In this study with historical controls and prospective evaluation, regulatory-cleared AI software was evaluated to prioritize iPE on routine chest CT scans with intravenous contrast agent in adult oncology patients. Diagnostic accuracy metrics were calculated, and temporal end points, including detection and notification times (DNTs), were assessed during three time periods (April 2019 to Sept. 2020): routine workflow without AI, human triage without AI, and worklist prioritization with AI.
In total, 11,736 CT scans in 6,447 oncology patients (mean age, 63 years ± 1 2 [SD]; 3,367 men) were included. Prevalence of iPE was 1.3% (51 of 3,837 scans), 1.4% (54 of 3,920 scans), and 1.0% (38 of 3,979 scans) for the respective time periods. The AI software detected 131 true-positive, 12 false-negative, 31 false-positive, and 11,559 true-negative results, achieving 91.6% sensitivity, 99.7% specificity, 99.9% negative predictive value, and 80.9% positive predictive value. During prospective evaluation, AI-based worklist prioritization reduced the median DNT for iPE-positive examinations to 87 minutes (vs. routine workflow of 7,714 minutes and human triage of 4,973 minutes). Radiologists’ missed rate of iPE was significantly reduced from 44.8% (47 of 105 scans) without AI to 2.6% (one of 38 scans) when assisted by the AI tool (P < .001).
AI-assisted workflow prioritization of IPE on routine CT scans in oncology patients showed high accuracy and significantly shortened the time to treatment in a setting with a backlog of examinations.
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