vRad Presents at SIIM Meeting on Artificial Intelligence Model to Assess Probability of Aortic Dissection

MINNEAPOLIS, Minn., June 27, 2019 – vRad (Virtual Radiologic), a MEDNAX Company and the nation’s leading teleradiology services and telemedicine company, announced it will make a scientific presentation, “ Screening for Aortic Dissection on CT Angiography Using a Convolutional Neural Network,” at the Society for Imaging Informatics in Medicine (SIIM) Annual Meeting on June 28 at Gaylord Rockies Resort in Aurora, Colo. The full session details can be found here.

The presentation highlights how vRad, using machine learning on retrospective aortic dissection data, created an artificial intelligence (AI) model for classifying and prioritizing aortic dissection and rupture for all post-contrast chest CT imaging data passing through vRad’s Patented Reading Platform. The model is integrated into vRad radiologists’ workflow to accelerate time-to-care. The presentation also outlines how vRad, in conjunction with the MEDNAX Radiology Solutions Artificial Intelligence (MDR-AI) Incubator, developed a convolutional neural network model trained on these aortic datasets to assess how likely these pathologies would be present in a chest CT.

“vRad and MEDNAX have had a collaborative and practical approach, which focused our AI efforts on helping radiologists do a better job by removing obstacles and improving their reading experience,” said Benjamin W. Strong, MD, Chief Medical Officer of vRad. “Both clinical and technical parties have shared this goal, and we see results that allow our radiologists to read ‘in the zone’ and maximize their technical efficiency and interpretive accuracy for the benefit of patients.”

“AI in medical imaging is having tangible impacts on patient care,” said Imad Nijim, Chief Information Officer of MEDNAX Radiology Solutions and vRad. “AI in medical imaging is now a tool that enables our radiologists to provide better care, and in some cases, save lives.”

Aortic dissection is a condition in which the aortal wall tears, causing blood to flow through the torn region as the middle and inner layers of the wall separate. This condition can be fatal if not recognized and treated immediately. The ideal imaging technique for identification of aortic dissection is computed tomography angiography (CTA) of the aorta. A fast method of screening chest CT studies for this pathology can assist with efficient patient triage and diagnosis.

“The best innovations are ones that directly help patients, and prioritizing radiologist reading worklists based on the potential presence of a life-threatening condition is a great example,” continued Nijim. “AI is not about replacing radiologists; it is about helping radiologists do what they do best—read images and care for patients as quickly and accurately as possible.”

The work developing the model also includes efforts of the MDR-AI Incubator, which launched in November 2018. The Incubator brings together radiologists, rich clinical datasets and technology partners to stimulate innovation and product development in radiology, improving clinical accuracy, efficiency, and the overall quality of patient care. The AI Incubator’s approach involves developing models that have immediate and measurable impact, so that medical and technical leaders can build tools that improve the practice of radiology for physicians and their patients.