A French retrospective study evaluated CINA-PE (Avicenna.AI), an AI tool for pulmonary embolism (PE) detection, using 196 CT pulmonary angiograms (15.8% PE-positive). Radiology residents initially read the CTPAs without AI, and then, after a 2-month interval, with AI assistance, to determine the presence, absence, or indeterminacy of PE. The assistance of CINA-PE led to increased diagnostic sensitivity (from 81.7% to 92.5%) and specificity (from 97.8% to 99.2%). Additionally, the AI support significantly enhanced reader agreement, elevating the kappa value from 0.77 to 0.88.
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Abstract
Purpose
To compare radiology residents’ diagnostic performances to detect pulmonary emboli (PEs) on CT pulmonary angiographies (CTPAs) with deep-learning (DL)–based algorithm support and without.
Methods
Fully anonymized CTPAs (n = 207) of patients suspected of having acute PE served as input for PE detection using a previously trained and validated DL-based algorithm. Three residents in their first three years of training, blinded to the index report and clinical history, read the CTPAs first without, and 2 months later with the help of artificial intelligence (AI) output, to diagnose PE as present, absent or indeterminate. We evaluated concordances and discordances with the consensus-reading results of two experts in chest imaging.
Results
Because the AI algorithm failed to analyze 11 CTPAs, 196 CTPAs were analyzed; 31 (15.8 %) were PE-positive. Good-classification performance was higher for residents with AI-algorithm support than without (AUROCs: 0.958 [95 % CI: 0.921–0.979] vs. 0.894 [95 % CI: 0.850–0.931], p < 0.001, respectively). The main finding was the increased sensitivity of residents’ diagnoses using the AI algorithm (92.5 % vs. 81.7 %, respectively). Concordance between residents (kappa: 0.77 [95 % CI: 0.76–0.78]; p < 0.001) improved with AI-algorithm use (kappa: 0.88 [95 % CI: 0.87–0.89]; p < 0.001).
Conclusion
The AI algorithm we used improved between-resident agreements to interpret CTPAs for suspected PE and, hence, their diagnostic performances.
