News | Artificial Intelligence | May 20, 2019

AI Detects Unsuspected Lung Cancer in Radiology Reports, Augments Clinical Follow-up

Research study demonstrates healthcare artificial intelligence application to detect and triage pulmonary nodules

AI Detects Unsuspected Lung Cancer in Radiology Reports, Augments Clinical Follow-up

May 20, 2019 — Digital Reasoning announced results from its automated radiology report analytics research. In a series of experiments on radiology reports from emergency departments, inpatient and outpatient healthcare facilities, Digital Reasoning used natural language processing (NLP) and machine learning (ML) to identify and triage high-risk lung nodules, achieving queue precision of 90.2 percent. The findings have now been published in the Journal of Clinical Oncology as part of the 2019 American Society of Clinical Oncology (ASCO) meeting proceedings.1

For health systems, reviewing incidental findings can be a time and labor intensive process.2 Other studies show the rate for timely clinical follow-up can fall as low as 29 percent across the industry.3 Applying advanced artificial intelligence (AI) to radiology reports to automate the identification and triage of pulmonary nodules, empowers doctors to focus on reviewing and acting on the most high-risk cases. This results in improved patient safety and faster time-to-treatment without excess labor.

During the research study, Digital Reasoning analyzed 8,879 free-text, narrative computed tomography (CT) radiology reports from Dec. 8, 2015 through April 23, 2017.  Today, those analytics are embedded in an enterprise solution utilized across more than 150 hospitals and 60 cancer centers in the United States.

For more information: www.ascopubs.org/journal/jco

References

1. French C., Makowski M., Terker S., et al. Automating incidental findings in radiology reports using natural language processing and machine learning to identify and classify pulmonary nodules. Presented at ASCO 2019. J Clin Oncol 37, 2019 (suppl; abstr e18093)

2. Rosenkrantz A.B., Xue X., Gyftopoulos S., et al. Downstream Costs Associated with Incidental Pulmonary Nodules Detected on CT. Acad Radiol., published online Aug. 6, 2018. pii: S1076-6332(18)30372-6, 2018

3. Blagev D.P., Lloyd J.F., Conner K., et al. Follow-up of Incidental Pulmonary Nodules and the Radiology Report. J Am Coll Radiol., published online Dec. 6, 2013. 13(2 Suppl):R18-24, 2016

Related Content

FDA Clears AiCE Image Reconstruction on Canon's Aquilion Precision CT

Image courtesy of Canon Medical Systems

Technology | Computed Tomography (CT) | October 21, 2019
Canon Medical Systems USA Inc. has received U.S. Food and Drug Administration (FDA) 510(k) clearance on its Advanced...
The Revolution Apex intelligent computed tomography (CT) scanner

The Revolution Apex intelligent computed tomography (CT) scanner. Image courtesy of GE Healthcare.

News | RSNA | October 18, 2019
At the 2019 annual meeting of the Radiological Society of North America (RSNA 2019), Dec. 1-6 in Chicago, GE Healthcare...
Selecting an AI Marketplace for Radiology: Key Considerations for Healthcare Providers
Feature | Artificial Intelligence | October 18, 2019 | Sanjay Parekh, Ph.D.
October 18, 2019 — As the nascent market for...
MaxQ AI's Intracranial Hemorrhage Software to be Integrated on Philips CT Systems
News | Artificial Intelligence | October 18, 2019
Medical diagnostic artificial intelligence (AI) company MaxQ AI announced that its Accipio intracranial hemorrhage (ICH...
While electronic medical record systems have helped consolidate most patient data into one location, medical imaging IT systems has proved to be more difficult to replicate by large EMR vendors. This has made room in the market for third-party radiology IT vendors that allow easy integration with the larger EMRs like Epic and Cerner. This image shows Agfa's enterprise imaging system, leveraging its ability to be accessed anywhere with internet connection and pull images from radiology and surgery.

While electronic medical record systems have helped consolidate most patient data into one location, medical imaging IT systems has proved to be more difficult to replicate by large EMR vendors. This has made room in the market for third-party radiology information system vendors that allow easy integration with the larger EMRs like Epic and Cerner. This image shows Agfa's enterprise imaging system, leveraging its ability to be accessed anywhere with an internet connection and able to pull in images from both radiology and surgery. 

Feature | Enterprise Imaging | October 17, 2019 | Steve Holloway
October 17, 2019 — The growing influence and uptake of electronic medical records (EMRs) in healthcare has driven deb
Sectra Adds DePuy Synthes 3-D Templates to Pre-Operative Orthopedic Solution
News | Orthopedic Imaging | October 17, 2019
International medical imaging information technology (IT) and cybersecurity company Sectra is extending its pre-...
Guerbet Signs Agreement With Icometrix for Exclusive Distribution of Icobrain
News | Neuro Imaging | October 16, 2019
Guerbet announced it has signed an exclusive agreement with Icometrix for the distribution in France, Italy and Brazil...
Subtle Medical Receives FDA 510(k) Clearance for AI-powered SubtleMR
Technology | Artificial Intelligence | October 16, 2019
Subtle Medical announced 510(k) clearance from the U.S. Food and Drug Administration (FDA) to market SubtleMR. SubtleMR...
Feature | Artificial Intelligence | October 16, 2019 | By Siddharth (Sid) Shah
The period between November through February is pretty interesting for the field of medical imaging — two major confe
As the role of artificial intelligence continues to expand, many companies are making significant investments in this technology to offer solutions
Feature | Artificial Intelligence | October 09, 2019 | By Sharmistha Sarkar
Artificial intelligence (AI) is a technology