News | Mammography | March 07, 2020

Artificial Intelligence to Improve the Precision of Mammograms

The study concludes that a combination of Artificial Intelligence algorithms and the interpretations of radiologists could, in the U.S. alone, result in a half million women not having to undergo unnecessary diagnostic tests every year

The study concludes that a combination of Artificial Intelligence algorithms and the interpretations of radiologists could, in the U.S. alone, result in a half million women not having to undergo unnecessary diagnostic tests every year

Researchers who participated in the DM (digital mammography) DREAM Challenge.

March 7, 2020 — The study is based on the results obtained in the Digital Mammography (DM) DREAM Challenge, an international competition led by IBM where researchers from the Instituto de Física Corpuscular (IFIC, CSIC-UV) have participated along with scientists from the UPV's Institute of Telecommunications and Multimedia Applications (iTEAM).

The team of researchers from IFIC and the iTEAM UPV was the only Spanish group that reached the end of the challenge. To do so, they developed a prediction algorithm based on convolutional neuron networks, an artificial intelligence technique that simulates the neurons of the visual cortex and allows classifying images, as well as self-learning of the system. Principles related to interpreting x-rays were also applied, where the group has several patents. The Valencian team's results, along with the rest of the finalists, are now published in the Journal of the American Medical Association (JAMA Network Open).

"Participating in this challenge has allowed our group to collaborate in Artificial Intelligence projects with clinical groups of the Comunidad Valenciana," stated Alberto Albiol, tenured professor at UPV and member of the iTEAM group. "This has opened opportunities for us to apply the Machine Learning techniques, as they are proposed in the article," he added.

For example, the work carried out by Valencian researchers is being carried out in Artemisa, the new computing platform for artificial intelligence at the Instituto de Física Corpuscular funded by the European Union and the Generalitat Valenciana within the FEDER operating program of the Comunitat Valenciana for 2014-2020 for the acquisition of R+D+i infrastructures and equipment.

"Designing strategies to reduce operating costs of health care is one of the objectives of sustainably applying Artificial Intelligence," pointed out Francisco Albiol, researcher of the IFIC and participant in the study. "The challenges cover from the algorithm part to jointly designing evidence-based strategies along with the medical sector. Artificial Intelligence applied at a large scale is one of the most promising technologies to make health care sustainable," he noted.

The goal of the Digital Mammography (DM) DREAM Challenge is to involve a broad international scientific community (over 1,200 researchers from around the world) to evaluate whether or not Artificial Intelligence algorithms can be equal to or improve the interpretations of the mammograms carried out by radiologists.

"This DREAM Challenge allowed carrying out a rigorous and adequate evaluation of dozens of advanced deep learning algorithms in two independent databases," explained Justin Guinney, vice president of Computational Oncology at Sage Bionetworks and president of DREAM Challenges.

 

A half million fewer mammograms per year in the U.S.

Led by IBM Research, Sage Bionetworks and Kaiser Permanente Washington Research Institute, the Digital Mammography DREAM Challenge concluded that, no algorithm by itself surpassed the radiologists, a combination of methods added to the evaluations of experts improved the accuracy of the exams. Kaiser Permanente Washington (KPW) and the Karolinska Institute (KI) of Sweden provided hundreds of thousands of unidentified mammograms and clinical data.

"Our study suggests that a combination of algorithms of Artificial Intelligence and the interpretations of the radiologists could result in a half million women per year not having to undergo unnecessary diagnostic tests in the United States alone," stated Gustavo Stolovitzky, the director of the IBM program dedicated to Translational Systems Biology and Nanotechnology in the Thomas J. Watson Research Center and founder of DREAM Challenges.

To guarantee the privacy of data and prevent the participants from downloading mammograms with sensitive data, the organizers of the study applied a working system from the model to the data. In the system, participants sent their algorithms to the organizers, who developed a system that applied them directly to the data.

"This focus on sharing data is particularly innovative and essential for preserving the privacy of the data," ensured Diana Buist, of the Kaiser Permanente Washington Health Research Institute. "In addition, the inclusion of data from different countries, with different practices for carrying out mammograms, indicates important translational differences in the way in which Artificial Intelligence can be used on different populations."

Mammograms are the most used diagnostic technique for the early detection of breast cancer. Though this detection tool is commonly effective, mammograms must be evaluated and interpreted by a radiologist, who uses their human visual perception to identify signs of cancer. Thus, it is estimated that there are 10% false positives in the 40 million women who undergo scheduled mammograms each year in the United States.

An effective artificial intelligence algorithm that can increase the radiologist's ability to reduce repeating unnecessary tests while also detecting clinically significant cancers would help increase mammograms' detection value.

For more information: www.upv.es

Related Content

#COVID19 #Coronavirus #2019nCoV #Wuhanvirus #SARScov2

Getty Images

Feature | Coronavirus (COVID-19) | April 03, 2020 | By Melinda Taschetta-Millane and Dave Fornell
In an effort to keep the imaging field updated on the latest information being released on coronavirus (COVID-19), th
Varian received FDA clearance for its Ethos therapy in February 2020. It is an adaptive intelligence solution that uses onboard AI in the treatment system to take the cone beam CT imaging on the system, compare it to the treatment plan and deliver an entire adaptive treatment plan in a typical 15-minute treatment time slot, from patient setup through treatment delivery.

Varian received FDA clearance for its Ethos therapy in February 2020, shown here displayed for the first time at ASTRO 2019. It is an adaptive intelligence solution that uses onboard AI in the treatment system to take the cone beam CT imaging on the system, compare it to the treatment plan and deliver an entire adaptive treatment plan in a typical 15-minute treatment time slot, from patient setup through treatment delivery.

Feature | Treatment Planning | April 03, 2020 | Dave Fornell, Editor
The traditional treatment planning process takes days to create an optimized radiation therapy delivery plan, but new
Category A

Category A

Feature | Breast Density | April 03, 2020 | By Dayna Williams M.D., Shivani Chaudhry, M.D., and Laurie R. Margolies, M.D.
Breast cancer is the most common cance
An example of Philips’ TrueVue technology, which offers photo-realistic rendering and the ability to change the location of the lighting source on 3-D ultrasound images. In this example of two Amplazer transcatheter septal occluder devices in the heart, the operator demonstrating the product was able to push the lighting source behind the devices into the other chamber of the heart. This illuminated a hole that was still present that the occluders did not seal.

An example of Philips’ TrueVue technology, which offers photo-realistic rendering and the ability to change the location of the lighting source on 3-D ultrasound images. In this example of two Amplazer transcatheter septal occluder devices in the heart, the operator demonstrating the product was able to push the lighting source behind the devices into the other chamber of the heart. This illuminated a hole that was still present that the occluders did not seal. Photo by Dave Fornell

Feature | Radiology Imaging | April 02, 2020 | By Katie Caron
A new year — and decade — offers the opportunity to reflect on the advancements and challenges of years gone by and p
#COVID19 #Coronavirus #2019nCoV #Wuhanvirus

Getty Images

Feature | Coronavirus (COVID-19) | April 02, 2020 | Jilan Liu and HIMSS Greater China Team
Information technologies have played a pivotal role in China’s response to the novel coronavirus...
#COVID19 #Coronavirus #2019nCoV #Wuhanvirus #SARScov2 the company is now offering a suite of AI solutions Vuno Med-LungQuant and Vuno Med-Chest X-ray for COVID-19, encompassing both lung X-ray and computed tomography (CT) modalities respectively all at once
News | Artificial Intelligence | April 02, 2020
April 2, 2020 — In the face of the COVID-19 pand
#COVID19 #Coronavirus #2019nCoV #Wuhanvirus #SARScov2 The Chinese start-up company Infervision launches its AI-based solution InferRead CT Lung Covid-19 also in Europe
News | Artificial Intelligence | March 31, 2020
March 31, 2020 — Lung infections generated by the coronavirus can be detected in...
#COVID19 #Coronavirus #2019nCoV #Wuhanvirus #SARScov2 Doctor in our hospital is using this intelligent system for accurate diagnosis

Doctor in our hospital is using this intelligent system for accurate diagnosis. (Photo: Business Wire)

News | Artificial Intelligence | March 31, 2020
March 31, 2020 — The Intelligent Evalua...