Dynamic Data-Driven Simulation-Based Decision Support System for Medical Procedures

Abstract

Medical procedures are often performed under the most challenging work environments, demanding higher accuracy and precision in cognitive and technical (psychomotor) skills. More specifically, minimally invasive procedures require higher dexterity to perform the treatment/diagnosis using camera equipment in one hand, and surgical/procedural equipment in the other hand. Thus, it poses a grand challenge to provide a comprehensive system that can provide progressive training, perform objective assessment of psychomotor skills, and assist in studying the decision-making under time and situational uncertainties. To overcome these challenges, a Dynamic Data-Driven Simulation-based decision support system is designed and developed for medical procedures in this dissertation research. To effectively cover the wide spectrum of medical procedures, two types of medical procedures are considered: surgical procedure (Functional Endoscopic Sinus Surgery (FESS)) and emergency procedure (Airway Management). Four particular objectives of this dissertation are to: (1) design and develop a high-fidelity VR-based simulation environment for procedural training and planning, (2) ensure the system calibration from two aspects including positional and parameteric for accurate tracking and realistic representation of anatomical structures, (3) devise computationally efficient data-driven simulation to handle soft-tissue deformation during execution, (4) perform objective proficiency assessment utilizing high-fidelity sensory data acquired during trainee’s performance, (5) conduct the thorough literature review to understand decision-making behavior of caregivers under time and situational uncertainties, and (6) develop a formal process representation while identifying key decision points, and examine a the decision-theoretic model to study the decision-making of caregivers. Firstly, to develop a foundation and medium of feedback delivery to the caregivers, a systems engineering framework (V-model) was utilized in designing a VR-based simulation with a high-fidelity operating room environment with all critical features, including procedural tools, patients, and relevant equipment. In specific, real data of patient-specific anatomical structures (CT scans) and commercially used surgical/procedural tools have been utilized at a system design phase to provide patient-specific and procedure-specific simulations to the trainees. For the system implementation phase, CAD modelling and CT segmentation have been used to develop 3D models of procedure-specific tools and anatomical structures, respectively. The CAD models have also been imported into the Physics-based simulation platform (Unity 3D), where the virtual models have been overlaid over the physical models (3D-printed). Secondly, system calibration for positional and parameteric aspects were achieved to facilitate realistic feel to the user regarding equipment handling and haptic feedback based on the physical interaction between the procedure-specific tools and anatomical structures. Thirdly, a data-driven approach based on sensory data was devised to achieve a computationally efficient method to handle and realistically represent the soft-tissue deformation for the procedure. Fourthly, a hierarchical task analysis of a medical procedure was developed for data acquisition of relevant sensory data (HTC Vive controllers) at the procedural, task, and surgeme levels to provide a robust objective assessment framework. A comprehensive framework was developed for performance assessment based on collected discrete and continuous data for a medical procedure. In specific, dynamic time warping (DTW) algorithm was implemented for data-preprocessing before the actual classification. Hence, DTW was utilized in two folds: (1) to obtain the estimated motion trajectory of an expert (due to lack of ground truth pertaining to ideal procedural motion), (2) to acquire the distance measures pertaining to all attributes of the trainee’s motion by comparison with (1). The output distance measures corresponding to each attribute served as an input to the classification algorithm (decision tree) to classify the trainees based on their current proficiency level (novice and experts). Finally, comprehensive literature review was conducted to gain deeper insights on decision-making behavior of caregivers during a medical procedure, which helped in achieving a formal process modeling of a medical procedure was performed to facilitate research studies understanding the cognitive behavior of caregivers. Specifically, process planning and execution representation using AND/OR graphs was derived for a medical procedure to capture key decision/task alternatives and sequencing problems within the caregiver’s decision-making. Moreover, discussions on the caregiver's decision-making strategy and the application of the decision-theoretic model (R-DFT) for solving AND/OR junctions based on risk and time-urgency have been proposed. The utilization of R-DFT allows the incorporation of intuitionistic and deliberative decision making under a unified framework. The works developed in this dissertation research have been implemented and validated with numerous experiments comprising expert and novice users for two applications (i.e., FESS and Airway Management). Dissemination of the developed works in resident training and study protocols will significantly impact a steady progression of trainee’s skills and gain deeper insights behind decision-making behavior of caregivers. Furthermore, the developed work will open avenues in digital education by offering cost-effective methods for medical education.