PDF(Pubmed)
Abstract：
Craniosynostosis refers to the premature fusion of one or more of the fibrous cranial sutures connecting the bones of the skull. Machine learning (ML) is an emerging technology and its application to craniosynostosis detection and management is underexplored. This systematic review aims to evaluate the application of ML techniques in the diagnosis, severity assessment, and predictive modeling of craniosynostosis. A comprehensive search was conducted on the PubMed and Google Scholar databases using predefined keywords related to craniosynostosis and ML. Inclusion criteria encompassed peer-reviewed studies in English that investigated ML algorithms in craniosynostosis diagnosis, severity assessment, or treatment outcome prediction. Three independent reviewers screened the search results, performed full-text assessments, and extracted data from selected studies using a standardized form. Thirteen studies met the inclusion criteria and were included in the review. Of the thirteen papers examined on the application of ML to the identification and treatment of craniosynostosis, two papers were dedicated to sagittal craniosynostosis, five papers utilized several different types of craniosynostosis in the training and testing of their ML models, and six papers were dedicated to metopic craniosynostosis. ML models demonstrated high accuracy in identifying different types of craniosynostosis and objectively quantifying severity using innovative metrics such as metopic severity score and cranial morphology deviation. The findings highlight the significant strides made in utilizing ML techniques for craniosynostosis diagnosis, severity assessment, and predictive modeling. Predictive modeling of treatment outcomes following surgical interventions showed promising results, aiding in personalized treatment strategies. Despite methodological diversities among studies, the collective evidence underscores ML\'s transformative potential in revolutionizing craniosynostosis management.
摘要：
颅骨融合是指连接颅骨骨的一个或多个纤维性颅骨缝合线的过早融合。机器学习（ML）是一种新兴技术，其在颅骨融合的检测和管理中的应用尚未得到充分的开发。本系统综述旨在评估ML技术在诊断中的应用，严重性评估，和预测模型的颅骨融合。使用与颅骨融合和ML相关的预定义关键字在PubMed和GoogleScholar数据库上进行了全面搜索。纳入标准包括英语同行评审的研究，这些研究调查了颅骨融合诊断中的ML算法，严重性评估，或治疗结果预测。三名独立审稿人筛选了搜索结果，进行了全文评估，并使用标准化表格从选定的研究中提取数据。13项研究符合纳入标准，被纳入审查。在关于ML在颅骨融合的识别和治疗中的应用的13篇论文中，两篇论文致力于矢状位颅骨融合,五篇论文在训练和测试他们的ML模型时利用了几种不同类型的颅骨融合，六篇论文专门研究了颅骨融合。ML模型在识别不同类型的颅骨融合和使用创新的指标（如metopic严重程度评分和颅骨形态偏差）客观量化严重程度方面表现出很高的准确性。这些发现强调了在利用ML技术诊断颅骨融合方面取得的重大进展，严重性评估，和预测建模。手术干预后治疗结果的预测模型显示了有希望的结果，帮助个性化治疗策略。尽管研究方法不同，集体证据强调了ML在革命性的颅骨融合管理方面的变革潜力。
