The algorithm is designed to analyze CT pulmonary angiography (CTPA) scans for signs of chronic thromboembolic pulmonary hypertension (CTEPH), a progressive and life-threatening condition that affects 5 million people per year around the world. CTEPH is difficult to diagnose because its symptoms are similar to those of other conditions such as chronic obstructive pulmonary disease (COPD).
CTPA and ventilation/perfusion (V/Q) scans are used to determine if a thromboembolic occlusion is causing the pulmonary hypertension; pulmonary thromboendarterectomy (PTE) can then be used to clear the occlusion.
The CTEPH algorithm that Bayer is developing with Merck uses deep learning to support radiologists by identifying signs of CTEPH in CTPA scans. The algorithm analyzes image findings from cardiac, lung perfusion, and pulmonary vessels along with the patient's clinical history.
If the development project proves successful, the software could be deployed via Bayer's Radimetrics software, which connects contrast medium, injector, and scan information to provide important insights for users. Receiving breakthrough device designation could speed up the algorithm's progress through the FDA, but the company cautioned that commercialization of the algorithm depends on the progress of the project.
Copyright © 2018 AuntMinnie.com