AI-Based Video Segmentation: Procedural Steps or Basic Maneuvers?
AI-Based Video Segmentation: Procedural Steps or Basic Maneuvers?
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DOI：
10.1016/j.jss.2022.10.069
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发表时间：
2023-03
影响因子：
2.2
通讯作者：
Pugh, Carla
中科院分区：
文献类型：
--
作者：
Perumalla, Calvin;Kearse, LaDonna;Peven, Michael;Laufer, Shlomi;Goll, Cassidi;Wise, Brett;Yang, Su;Pugh, Carla
基于视频的外科手术回顾已被证明在培训中是有用的，可以有效地定性评估手术技能和术中决策。目前的视频分割协议主要集中在程序步骤。虽然有些手术比其他手术更复杂，但任何给定手术中的许多步骤都涉及基本操作的复杂编排，如打结、打结和切割。在某些手术步骤中使用这些操作可以传达有助于评估手术复杂性、手术偏好和技能的信息。我们的研究旨在开发和评估一种算法来识别这些演习。使用标准深度学习架构在数据集上区分缝线抛出、打结和缝线切割，该数据集由参与模拟肠切开术修复的执业临床医生（N = 52）的视频组成。通过定性检查在开放式结肠切除术的步骤子集中识别机动的效用，探索了对传统人工智能分割的附加值的感知。判别机动的准确率达到84%。对于缝线抛出、打结和缝线切割，检测基本操作的精确度分别为87.9%、60%和90.9%。定性概念图确定了可从基本机动识别中受益的现实情景。基本操作可以指示错误管理活动或安全措施，并允许评估技能。我们的深度学习算法以合理的准确度识别基本动作。此类模型可以通过提供可以补充传统视频分割协议的额外信息来帮助人工智能辅助视频审查。
Video-based review of surgical procedures has proven to be useful in training by enabling efficiency in the qualitative assessment of surgical skill and intraoperative decision-making. Current video segmentation protocols focus largely on procedural steps. Although some operations are more complex than others, many of the steps in any given procedure involve an intricate choreography of basic maneuvers such as suturing, knot tying, and cutting. The use of these maneuvers at certain procedural steps can convey information that aids in the assessment of the complexity of the procedure, surgical preference, and skill. Our study aims to develop and evaluate an algorithm to identify these maneuvers. A standard deep learning architecture was used to differentiate between suture throws, knot ties, and suture cutting on a data set comprised of videos from practicing clinicians (N = 52) who participated in a simulated enterotomy repair. Perception of the added value to traditional artificial intelligence segmentation was explored by qualitatively examining the utility of identifying maneuvers in a subset of steps for an open colon resection. An accuracy of 84% was reached in differentiating maneuvers. The precision in detecting the basic maneuvers was 87.9%, 60%, and 90.9% for suture throws, knot ties, and suture cutting, respectively. The qualitative concept mapping confirmed realistic scenarios that could benefit from basic maneuver identification. Basic maneuvers can indicate error management activity or safety measures and allow for the assessment of skill. Our deep learning algorithm identified basic maneuvers with reasonable accuracy. Such models can aid in artificial intelligence-assisted video review by providing additional information that can complement traditional video segmentation protocols.
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影响因子：
2.4
作者：
Bromley, Miluska;Marrou, Walter;Charles-de-Sa, Luiz
通讯作者：
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DOI：
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发表时间：
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10.6
作者：
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