A newly developed deep learning algorithm could help assess a patient’s risk of cardiovascular disease with the same low-dose computerized tomography (CT) scan used to screen for lung cancer, according to a study published in Nature Communications.
Researchers acquired more than 30,000 low-dose CT images from the National Lung Screening Trial, to develop, train, and validate a deep learning algorithm capable of filtering out unwanted artifacts and noise, and extracting features needed to make a diagnosis. They then validated the algorithm using an additional 2,085 images.
The algorithm proved to be highly effective in analyzing the risk of cardiovascular disease in high-risk patients using low-dose CT scans. More so, the algorithm closely mimicked the performance of dedicated cardiac CT scans when it was tested on an independent dataset. This approach, researchers say, paves the way for more efficient, more cost-effective, and lower radiation diagnoses, without requiring patients to undergo a second CT scan. The study was funded by NHLBI.