Evaluation of AI-based detection of incidental pulmonary emboli in cardiac CT angiography scans.

Abstract

Incidental pulmonary embolism (PE) is detected in 1% of cardiac CT angiography (CCTA) scans, despite the targeted aortic opacification and limited field of view. While artificial intelligence (AI) algorithms have proven effective in detecting PE in CT pulmonary angiography (CTPA), their use in CCTA remains unexplored. This study aimed to evaluate the feasibility of an AI algorithm for detecting incidental PE in CCTA scans. A dedicated AI algorithm was retrospectively applied to CCTA scans to detect PE. Radiology reports were reviewed using a natural language processing (NLP) tool to detect mentions of PE. Discrepancies between the AI and radiology reports triggered a blinded review by a cardiothoracic radiologist. All scans identified as positive for PE were thoroughly assessed for radiographic features, including the location of emboli and right ventricular (RV) strain. The performance of the AI algorithm for PE detection was compared to the original radiology report. Between 2021 and 2023, 1534 CCTA scans were analyzed. The AI algorithm identified 27 positive PE scans, with a subsequent review confirming PE in 22/27 cases. Of these, 10 (45.5%) were missed in the initial radiology report, all involving segmental or subsegmental arteries (P < 0.05) with no evidence of RV strain. This study demonstrates the feasibility of using an AI algorithm to detect incidental PE in CCTA scans. A notable radiology report miss rate (45.5%) of segmental and subsegmental emboli was documented. While these findings emphasize the potential value of AI for PE detection in the daily radiology workflow, further research is needed to fully determine its clinical impact.