Enhancing Surgical Precision with Convolutional Neural Networks and Iot in Robotic Surgery - Megelin Star, A. Anbu; Brindha, G.; S Chooralil, Varghese; F. Stanley, Berakhah.; Brilly Sangeetha, S.; Yuvaraj, N.; Arshath Raja, R.; Antony Joe, S. Sebastin (Journal of Neonatal Surgery, 2025)
Advancements in robotic surgery have significantly improved surgical precision, yet challenges related to real-time decision-making and adaptive control persist. Integrating Convolutional Neural Networks (CNNs) with Internet of Things (IoT) technology offers a promising approach to enhance the accuracy and responsiveness of robotic-assisted surgeries. CNNs can process high-resolution medical imaging data to identify critical anatomical structures and potential complications, while IoT enables real-time data acquisition and feedback from surgical instruments and patient monitoring systems. The proposed method involves a CNN-based deep learning model integrated with IoT sensors to enhance intraoperative decision-making. High-resolution surgical images and sensor data are fed into a multi-layer CNN model that extracts features and classifies anatomical structures in real-time. Feedback from IoT-enabled surgical tools is processed using a recurrent feedback mechanism to adjust the surgical path dynamically. Results indicate that the proposed method improves accuracy in anatomical structure identification by 9.5% and reduces intraoperative errors by 12.3% compared to existing methods. Enhanced real-time responsiveness and surgical precision were achieved with a reduced feedback latency of 23 ms. This approach provides a scalable and adaptive framework for improving robotic surgical outcomes through real-time learning and feedback integration.