We retrospectively identified all CTPAs conducted at our institution in 2017 (n=1,499). Exams with clinical questions other than PE were excluded from the analysis (n=34). The remaining exams were classified into positive (n=232) and negative (n=1,233) for PE based on the final written reports, which defined the reference standard. The fully anonymized 1-mm series in soft tissue reconstruction served as input for the PE detection prototype algorithm that was based on a deep convolutional neural network comprising a Resnet architecture. It was trained and validated on 28,000 CTPAs acquired at other institutions.
The algorithm correctly identified 215 of 232 exams positive for pulmonary embolism (sensitivity 92.7%; 95% confidence interval [CI] 88.3–95.5%) and 1,178 of 1,233 exams negative for pulmonary embolism (specificity 95.5%; 95% CI 94.2–96.6%). On a per finding level, 1,174 of 1,352 findings marked as embolus by the algorithm were true emboli. Most of the false positive findings were due to contrast agent–related flow artifacts, pulmonary veins, and lymph nodes.
The AI solution can automatically detect PE cases including a high sensitivity of 92.7% in identifying positive exams for PE and a high specificity of 95.5% in identifying negative PE exams.
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