Imaging Pearls ❯ Vascular ❯ Artificial Intelligence (AI)
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- Objectives: To evaluate and compare the diagnostic performances of a commercialized artificial intelligence (AI) algorithm for diagnosing pulmonary embolism (PE) on CT pulmonary angiogram (CTPA) with those of emergency radiologists in routine clinical practice.
Methods: This was an IRB-approved retrospective multicentric study including patients with suspected PE from September to December 2019 (i.e., during a preliminary evaluation period of an approved AI algorithm). CTPA quality and conclusions by emergency radiologists were retrieved from radiological reports. The gold standard was a retrospective review of CTPA, radiological and clinical reports, AI outputs, and patient outcomes. Diagnostic performance metrics for AI and radiologists were assessed in the entire cohort and depending on CTPA quality.
How artificial intelligence improves radiological interpretation in suspected pulmonary embolism
Alexandre Ben Cheikh et al.
European Radiology 2022https://doi.org/10.1007/s00330-022-08645-2 - Results: Overall, 1202 patients were included (median age: 66.2 years). PE prevalence was 15.8% (190/1202). The AI algorithm detected 219 suspicious PEs, of which 176 were true PEs, including 19 true PEs missed by radiologists. In the cohort, the highest sensitivity and negative predictive values (NPVs) were obtained with AI (92.6% versus 90% and 98.6% versus 98.1%, respectively), while the highest specificity and positive predictive value (PPV) were found with radiologists (99.1% versus 95.8% and 95% versus 80.4%, respectively). Accuracy, specificity, and PPV were significantly higher for radiologists except in subcohorts with poor-to-average injection quality. Radiologists positively evaluated the AI algorithm to improve their diagnostic comfort (55/79 [69.6%]).
Conclusion: Instead of replacing radiologists, AI for PE detection appears to be a safety net in emergency radiology practice due to high sensitivity and NPV, thereby increasing the self-confidence of radiologists.
How artificial intelligence improves radiological interpretation in suspected pulmonary embolism
Alexandre Ben Cheikh et al.
European Radiology 2022 https://doi.org/10.1007/s00330-022-08645-2 - Key Points
• Both the AI algorithm and emergency radiologists showed excellent performance in diagnosing PE on CTPA (sensitivity and specificity ≥ 90%; accuracy ≥ 95%).
• The AI algorithm for PE detection can help increase the sensitivity and NPV of emergency radiologists in clinical practice, especially in cases of poor-to-moderate injection quality.
• Emergency radiologists recommended the use of AI for PE detection in satisfaction surveys to increase their confidence and comfort in their final diagnosis.
How artificial intelligence improves radiological interpretation in suspected pulmonary embolism
Alexandre Ben Cheikh et al.
European Radiology 2022https://doi.org/10.1007/s00330-022-08645-2 - “Indeed, in the entire cohort-2019, AIDOC captured 19 PEs that were not diagnosed by radiologists in 19 distinct patients. In other words, the AI algorithm could correct a misdiagnosed PE approximately every 63 CTPAs (≈1202/19). This estimation must be considered in parallel with the high number of CTPAs required by emergency physicians (≈18,000 CTPAs in 2020 in our group—so approximately 285 [≈18000/1202 × 19] true PEs detected by AI but initially misdiagnosed by radiologists in 2020) and with human and financial consequences of missed PEs [32]. Indeed, mortality and recurrence rates for untreated or missed PE range between 5 and 30%.”
How artificial intelligence improves radiological interpretation in suspected pulmonary embolism
Alexandre Ben Cheikh et al.
European Radiology 2022https://doi.org/10.1007/s00330-022-08645-2 - “In conclusion, this study confirms the high diagnostic performances of AI algorithms relying on DCNN to diagnose PE on CTPA in a large multicentric retrospective emergency series. It also underscores where and how AI algorithms could better support (or “augment”) radiologists, i.e., for poor- quality examinations and by increasing their diagnostic con- fidence through the high sensitivity and high NPV of AI. Thus, our work provides more scientific ground for the concept of “AI-augmented” radiologists instead of supporting the theory of radiologists’ replacement by AI.”
How artificial intelligence improves radiological interpretation in suspected pulmonary embolism
Alexandre Ben Cheikh et al.
European Radiology 2022https://doi.org/10.1007/s00330-022-08645-2