Video Analysis of Neurosurgical Technical Performance and Adverse Events

项目摘要 拟议研究和职业发展计划旨在为候选人提供知识， 经验和资源，成为一个独立的神经外科医生，科学家，其研究 通过机器的设计和实施， 机器学习（ML）和计算机视觉（CV）系统，为外科医生提供反馈，以改善外科手术 性能经过正式培训后，执业神经外科医生很少收到反馈，而是通过学习 在程序中积累的经验。然而，大多数没有积累足够的案件量，以实现最佳的 每一个过程的结果。最终，超过16，000名患者因可预防的神经外科错误而受到伤害 每年，导致高达70%的中风和神经系统残疾，高达16%的患者死亡。 患者不幸的是，由于缺乏数据集，手术期间有害不良事件的研究受到阻碍 包含导致不良事件或结局的手术措施。候选人提出要克服 通过使用先进的CV和ML方法来分析以前未研究的多模态数据集， 结合垂体手术视频和临床数据。每年进行超过10，000次包皮手术， 美国，可以记录下来进行分析，其步骤、错误和不良事件最近在 国际共识声明。具体来说，候选人将检验中心假设， 可视外科医生技能因素与可视化特征（包括患者解剖结构和疾病）的相互作用 病理学，产生可识别的步骤特异性手术错误，导致术后卒中，神经系统 残疾和其他不良事件。具体目标：1）使用CV识别步骤特定错误（由工具定义 使用、步骤进展和阶段特征）; 2）训练ML模型以预测 来自外科医生技能的先前度量和手术野的当前视觉特征的即将发生的不良事件。 方法识别高风险神经外科手术，预测即将发生的不良事件， 运动，并回顾性地突出与不良事件相关的关键时间点和视觉特征 有必要合理设计和实施干预措施，以减少中风，神经功能障碍， 其他不良事件。这项工作的可行性和成功将由候选人的杰出的 指导团队，包括一名具有CV手术经验的外科医生-科学家 使用医学图像和临床数据，生物医学， 深度学习在复杂预测模型中的应用，以及多机构垂体手术研究。在 在该奖项的最后一年，候选人将申请R 01奖，以前瞻性地实施可推广的 预测模型（在目标2中开发）使用来自几个外科手术的视频的更大数据集， 开发实时收集手术视频数据并对其采取行动的方法。

PROJECT SUMMARY The proposed research and career development plan aim to provide the candidate with the knowledge, experience, and resources necessary to become an independent neurosurgeon-scientist whose research reduces stroke and neurologic disability after neurosurgery through the design and implementation of machine learning (ML) and computer vision (CV) systems that provide surgeons with feedback to improve surgical performance. After formal training, practicing neurosurgeons receive little feedback and instead learn by experience accrued during procedures. However, most do not accrue sufficient case volume to achieve optimal outcomes in every procedure. Ultimately, >16,000 patients are harmed by preventable neurosurgical errors each year, resulting in stroke and neurologic disability in up to 70% and death in up to 16% of affected patients. Unfortunately, the study of harmful adverse events during surgery is obstructed by a lack of datasets containing surgical actions leading up to adverse events or outcomes. The candidate proposes to overcome this limitation by using advanced CV and ML methods to analyze a previously unstudied, multimodal dataset combining pituitary surgical video and clinical data. Pituitary surgery is performed >10,000 times annually in the U.S., can be recorded for analysis, and its steps, errors, and adverse events were recently standardized in an international consensus statement. Specifically, the candidate will test the central hypothesis that the interaction of visible surgeon skill factors with visualized features, including patient anatomy and disease pathology, produces identifiable step-specific surgical errors that result in postoperative stroke, neurologic disability, and other adverse events. Specific Aims: 1) Use CV to identify step-specific errors (defined by tool usage, step progression, and phase characteristics) preceding adverse events; 2) Train ML models to predict upcoming adverse events from prior metrics of surgeon skill and current visual features of the surgical field. Methods to identify high-risk neurosurgical actions, predict upcoming adverse events from a surgeon’s movements, and retrospectively highlight critical timepoints and visual features associated with adverse events are necessary to rationally design and implement interventions to reduce stroke, neurologic disability, and other adverse events. The feasibility and success of this work will be facilitated by the candidate’s outstanding mentoring team, including a surgeon-scientist with experience conducting CV-based surgical performance assessments from procedural visual data and experts in ML using medical image and clinical data, biomedical applications of deep learning in complex prediction models, and multi-institutional pituitary surgical research. In the final year of the award, the candidate will apply for an R01 award to prospectively implement generalizable predictive models (developed in Aim 2) using a larger dataset of videos from several surgical procedures and to develop methods to collect and act upon data from operative video in real-time.