Data Science for Improved Outcomes in Interventional Healthcare

我在手术数据科学研究的长期目标是开发机器学习方法，用于术中手术数据的定量分析，以改善患者的预后。由于疾病和手术技术的多样性，机器学习方法必须结合领域知识。我的工作将集中在：（1）实时决策支持和（2）绩效评估和辅导。在此资助期间，我建议研究两个主要应用：即时超声和眼科手术。这些徒手干预具有许多自由度，并且需要对环境进行精确的传感。床旁超声的性能评估和指导这项研究致力于机器学习，以进行床旁超声的综合性能评估和指导。我们将探索使用神经网络架构进行多方面的技能评估，基于来自高精度跟踪系统和低成本惯性测量单元的传感器数据。我们还将建立干预工作流程的模型，并利用几个数据源（例如姿势跟踪器，超声视频，网络摄像头视频）来识别正在进行的步骤和使用状态转换建模的最佳反馈。此外，我们还将研究培训课程中不同的x现实模式的附加值。这项工作促进了世界范围内医学教育向基于能力的模式的转变（其中认证仅基于是否达到能力）。这将减少对专家时间的限制，并通过提供自动评估和反馈来提高客观性。 眼科手术的质量保证这项工作将研究真实的白内障手术的质量保证方法，其目标是直接改善患者的预后。我们将通过结合卷积和递归神经网络来研究手术视频的相位检测方法。随后，使用因果推理，我们可以确定将优化患者结果的行动方案：迈向实时决策支持的一步。我们还将通过视频分类网络调查手术每个阶段的质量指标，以及低成本光学工具跟踪器在此评估中的附加值。这项研究通过提供实时质量和决策支持措施来改善患者的治疗效果。这些信息可以集成到手术导航软件中，以使用数据驱动方法优化患者结果。

The long-term goal of my research in surgical data science is to develop machine learning methods for quantitative analysis of intra-operative surgical data to improve patient outcomes. Due to the diversity in disease and surgical techniques, it is imperative machine learning methods incorporate domain knowledge. My work will focus on: (1) real-time decision support and (2) performance assessment and coaching. For this funding period, I propose research in two primary applications: point-of-care ultrasound and ophthalmological surgery. These freehand interventions have many degrees of freedom and require precise sensorization of the environment. Performance Assessment and Coaching in Point-of-care Ultrasound This research works towards machine learning for comprehensive performance assessment and coaching in point-of-care ultrasound. We will explore the use of neural network architectures for multi-faceted skills assessment based on sensor data from both high accuracy tracking systems as well as low-cost inertial measurement units. We will also build models of the intervention's workflow and exploit several data sources (e.g. pose trackers, ultrasound video, webcam video) to identify the ongoing step and optimal feedback using state transition modelling. Furthermore, we will examine the added value if different x-reality modalities within training curricula. This work facilitates the worldwide shift in medical education to a competency-based model (one where accreditation is based solely upon whether competencies are achieved). This will alleviate constraints on expert time and increase objectivity by offering automated assessment and feedback. Quality Assurance in Ophthalmological Surgery This work will investigate methods for quality assurance in real cataract surgeries with the goal of directly improving patient outcomes. We will investigate methods for phase detection from surgical videos by combining convolutional and recurrent neural networks. Subsequently, using causal inference, we can determine the course of action that will optimize patient outcomes: a step towards real-time decision support. We will also investigate measures of quality in each phase of surgery via video classification networks, and the added value of low-cost optical tool trackers in this assessment. This research works towards improved patient outcomes by providing real-time measures of quality and decision support. Such information can be integrated into surgical navigation software to optimize patient outcomes using a data-driven approach.