Data Science for Improved Outcomes in Interventional Healthcare

数据科学改善介入医疗保健的结果

• 批准号: RGPIN-2020-05582
• 负责人: Holden, Matthew
• 金额: $ 1.75万
• 依托单位: Carleton University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=717303
• 关键词: Data; Science; Improved; Outcomes; Interventional

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.

我在外科数据科学研究的长期目标是开发机器学习方法对术中手术数据进行量化分析，以改善患者的预后。由于疾病和外科技术的多样性，将领域知识融入机器学习方法势在必行。我的工作将集中在：(1)实时决策支持和(2)绩效评估和指导。 在这一资助期间，我建议在两个主要应用领域进行研究：护理点式超声波和眼科手术。这些徒手干预具有许多自由度，需要对环境进行精确的传感。 临床式超声检查的绩效评估与辅导 这项研究致力于机器学习，以全面评估和指导护理点超声。我们将探索使用神经网络结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、超声波视频、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，我们将审查培训课程中不同的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(在这种模式下，认证仅基于是否达到能力)。这将缓解对专家时间的限制，并通过提供自动评估和反馈来增加客观性。 眼科手术中的质量保证 这项工作将研究在真实的白内障手术中保证质量的方法，目的是直接改善患者的预后。我们将研究结合卷积和递归神经网络从手术视频中进行相位检测的方法。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针：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衡量标准，以及低成本光学工具追踪器在此次评估中的附加值。 这项研究通过提供质量和决策支持的实时测量，致力于改善患者的结果。这样的信息可以集成到手术导航软件中，使用数据驱动的方法来优化患者的结果。