Abstract：
BACKGROUND: This systematic review aims to evaluate the use of machine learning and artificial intelligence in hernia surgery.
METHODS: The PRISMA guidelines were followed throughout this systematic review. The ROBINS-I and Rob 2 tools were used to perform qualitative assessment of all studies included in this review. Recommendations were then summarized for the following pre-defined key items: protocol, research question, search strategy, study eligibility, data extraction, study design, risk of bias, publication bias, and statistical analysis.
RESULTS: A total of 13 articles were ultimately included for this review, describing the use of machine learning and deep learning for hernia surgery. All studies were published from 2020 to 2023. Articles varied regarding the population studied, type of machine learning or Deep Learning Model (DLM) used, and hernia type. Of the thirteen included studies, all included either inguinal, ventral, or incisional hernias. Four studies evaluated recognition of surgical steps during inguinal hernia repair videos. Two studies predicted outcomes using image-based DMLs. Seven studies developed and validated deep learning algorithms to predict outcomes and identify factors associated with postoperative complications.
CONCLUSIONS: The use of ML for abdominal wall reconstruction has been shown to be a promising tool for predicting outcomes and identifying factors that could lead to postoperative complications.
METHODS: The PRISMA guidelines were followed throughout this systematic review. The ROBINS-I and Rob 2 tools were used to perform qualitative assessment of all studies included in this review. Recommendations were then summarized for the following pre-defined key items: protocol, research question, search strategy, study eligibility, data extraction, study design, risk of bias, publication bias, and statistical analysis.
RESULTS: A total of 13 articles were ultimately included for this review, describing the use of machine learning and deep learning for hernia surgery. All studies were published from 2020 to 2023. Articles varied regarding the population studied, type of machine learning or Deep Learning Model (DLM) used, and hernia type. Of the thirteen included studies, all included either inguinal, ventral, or incisional hernias. Four studies evaluated recognition of surgical steps during inguinal hernia repair videos. Two studies predicted outcomes using image-based DMLs. Seven studies developed and validated deep learning algorithms to predict outcomes and identify factors associated with postoperative complications.
CONCLUSIONS: The use of ML for abdominal wall reconstruction has been shown to be a promising tool for predicting outcomes and identifying factors that could lead to postoperative complications.
摘要：
背景：本系统综述旨在评估机器学习和人工智能在疝气手术中的使用。
方法：本系统综述遵循PRISMA指南。使用ROBINS-I和Rob2工具对纳入本综述的所有研究进行定性评估。然后总结了以下预定义关键项目的建议：协议，研究问题,搜索策略,研究资格，数据提取,研究设计，偏见的风险，出版偏见，和统计分析。
结果：本综述最终共纳入13篇文章，描述机器学习和深度学习在疝气手术中的应用。所有研究均于2020年至2023年发表。关于所研究人群的文章各不相同，使用的机器学习或深度学习模型(DLM)的类型，和疝气类型。在13项纳入的研究中，都包括腹股沟，腹侧,或者是切口疝.四项研究评估了腹股沟疝修补术视频中手术步骤的识别。两项研究使用基于图像的DML预测结果。七项研究开发并验证了深度学习算法，以预测结果并确定与术后并发症相关的因素。
结论：使用ML进行腹壁重建已被证明是预测结果和确定可能导致术后并发症的因素的有希望的工具。
方法：本系统综述遵循PRISMA指南。使用ROBINS-I和Rob2工具对纳入本综述的所有研究进行定性评估。然后总结了以下预定义关键项目的建议：协议，研究问题,搜索策略,研究资格，数据提取,研究设计，偏见的风险，出版偏见，和统计分析。
结果：本综述最终共纳入13篇文章，描述机器学习和深度学习在疝气手术中的应用。所有研究均于2020年至2023年发表。关于所研究人群的文章各不相同，使用的机器学习或深度学习模型(DLM)的类型，和疝气类型。在13项纳入的研究中，都包括腹股沟，腹侧,或者是切口疝.四项研究评估了腹股沟疝修补术视频中手术步骤的识别。两项研究使用基于图像的DML预测结果。七项研究开发并验证了深度学习算法，以预测结果并确定与术后并发症相关的因素。
结论：使用ML进行腹壁重建已被证明是预测结果和确定可能导致术后并发症的因素的有希望的工具。
