Efficacy of an Automated Pulmonary Embolism (PE) Detection Algorithm on Routine Contrast-Enhanced Chest CT Imaging for Non-PE Studies.

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Abstract

The urgency to accelerate PE management and minimize patient risk has driven the development of artificial intelligence (AI) algorithms designed to provide a swift and accurate diagnosis in dedicated chest imaging (computed tomography pulmonary angiogram; CTPA) for suspected PE; however, the accuracy of AI algorithms in the detection of incidental PE in non-dedicated CT imaging studies remains unclear and untested. This study explores the potential for a commercial AI algorithm to identify incidental PE in non-dedicated contrast-enhanced CT chest imaging studies. The Viz PE algorithm was deployed to identify the presence of PE on 130 dedicated and 63 non-dedicated contrast-enhanced CT chest exams. The predictions for non-dedicated contrast-enhanced chest CT imaging studies were 90.48% accurate, with a sensitivity of 0.14 and specificity of 1.00. Our findings reflect that the Viz PE algorithm demonstrated an overall accuracy of 90.16%, with a specificity of 96% and a sensitivity of 41%. Although the high specificity is promising for ruling in PE, the low sensitivity highlights a limitation, as it indicates the algorithm may miss a substantial number of true-positive incidental PEs. This study demonstrates that commercial AI detection tools hold promise as integral support for detecting PE, particularly when there is a strong clinical indication for their use; however, current limitations in sensitivity, especially for incidental cases, underscore the need for ongoing radiologist oversight.

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