Applications of Machine Learning in Image Analysis to Identify Craniosynostosis: A Systematic Review and Meta-Analysis

IF 1.7 3区医学 Q2 DENTISTRY, ORAL SURGERY & MEDICINE

Orthodontics & Craniofacial Research Pub Date : 2025-04-11 DOI:10.1111/ocr.12918

Nozhan Azimi, Katayoun Talebi Rafsanjan, Mohammad Mahdi Khanmohammadi Khorami, Asghar Ebadifar, Ali Azadi

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引用次数: 0

Abstract

Craniosynostosis is a condition characterised by the premature fusion of cranial sutures, which can lead to significant neurodevelopmental and aesthetic issues if not diagnosed and treated early. This study aimed to systematically review and conduct a meta-analysis of studies utilising machine learning (ML) models to diagnose craniosynostosis in photographs or radiographs from humans, evaluating their accuracy through sensitivity, specificity and diagnostic odds ratio. A comprehensive search was conducted on PubMed, Web of Science and Scopus until October 2024 regarding the following PECO question: ‘Should ML models (E) be used to diagnose craniosynostosis in photographs or radiographs from humans (P) compared to a reference standard (C) based on their sensitivity, specificity, and diagnostic odds ratio (O)?’. Studies employing ML to diagnose craniofacial deformities on photographs and radiographs of human subjects were included. Using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2), the risk of bias was assessed. A bivariate random-effect meta-analysis was conducted to pool the diagnostic odds ratio, sensitivity and specificity of the included studies. The GRADE approach was used to evaluate the overall strength of the clinical recommendation and estimated meta-evidence. An initial search yielded 685 articles. After screening, 47 articles were selected for a full-text review. Eventually, 28 studies were selected for the systematic review, and 17 were included in the meta-analysis. The results, with an overall moderate certainty, indicated an AUC of 0.99 (95% CI: 0.98–1.00), an overall sensitivity of 97% (95% CI: 94%–98%) and an overall specificity of 97% (95% CI: 94%–99%). The estimated pooled diagnostic odds ratio was 1131 (95% CI: 290–4419). The present study showed that the ML approaches possess high efficiency and applicability in the diagnosis of craniosynostosis in photographs or radiographs from humans. These findings affirm that ML models should be considered viable diagnostic tools for craniosynostosis.

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机器学习在图像分析中识别颅缝闭合的应用：系统综述和荟萃分析。

颅缝闭锁是一种以颅缝合线过早融合为特征的疾病，如果不及早诊断和治疗，会导致严重的神经发育和美学问题。本研究旨在系统回顾并对利用机器学习（ML）模型诊断人类照片或x光片颅缝闭锁的研究进行荟萃分析，通过灵敏度、特异性和诊断优势比评估其准确性。在PubMed、Web of Science和Scopus上进行了全面的搜索，直到2024年10月，关于以下PECO问题：“基于灵敏度、特异性和诊断优势比(O)，与参考标准(C)相比，ML模型(E)是否应用于诊断人类照片或x线片中的颅缝闭锁(P) ?”研究采用机器学习诊断颅面畸形的照片和x线片的人类受试者。使用诊断准确性质量评估研究（QUADAS-2），评估偏倚风险。进行双变量随机效应荟萃分析，汇总纳入研究的诊断优势比、敏感性和特异性。GRADE方法用于评估临床推荐和估计的meta证据的总体强度。最初的搜索产生了685篇文章。经筛选，选取47篇文章进行全文综述。最终，28项研究被纳入系统评价，17项研究被纳入meta分析。结果总体上具有中等确定性，AUC为0.99 (95% CI: 0.98-1.00)，总灵敏度为97% (95% CI: 94%-98%)，总特异性为97% （95% CI: 94%-99%）。估计合并诊断优势比为1131 （95% CI: 290-4419）。本研究表明，机器学习方法在人类照片或x线片上诊断颅缝闭锁具有很高的效率和适用性。这些发现证实ML模型应该被认为是颅缝闭合的可行诊断工具。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Orthodontics & Craniofacial Research 医学-牙科与口腔外科

CiteScore

5.30

自引率

3.20%

发文量

审稿时长

>12 weeks

期刊介绍： Orthodontics & Craniofacial Research - Genes, Growth and Development is published to serve its readers as an international forum for the presentation and critical discussion of issues pertinent to the advancement of the specialty of orthodontics and the evidence-based knowledge of craniofacial growth and development. This forum is based on scientifically supported information, but also includes minority and conflicting opinions. The objective of the journal is to facilitate effective communication between the research community and practicing clinicians. Original papers of high scientific quality that report the findings of clinical trials, clinical epidemiology, and novel therapeutic or diagnostic approaches are appropriate submissions. Similarly, we welcome papers in genetics, developmental biology, syndromology, surgery, speech and hearing, and other biomedical disciplines related to clinical orthodontics and normal and abnormal craniofacial growth and development. In addition to original and basic research, the journal publishes concise reviews, case reports of substantial value, invited essays, letters, and announcements. The journal is published quarterly. The review of submitted papers will be coordinated by the editor and members of the editorial board. It is policy to review manuscripts within 3 to 4 weeks of receipt and to publish within 3 to 6 months of acceptance.