A Real-Time AI-Driven Surgical Monitoring Platform Using Robotics, 3D Convolutional Neural Networks (3D-CNNs), and Bayesian Optimization for Enhanced Precision

Abstract

In this work proposed an AI powered surgical monitoring platform to enhance robotic precision-made using 3DCNNs and Bayesian optimization. For example, real time tracking of tools and tissue identification can be used to improve the accuracy and efficiency of complex robot assisted surgeries. 3DCNNs combined with Bayesian optimization to build a real-time ai platform. This will enhance the precision of robotically assisted surgery by improving such features as surgical tool tracking and tissue recognition, which are critically important parameters for decision-making during complex surgeries. A 3D-CNN is employed to automatically learn discriminative spatiotemporal features from high resolution 3D medical images. By fine tuning the model parameters using Bayesian Optimization, tool tracking and tissue classification accuracy are improved. Surgeons receive immediate feedback in critical surgical settings. The use of Bayesian optimization helped increase the accuracy of our platform by 7.6% in achieving an overall user validation rate at a staggering $\mathbf{9 9. 5 0 \%}$. Reduced latency to 180 MS for real-time tool tracking and tissue identification in robot-assisted surgeries. 3DCNNs provide a robust, real-time solution to higher precision robotic surgeries by lowering the overall error rate in Bayesian optimization while also allowing doctors complete situational awareness for important surgical choices.

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