Please use this identifier to cite or link to this item:
http://hdl.handle.net/1893/36698
Appears in Collections: | Computing Science and Mathematics Journal Articles |
Peer Review Status: | Refereed |
Title: | Deep learning for surgical instrument recognition and segmentation in robotic-assisted surgeries: a systematic review |
Author(s): | Ahmed, Fatimaelzahraa Ali Yousef, Mahmoud Ahmed, Mariam Ali Ali, Hasan Omar Mahboob, Anns Ali, Hazrat Shah, Zubair Aboumarzouk, Omar Al Ansari, Abdulla Balakrishnan, Shidin |
Contact Email: | ali.hazrat@stir.ac.uk |
Keywords: | Deep learning Surgical tool annotation Robotic surgery Minimally invasive surgery Convolutional neural networks U-Net ResNet |
Issue Date: | 4-Nov-2024 |
Date Deposited: | 12-Dec-2024 |
Citation: | Ahmed FA, Yousef M, Ahmed MA, Ali HO, Mahboob A, Ali H, Shah Z, Aboumarzouk O, Al Ansari A & Balakrishnan S (2024) Deep learning for surgical instrument recognition and segmentation in robotic-assisted surgeries: a systematic review. <i>Artificial Intelligence Review</i>, 58 (1). https://doi.org/10.1007/s10462-024-10979-w |
Abstract: | Applying deep learning (DL) for annotating surgical instruments in robot-assisted minimally invasive surgeries (MIS) represents a significant advancement in surgical technology. This systematic review examines 48 studies that utilize advanced DL methods and architectures. These sophisticated DL models have shown notable improvements in the precision and efficiency of detecting and segmenting surgical tools. The enhanced capabilities of these models support various clinical applications, including real-time intraoperative guidance, comprehensive postoperative evaluations, and objective assessments of surgical skills. By accurately identifying and segmenting surgical instruments in video data, DL models provide detailed feedback to surgeons, thereby improving surgical outcomes and reducing complication risks. Furthermore, the application of DL in surgical education is transformative. The review underscores the significant impact of DL on improving the accuracy of skill assessments and the overall quality of surgical training programs. However, implementing DL in surgical tool detection and segmentation faces challenges, such as the need for large, accurately annotated datasets to train these models effectively. The manual annotation process is labor-intensive and time-consuming, posing a significant bottleneck. Future research should focus on automating the detection and segmentation process and enhancing the robustness of DL models against environmental variations. Expanding the application of DL models across various surgical specialties will be essential to fully realize this technology’s potential. Integrating DL with other emerging technologies, such as augmented reality (AR), also offers promising opportunities to further enhance the precision and efficacy of surgical procedures. |
DOI Link: | 10.1007/s10462-024-10979-w |
Rights: | This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
Licence URL(s): | http://creativecommons.org/licenses/by/4.0/ |
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s10462-024-10979-w-1.pdf | Fulltext - Published Version | 2.16 MB | Adobe PDF | View/Open |
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