  Coronary Computed Tomography Angiography (CCTA) evaluation of chest-pain
patients in an Emergency Department (ED) is considered appropriate. While a
negative CCTA interpretation supports direct patient discharge from an ED,
labor-intensive analyses are required, with accuracy in jeopardy from
distractions. We describe the development of an Artificial Intelligence (AI)
algorithm and workflow for assisting interpreting physicians in CCTA screening
for the absence of coronary atherosclerosis. The two-phase approach consisted
of (1) Phase 1 - focused on the development and preliminary testing of an
algorithm for vessel-centerline extraction classification in a balanced study
population (n = 500 with 50% disease prevalence) derived by retrospective
random case selection; and (2) Phase 2 - concerned with simulated-clinical
Trialing of the developed algorithm on a per-case basis in a more real-world
study population (n = 100 with 28% disease prevalence) from an ED chest-pain
series. This allowed pre-deployment evaluation of the AI-based CCTA screening
application which provides a vessel-by-vessel graphic display of algorithm
inference results integrated into a clinically capable viewer. Algorithm
performance evaluation used Area Under the Receiver-Operating-Characteristic
Curve (AUC-ROC); confusion matrices reflected ground-truth vs AI
determinations. The vessel-based algorithm demonstrated strong performance with
AUC-ROC = 0.96. In both Phase 1 and Phase 2, independent of disease prevalence
differences, negative predictive values at the case level were very high at
95%. The rate of completion of the algorithm workflow process (96% with
inference results in 55-80 seconds) in Phase 2 depended on adequate image
quality. There is potential for this AI application to assist in CCTA
interpretation to help extricate atherosclerosis from chest-pain presentations.
