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.
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.