Videos | Enterprise Imaging | May 22, 2018

VIDEO INTERVIEW: Building An Effective Enterprise Imaging Strategy

ITN Contributing Editor Greg Freiherr interviews Kim Garriott, Principal Consultant, Logicalis

The rapidly expanding volume of unstructured data, exemplified by medical images, underscores the need to develop an enterprise imaging strategy, says Kim Garriott, principal consultant at Logicalis, a global provider of IT solutions and managed services.

In the accompanying video interview, Garriott explains how the combination of these two may form a convincing argument for the leadership of your organization to move forward with enterprise imaging.

On June 1, at the upcoming SIIM 2018 convention, she will co-host the continuing education session “Achieving Enterprise Imaging Maturity,” during which she and the HIMSS-SIIM Workgroup will present the new Digital Imaging Adoption Maturity Model. DIAM, as it is called, is designed to support real-time collaboration and patient-centric care by providing a method to address enterprise-wide imaging challenges. 

When developing an enterprise imaging strategy, organizations must consider their specific needs, which are characterized in “use cases.” Garriott, who is the global chair of the HIMSS analytic DIAM Development team and co-chair of the HIMSS-SIIM Enterprise Imaging Workgroup, explains that these use cases constitute one very important element of the enterprise imaging strategy. Another is construction of a “data governance model.” This model is needed to guide the capture of data and is essential to the success of enterprise imaging.

Editor’s Note: This pre-SIIM video interview is the first in a series of three by ITN Contributing Editor, Greg Freiherr. The series features industry luminaries discussing key issues associated with the upcoming SIIM conference.

Related Video: ITN Associate Editor Jeff Zagoudis discusses, "Enterprise Imaging and the Digital Imaging Adoption Model" with Kim Garriott at HIMSS 2018.

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Artificial Intelligence | July 22, 2021
Technology Report: Artificial Intelligence in Radiology 2021

This is an overview of trends and technologies in radiology artificial intelligence (AI) applications in 2021. Views were shared by 11 radiologists using AI and industry leaders, which include:

• Randy Hicks, M.D., MBA, radiologist and CEO of Reginal Medical Imaging (RMI), and an iCAD Profound AI user.

• Prof. Dr. Thomas Frauenfelder, University of Zurich, Institute for Diagnostic and Interventional Radiology, and Riverain AI user.

• Amy Patel, M.D., medical director of Liberty Hospital Women’s Imaging, assistant professor of radiology at UMKC, and user of Kios AI for breast ultrasound.

• Sham Sokka, Ph.D., vice president and head of innovation, precision diagnosis, Philips Healthcare.

• Ivo Dreisser, Siemens Healthineers, global marketing manager for the AI Rad Companion.

• Bill Lacey, vice president of medical informatics, Fujifilm Medical Systems USA.

• Karley Yoder, vice president and general manager, artificial intelligence, GE Healthcare.

• Georges Espada, head of Agfa Healthcare digital and computed radiography business unit.

• Pooja Rao, head of research and development and co-founder of Qure.ai.

• Jill Hamman, world-wide marketing manager at Carestream Health.

• Sebastian Nickel, Siemens Healthineers, global product manager for the AI Pathway Companion. 

There has been a change in attitudes about AI on the expo floor at the Radiological Society of North America (RSNA) over the last two years. AI conversations were originally 101 level and discussed how AI technology could be trained to sort photos of dogs and cats. However, in 2020, with numerous FDA approvals for various AI applications, the conversations at RSNA, and industry wide, have shifted to that of accepting the validity of AI. Radiologists now want to discuss how a specific AI algorithm is going to help them save time, make more accurate diagnoses and make them more efficient.

With a higher level of maturity in AI and the technology seeing wider adoption, radiologists using it say AI gives them additional confidence in their diagnoses, and can even help readers who may not be deep experts in the exam type they are being asked to read. 

With a myriad of new AI apps gaining regulatory approval from scores of imaging vendors, the biggest challenge for getting this technology into hospitals is an easy to integrate format. This has led to several vendors creating AI app stores. These allow AI apps to integrate easily into radiology workflows because the apps are already integrated as third-party software into a larger radiology vendors' IT platform.  

There are now hundreds of AI applications that do a wide variety of analysis, from data analytics, image reconstruction, disease and anatomy identification, automating measurements and advanced visualization. The AI applications can be divided into 2 basic types — AI to improve workflow, and AI for clinical decision support, such as diagnostic aids.

On the workflow side, several vendors are leveraging AI to pull together all of a patients' information, prior exams and reports in one location and to digest the information so it is easier for the radiologist to consume. Often the AI pulls only data and priors that relate to a specific question being asked, based on the imaging protocol used for the exam. One example of this is the Siemens Healthineers AI Clinical Pathway and Siemens AI integrations with PACS to automate measurements and advanced visualization.

AI is also helping simplify complex tasks and help reduce the reading time on involved exams. One example of this is in 3-D breast tomosythesis with hundreds of images, which is rapidly replacing 2-D mammography, which only produces 4 images. Another example is automated image reconstruction algorithms to significantly reduce manual work. AI also is now being integrated directly into several vendors' imaging systems to speed workflow and improve image quality.

Vendors say AI is here to stay. They explain the future of AI will be automation to help improve image quality, simplify manual processes, improved diagnostic quality, new ways to analyze data, and workflow aids that operate in the background as part of a growing number of software solutions. 

Several vendors at RSNA 2020 noted that AI's biggest impact in the coming years will be its ability to augment and speed the workflow for the small number of radiologists compared to the quickly growing elder patient populations worldwide. There also are applications in rural and developing countries were there are very low numbers of physicians or specialists.

 

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Coronavirus (COVID-19) | May 11, 2021
VIDEO: COVID Vaccine Adenopathy Can Last Up to 10 Weeks

Yael Eshet, M.D., MSc, a diagnostic radiology specialist at Sheba Medical Center in Israel, was the lead author on a recent study that showed COVID-19 (SARS-CoV-2) vaccine adenopathy can persist more than 6 weeks. This swelling of lymph nodes is similar to what is seen cancer and infections and the new findings show it can last longer than 7-10 weeks. The current recommended time people should delay medical imaging is 6 weeks after receiving a COVID vaccine to avoid a misdiagnosis,[2] but this new study shows there is increased inflammation shown on PET-CT imaging for much longer.

These were the findings in the Radiology published study "Prevalence of Increased FDG PET/CT Axillary Lymph Node Uptake Beyond 6 Weeks after mRNA COVID-19 Vaccination."[1]

Researchers using fluorodeoxyglucose (FDG)-positron emission tomography (PET) have found increased FDG uptake in the lymph nodes of patients 7-10 weeks past their second mRNA-based Pfizer-BioNTech COVID-19 vaccination. This new information indicates a persistent immune response that could be mistaken on imaging exams for serious conditions like lymphoma over a much longer period of time.

Recent recommendations for post-vaccine lymphadenopathy advise scheduling routine imaging, such as screening mammography, before, or at least 6 weeks after, the final vaccination dose to eliminate false positive results. However, this new research showed that avid axillary lymph node uptake was present beyond 6 weeks after the second vaccination in more than 29% of the patients in the study cohort.

The authors stated “This study shows that avid axillary lymph node uptake on FDG PET/CT can be detected in more than a quarter of our patient population even beyond 6 weeks after the second dose of the mRNA-based COVID-19 vaccination. Compared to a previous study showing normalization of FDG uptake within 40 days of receiving an inactivated H1N1 influenza vaccine, we found uptake persistence even at 70 days. Physicians should be aware of this potential pitfall.”

Some images in this video are from another Radiology study, which showed PET tracer uptake at the COVID vaccine injection site and other examples of axillary adenopathy.[3]

 

Related COVID Vaccine Axillary Adenapathy Content:

COVID-19 Vaccine Can Cause False Positive Cancer Diagnosis

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VIDEO: COVID Vaccine May Cause Enlarged Lymph Nodes on Mammograms — Interview with Constance "Connie" Lehman, M.D.

COVID-19 Vaccination Axillary Adenopathy Detected During Breast Imaging

PHOTO GALLERY: How COVID-19 Appears on Medical Imaging

Find more radiology related COVID content 

References:

1. Yael Eshet, Noam Tau1, Yousef Alhoubani, Nayroz Kanana, Liran Domachevsky, Michal Eifer. Prevalence of Increased FDG PET/CT Axillary Lymph Node Uptake Beyond 6 Weeks after mRNA COVID-19 Vaccination. Radiology. Published Online:Apr 27 2021https://doi.org/10.1148/radiol.2021210886.

2. Constance D. Lehman, Leslie R. Lamb, and Helen Anne D'Alessandro. Mitigating the Impact of Coronavirus Disease (COVID-19) Vaccinations on Patients Undergoing Breast Imaging Examinations: A Pragmatic Approach American Journal of Roentgenology. 10.2214/AJR.21.25688.

3. Can Özütemiz, Luke A. Krystosek, An L. Church, Anil Chauhan, Jutta M. Ellermann, Evidio Domingo-Musibay, Daniel Steinberger. Lymphadenopathy in COVID-19 Vaccine Recipients: Diagnostic Dilemma in Oncology Patients. Radiology. Published Online:Feb 24 2021https://doi.org/10.1148/radiol.2021210275.

 

Point-of-Care Ultrasound (POCUS) | April 01, 2021
VIDEO: Examples of Lung B-lines and Cardiac Imaging on a Vscan Point of Care Ultrasound

Here are two quick clinical examples of point-of-care ultrasound (POCUS) lung imaging and cardiac imaging using a GE Vscan Air device. The examples show an abnormal lung image with B-lines. The second clip shows a healthy heart in a parasternal color Doppler image.

The GE Healthcare Vscan Air is a cutting-edge, wireless pocket-sized ultrasound that provides crystal clear image quality, whole-body scanning capabilities, and intuitive software. The pocket-sized ultrasound system was originally introduced in 2010, and as of early 2021, there are over 30,000 Vscan systems in use. The new Vscan Air features a wireless ultrasound probe.

Read more in the article GE Healthcare Unveils Vscan Air Wireless Handheld Ultrasound

Find more POCUS news and video

Breast Imaging | March 26, 2021
VIDEO: COVID Vaccine May Cause Enlarged Lymph Nodes on Mammograms

Constance "Connie" Lehman, M.D., Ph.D., chief of breast imaging, co-director of the Avon Comprehensive Breast Evaluation Center at the Massachusetts General Hospital, and professor of radiology at Harvard Medical School, explains issues and suggested guidelines for women who receive the COVID-19 vaccine and need to get a mammogram. In the first three months since the vaccines have been released, there have been numerous case reports of the vaccine causing swollen lymph nodes. This is would usually raise a red flag for breast cancer, but is normal for many women receiving the vaccine as their body's immune system gears up against the virus. 

Lehman said cases reports of axillary adenopathy have been identified on breast imaging after coronavirus disease (COVID-19) vaccination and are rising. Lehman et al. proposed a pragmatic management approach in a recent article in the American Journal of Roentgenology (AJR).[1]

In the settings of screening mammography, screening MRI and diagnostic imaging work-up of breast symptoms, with no imaging findings beyond unilateral axillary adenopathy ipsilateral to recent (prior six weeks) vaccination, they report the adenopathy as benign with no further imaging indicated if no nodes are palpable six weeks after the last vaccine dose. 

For patients with palpable axillary adenopathy in the setting of ipsilateral recent vaccination, clinical follow-up of the axilla is recommended. In all these scenarios, axillary ultrasound is recommended if clinical concern persists six weeks after vaccination. 

In patients with recent breast cancer diagnosis in the pre- or peri-treatment setting, prompt recommended imaging is encouraged as well as vaccination (in the thigh or contralateral arm). The recommendations align with the ACR BI-RADS Atlas and aim to: 1) reduce patient anxiety, provider burden, and costs of unnecessary evaluation of enlarged nodes in the setting of recent vaccination, and 2) avoid further delays in vaccinations and breast cancer screening during the pandemic.

 

Related Medical Imaging of COVID Content:

COVID-19 Vaccination Axillary Adenopathy Detected During Breast Imaging

PHOTO GALLERY: How COVID-19 Appears on Medical Imaging

VIDEO: Imaging COVID-19 With Point-of-Care Ultrasound (POCUS) — Interview with Mike Stone, M.D.

VIDEO: Use of Teleradiology During the COVID-19 Pandemic — Interview with John Kim, M.D.

Find more radiology related COVID content 

 

Reference:

1. Constance D. Lehman, Leslie R. Lamb, and Helen Anne D'Alessandro. Mitigating the Impact of Coronavirus Disease (COVID-19) Vaccinations on Patients Undergoing Breast Imaging Examinations: A Pragmatic Approach American Journal of Roentgenology. 10.2214/AJR.21.25688

 

 

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Information Technology | December 01, 2020
Talking Trends with Elekta

Treating cancer effectively often includes a combination of patient therapies. In recent years, technology advancements have led to a more efficient and personalized approach to treatment. Andrew Wilson, President of Oncology Informatics at Elekta, discussed the latest software advancements with ITN.

X-Ray | November 28, 2020
Talking Trends with Agfa

Agfa is looking to transform X-ray with new advancements in volumetric imaging, and with new mobile concepts and implementation of intelligent tools. ITN had a conversation with Georges Espada on Transforming X-ray with Intelligent Tools.

Enterprise Imaging | November 23, 2020
Talking Trends with Fujifilm

Fujifilm's next generation secure server-side viewer platform extends across enterprise imaging areas to bring together radiology, mammography and cardiology into a single zero footprint platform. Bill Lacy, vice president of medical informatics for Fujifilm Medical Systems USA recently talked with ITN about their Synapse 7x platform.

PACS | June 29, 2020
Talking Trends with Konica Minolta

Kevin Borden, Vice President of Product, Healthcare IT for Konica Minolta, talks about Improving Access and Aiding Workflow with itnTV. He explains how the server-side rendering and zero-footprint viewer in its Exa PACS make it well-suited for remote reading.

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Artificial Intelligence | July 22, 2021
Technology Report: Artificial Intelligence in Radiology 2021

This is an overview of trends and technologies in radiology artificial intelligence (AI) applications in 2021. Views were shared by 11 radiologists using AI and industry leaders, which include:

• Randy Hicks, M.D., MBA, radiologist and CEO of Reginal Medical Imaging (RMI), and an iCAD Profound AI user.

• Prof. Dr. Thomas Frauenfelder, University of Zurich, Institute for Diagnostic and Interventional Radiology, and Riverain AI user.

• Amy Patel, M.D., medical director of Liberty Hospital Women’s Imaging, assistant professor of radiology at UMKC, and user of Kios AI for breast ultrasound.

• Sham Sokka, Ph.D., vice president and head of innovation, precision diagnosis, Philips Healthcare.

• Ivo Dreisser, Siemens Healthineers, global marketing manager for the AI Rad Companion.

• Bill Lacey, vice president of medical informatics, Fujifilm Medical Systems USA.

• Karley Yoder, vice president and general manager, artificial intelligence, GE Healthcare.

• Georges Espada, head of Agfa Healthcare digital and computed radiography business unit.

• Pooja Rao, head of research and development and co-founder of Qure.ai.

• Jill Hamman, world-wide marketing manager at Carestream Health.

• Sebastian Nickel, Siemens Healthineers, global product manager for the AI Pathway Companion. 

There has been a change in attitudes about AI on the expo floor at the Radiological Society of North America (RSNA) over the last two years. AI conversations were originally 101 level and discussed how AI technology could be trained to sort photos of dogs and cats. However, in 2020, with numerous FDA approvals for various AI applications, the conversations at RSNA, and industry wide, have shifted to that of accepting the validity of AI. Radiologists now want to discuss how a specific AI algorithm is going to help them save time, make more accurate diagnoses and make them more efficient.

With a higher level of maturity in AI and the technology seeing wider adoption, radiologists using it say AI gives them additional confidence in their diagnoses, and can even help readers who may not be deep experts in the exam type they are being asked to read. 

With a myriad of new AI apps gaining regulatory approval from scores of imaging vendors, the biggest challenge for getting this technology into hospitals is an easy to integrate format. This has led to several vendors creating AI app stores. These allow AI apps to integrate easily into radiology workflows because the apps are already integrated as third-party software into a larger radiology vendors' IT platform.  

There are now hundreds of AI applications that do a wide variety of analysis, from data analytics, image reconstruction, disease and anatomy identification, automating measurements and advanced visualization. The AI applications can be divided into 2 basic types — AI to improve workflow, and AI for clinical decision support, such as diagnostic aids.

On the workflow side, several vendors are leveraging AI to pull together all of a patients' information, prior exams and reports in one location and to digest the information so it is easier for the radiologist to consume. Often the AI pulls only data and priors that relate to a specific question being asked, based on the imaging protocol used for the exam. One example of this is the Siemens Healthineers AI Clinical Pathway and Siemens AI integrations with PACS to automate measurements and advanced visualization.

AI is also helping simplify complex tasks and help reduce the reading time on involved exams. One example of this is in 3-D breast tomosythesis with hundreds of images, which is rapidly replacing 2-D mammography, which only produces 4 images. Another example is automated image reconstruction algorithms to significantly reduce manual work. AI also is now being integrated directly into several vendors' imaging systems to speed workflow and improve image quality.

Vendors say AI is here to stay. They explain the future of AI will be automation to help improve image quality, simplify manual processes, improved diagnostic quality, new ways to analyze data, and workflow aids that operate in the background as part of a growing number of software solutions. 

Several vendors at RSNA 2020 noted that AI's biggest impact in the coming years will be its ability to augment and speed the workflow for the small number of radiologists compared to the quickly growing elder patient populations worldwide. There also are applications in rural and developing countries were there are very low numbers of physicians or specialists.

 

Related AI in Medical Imaging Content:

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VIDEO: Artificial Intelligence For MRI Helps Overcome Backlog of Exams Due to COVID

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VIDEO: Use of Artificial Intelligence in Nuclear Imaging

3 High-impact AI Market Trends in Radiology at RSNA 2019

 

Photo Gallery of New Imaging Technologies at RSNA 2019

VIDEO: Editors Choice of the Most Innovative New Radiology Technology at RSNA 2019

Study Reveals New Comprehensive AI Chest X-ray Solution Improves Radiologist Accuracy

VIDEO: Real-world Use of AI to Detect Hemorrhagic Stroke

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Eliminating Bias from Healthcare AI Critical to Improve Health Equity

VIDEO: FDA Cleared Artificial Intelligence for Immediate Results of Head CT Scans

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Selecting an AI Marketplace for Radiology: Key Considerations for Healthcare Providers

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WEBINAR: Building the Bridge - How Imaging AI is Delivering Clinical Value Across the Care Continuum

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VIDEO: AI-Assisted Automatic Ejection Fraction for Point-of-Care Ultrasound

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VIDEO: Artificial Intelligence to Automate CT Calcium Scoring and Radiomics

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VIDEO: Integrating Artificial Intelligence Into Radiologists Workflow

 

Northwestern Medicine Introduces Artificial Intelligence to Improve Ultrasound Imaging

Find more artificial intelligence news and video

 

 

 

Enterprise Imaging | February 21, 2020
Technology Report: Enterprise Imaging 2019

In Enterprise Imaging at RSNA 2019, ITN Contributing Editor Greg Freiherr offers an overview of enterprise imaging advances at the Radiological Society of North America (RSNA) 2019 annual meeting.

Artificial Intelligence | January 15, 2019
Technology Report: Artificial Intelligence 2018

In Artificial Intelligence 2018: What Radiologists Need to Know About AI, ITN Contributing Editor Greg Freiherr offers an overview of artificial intelligence (AI) advances at the Radiological Society of North America (RSNA) 2018 annual meeting.

 

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Enterprise Imaging | January 14, 2019
Technology Report: Enterprise Imaging 2018

In Enterprise Imaging 2018: Balancing Strategy and Technology in Enterprise Imaging, ITN Contributing Editor Greg Freiherr offers an overview of enterprise imaging advances at the Radiological Society of North America (RSNA) 2018 annual meeting.

 

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Artificial Intelligence | July 22, 2021
Technology Report: Artificial Intelligence in Radiology 2021

This is an overview of trends and technologies in radiology artificial intelligence (AI) applications in 2021. Views were shared by 11 radiologists using AI and industry leaders, which include:

• Randy Hicks, M.D., MBA, radiologist and CEO of Reginal Medical Imaging (RMI), and an iCAD Profound AI user.

• Prof. Dr. Thomas Frauenfelder, University of Zurich, Institute for Diagnostic and Interventional Radiology, and Riverain AI user.

• Amy Patel, M.D., medical director of Liberty Hospital Women’s Imaging, assistant professor of radiology at UMKC, and user of Kios AI for breast ultrasound.

• Sham Sokka, Ph.D., vice president and head of innovation, precision diagnosis, Philips Healthcare.

• Ivo Dreisser, Siemens Healthineers, global marketing manager for the AI Rad Companion.

• Bill Lacey, vice president of medical informatics, Fujifilm Medical Systems USA.

• Karley Yoder, vice president and general manager, artificial intelligence, GE Healthcare.

• Georges Espada, head of Agfa Healthcare digital and computed radiography business unit.

• Pooja Rao, head of research and development and co-founder of Qure.ai.

• Jill Hamman, world-wide marketing manager at Carestream Health.

• Sebastian Nickel, Siemens Healthineers, global product manager for the AI Pathway Companion. 

There has been a change in attitudes about AI on the expo floor at the Radiological Society of North America (RSNA) over the last two years. AI conversations were originally 101 level and discussed how AI technology could be trained to sort photos of dogs and cats. However, in 2020, with numerous FDA approvals for various AI applications, the conversations at RSNA, and industry wide, have shifted to that of accepting the validity of AI. Radiologists now want to discuss how a specific AI algorithm is going to help them save time, make more accurate diagnoses and make them more efficient.

With a higher level of maturity in AI and the technology seeing wider adoption, radiologists using it say AI gives them additional confidence in their diagnoses, and can even help readers who may not be deep experts in the exam type they are being asked to read. 

With a myriad of new AI apps gaining regulatory approval from scores of imaging vendors, the biggest challenge for getting this technology into hospitals is an easy to integrate format. This has led to several vendors creating AI app stores. These allow AI apps to integrate easily into radiology workflows because the apps are already integrated as third-party software into a larger radiology vendors' IT platform.  

There are now hundreds of AI applications that do a wide variety of analysis, from data analytics, image reconstruction, disease and anatomy identification, automating measurements and advanced visualization. The AI applications can be divided into 2 basic types — AI to improve workflow, and AI for clinical decision support, such as diagnostic aids.

On the workflow side, several vendors are leveraging AI to pull together all of a patients' information, prior exams and reports in one location and to digest the information so it is easier for the radiologist to consume. Often the AI pulls only data and priors that relate to a specific question being asked, based on the imaging protocol used for the exam. One example of this is the Siemens Healthineers AI Clinical Pathway and Siemens AI integrations with PACS to automate measurements and advanced visualization.

AI is also helping simplify complex tasks and help reduce the reading time on involved exams. One example of this is in 3-D breast tomosythesis with hundreds of images, which is rapidly replacing 2-D mammography, which only produces 4 images. Another example is automated image reconstruction algorithms to significantly reduce manual work. AI also is now being integrated directly into several vendors' imaging systems to speed workflow and improve image quality.

Vendors say AI is here to stay. They explain the future of AI will be automation to help improve image quality, simplify manual processes, improved diagnostic quality, new ways to analyze data, and workflow aids that operate in the background as part of a growing number of software solutions. 

Several vendors at RSNA 2020 noted that AI's biggest impact in the coming years will be its ability to augment and speed the workflow for the small number of radiologists compared to the quickly growing elder patient populations worldwide. There also are applications in rural and developing countries were there are very low numbers of physicians or specialists.

 

Related AI in Medical Imaging Content:

AI Outperforms Humans in Creating Cancer Treatments, But Do Doctors Trust It?

VIDEO: Artificial Intelligence For MRI Helps Overcome Backlog of Exams Due to COVID

How AI is Helping the Fight Against Breast Cancer

VIDEO: Use of Artificial Intelligence in Nuclear Imaging

3 High-impact AI Market Trends in Radiology at RSNA 2019

 

Photo Gallery of New Imaging Technologies at RSNA 2019

VIDEO: Editors Choice of the Most Innovative New Radiology Technology at RSNA 2019

Study Reveals New Comprehensive AI Chest X-ray Solution Improves Radiologist Accuracy

VIDEO: Real-world Use of AI to Detect Hemorrhagic Stroke

The Radiology AI Evolution at RSNA 2019

 

Eliminating Bias from Healthcare AI Critical to Improve Health Equity

VIDEO: FDA Cleared Artificial Intelligence for Immediate Results of Head CT Scans

Building the Future of AI Through Data

WEBINAR: Do More, Perform Better: Delivering Clinical Quality through Advanced Radiology and Artificial Intelligence

Integrating Artificial Intelligence in Treatment Planning

 

Selecting an AI Marketplace for Radiology: Key Considerations for Healthcare Providers

Artificial Intelligence Improves Accuracy of Breast Ultrasound Diagnoses

Artificial Intelligence Greatly Speeds Radiation Therapy Treatment Planning

WEBINAR: Building the Bridge - How Imaging AI is Delivering Clinical Value Across the Care Continuum

AI in Medical Imaging Market to Reach $1.5B by 2024

 

VIDEO: AI-Assisted Automatic Ejection Fraction for Point-of-Care Ultrasound

5 Trends in Enterprise Imaging and PACS Systems

VIDEO: Artificial Intelligence to Automate CT Calcium Scoring and Radiomics

Scale AI in Imaging Now for the Post-COVID Era

VIDEO: Integrating Artificial Intelligence Into Radiologists Workflow

 

Northwestern Medicine Introduces Artificial Intelligence to Improve Ultrasound Imaging

Find more artificial intelligence news and video

 

 

 

Coronavirus (COVID-19) | January 26, 2021
VIDEO: What Does COVID-19 Look Like in Lung CT Scans

This is an example of a COVID-19 (SARS-CoV-2) positive patient's lung computed tomography (CT) scan. The video scrolls through the image slices of the scan and shows the typical white, ground glass opacities (GGO) caused by COVID pneumonia. The pneumonia typically appears along the walls of each lobe of the lung, especially the chest wall and the lower portions of the lungs. This scan is from a Canon Aquilion Prime SP CT scanner and used Advanced intelligent Clear-IQ Engine (AiCE), an artificial intelligence-driven image reconstruction software to improve image quality of lower-dose scans. This was shown by Canon Medical as an exmaple of CT image quality for the virus at the 2020 Radiological Society of North American (RSNA) meeting. 

Read more about this system and its launch in 2020 to address COVID, Canon Medical Launches CT Solution for Patients with Viral Infectious Diseases.

VIDEO: How to Image COVID-19 and Radiological Presentations of the Virus interview with Margarita Revzin, M.D., associate professor of radiology and biomedical imaging, Yale School of Medicine.

Find more radiology clinical images of coronavirus in this photo gallery.

Find more radiology related COVID news and video

Artificial Intelligence | December 02, 2020
VIDEO: Deep Learning Analysis of Abdominal Fat to Assess Heart Attack and Stroke Risk

Kirti Magudia, M.D., Ph.D., an abdominal imaging and ultrasound fellow at the University of California San Francisco, explains how an automated deep learning analysis of abdominal computed tomography (CT) images can produce a more precise measurement of body composition and better predicts major cardiovascular events, such as heart attack and stroke, better than overall weight or body mass index (BMI). This was according to a study she presented at the 2020 Radiological Society of North America (RSNA) virtual meeting.

Unlike BMI, which is based on height and weight, a single axial CT slice of the abdomen visualizes the volume of subcutaneous fat area, visceral fat area and skeletal muscle area. However, manually measuring these individual areas is time intensive and costly. A multidisciplinary team of researchers, including radiologists, a data scientist and biostatistician, developed a fully automated artificial intelligence (AI) method to determine body composition metrics from abdominal CT images.

Statistical analysis demonstrated that visceral fat area was independently associated with future heart attack and stroke. BMI was not associated with heart attack or stroke.

Read more about this study

Find more RSNA news

Information Technology | December 01, 2020
Talking Trends with Elekta

Treating cancer effectively often includes a combination of patient therapies. In recent years, technology advancements have led to a more efficient and personalized approach to treatment. Andrew Wilson, President of Oncology Informatics at Elekta, discussed the latest software advancements with ITN.

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Information Technology | December 01, 2020
Talking Trends with Elekta

Treating cancer effectively often includes a combination of patient therapies. In recent years, technology advancements have led to a more efficient and personalized approach to treatment. Andrew Wilson, President of Oncology Informatics at Elekta, discussed the latest software advancements with ITN.

Radiation Therapy | November 15, 2020
VIDEO: Explaining Boron Neutron Capture Therapy for Targeted Cancer Radiotherapy

Bruce Bauer, Ph.D., CEO of TAE Life Sciences. The company is developing boron neutron capture therapy (BNCT) as a new radiation therapy for cancer. A patient is first infused with a non-toxic boron-10 compound, which selectively accumulates in tumor tissue. A neutron beam is then focused on the tumor and the neutrons are captured by the boron and causes emission of alpha radiation particles within the tumor. Alpha particles have a a very short range, so this helps spare surrounding healthy tissue from radiation damage. 

Historically, BNCT clinical studies have been carried out using boronophenylalanine (BPA) and neutrons derived from the core of a nuclear reactor. While the clinical outcomes have been encouraging, the availability of better boron-10 compounds and access to a neutron source posed a significant barrier to clinical research and adoption of BNCT as a practical cancer therapy.

There is now a renaissance in BNCT with the availability of new accelerator-based neutrons sources and novel synthesis of boron-10 target drugs, allowing clinical research to expand with the goal to have BNCT available as a new treatment option for patients.

The secondary radiation reaction from BNCT, with cellular-level precision, spares more healthy tissues and can potentially treat cancers that otherwise have few treatment options.

The system requires a neutron accelerator, but this is smaller than a proton system and operates at much lower energy, so the shielding requirement is much lower, cutting construction costs.

Find more news and video on radiation therapy

 

Contrast Media Injectors | May 22, 2020
itnTV “Conversations”: Radiology Trends with Bayer’s Dennis Durmis

At this year’s RSNA ITN sat down with Dennis Durmis, Senior Vice President, Bayer Radiology to discuss Radiology trends. Discussion topics centered around three key areas where Bayer Radiology is responding to trends; including digitalization, workflow efficiencies and efforts to bring more focus to the Radiology patient experience. During the interview Dennis discussed Bayer’s digital strategy, features and benefits of their new injector, the MEDRAD® Stellant FLEX Injector and Bayer’s education efforts of the imaging needs of women with Dense Breast.

Proton Therapy | December 16, 2019
VIDEO: Varian Showcases Latest Developments at ASTRO 2019

Join Chris Toth, president of Varian’s Oncology Systems business, for a peek at the history of machine learning/AI in radiation oncology, plus other highlights in 2019:

  • Ethos therapy: the world’s first AI-powered adaptive radiotherapy.
  • Noona cloud-based application for capturing patient-reported outcomes.
  • Varian’s multi-room configuration for ProBeam 360 proton therapy.
  • The promise of FLASH, an ultra-high-speed treatment that is in pre-clinical testing, and represents an exciting and potentially promising new direction in the treatment of cancer. 

Radiology Imaging View all 361 items

Coronavirus (COVID-19) | May 11, 2021
VIDEO: COVID Vaccine Adenopathy Can Last Up to 10 Weeks

Yael Eshet, M.D., MSc, a diagnostic radiology specialist at Sheba Medical Center in Israel, was the lead author on a recent study that showed COVID-19 (SARS-CoV-2) vaccine adenopathy can persist more than 6 weeks. This swelling of lymph nodes is similar to what is seen cancer and infections and the new findings show it can last longer than 7-10 weeks. The current recommended time people should delay medical imaging is 6 weeks after receiving a COVID vaccine to avoid a misdiagnosis,[2] but this new study shows there is increased inflammation shown on PET-CT imaging for much longer.

These were the findings in the Radiology published study "Prevalence of Increased FDG PET/CT Axillary Lymph Node Uptake Beyond 6 Weeks after mRNA COVID-19 Vaccination."[1]

Researchers using fluorodeoxyglucose (FDG)-positron emission tomography (PET) have found increased FDG uptake in the lymph nodes of patients 7-10 weeks past their second mRNA-based Pfizer-BioNTech COVID-19 vaccination. This new information indicates a persistent immune response that could be mistaken on imaging exams for serious conditions like lymphoma over a much longer period of time.

Recent recommendations for post-vaccine lymphadenopathy advise scheduling routine imaging, such as screening mammography, before, or at least 6 weeks after, the final vaccination dose to eliminate false positive results. However, this new research showed that avid axillary lymph node uptake was present beyond 6 weeks after the second vaccination in more than 29% of the patients in the study cohort.

The authors stated “This study shows that avid axillary lymph node uptake on FDG PET/CT can be detected in more than a quarter of our patient population even beyond 6 weeks after the second dose of the mRNA-based COVID-19 vaccination. Compared to a previous study showing normalization of FDG uptake within 40 days of receiving an inactivated H1N1 influenza vaccine, we found uptake persistence even at 70 days. Physicians should be aware of this potential pitfall.”

Some images in this video are from another Radiology study, which showed PET tracer uptake at the COVID vaccine injection site and other examples of axillary adenopathy.[3]

 

Related COVID Vaccine Axillary Adenapathy Content:

COVID-19 Vaccine Can Cause False Positive Cancer Diagnosis

Help Spread Awareness of Potential COVID-19 Vaccine Imaging Side-effects

VIDEO: COVID Vaccine May Cause Enlarged Lymph Nodes on Mammograms — Interview with Constance "Connie" Lehman, M.D.

COVID-19 Vaccination Axillary Adenopathy Detected During Breast Imaging

PHOTO GALLERY: How COVID-19 Appears on Medical Imaging

Find more radiology related COVID content 

References:

1. Yael Eshet, Noam Tau1, Yousef Alhoubani, Nayroz Kanana, Liran Domachevsky, Michal Eifer. Prevalence of Increased FDG PET/CT Axillary Lymph Node Uptake Beyond 6 Weeks after mRNA COVID-19 Vaccination. Radiology. Published Online:Apr 27 2021https://doi.org/10.1148/radiol.2021210886.

2. Constance D. Lehman, Leslie R. Lamb, and Helen Anne D'Alessandro. Mitigating the Impact of Coronavirus Disease (COVID-19) Vaccinations on Patients Undergoing Breast Imaging Examinations: A Pragmatic Approach American Journal of Roentgenology. 10.2214/AJR.21.25688.

3. Can Özütemiz, Luke A. Krystosek, An L. Church, Anil Chauhan, Jutta M. Ellermann, Evidio Domingo-Musibay, Daniel Steinberger. Lymphadenopathy in COVID-19 Vaccine Recipients: Diagnostic Dilemma in Oncology Patients. Radiology. Published Online:Feb 24 2021https://doi.org/10.1148/radiol.2021210275.

 

Point-of-Care Ultrasound (POCUS) | April 01, 2021
VIDEO: Examples of Lung B-lines and Cardiac Imaging on a Vscan Point of Care Ultrasound

Here are two quick clinical examples of point-of-care ultrasound (POCUS) lung imaging and cardiac imaging using a GE Vscan Air device. The examples show an abnormal lung image with B-lines. The second clip shows a healthy heart in a parasternal color Doppler image.

The GE Healthcare Vscan Air is a cutting-edge, wireless pocket-sized ultrasound that provides crystal clear image quality, whole-body scanning capabilities, and intuitive software. The pocket-sized ultrasound system was originally introduced in 2010, and as of early 2021, there are over 30,000 Vscan systems in use. The new Vscan Air features a wireless ultrasound probe.

Read more in the article GE Healthcare Unveils Vscan Air Wireless Handheld Ultrasound

Find more POCUS news and video

Breast Imaging | March 26, 2021
VIDEO: COVID Vaccine May Cause Enlarged Lymph Nodes on Mammograms

Constance "Connie" Lehman, M.D., Ph.D., chief of breast imaging, co-director of the Avon Comprehensive Breast Evaluation Center at the Massachusetts General Hospital, and professor of radiology at Harvard Medical School, explains issues and suggested guidelines for women who receive the COVID-19 vaccine and need to get a mammogram. In the first three months since the vaccines have been released, there have been numerous case reports of the vaccine causing swollen lymph nodes. This is would usually raise a red flag for breast cancer, but is normal for many women receiving the vaccine as their body's immune system gears up against the virus. 

Lehman said cases reports of axillary adenopathy have been identified on breast imaging after coronavirus disease (COVID-19) vaccination and are rising. Lehman et al. proposed a pragmatic management approach in a recent article in the American Journal of Roentgenology (AJR).[1]

In the settings of screening mammography, screening MRI and diagnostic imaging work-up of breast symptoms, with no imaging findings beyond unilateral axillary adenopathy ipsilateral to recent (prior six weeks) vaccination, they report the adenopathy as benign with no further imaging indicated if no nodes are palpable six weeks after the last vaccine dose. 

For patients with palpable axillary adenopathy in the setting of ipsilateral recent vaccination, clinical follow-up of the axilla is recommended. In all these scenarios, axillary ultrasound is recommended if clinical concern persists six weeks after vaccination. 

In patients with recent breast cancer diagnosis in the pre- or peri-treatment setting, prompt recommended imaging is encouraged as well as vaccination (in the thigh or contralateral arm). The recommendations align with the ACR BI-RADS Atlas and aim to: 1) reduce patient anxiety, provider burden, and costs of unnecessary evaluation of enlarged nodes in the setting of recent vaccination, and 2) avoid further delays in vaccinations and breast cancer screening during the pandemic.

 

Related Medical Imaging of COVID Content:

COVID-19 Vaccination Axillary Adenopathy Detected During Breast Imaging

PHOTO GALLERY: How COVID-19 Appears on Medical Imaging

VIDEO: Imaging COVID-19 With Point-of-Care Ultrasound (POCUS) — Interview with Mike Stone, M.D.

VIDEO: Use of Teleradiology During the COVID-19 Pandemic — Interview with John Kim, M.D.

Find more radiology related COVID content 

 

Reference:

1. Constance D. Lehman, Leslie R. Lamb, and Helen Anne D'Alessandro. Mitigating the Impact of Coronavirus Disease (COVID-19) Vaccinations on Patients Undergoing Breast Imaging Examinations: A Pragmatic Approach American Journal of Roentgenology. 10.2214/AJR.21.25688

 

 

Magnetic Resonance Imaging (MRI) | March 19, 2021
VIDEO: Artificial Intelligence For MRI Helps Overcome Backlog of Exams Due to COVID

Darryl B. Sneag, M.D., a radiologist and director of peripheral nerve MRI at the Hospital for Special Surgery (HSS) in New York City, explains how artificial intelligence (AI) magnetic resonance imaging (MRI) reconstruction algorithms have cut imaging times by 50 percent. This has enabled his facility to maintain the same number of patients as it did prior to the pandemic, while still having time to sterilize the scanners after each patient. 

Many radiology departments are now experiencing a backlog of cases due to COVID-19 shutdowns in 2020 and the limits on the number of patients that can be in the hospital for imaging exams due to pandemic containment precautions. Sneag said AI is now playing a role in helping streamline workflow.

HSS has 19 GE Healthcare MRI scanners and uses the Air Recon DL AI image reconstruction algorithm. This allows for shorter scan times, so the same number of patients as pre-pandemic can be imaged per day, even with deeper cleaning of the MRI bore. Sneag explains the algorithm has greatly helped with patient throughput, but the trade off is sometimes getting a ringing artifact on images.

HSS also uses GE's Air Coil flexible pad MRI coils. These can wrap around the patient to improve comfort and get the coils closer to the anatomy being imaged.

 

Related MRI and COVID Content:

Business During COVID-19 and Beyond

Imaging Volumes Hold Steady Post COVID-19 Closures

GE Healthcare Addresses Growing Radiology Data Challenges at RSNA 2019

Technology is Driving the MRI Market

VIDEO: How to Image COVID-19 and Radiological Presentations of the Virus

Top Trend Takeaways in Radiology From RSNA 2020

Post-COVID Pain or Weakness? Request an Ultrasound or MRI

Find more COVID radiology-related content

 

Molecular Imaging View all 30 items

Coronavirus (COVID-19) | May 11, 2021
VIDEO: COVID Vaccine Adenopathy Can Last Up to 10 Weeks

Yael Eshet, M.D., MSc, a diagnostic radiology specialist at Sheba Medical Center in Israel, was the lead author on a recent study that showed COVID-19 (SARS-CoV-2) vaccine adenopathy can persist more than 6 weeks. This swelling of lymph nodes is similar to what is seen cancer and infections and the new findings show it can last longer than 7-10 weeks. The current recommended time people should delay medical imaging is 6 weeks after receiving a COVID vaccine to avoid a misdiagnosis,[2] but this new study shows there is increased inflammation shown on PET-CT imaging for much longer.

These were the findings in the Radiology published study "Prevalence of Increased FDG PET/CT Axillary Lymph Node Uptake Beyond 6 Weeks after mRNA COVID-19 Vaccination."[1]

Researchers using fluorodeoxyglucose (FDG)-positron emission tomography (PET) have found increased FDG uptake in the lymph nodes of patients 7-10 weeks past their second mRNA-based Pfizer-BioNTech COVID-19 vaccination. This new information indicates a persistent immune response that could be mistaken on imaging exams for serious conditions like lymphoma over a much longer period of time.

Recent recommendations for post-vaccine lymphadenopathy advise scheduling routine imaging, such as screening mammography, before, or at least 6 weeks after, the final vaccination dose to eliminate false positive results. However, this new research showed that avid axillary lymph node uptake was present beyond 6 weeks after the second vaccination in more than 29% of the patients in the study cohort.

The authors stated “This study shows that avid axillary lymph node uptake on FDG PET/CT can be detected in more than a quarter of our patient population even beyond 6 weeks after the second dose of the mRNA-based COVID-19 vaccination. Compared to a previous study showing normalization of FDG uptake within 40 days of receiving an inactivated H1N1 influenza vaccine, we found uptake persistence even at 70 days. Physicians should be aware of this potential pitfall.”

Some images in this video are from another Radiology study, which showed PET tracer uptake at the COVID vaccine injection site and other examples of axillary adenopathy.[3]

 

Related COVID Vaccine Axillary Adenapathy Content:

COVID-19 Vaccine Can Cause False Positive Cancer Diagnosis

Help Spread Awareness of Potential COVID-19 Vaccine Imaging Side-effects

VIDEO: COVID Vaccine May Cause Enlarged Lymph Nodes on Mammograms — Interview with Constance "Connie" Lehman, M.D.

COVID-19 Vaccination Axillary Adenopathy Detected During Breast Imaging

PHOTO GALLERY: How COVID-19 Appears on Medical Imaging

Find more radiology related COVID content 

References:

1. Yael Eshet, Noam Tau1, Yousef Alhoubani, Nayroz Kanana, Liran Domachevsky, Michal Eifer. Prevalence of Increased FDG PET/CT Axillary Lymph Node Uptake Beyond 6 Weeks after mRNA COVID-19 Vaccination. Radiology. Published Online:Apr 27 2021https://doi.org/10.1148/radiol.2021210886.

2. Constance D. Lehman, Leslie R. Lamb, and Helen Anne D'Alessandro. Mitigating the Impact of Coronavirus Disease (COVID-19) Vaccinations on Patients Undergoing Breast Imaging Examinations: A Pragmatic Approach American Journal of Roentgenology. 10.2214/AJR.21.25688.

3. Can Özütemiz, Luke A. Krystosek, An L. Church, Anil Chauhan, Jutta M. Ellermann, Evidio Domingo-Musibay, Daniel Steinberger. Lymphadenopathy in COVID-19 Vaccine Recipients: Diagnostic Dilemma in Oncology Patients. Radiology. Published Online:Feb 24 2021https://doi.org/10.1148/radiol.2021210275.

 

PET-CT | December 04, 2020
VIDEO: Example of PET-CT Artificial Intelligence Driven Image Reconstruction

This is an example of Canon's Advanced intelligent Clear-IQ Engine (AiCE) AI-driven image reconstruction software that is now being used to improve image quality on the Canon Celesteion Prime PET/CT nuclear imaging system. The deep learning is used to enhance the iterative reconstruction used to reduce noise and sharped high contrast resolution on positron emission tomography (PET) images from the digital PET detector used on the system. 

This example is a whole-body FGD PET scan of a patient with a large BMI with lung cancer.

The Cartesion Prime PET/CT is the industry’s only air-cooled digital PET/CT, provides variable bed time (vBT) acquisition as a standard feature. This and the new FDA 510(k)-pending AiCE technology were highlighted at the 2020 Radiological Society of North America (RSNA) virtual meeting. 

Find more RSNA news

 

Artificial Intelligence | September 25, 2020
VIDEO: Use of Artificial Intelligence in Nuclear Imaging

Ernest Garcia, Ph.D., MASNC, FAHA, endowed professor in cardiac imaging, director of nuclear cardiology R&D laboratory, Emory University, developer of the Emory Cardiac Tool Box used in nuclear imaging and past-president of the American Society of Nuclear Cardiology (ASNC), explains the use of artificial intelligence (AI) in cardiac imaging. He said there is a tsunami of new AI applications that are starting to flood the FDA for market approval, and there are several examples of AI already in use in radiology. He spoke on this topic in a keynote session at the 2020 ASNC meeting.

 

Related Artificial Intelligence in Cardiology Content:

VIDEO: Machine Learning for Diagnosis and Risk Prediction in Nuclear Cardiology — Interview with Piotr J. Slomka, Ph.D.,

Artificial Intelligence Applications in Cardiology

VIDEO: Artificial Intelligence May Improve Cath Lab Interventions — Interview with Nick West, M.D., Abbott CMO

How Artificial Intelligence Will Change Medical Imaging

VIDEO: Artificial Intelligence for Echocardiography at Mass General — Interview with Judy Hung, M.D.

VIDEO: ACC Efforts to Advance Evidence-based Implementation of AI in Cardiovascular Care — Interview with John Rumsfeld, M.D.

VIDEO: Overview of Artificial Intelligence and its Use in Cardiology — Interview with Anthony Chang, M.D.

For more AI in cardiology content

Coronavirus (COVID-19) | April 18, 2020
VIDEO: COVID-19 Precautions for Cardiac Imaging

Stephen Bloom, M.D., FASNC, director of noninvasive cardiology (cardiac CT, nuclear cardiology and echocardiography) at Midwest Heart and Vascular Associates, Overland Park, Kansas. He is also a member of the American Society of Nuclear Cardiology (ASNC) Board of Directors, explains some of the issues involved and protocols used for cardiac imaging during the COVID-19 pandemic. His discussion includes computed tomography, cardiac ultrasound and nuclear imaging.

Right now, Bloom said it is difficult to test everybody and there is a shortage of masks, gowns and other personal protective equipment (PPE), and the imaging equipment needs to be sanitized each time it is used. He said it is just is not possible to image all the patients who need imaging right now. Hospitals also are trying to limit the number of healthy people people coming into hospitals for routine visits and tests to reduce their potential exposure to the novel coronavirus (COVID-19, SARS-CoV-2) and help containment efforts. 

"The tests should be done, very simply, if it changes the care of the patient. If it doesn't change the care of the patient, and it can be postponed, it should be postponed," Bloom explained. "I would say 80 percent of our cardiac imaging exams have stopped. It has been very dramatic."

 

Related Imaging Precautions During COVID-19 Content:

Cardiac Imaging Best Practices During the COVID-19 Pandemic

Best Practices for Nuclear Cardiology Laboratories During the Coronavirus (COVID-19) Pandemic

ASE Guidelines for the Protection of Echocardiography Providers During the COVID-19 Outbreak 

VIDEO: Best Practices for Nuclear Cardiology During the COVID-19 Pandemic — Interview with Hicham Skali, M.D.

VIDEO: Cancelling Non-essential Cardiac Procedures During the COVID-19 Outbreak — Interview with Ehtisham Mahmud, M.D. 

VIDEO: 9 Cardiologists Share COVID-19 Takeaways From Across the U.S.  

VIDEO: Telemedicine in Cardiology and Medical Imaging During COVID-19 — Interview with Regina Druz, M.D.

VIDEO: Use of Teleradiology During the COVID-19 Pandemic — an interview with radiologist John Kim, M.D.

Study Looks at CT Findings of COVID-19 Through Recovery

Experts Stress Radiology Preparedness for COVID-19

VIDEO: Imaging COVID-19 With Point-of-Care Ultrasound (POCUS) — Interview with emergency physician Mike Stone, M.D.,

VIDEO: How China Leveraged Health IT to Combat COVID-19 — Interview with Jilan Liu, M.D., CEO for the HIMSS Greater China

ACR Recommendations for the Use of Chest Radiography and CT for Suspected COVID-19 Cases

VIDEO: What Cardiologists Need to Know about COVID-19 — Interview with Thomas Maddox, M.D.

The Cardiac Implications of Novel Coronavirus

Information Technology View all 264 items

Artificial Intelligence | July 22, 2021
Technology Report: Artificial Intelligence in Radiology 2021

This is an overview of trends and technologies in radiology artificial intelligence (AI) applications in 2021. Views were shared by 11 radiologists using AI and industry leaders, which include:

• Randy Hicks, M.D., MBA, radiologist and CEO of Reginal Medical Imaging (RMI), and an iCAD Profound AI user.

• Prof. Dr. Thomas Frauenfelder, University of Zurich, Institute for Diagnostic and Interventional Radiology, and Riverain AI user.

• Amy Patel, M.D., medical director of Liberty Hospital Women’s Imaging, assistant professor of radiology at UMKC, and user of Kios AI for breast ultrasound.

• Sham Sokka, Ph.D., vice president and head of innovation, precision diagnosis, Philips Healthcare.

• Ivo Dreisser, Siemens Healthineers, global marketing manager for the AI Rad Companion.

• Bill Lacey, vice president of medical informatics, Fujifilm Medical Systems USA.

• Karley Yoder, vice president and general manager, artificial intelligence, GE Healthcare.

• Georges Espada, head of Agfa Healthcare digital and computed radiography business unit.

• Pooja Rao, head of research and development and co-founder of Qure.ai.

• Jill Hamman, world-wide marketing manager at Carestream Health.

• Sebastian Nickel, Siemens Healthineers, global product manager for the AI Pathway Companion. 

There has been a change in attitudes about AI on the expo floor at the Radiological Society of North America (RSNA) over the last two years. AI conversations were originally 101 level and discussed how AI technology could be trained to sort photos of dogs and cats. However, in 2020, with numerous FDA approvals for various AI applications, the conversations at RSNA, and industry wide, have shifted to that of accepting the validity of AI. Radiologists now want to discuss how a specific AI algorithm is going to help them save time, make more accurate diagnoses and make them more efficient.

With a higher level of maturity in AI and the technology seeing wider adoption, radiologists using it say AI gives them additional confidence in their diagnoses, and can even help readers who may not be deep experts in the exam type they are being asked to read. 

With a myriad of new AI apps gaining regulatory approval from scores of imaging vendors, the biggest challenge for getting this technology into hospitals is an easy to integrate format. This has led to several vendors creating AI app stores. These allow AI apps to integrate easily into radiology workflows because the apps are already integrated as third-party software into a larger radiology vendors' IT platform.  

There are now hundreds of AI applications that do a wide variety of analysis, from data analytics, image reconstruction, disease and anatomy identification, automating measurements and advanced visualization. The AI applications can be divided into 2 basic types — AI to improve workflow, and AI for clinical decision support, such as diagnostic aids.

On the workflow side, several vendors are leveraging AI to pull together all of a patients' information, prior exams and reports in one location and to digest the information so it is easier for the radiologist to consume. Often the AI pulls only data and priors that relate to a specific question being asked, based on the imaging protocol used for the exam. One example of this is the Siemens Healthineers AI Clinical Pathway and Siemens AI integrations with PACS to automate measurements and advanced visualization.

AI is also helping simplify complex tasks and help reduce the reading time on involved exams. One example of this is in 3-D breast tomosythesis with hundreds of images, which is rapidly replacing 2-D mammography, which only produces 4 images. Another example is automated image reconstruction algorithms to significantly reduce manual work. AI also is now being integrated directly into several vendors' imaging systems to speed workflow and improve image quality.

Vendors say AI is here to stay. They explain the future of AI will be automation to help improve image quality, simplify manual processes, improved diagnostic quality, new ways to analyze data, and workflow aids that operate in the background as part of a growing number of software solutions. 

Several vendors at RSNA 2020 noted that AI's biggest impact in the coming years will be its ability to augment and speed the workflow for the small number of radiologists compared to the quickly growing elder patient populations worldwide. There also are applications in rural and developing countries were there are very low numbers of physicians or specialists.

 

Related AI in Medical Imaging Content:

AI Outperforms Humans in Creating Cancer Treatments, But Do Doctors Trust It?

VIDEO: Artificial Intelligence For MRI Helps Overcome Backlog of Exams Due to COVID

How AI is Helping the Fight Against Breast Cancer

VIDEO: Use of Artificial Intelligence in Nuclear Imaging

3 High-impact AI Market Trends in Radiology at RSNA 2019

 

Photo Gallery of New Imaging Technologies at RSNA 2019

VIDEO: Editors Choice of the Most Innovative New Radiology Technology at RSNA 2019

Study Reveals New Comprehensive AI Chest X-ray Solution Improves Radiologist Accuracy

VIDEO: Real-world Use of AI to Detect Hemorrhagic Stroke

The Radiology AI Evolution at RSNA 2019

 

Eliminating Bias from Healthcare AI Critical to Improve Health Equity

VIDEO: FDA Cleared Artificial Intelligence for Immediate Results of Head CT Scans

Building the Future of AI Through Data

WEBINAR: Do More, Perform Better: Delivering Clinical Quality through Advanced Radiology and Artificial Intelligence

Integrating Artificial Intelligence in Treatment Planning

 

Selecting an AI Marketplace for Radiology: Key Considerations for Healthcare Providers

Artificial Intelligence Improves Accuracy of Breast Ultrasound Diagnoses

Artificial Intelligence Greatly Speeds Radiation Therapy Treatment Planning

WEBINAR: Building the Bridge - How Imaging AI is Delivering Clinical Value Across the Care Continuum

AI in Medical Imaging Market to Reach $1.5B by 2024

 

VIDEO: AI-Assisted Automatic Ejection Fraction for Point-of-Care Ultrasound

5 Trends in Enterprise Imaging and PACS Systems

VIDEO: Artificial Intelligence to Automate CT Calcium Scoring and Radiomics

Scale AI in Imaging Now for the Post-COVID Era

VIDEO: Integrating Artificial Intelligence Into Radiologists Workflow

 

Northwestern Medicine Introduces Artificial Intelligence to Improve Ultrasound Imaging

Find more artificial intelligence news and video

 

 

 

Magnetic Resonance Imaging (MRI) | March 19, 2021
VIDEO: Artificial Intelligence For MRI Helps Overcome Backlog of Exams Due to COVID

Darryl B. Sneag, M.D., a radiologist and director of peripheral nerve MRI at the Hospital for Special Surgery (HSS) in New York City, explains how artificial intelligence (AI) magnetic resonance imaging (MRI) reconstruction algorithms have cut imaging times by 50 percent. This has enabled his facility to maintain the same number of patients as it did prior to the pandemic, while still having time to sterilize the scanners after each patient. 

Many radiology departments are now experiencing a backlog of cases due to COVID-19 shutdowns in 2020 and the limits on the number of patients that can be in the hospital for imaging exams due to pandemic containment precautions. Sneag said AI is now playing a role in helping streamline workflow.

HSS has 19 GE Healthcare MRI scanners and uses the Air Recon DL AI image reconstruction algorithm. This allows for shorter scan times, so the same number of patients as pre-pandemic can be imaged per day, even with deeper cleaning of the MRI bore. Sneag explains the algorithm has greatly helped with patient throughput, but the trade off is sometimes getting a ringing artifact on images.

HSS also uses GE's Air Coil flexible pad MRI coils. These can wrap around the patient to improve comfort and get the coils closer to the anatomy being imaged.

 

Related MRI and COVID Content:

Business During COVID-19 and Beyond

Imaging Volumes Hold Steady Post COVID-19 Closures

GE Healthcare Addresses Growing Radiology Data Challenges at RSNA 2019

Technology is Driving the MRI Market

VIDEO: How to Image COVID-19 and Radiological Presentations of the Virus

Top Trend Takeaways in Radiology From RSNA 2020

Post-COVID Pain or Weakness? Request an Ultrasound or MRI

Find more COVID radiology-related content

 

PET-CT | December 04, 2020
VIDEO: Example of PET-CT Artificial Intelligence Driven Image Reconstruction

This is an example of Canon's Advanced intelligent Clear-IQ Engine (AiCE) AI-driven image reconstruction software that is now being used to improve image quality on the Canon Celesteion Prime PET/CT nuclear imaging system. The deep learning is used to enhance the iterative reconstruction used to reduce noise and sharped high contrast resolution on positron emission tomography (PET) images from the digital PET detector used on the system. 

This example is a whole-body FGD PET scan of a patient with a large BMI with lung cancer.

The Cartesion Prime PET/CT is the industry’s only air-cooled digital PET/CT, provides variable bed time (vBT) acquisition as a standard feature. This and the new FDA 510(k)-pending AiCE technology were highlighted at the 2020 Radiological Society of North America (RSNA) virtual meeting. 

Find more RSNA news

 

Artificial Intelligence | December 02, 2020
VIDEO: Deep Learning Analysis of Abdominal Fat to Assess Heart Attack and Stroke Risk

Kirti Magudia, M.D., Ph.D., an abdominal imaging and ultrasound fellow at the University of California San Francisco, explains how an automated deep learning analysis of abdominal computed tomography (CT) images can produce a more precise measurement of body composition and better predicts major cardiovascular events, such as heart attack and stroke, better than overall weight or body mass index (BMI). This was according to a study she presented at the 2020 Radiological Society of North America (RSNA) virtual meeting.

Unlike BMI, which is based on height and weight, a single axial CT slice of the abdomen visualizes the volume of subcutaneous fat area, visceral fat area and skeletal muscle area. However, manually measuring these individual areas is time intensive and costly. A multidisciplinary team of researchers, including radiologists, a data scientist and biostatistician, developed a fully automated artificial intelligence (AI) method to determine body composition metrics from abdominal CT images.

Statistical analysis demonstrated that visceral fat area was independently associated with future heart attack and stroke. BMI was not associated with heart attack or stroke.

Read more about this study

Find more RSNA news

Women's Health View all 77 items

Coronavirus (COVID-19) | May 11, 2021
VIDEO: COVID Vaccine Adenopathy Can Last Up to 10 Weeks

Yael Eshet, M.D., MSc, a diagnostic radiology specialist at Sheba Medical Center in Israel, was the lead author on a recent study that showed COVID-19 (SARS-CoV-2) vaccine adenopathy can persist more than 6 weeks. This swelling of lymph nodes is similar to what is seen cancer and infections and the new findings show it can last longer than 7-10 weeks. The current recommended time people should delay medical imaging is 6 weeks after receiving a COVID vaccine to avoid a misdiagnosis,[2] but this new study shows there is increased inflammation shown on PET-CT imaging for much longer.

These were the findings in the Radiology published study "Prevalence of Increased FDG PET/CT Axillary Lymph Node Uptake Beyond 6 Weeks after mRNA COVID-19 Vaccination."[1]

Researchers using fluorodeoxyglucose (FDG)-positron emission tomography (PET) have found increased FDG uptake in the lymph nodes of patients 7-10 weeks past their second mRNA-based Pfizer-BioNTech COVID-19 vaccination. This new information indicates a persistent immune response that could be mistaken on imaging exams for serious conditions like lymphoma over a much longer period of time.

Recent recommendations for post-vaccine lymphadenopathy advise scheduling routine imaging, such as screening mammography, before, or at least 6 weeks after, the final vaccination dose to eliminate false positive results. However, this new research showed that avid axillary lymph node uptake was present beyond 6 weeks after the second vaccination in more than 29% of the patients in the study cohort.

The authors stated “This study shows that avid axillary lymph node uptake on FDG PET/CT can be detected in more than a quarter of our patient population even beyond 6 weeks after the second dose of the mRNA-based COVID-19 vaccination. Compared to a previous study showing normalization of FDG uptake within 40 days of receiving an inactivated H1N1 influenza vaccine, we found uptake persistence even at 70 days. Physicians should be aware of this potential pitfall.”

Some images in this video are from another Radiology study, which showed PET tracer uptake at the COVID vaccine injection site and other examples of axillary adenopathy.[3]

 

Related COVID Vaccine Axillary Adenapathy Content:

COVID-19 Vaccine Can Cause False Positive Cancer Diagnosis

Help Spread Awareness of Potential COVID-19 Vaccine Imaging Side-effects

VIDEO: COVID Vaccine May Cause Enlarged Lymph Nodes on Mammograms — Interview with Constance "Connie" Lehman, M.D.

COVID-19 Vaccination Axillary Adenopathy Detected During Breast Imaging

PHOTO GALLERY: How COVID-19 Appears on Medical Imaging

Find more radiology related COVID content 

References:

1. Yael Eshet, Noam Tau1, Yousef Alhoubani, Nayroz Kanana, Liran Domachevsky, Michal Eifer. Prevalence of Increased FDG PET/CT Axillary Lymph Node Uptake Beyond 6 Weeks after mRNA COVID-19 Vaccination. Radiology. Published Online:Apr 27 2021https://doi.org/10.1148/radiol.2021210886.

2. Constance D. Lehman, Leslie R. Lamb, and Helen Anne D'Alessandro. Mitigating the Impact of Coronavirus Disease (COVID-19) Vaccinations on Patients Undergoing Breast Imaging Examinations: A Pragmatic Approach American Journal of Roentgenology. 10.2214/AJR.21.25688.

3. Can Özütemiz, Luke A. Krystosek, An L. Church, Anil Chauhan, Jutta M. Ellermann, Evidio Domingo-Musibay, Daniel Steinberger. Lymphadenopathy in COVID-19 Vaccine Recipients: Diagnostic Dilemma in Oncology Patients. Radiology. Published Online:Feb 24 2021https://doi.org/10.1148/radiol.2021210275.

 

Breast Imaging | March 26, 2021
VIDEO: COVID Vaccine May Cause Enlarged Lymph Nodes on Mammograms

Constance "Connie" Lehman, M.D., Ph.D., chief of breast imaging, co-director of the Avon Comprehensive Breast Evaluation Center at the Massachusetts General Hospital, and professor of radiology at Harvard Medical School, explains issues and suggested guidelines for women who receive the COVID-19 vaccine and need to get a mammogram. In the first three months since the vaccines have been released, there have been numerous case reports of the vaccine causing swollen lymph nodes. This is would usually raise a red flag for breast cancer, but is normal for many women receiving the vaccine as their body's immune system gears up against the virus. 

Lehman said cases reports of axillary adenopathy have been identified on breast imaging after coronavirus disease (COVID-19) vaccination and are rising. Lehman et al. proposed a pragmatic management approach in a recent article in the American Journal of Roentgenology (AJR).[1]

In the settings of screening mammography, screening MRI and diagnostic imaging work-up of breast symptoms, with no imaging findings beyond unilateral axillary adenopathy ipsilateral to recent (prior six weeks) vaccination, they report the adenopathy as benign with no further imaging indicated if no nodes are palpable six weeks after the last vaccine dose. 

For patients with palpable axillary adenopathy in the setting of ipsilateral recent vaccination, clinical follow-up of the axilla is recommended. In all these scenarios, axillary ultrasound is recommended if clinical concern persists six weeks after vaccination. 

In patients with recent breast cancer diagnosis in the pre- or peri-treatment setting, prompt recommended imaging is encouraged as well as vaccination (in the thigh or contralateral arm). The recommendations align with the ACR BI-RADS Atlas and aim to: 1) reduce patient anxiety, provider burden, and costs of unnecessary evaluation of enlarged nodes in the setting of recent vaccination, and 2) avoid further delays in vaccinations and breast cancer screening during the pandemic.

 

Related Medical Imaging of COVID Content:

COVID-19 Vaccination Axillary Adenopathy Detected During Breast Imaging

PHOTO GALLERY: How COVID-19 Appears on Medical Imaging

VIDEO: Imaging COVID-19 With Point-of-Care Ultrasound (POCUS) — Interview with Mike Stone, M.D.

VIDEO: Use of Teleradiology During the COVID-19 Pandemic — Interview with John Kim, M.D.

Find more radiology related COVID content 

 

Reference:

1. Constance D. Lehman, Leslie R. Lamb, and Helen Anne D'Alessandro. Mitigating the Impact of Coronavirus Disease (COVID-19) Vaccinations on Patients Undergoing Breast Imaging Examinations: A Pragmatic Approach American Journal of Roentgenology. 10.2214/AJR.21.25688

 

 

Artificial Intelligence | November 11, 2020
VIDEO: Integrating Artificial Intelligence Into Radiologists Workflow

Artificial Intelligence (AI) is becoming more common place in radiology practices, and emerging technologies are providing radiologists with sophisticated detection software to aid their reading and provide support for a busy workflow. With the progression of AI technology, vendors must look not only at what AI can do for the radiologist, but how the radiologist and the technician interact with that technology –  the goal should be increasing accuracy while also positively improving workflow. GE Healthcare is working to improve radiology AI workflow in its Centricity Universal Viewer.

Three key opinion leaders offers their views on what is needed to make AI more valauble and accessible to radiologists. These include:

   • Amy Patel, M.D., breast radiologist, medical director, Liberty Hospital Women's Imaging, assistant professor of radiology, University of Missouri-Kansas City.

   • Prof. Dr. Thomas Frauenfelder, M.D., vice chairman and professor of thoracic radiology, Institute for Diagnostic and Interventional Radiology, University of Zurich.

   • Randy Hicks, M.D., chief executive officer, Regional Medical Imaging.

 

Learn more about the Centricity Universal Viewer in the VIDEO: How GE Healthcare’s Zero Footprint Remote Image Viewer Supports Clinical Care

 

 

 

 

 

MRI Breast | October 14, 2020
VIDEO: Use of Breast MRI Screening in Women With Dense Breasts

Professor Christiane Kuhl, M.D., director of radiology, University Hospital Aachen, Germany, explains how breast magnetic resonance imaging (MRI) can be used to clearly identify breast cancers in women with dense breast tissue. In women with dense breasts, it can be very difficult to detect many cancers on standard mammograms because the cancers and dense tissue both appear white. MRI can help clearly define tumors and identify which nodules are cancer and which are benign, which can help greatly reduce the need for biopsies.

Kuhl is an expert in breast imaging and breast MRI. She helped develop an a shortened MRI protocol that allows breast MR images to be created in 3 minutes or less, rather than standard protocols that can take up to 30 minutes. In the interview she shows patient case examples of standard mammograms and the MRI supplemental imaging for the same patient to show the hidden tumors. 

She also explains the differences between standard 2-d mammography, the current standard of care, and the newer 3-D mammogram tomosythnesis technology, breast ultrasound and breast MRI technologies.

Read the related article Use of Breast MRI Screening in Women With Dense Breasts, which includes case examples comparing mammograms to the patients' breast MRIs.

Other video interviews with Dr. Kuhl:

VIDEO: Explaining Dense Breasts

VIDEO: The Impact of COVID-19 on Breast Imaging

 

Related Breast MRI Content:

Abbreviated MRI Outperforms 3-D Mammograms at Finding Cancer in Dense Breasts

VIDEO: Explaining Dense Breasts — Interview with Christiane Kuhl, M.D.

VIDEO: Use of Breast MRI Improved Cancer Detection in Dense Breasts in Dutch Study — Interview with Gillian Newstead, M.D.

Technologies to Watch in Breast Imaging

Screening MRI Detects BI-RADS 3 Breast Cancer in High-risk Patients

Rapid Breast MRI Screening Improves Cancer Detection in Dense Breasts

Breast MRI in Cancer Diagnosis
 

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