Clinical imaging · 2024
Diagnostic performance of an artificial intelligence model for the detection of pneumothorax at chest X-ray.
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Monti Caterina Beatrice, Bianchi Lorenzo Maria Giuseppe, Rizzetto Francesco, Carbonaro Luca Alessandro, Vanzulli Angelo
Аннотация
PURPOSE: Pneumothorax (PTX) is a common clinical urgency, its diagnosis is usually performed on chest radiography (CXR), and it presents a setting where artificial intelligence (AI) methods could find terrain in aiding radiologists in facing increasing workloads. Hence, the purpose of our study was to test an AI system for the detection of PTX on CXR examinations, to review its diagnostic performance in such setting alongside that of reading radiologists.
METHOD: We retrospectively ran an AI system on CXR examinations of patients who were imaged for the suspicion of PTX, and who also underwent computed tomography (CT) within the same day, the latter being used as reference standard. The performance of the proposed AI system was compared to that of reading radiologists, obtained from CXR reports.
RESULTS: Overall, the AI system achieved an accuracy of 74 % (95%CI 68-79 %), with a sensitivity of 66 % (95%CI 59-73 %) and a specificity of 93 % (95%CI 85-97 %). Human readers displayed a comparable accuracy (77 %, 95%CI 71-82 %, p = 0.355), with higher sensitivity (73 %, 95%CI 66-79 %, p = 0.040), albeit lower specificity (85 %, 95%CI 75-91 %, p = 0.034). The performance of AI was influenced by patient positioning at CXR (p = 0.040).
CONCLUSIONS: The proposed tool could represent an aid to radiologists in detecting PTX, improving specificity. Further improvement with training on more challenging cases may pave the way for its use as a screening or standalone tool.
METHOD: We retrospectively ran an AI system on CXR examinations of patients who were imaged for the suspicion of PTX, and who also underwent computed tomography (CT) within the same day, the latter being used as reference standard. The performance of the proposed AI system was compared to that of reading radiologists, obtained from CXR reports.
RESULTS: Overall, the AI system achieved an accuracy of 74 % (95%CI 68-79 %), with a sensitivity of 66 % (95%CI 59-73 %) and a specificity of 93 % (95%CI 85-97 %). Human readers displayed a comparable accuracy (77 %, 95%CI 71-82 %, p = 0.355), with higher sensitivity (73 %, 95%CI 66-79 %, p = 0.040), albeit lower specificity (85 %, 95%CI 75-91 %, p = 0.034). The performance of AI was influenced by patient positioning at CXR (p = 0.040).
CONCLUSIONS: The proposed tool could represent an aid to radiologists in detecting PTX, improving specificity. Further improvement with training on more challenging cases may pave the way for its use as a screening or standalone tool.