Enhancement of Postoperative Surgery and Interventional Healthcare using Surgical Data Science(SDS)

Abstract

This paper addresses the challenge of colorectal cancer recurrence despite its preventability, emphasizing the critical need for effective endoscopic interventions. Existing approaches often fail to classify polyps, hindering accurate prediction and timely treatment sufficiently. This paper proposes leveraging AI and machine learning, particularly supervised learning techniques, to enhance polyp classification across eight categories and improve prediction accuracy. The methodology involves a comprehensive review of AI applications in endoscopic surgery, evaluating their clinical effectiveness. Additionally, the paper introduces Surgical Data Science (SDS) as a solution for optimizing postoperative care in colorectal surgery. SDS utilizes machine learning and detailed data analysis to personalize preoperative planning, monitor surgical outcomes in real time, and enhance disease surveillance at a population level. This approach offers a promising pathway to significantly improve colorectal surgery outcomes through precise diagnostics and proactive intervention strategies. In our study, we achieved a diagnostic and prediction accuracy of 99% using a combination of Convolutional Neural Networks (CNN) and Support Vector Machine (SVM) for the 8-class classification of colorectal polyps. Additionally, we obtained a 95% accuracy rate using CNN and Multilayer Perceptron (MLP). This demonstrates the potential of AI-driven techniques in advancing the effectiveness of colorectal cancer treatment.