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Artificial Intelligence Diagnostic Accuracy in Fracture Detection From Plain Radiographs and Comparing It With Clinicians: A Systematic Review and Meta-Analysis Publisher Pubmed



Nowroozi A1 ; Salehi MA1 ; Shobeiri P1 ; Agahi S1 ; Momtazmanesh S1 ; Kaviani P2 ; Kalra MK2
Authors
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Authors Affiliations
  1. 1. School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
  2. 2. Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, 02114, MA, United States

Source: Clinical Radiology Published:2024


Abstract

Purpose: Fracture detection is one of the most commonly used and studied aspects of artificial intelligence (AI) in medicine. In this systematic review and meta-analysis, we aimed to summarize available literature and data regarding AI performance in fracture detection on plain radiographs and various factors affecting it. Methods: We systematically reviewed studies evaluating AI algorithms in detecting bone fractures in plain radiographs, combined their performance using meta-analysis (a bivariate regression approach), and compared it with that of clinicians. We also analyzed the factors potentially affecting algorithm performance using meta-regression. Results: Our analysis included 100 studies. In 83 studies with confusion matrices, AI algorithms showed a sensitivity of 91.43% and a specificity of 92.12% (Area under the summary receiver operator curve = 0.968). After adjustment and false discovery rate correction, tibia/fibula (excluding ankle) fractures were associated with higher (7.0%, p=0.004) AI sensitivity, while more recent publications (5.5%, p=0.003) and Xception architecture (6.6%, p<0.001) were associated with higher specificity. Clinicians and AI showed similar specificity in fracture identification, although AI leaned to higher sensitivity (7.6%, p=0.07). Radiologists, on the other hand, were more specific than AI overall and in several subgroups, and more sensitive to hip fractures before FDR correction. Conclusions: Currently available AI aids could result in a significant improvement in care where radiologists are not readily available. Moreover, identifying factors affecting algorithm performance could guide AI development teams in their process of optimizing their products. © 2024
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