Automated Assessment for Robotic Suturing Utilizing Deep Learning Algorithms

摘要 医疗差错是美国第三大死亡原因，每年造成200亿美元的损失。 手术并发症占这些死亡和费用的三分之一。外科手术表现 直接影响患者的预后。前列腺癌是男性最常见的癌症， 手术（机器人辅助根治性乳房切除术（RARP））可能导致阳痿， 失禁甚至死亡需要客观评估技术的可靠手段。 在本项目中，我们将重点评估外科医生在RARP期间通过虚拟 现实（VR）模拟。缝合是许多类型手术中的常见技能，可以跟踪 与性能指标相关，并与RARP后的患者结局相关。 在这个建议中，我们试图首先确定临界子步骤机动的飞行器和 成功实现这些目标所需的技术技能（目标1a）。此外，我们还打算开发 通过分析原始运动学数据建立自动化技能评估管道（目标1b）， 视频（Aim 2b）和运动学/视频（Aim 2c），来自VR模拟性能， 创新的机器学习策略和基于深度学习的计算机视觉。 所提出的工作的主要区别是确定如何以及粒度子步骤 在空中进行机动。参与这项研究的外科医生不仅将提供 通过他们的VR模拟性能提供数据，但也将提供来自 RARP旨在确定外科医生技能、患者因素和手术 结果。统计建模将测量外科医生技能和患者 患者结局（目标3）。我们假设机器的创新应用 学习算法可以准确地评估外科医生的技术技能，并且可以进一步预测 相关临床结果的可能性。 拟议的工作将使VR中的可扩展和可操作的反馈成为可能， 具有宝贵的知识，以最大限度地减少手术风险，在现场手术。

ABSTRACT Medical errors are the third leading cause of death in the US at a cost of $20 billion annually. Surgical complications account for a third of these deaths and cost. Surgical performance directly impacts patient outcomes. Prostate cancer, the most common cancer in men, is treated with surgery (robot-assisted radical prostatectomy (RARP)) that can lead to impotence, incontinence, and even death. Reliable means of objectively assessing technique are required. In this project we will focus on assessing surgeon suturing skills during RARP through virtual reality (VR) simulation. Suturing is a common skill in many types of surgeries, can be tracked with performance metrics, and has been correlated with patient outcomes after RARP. In this proposal we seek to first determine the critical sub-step maneuvers of suturing and the technical skills necessary to achieve them successfully (Aim 1a). Further, we intend to develop an automated skills assessment pipeline through the analysis of raw kinematic data (Aim 1b), video (Aim 2b), and both kinematic/video (Aim 2c), from VR simulation performance by innovative machine learning strategies and deep-learning-based computer vision. The primary differentiator of the proposed work is determining how well granular sub-step maneuvers in suturing are performed. Surgeons participating in this study will not only provide data through their VR simulation performance, but will also contribute real patient data from the RARP to establish the relationship between surgeon skill, patient factors, and surgical outcomes. Statistical modeling will measure the differential impact of surgeon skill and patient factors to patient outcomes (Aim 3). We hypothesize that innovative application of machine learning algorithms can accurately assess surgeon technical skills, and can further anticipate likelihood of relevant clinical outcomes. The proposed work will enable scalable and actionable feedback in VR, empowering surgeons with valuable knowledge to minimize surgical risk in live surgery.