Reproduce “Deep Learning for Surgical Instrument Recognition and Segmentation in Robotic-Assisted Surgeries: A Systematic Review” on OpenResearch

About this paper

Fatimaelzahraa Ali Ahmed, Mahmoud Yousef, Mariam Ali Ahmed, Hasan Omar Ali, Anns Mahboob, Hazrat Ali, Zubair Shah, Omar Aboumarzouk and 2 more

A systematic review evaluates 48 deep learning studies published between 2017 and 2024, assessing the effectiveness of deep learning techniques for surgical instrument detection and segmentation in robot-assisted minimally invasive surgeries. The analysis identifies key methodologies, datasets, and clinical applications, highlighting high accuracy metrics, with Dice coefficients reaching 97.10% and IoU up to 96% for binary segmentation.

Key insights

• Deep learning models, predominantly Convolutional Neural Networks (CNNs) like U-Net and ResNet, along with hybrid architectures, demonstrate substantial capability for accurate and efficient surgical instrument detection and segmentation.
• The reliance on both publicly available and numerous private datasets, often requiring intensive manual annotation, indicates a bottleneck in data availability and scalability for training robust DL models.
• DL-driven surgical tool annotation has diverse clinical applications, with strong emphasis on skill assessment, postoperative outcome analysis, and intraoperative guidance, but faces challenges in data scarcity and generalization across varied surgical environments.

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