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Background <p>Currently, the application of convolutional neural networks (CNNs) in artificial intelligence (AI) for medical imaging diagnosis has emerged as a highly promising tool. In particular, AI-assisted diagnosis holds significant potential for orthopedic and emergency department physicians by improving diagnostic efficiency and enhancing the overall patient experience. This systematic review and meta-analysis has the objective of assessing the application of AI in diagnosing facial fractures and evaluating its diagnostic performance.</p> Methods <p>This study adhered to the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and PRISMA-Diagnostic Test Accuracy (PRISMA-DTA). A comprehensive literature search was conducted in the PubMed, Cochrane Library, and Web of Science databases to identify original articles published up to December 2024. The risk of bias and applicability of the included studies were assessed using the QUADAS-2 tool. The results were analyzed using a Summary Receiver Operating Characteristic (SROC) curve.</p> Results <p>A total of 16 studies were included in the analysis, with contingency tables extracted from 11 of them. The pooled sensitivity was 0.889 (95% CI: 0.844–0.922), and the pooled specificity was 0.888 (95% CI: 0.834–0.926). The area under the Summary Receiver Operating Characteristic (SROC) curve was 0.911. In the subgroup analysis of nasal and mandibular fractures, the pooled sensitivity for nasal fractures was 0.851 (95% CI: 0.806–0.887), and the pooled specificity was 0.883 (95% CI: 0.862–0.902). For mandibular fractures, the pooled sensitivity was 0.905 (95% CI: 0.836–0.947), and the pooled specificity was 0.895 (95% CI: 0.824–0.940).</p> Conclusions <p>AI can be developed as an auxiliary tool to assist clinicians in diagnosing facial fractures. The results demonstrate high overall sensitivity and specificity, along with a robust performance reflected by the high area under the SROC curve.</p> Clinical trial number <p>This study has been prospectively registered on Prospero, ID:CRD42024618650, Creat Date:10 Dec 2024. <a href="https://www.crd.york.ac.uk/PROSPERO/view/CRD42024618650">https://www.crd.york.ac.uk/PROSPERO/view/CRD42024618650</a>.</p>

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Evaluation of Artificial Intelligence-based diagnosis for facial fractures, advantages compared with conventional imaging diagnosis: a systematic review and meta-analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月15日

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  • Jiangyi Ju,
  • Zhen Qu,
  • Han Qing,
  • Yunxia Ding,
  • Lihua Peng

摘要

Background

Currently, the application of convolutional neural networks (CNNs) in artificial intelligence (AI) for medical imaging diagnosis has emerged as a highly promising tool. In particular, AI-assisted diagnosis holds significant potential for orthopedic and emergency department physicians by improving diagnostic efficiency and enhancing the overall patient experience. This systematic review and meta-analysis has the objective of assessing the application of AI in diagnosing facial fractures and evaluating its diagnostic performance.

Methods

This study adhered to the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and PRISMA-Diagnostic Test Accuracy (PRISMA-DTA). A comprehensive literature search was conducted in the PubMed, Cochrane Library, and Web of Science databases to identify original articles published up to December 2024. The risk of bias and applicability of the included studies were assessed using the QUADAS-2 tool. The results were analyzed using a Summary Receiver Operating Characteristic (SROC) curve.

Results

A total of 16 studies were included in the analysis, with contingency tables extracted from 11 of them. The pooled sensitivity was 0.889 (95% CI: 0.844–0.922), and the pooled specificity was 0.888 (95% CI: 0.834–0.926). The area under the Summary Receiver Operating Characteristic (SROC) curve was 0.911. In the subgroup analysis of nasal and mandibular fractures, the pooled sensitivity for nasal fractures was 0.851 (95% CI: 0.806–0.887), and the pooled specificity was 0.883 (95% CI: 0.862–0.902). For mandibular fractures, the pooled sensitivity was 0.905 (95% CI: 0.836–0.947), and the pooled specificity was 0.895 (95% CI: 0.824–0.940).

Conclusions

AI can be developed as an auxiliary tool to assist clinicians in diagnosing facial fractures. The results demonstrate high overall sensitivity and specificity, along with a robust performance reflected by the high area under the SROC curve.

Clinical trial number

This study has been prospectively registered on Prospero, ID:CRD42024618650, Creat Date:10 Dec 2024. https://www.crd.york.ac.uk/PROSPERO/view/CRD42024618650.