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European Radiology
Erschienen in: European Radiology 10/2022

27.06.2022 | Imaging Informatics and Artificial Intelligence

Diagnostic accuracy and potential covariates of artificial intelligence for diagnosing orthopedic fractures: a systematic literature review and meta-analysis

verfasst von: Xiang Zhang, Yi Yang, Yi-Wei Shen, Ke-Rui Zhang, Ze-kun Jiang, Li-Tai Ma, Chen Ding, Bei-Yu Wang, Yang Meng, Hao Liu

Erschienen in: European Radiology | Ausgabe 10/2022

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Abstract

Objectives

To systematically quantify the diagnostic accuracy and identify potential covariates affecting the performance of artificial intelligence (AI) in diagnosing orthopedic fractures.

Methods

PubMed, Embase, Web of Science, and Cochrane Library were systematically searched for studies on AI applications in diagnosing orthopedic fractures from inception to September 29, 2021. Pooled sensitivity and specificity and the area under the receiver operating characteristic curves (AUC) were obtained. This study was registered in the PROSPERO database prior to initiation (CRD 42021254618).

Results

Thirty-nine were eligible for quantitative analysis. The overall pooled AUC, sensitivity, and specificity were 0.96 (95% CI 0.94–0.98), 90% (95% CI 87–92%), and 92% (95% CI 90–94%), respectively. In subgroup analyses, multicenter designed studies yielded higher sensitivity (92% vs. 88%) and specificity (94% vs. 91%) than single-center studies. AI demonstrated higher sensitivity with transfer learning (with vs. without: 92% vs. 87%) or data augmentation (with vs. without: 92% vs. 87%), compared to those without. Utilizing plain X-rays as input images for AI achieved results comparable to CT (AUC 0.96 vs. 0.96). Moreover, AI achieved comparable results to humans (AUC 0.97 vs. 0.97) and better results than non-expert human readers (AUC 0.98 vs. 0.96; sensitivity 95% vs. 88%).

Conclusions

AI demonstrated high accuracy in diagnosing orthopedic fractures from medical images. Larger-scale studies with higher design quality are needed to validate our findings.

Key Points

• Multicenter study design, application of transfer learning, and data augmentation are closely related to improving the performance of artificial intelligence models in diagnosing orthopedic fractures.
• Utilizing plain X-rays as input images for AI to diagnose fractures achieved results comparable to CT (AUC 0.96 vs. 0.96).
• AI achieved comparable results to humans (AUC 0.97 vs. 0.97) but was superior to non-expert human readers (AUC 0.98 vs. 0.96, sensitivity 95% vs. 88%) in diagnosing fractures.
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Metadaten
Titel
Diagnostic accuracy and potential covariates of artificial intelligence for diagnosing orthopedic fractures: a systematic literature review and meta-analysis
verfasst von
Xiang Zhang
Yi Yang
Yi-Wei Shen
Ke-Rui Zhang
Ze-kun Jiang
Li-Tai Ma
Chen Ding
Bei-Yu Wang
Yang Meng
Hao Liu
Publikationsdatum
27.06.2022
Verlag
Springer Berlin Heidelberg
Erschienen in
European Radiology / Ausgabe 10/2022
Print ISSN: 0938-7994
Elektronische ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-022-08956-4

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