Evaluating surgical expertise with AI‐based automated instrument recognition for robotic distal gastrectomy
| Title: | Evaluating surgical expertise with AI‐based automated instrument recognition for robotic distal gastrectomy |
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| Authors: | James S. Strong, Tasuku Furube, Masashi Takeuchi, Hirofumi Kawakubo, Yusuke Maeda, Satoru Matsuda, Kazumasa Fukuda, Rieko Nakamura, Yuko Kitagawa |
| Source: | Annals of Gastroenterological Surgery, Vol 8, Iss 4, Pp 611-619 (2024) |
| Publisher Information: | Wiley, 2024. |
| Publication Year: | 2024 |
| Collection: | LCC:Surgery LCC:Diseases of the digestive system. Gastroenterology |
| Subject Terms: | automated instrument recognition, gastric cancer, robotic distal gastrectomy, surgical skill, Surgery, RD1-811, Diseases of the digestive system. Gastroenterology, RC799-869 |
| More Details: | Abstract Introduction Complexities of robotic distal gastrectomy (RDG) give reason to assess physician's surgical skill. Varying levels in surgical skill affect patient outcomes. We aim to investigate how a novel artificial intelligence (AI) model can be used to evaluate surgical skill in RDG by recognizing surgical instruments. Methods Fifty‐five consecutive robotic surgical videos of RDG for gastric cancer were analyzed. We used Deeplab, a multi‐stage temporal convolutional network, and it trained on 1234 manually annotated images. The model was then tested on 149 annotated images for accuracy. Deep learning metrics such as Intersection over Union (IoU) and accuracy were assessed, and the comparison between experienced and non‐experienced surgeons based on usage of instruments during infrapyloric lymph node dissection was performed. Results We annotated 540 Cadiere forceps, 898 Fenestrated bipolars, 359 Suction tubes, 307 Maryland bipolars, 688 Harmonic scalpels, 400 Staplers, and 59 Large clips. The average IoU and accuracy were 0.82 ± 0.12 and 87.2 ± 11.9% respectively. Moreover, the percentage of each instrument's usage to overall infrapyloric lymphadenectomy duration predicted by AI were compared. The use of Stapler and Large clip were significantly shorter in the experienced group compared to the non‐experienced group. Conclusions This study is the first to report that surgical skill can be successfully and accurately determined by an AI model for RDG. Our AI gives us a way to recognize and automatically generate instance segmentation of the surgical instruments present in this procedure. Use of this technology allows unbiased, more accessible RDG surgical skill. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 2475-0328 34580433 |
| Relation: | https://doaj.org/toc/2475-0328 |
| DOI: | 10.1002/ags3.12784 |
| Access URL: | https://doaj.org/article/d52579c34580433199c067cc2cec8783 |
| Accession Number: | edsdoj.52579c34580433199c067cc2cec8783 |
| Database: | Directory of Open Access Journals |
| ISSN: | 24750328 34580433 |
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| DOI: | 10.1002/ags3.12784 |
| Published in: | Annals of Gastroenterological Surgery |
| Language: | English |