Department of General, Visceral, and Transplantation Surgery, University Hospital of Heidelberg, Heidelberg, Germany.
Department of Urology, University Medical Center Mannheim, Heidelberg University, Mannheim, Germany.
Ann Surg. 2021 Apr 1;273(4):684-693. doi: 10.1097/SLA.0000000000004425.
To provide an overview of ML models and data streams utilized for automated surgical phase recognition.
Phase recognition identifies different steps and phases of an operation. ML is an evolving technology that allows analysis and interpretation of huge data sets. Automation of phase recognition based on data inputs is essential for optimization of workflow, surgical training, intraoperative assistance, patient safety, and efficiency.
A systematic review was performed according to the Cochrane recommendations and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. PubMed, Web of Science, IEEExplore, GoogleScholar, and CiteSeerX were searched. Literature describing phase recognition based on ML models and the capture of intraoperative signals during general surgery procedures was included.
A total of 2254 titles/abstracts were screened, and 35 full-texts were included. Most commonly used ML models were Hidden Markov Models and Artificial Neural Networks with a trend towards higher complexity over time. Most frequently used data types were feature learning from surgical videos and manual annotation of instrument use. Laparoscopic cholecystectomy was used most commonly, often achieving accuracy rates over 90%, though there was no consistent standardization of defined phases.
ML for surgical phase recognition can be performed with high accuracy, depending on the model, data type, and complexity of surgery. Different intraoperative data inputs such as video and instrument type can successfully be used. Most ML models still require significant amounts of manual expert annotations for training. The ML models may drive surgical workflow towards standardization, efficiency, and objectiveness to improve patient outcome in the future.
CRD42018108907.
提供用于自动手术阶段识别的 ML 模型和数据流概述。
阶段识别可识别手术的不同步骤和阶段。机器学习是一种不断发展的技术，可用于分析和解释大型数据集。基于数据输入的阶段自动识别对于优化工作流程、手术培训、术中辅助、患者安全和效率至关重要。
根据 Cochrane 建议和系统评价和荟萃分析报告的首选报告项目进行了系统评价。检索了 PubMed、Web of Science、IEEExplore、GoogleScholar 和 CiteSeerX。纳入了描述基于机器学习模型的阶段识别以及在普通外科手术过程中捕获术中信号的文献。
共筛选了 2254 篇标题/摘要，纳入了 35 篇全文。最常用的机器学习模型是隐马尔可夫模型和人工神经网络，随着时间的推移，其复杂性呈上升趋势。最常用的数据类型是从手术视频中进行特征学习和手动注释器械使用情况。腹腔镜胆囊切除术最常被使用，准确率通常超过 90%，尽管没有对定义的阶段进行一致的标准化。
基于机器学习的手术阶段识别可以达到很高的准确率，具体取决于模型、数据类型和手术的复杂程度。不同的术中数据输入，如视频和器械类型，可以成功地被使用。大多数机器学习模型仍然需要大量的手动专家注释来进行训练。未来，机器学习模型可能会推动手术工作流程向标准化、效率和客观性发展，以改善患者的治疗效果。
CRD42018108907。
