Missed Incidental Pulmonary Embolism: Harnessing Artificial Intelligence to Assess Prevalence and Improve Quality Improvement Opportunities

Purpose Incidental pulmonary embolism (IPE) can be found on body CT. The aim of this study was to evaluate the feasibility of using artificial intelligence to identify missed IPE on a large number of CT examinations. Methods This retrospective analysis included all single-phase chest, abdominal, and pelvic (CAP) and abdominal and pelvic (AP) CT examinations performed at a single center over 1 year, for indications other than identification of PE. Proprietary visual classification and natural language processing software was used to analyze images and reports from all CT examinations, followed by a two-step human adjudication process to classify cases as true positive, false positive, true negative, or false negative. Descriptive statistics were assessed for prevalence of IPE and features (subsegmental versus central, unifocal versus multifocal, right heart strain or not) of missed IPE. Interrater agreement for radiologist readers was also calculated. Results A total of 11,913 CT examinations (6,398 CAP, 5,515 AP) were included. Thirty false-negative examinations were identified on CAP (0.47%; 95% confidence interval [CI], 0.32%-0.67%) and nineteen false-negative studies on AP (0.34%; 95% CI, 0.21%-0.54%) studies. During manual review, readers showed substantial agreement for identification of IPE on CAP (κ = 0.76; 95% CI, 0.66-0.86) and nearly perfect agreement for identification of IPE on AP (κ = 0.86; 95% CI, 0.76-0.97). Forty-nine missed IPEs (0.41%; 95% CI, 0.30%-0.54%) were ultimately identified, compared with seventy-nine IPEs (0.66%; 95% CI, 0.53%-0.83%) identified at initial clinical interpretation. Conclusions Artificial intelligence can efficiently analyze CT examinations to identify potential missed IPE. These results can inform peer-review efforts and quality control and could potentially be implemented in a prospective fashion.