PDF(Pubmed)
Abstract：
UNASSIGNED: The purpose of this study was to assess the current use and reliability of artificial intelligence (AI)-based algorithms for analyzing cataract surgery videos.
UNASSIGNED: A systematic review of the literature about intra-operative analysis of cataract surgery videos with machine learning techniques was performed. Cataract diagnosis and detection algorithms were excluded. Resulting algorithms were compared, descriptively analyzed, and metrics summarized or visually reported. The reproducibility and reliability of the methods and results were assessed using a modified version of the Medical Image Computing and Computer-Assisted (MICCAI) checklist.
UNASSIGNED: Thirty-eight of the 550 screened studies were included, 20 addressed the challenge of instrument detection or tracking, 9 focused on phase discrimination, and 8 predicted skill and complications. Instrument detection achieves an area under the receiver operator characteristic curve (ROC AUC) between 0.976 and 0.998, instrument tracking an mAP between 0.685 and 0.929, phase recognition an ROC AUC between 0.773 and 0.990, and complications or surgical skill performs with an ROC AUC between 0.570 and 0.970.
UNASSIGNED: The studies showed a wide variation in quality and pose a challenge regarding replication due to a small number of public datasets (none for manual small incision cataract surgery) and seldom published source code. There is no standard for reported outcome metrics and validation of the models on external datasets is rare making comparisons difficult. The data suggests that tracking of instruments and phase detection work well but surgical skill and complication recognition remains a challenge for deep learning.
UNASSIGNED: This overview of cataract surgery analysis with AI models provides translational value for improving training of the clinician by identifying successes and challenges.
UNASSIGNED: A systematic review of the literature about intra-operative analysis of cataract surgery videos with machine learning techniques was performed. Cataract diagnosis and detection algorithms were excluded. Resulting algorithms were compared, descriptively analyzed, and metrics summarized or visually reported. The reproducibility and reliability of the methods and results were assessed using a modified version of the Medical Image Computing and Computer-Assisted (MICCAI) checklist.
UNASSIGNED: Thirty-eight of the 550 screened studies were included, 20 addressed the challenge of instrument detection or tracking, 9 focused on phase discrimination, and 8 predicted skill and complications. Instrument detection achieves an area under the receiver operator characteristic curve (ROC AUC) between 0.976 and 0.998, instrument tracking an mAP between 0.685 and 0.929, phase recognition an ROC AUC between 0.773 and 0.990, and complications or surgical skill performs with an ROC AUC between 0.570 and 0.970.
UNASSIGNED: The studies showed a wide variation in quality and pose a challenge regarding replication due to a small number of public datasets (none for manual small incision cataract surgery) and seldom published source code. There is no standard for reported outcome metrics and validation of the models on external datasets is rare making comparisons difficult. The data suggests that tracking of instruments and phase detection work well but surgical skill and complication recognition remains a challenge for deep learning.
UNASSIGNED: This overview of cataract surgery analysis with AI models provides translational value for improving training of the clinician by identifying successes and challenges.
摘要：
■这项研究的目的是评估基于人工智能（AI）的算法用于分析白内障手术视频的当前使用和可靠性。
■对有关使用机器学习技术的白内障手术视频的术中分析的文献进行了系统回顾。排除白内障诊断和检测算法。结果算法进行了比较，描述性分析，和度量汇总或直观报告。使用修改版本的医学图像计算和计算机辅助（MICCAI）检查表评估方法和结果的可重复性和可靠性。
■纳入了550项筛选研究中的38项，20解决了仪器检测或跟踪的挑战，9专注于相位区分，和8个预测的技能和并发症。仪器检测使受试者操作员特征曲线下面积（ROCAUC）在0.976和0.998之间，仪器跟踪mAP在0.685和0.929之间，相位识别ROCAUC在0.773和0.990之间，并发症或手术技巧在ROCAUC在0.570和0.970之间。
■这些研究表明，质量差异很大，并且由于公共数据集的数量很少（对于手动小切口白内障手术没有）和很少发布的源代码，对复制提出了挑战。报告的结果指标没有标准，外部数据集上的模型验证很少，这使得比较困难。数据表明，仪器和相位检测的跟踪效果很好，但手术技能和并发症识别仍然是深度学习的挑战。
■使用AI模型进行白内障手术分析的概述通过确定成功和挑战，为改善临床医生的培训提供了转化价值。
■对有关使用机器学习技术的白内障手术视频的术中分析的文献进行了系统回顾。排除白内障诊断和检测算法。结果算法进行了比较，描述性分析，和度量汇总或直观报告。使用修改版本的医学图像计算和计算机辅助（MICCAI）检查表评估方法和结果的可重复性和可靠性。
■纳入了550项筛选研究中的38项，20解决了仪器检测或跟踪的挑战，9专注于相位区分，和8个预测的技能和并发症。仪器检测使受试者操作员特征曲线下面积（ROCAUC）在0.976和0.998之间，仪器跟踪mAP在0.685和0.929之间，相位识别ROCAUC在0.773和0.990之间，并发症或手术技巧在ROCAUC在0.570和0.970之间。
■这些研究表明，质量差异很大，并且由于公共数据集的数量很少（对于手动小切口白内障手术没有）和很少发布的源代码，对复制提出了挑战。报告的结果指标没有标准，外部数据集上的模型验证很少，这使得比较困难。数据表明，仪器和相位检测的跟踪效果很好，但手术技能和并发症识别仍然是深度学习的挑战。
■使用AI模型进行白内障手术分析的概述通过确定成功和挑战，为改善临床医生的培训提供了转化价值。
