Surgical Workflow Analysis: An Explainable Approach.

Surgical workflow analysis optimizes efficiency, resource use, and patient safety in catheterization labs. Traditional manual methods are labour-intensive and inconsistent, driving the need for automated solutions that utilize machine learning and computer vision. This thesis introduces an explainable two-stage model for workflow analysis using ceiling-mounted cameras. The approach combines a YOLOv8 object detection model with a Gaussian Mixture Model - Hidden Markov Model (GMM-HMM). The first stage detects key objects for input into the second stage, where the GMM-HMM infers workflow phases by modelling spatial and temporal dynamics for real-time classification. Validation on two hospital datasets achieves 95.2% accuracy for the RdGG dataset and 95.4% for HH Tampere, demonstrating generalizability across environments. Experimental results show high accuracy in detecting workflow phases, highlighting explainability and robustness. The combined efficiencies of YOLOv8 and GMM-HMM allow for precise phase transition identification. The model's real-time application and adaptability across hospitals suggest its clinical implementation potential. This research furthers automated workflow analysis by enhancing interpretability and adaptability. Future work aims to improve robustness against occlusions, integrate audio data, and explore applications in other surgical settings.