Enhancing Surgical Tool Classification Using a Semi-supervised Learning Autoencoder Approach

The purpose of this study is to develop a robust approach for identifying and classifying surgical instruments while addressing the challenges associated with traditional supervised learning methods, which require large annotated datasets. By comparing the performance of supervised machine learning and deep learning models with a semi-supervised autoencoder approach, this study aims to enhance efficiency and accuracy in automated and robotic-assisted surgical procedures. This will ultimately improve workflow optimization, error minimization, and real-time decision-making in the operating room