Deep Learning Model for Enhanced Surgical Phase Recognition and Tool Detection in Robot Assisted Surgery

Abstract

This study addresses the advancement of surgical phase recognition and tool detection in robot-assisted minimally invasive surgery (RMIS) through the development of a deep learning-based framework. The transition from conventional open surgery to MIS, and further to RMIS, has resulted in significant improvements in patient outcomes, including reduced trauma, lower infection risk, and faster recovery. Despite these benefits, challenges persist in accurately recognizing surgical phases and detecting tools due to high variability in surgical procedures, complex and dynamic visual scenes, and limitations in existing temporal modeling approaches. Traditional methods often rely on fixed temporal convolutions or constant-length video representations, which inadequately capture the diverse temporal dynamics inherent in surgical workflows. Recent advances leverage convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and transformer-based models to enhance feature discrimination and temporal dependency modeling. However, gaps remain in integrating the semantic structure of surgeries, efficiently exploiting multi-task learning, and handling challenging video frames. This research proposes a cascaded deep learning model that jointly addresses surgical phase recognition and tool detection, incorporating advanced architectures and multi-task strategies. The model's performance is evaluated against established benchmarks using various datasets and metrics, demonstrating improved accuracy and robustness. The findings highlight the potential of deep learning to enhance intraoperative decision support, workflow analysis, and surgical quality assurance in RMIS1.

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