Data Science for Improved Outcomes in Interventional Healthcare

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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.