How artificial intelligence improves radiological interpretation in suspected pulmonary embolism

Abstract

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. 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. 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. Supplementary Information The online version contains supplementary material available at 10.1007/s00330-022-08645-2.

Deep Learning-Based Algorithm for Automatic Detection of Pulmonary Embolism in Chest CT Angiograms

“…However, the dataset selection for the test was made from a single institution, missing the evaluation of potential generalizability. On the other hand, in a more recent publication, investigators used the Briefcase software to retrospectively assess the presence or absence of PE on CTA examinations performed in various emergency departments and sent to interpretation centers of three French cities (Bordeaux, Lyon, and Marseille), thus reinforcing the validation of the algorithm [ 27 ].…”

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“…However, another study comparing an AI‐based model with radiologist evaluations found that accuracy, specificity and positive predictive value (PPV) were significantly higher for radiologists, except in subcohorts with poor‐to‐average injection quality 10 . Ma et al.…”

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“…Nowadays, there is a growing interest in developing reliable and fast AI-based systems to assist radiologists as second readers. Cheik et al evaluated the diagnostic performances of an AI-powered algorithm for the automatic detection of pulmonary embolism in the CTPAs of 1202 patients, and compared the results with those of emergency radiologists in routine clinical practice [ 86 ].…”