Real-World evaluation of an AI triaging system for chest X-rays: A prospective clinical study.

Autor: Sridharan S; Data Management and Informatics, Changi General Hospital, Singapore., Seah Xin Hui A; Data Management and Informatics, Changi General Hospital, Singapore., Venkataraman N; Data Management and Informatics, Changi General Hospital, Singapore; Singapore University of Technology and Design, Singapore., Sivanath Tirukonda P; Department of Diagnostic Radiology, Changi General Hospital, Singapore., Pratab Jeyaratnam R; Department of Diagnostic Radiology, Changi General Hospital, Singapore., John S; Department of Diagnostic Radiology, Changi General Hospital, Singapore., Suresh Babu S; Department of Diagnostic Radiology, Changi General Hospital, Singapore., Liew P; Department of Diagnostic Radiology, Changi General Hospital, Singapore., Francis J; Department of Diagnostic Radiology, Changi General Hospital, Singapore., Koh Tzan T; Radiography Department, Changi General Hospital, Singapore., Kang Min W; Department of Diagnostic Radiology, Changi General Hospital, Singapore., Min Liong G; Executive Office, Changi General Hospital, Singapore; Executive Office, Singapore Health Services, Singapore; Department of General Surgery, Changi General Hospital, Singapore., Liew Jin Yee C; Department of Diagnostic Radiology, Changi General Hospital, Singapore; Department of Biomedical Engineering, National University of Singapore, Singapore; Duke-NUS Medical School, Singapore. Electronic address: charlene.liew.j.y@singhealth.com.sg.
Jazyk: angličtina
Zdroj: European journal of radiology [Eur J Radiol] 2024 Dec; Vol. 181, pp. 111783. Date of Electronic Publication: 2024 Oct 10.
DOI: 10.1016/j.ejrad.2024.111783
Abstrakt: Chest X-rays (CXRs) are crucial for diagnosing and managing lung conditions. While CXR is a common and cost-effective diagnostic tool, interpreting the high volume of CXRs is challenging due to workforce limitations. Artificial intelligence (AI) offers promise in enhancing efficiency and accuracy. However, real-world applicability and generalizability across diverse patient cohorts remain areas of concerns. In our study, the LUNIT INSIGHT CXR Triage software was evaluated in a diverse patient cohort. Forty-three radiologists, blinded to AI results, assessed CXRs categorized into normal, non-urgent, and urgent using a 3-tier classification system. Performance metrics and turnaround times were analyzed. The AI system demonstrated sensitivity of 89% for normal CXRs, specificity of 93%, PPV of 83%, and NPV of 95%, with an F1 score of 0.86 and an AUC of 0.91. For non-urgent CXRs, sensitivity and specificity were 93% and 91%, with PPV and NPV at 94% and 89%, respectively, and an F1 score of 0.94 and an AUC of 0.92. In the urgent category, sensitivity was 82%, specificity 99%, PPV 90%, and NPV 98%. Subgroup analysis revealed consistently high accuracy across various age groups (Young, Adult, Senior), genders, and ethnicities (Chinese, Malay, Indian, Others), with sensitivity, specificity, and AUC consistently above 84%. The AI system also significantly reduced turnaround times across all subgroups, indicating its robust performance and generalizability in diverse healthcare settings.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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