Integrating Internet of Medical Things and Deep Learning for Reducing Retained Surgical Items: A Comprehensive Survey

Abstract

Retained Surgical Items (RSIs) pose significant health risks to patients, making their prevention a critical concern in surgical practices. This paper explores the integration of the Internet of Medical Things (IoMT) with deep learning technologies to enhance real-time tracking and monitoring of surgical items. By utilizing advanced deep learning algorithms, we can improve the accuracy of item detection and counting, thereby minimizing the occurrence of RSIs. The paper discusses various frameworks and platforms that can be employed to develop comprehensive tracking systems tailored for healthcare applications. Additionally, it highlights the potential of smart devices and wearable technologies in facilitating continuous monitoring of surgical items. Despite the advancements in healthcare technology, integrating deep learning and IoMT remains underexplored in the context of RSIs. This paper aims to bridge that gap by proposing innovative solutions that leverage these technologies to enhance patient safety and surgical outcomes. The creative solutions include a smart surgical instrument tracking system, wearable monitoring devices for surgeons, AI-powered surgical workflow management, and remote monitoring and data-sharing platforms. These solutions not only aim to minimize the occurrence of RSIs but also enhance the overall efficiency and safety of surgical procedures through the innovative use of technology.