Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, Washington, DC, USA.
Department of Neurosurgery, Mayo Clinic, Jacksonville, Florida, USA.
Int J Comput Assist Radiol Surg. 2024 Oct;19(10):1947-1952. doi: 10.1007/s11548-024-03203-1. Epub 2024 Jul 19.
Lumbar discectomy is among the most common spine procedures in the US, with 300,000 procedures performed each year. Like other surgical procedures, this procedure is not excluded from potential complications. This paper presents a video annotation methodology for microdiscectomy including the development of a surgical workflow. In future work, this methodology could be combined with computer vision and machine learning models to predict potential adverse events. These systems would monitor the intraoperative activities and possibly anticipate the outcomes.
A necessary step in supervised machine learning methods is video annotation, which involves labeling objects frame-by-frame to make them recognizable for machine learning applications. Microdiscectomy video recordings of spine surgeries were collected from a multi-center research collaborative. These videos were anonymized and stored in a cloud-based platform. Videos were uploaded to an online annotation platform. An annotation framework was developed based on literature review and surgical observations to ensure proper understanding of the instruments, anatomy, and steps.
An annotated video of microdiscectomy was produced by a single surgeon. Multiple iterations allowed for the creation of an annotated video complete with labeled surgical tools, anatomy, and phases. In addition, a workflow was developed for the training of novice annotators, which provides information about the annotation software to assist in the production of standardized annotations.
A standardized workflow for managing surgical video data is essential for surgical video annotation and machine learning applications. We developed a standard workflow for annotating surgical videos for microdiscectomy that may facilitate the quantitative analysis of videos using supervised machine learning applications. Future work will demonstrate the clinical relevance and impact of this workflow by developing process modeling and outcome predictors.
腰椎间盘切除术是美国最常见的脊柱手术之一，每年约有 30 万例手术。与其他手术一样，该手术也不能排除潜在的并发症。本文提出了一种用于显微椎间盘切除术的视频标注方法，包括手术流程的开发。在未来的工作中，该方法可以与计算机视觉和机器学习模型相结合，以预测潜在的不良事件。这些系统将监测术中活动，并可能预测结果。
监督机器学习方法的必要步骤是视频标注，即通过逐帧标记对象，使其可被机器学习应用程序识别。从一个多中心研究协作中收集了显微椎间盘切除术的脊柱手术视频记录。这些视频经过匿名处理并存储在基于云的平台中。将视频上传到在线标注平台。根据文献综述和手术观察结果开发了一个标注框架，以确保对器械、解剖结构和步骤有正确的理解。
由一名外科医生制作了一个显微椎间盘切除术的标注视频。通过多次迭代，创建了一个带有标记手术工具、解剖结构和阶段的标注视频。此外，还为新手标注员开发了一个工作流程，其中提供了有关标注软件的信息，以协助生成标准化的标注。
管理手术视频数据的标准化工作流程对于手术视频标注和机器学习应用至关重要。我们开发了一种用于显微椎间盘切除术视频标注的标准工作流程，这可能有助于使用监督机器学习应用程序对视频进行定量分析。未来的工作将通过开发过程建模和结果预测来展示该工作流程的临床相关性和影响。
