利用基于人工智能的自动器械识别技术评估机器人远端胃切除术的外科专业知识

IF 2.9 4区 医学 Q2 GASTROENTEROLOGY & HEPATOLOGY
James S. Strong, Tasuku Furube, Masashi Takeuchi, Hirofumi Kawakubo, Yusuke Maeda, Satoru Matsuda, Kazumasa Fukuda, Rieko Nakamura, Yuko Kitagawa
{"title":"利用基于人工智能的自动器械识别技术评估机器人远端胃切除术的外科专业知识","authors":"James S. Strong,&nbsp;Tasuku Furube,&nbsp;Masashi Takeuchi,&nbsp;Hirofumi Kawakubo,&nbsp;Yusuke Maeda,&nbsp;Satoru Matsuda,&nbsp;Kazumasa Fukuda,&nbsp;Rieko Nakamura,&nbsp;Yuko Kitagawa","doi":"10.1002/ags3.12784","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Introduction</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>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.</p>\n </section>\n </div>","PeriodicalId":8030,"journal":{"name":"Annals of Gastroenterological Surgery","volume":"8 4","pages":"611-619"},"PeriodicalIF":2.9000,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ags3.12784","citationCount":"0","resultStr":"{\"title\":\"Evaluating surgical expertise with AI-based automated instrument recognition for robotic distal gastrectomy\",\"authors\":\"James S. Strong,&nbsp;Tasuku Furube,&nbsp;Masashi Takeuchi,&nbsp;Hirofumi Kawakubo,&nbsp;Yusuke Maeda,&nbsp;Satoru Matsuda,&nbsp;Kazumasa Fukuda,&nbsp;Rieko Nakamura,&nbsp;Yuko Kitagawa\",\"doi\":\"10.1002/ags3.12784\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Introduction</h3>\\n \\n <p>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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusions</h3>\\n \\n <p>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.</p>\\n </section>\\n </div>\",\"PeriodicalId\":8030,\"journal\":{\"name\":\"Annals of Gastroenterological Surgery\",\"volume\":\"8 4\",\"pages\":\"611-619\"},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2024-02-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ags3.12784\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Annals of Gastroenterological Surgery\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/ags3.12784\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"GASTROENTEROLOGY & HEPATOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of Gastroenterological Surgery","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/ags3.12784","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"GASTROENTEROLOGY & HEPATOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

机器人远端胃切除术(RDG)的复杂性使我们有理由对医生的手术技能进行评估。不同水平的手术技能会影响患者的预后。我们旨在研究如何利用新型人工智能(AI)模型,通过识别手术器械来评估 RDG 的手术技能。我们使用了多级时空卷积网络 Deeplab,并在 1234 张人工标注的图像上进行了训练。然后在 149 张注释图像上测试了模型的准确性。我们标注了 540 把卡迪尔镊子、898 把瘘管双刀、359 把吸管、307 把马里兰双刀、688 把谐波手术刀、400 把订书机和 59 把大夹子。平均 IoU 和准确率分别为 0.82 ± 0.12 和 87.2 ± 11.9%。此外,还比较了每种器械的使用时间占人工智能预测的幽门下淋巴腺切除术总时间的百分比。与无经验组相比,有经验组使用订书机和大夹子的时间明显更短。这项研究首次报道了人工智能模型可以成功、准确地确定 RDG 的手术技巧。我们的人工智能让我们有办法识别并自动生成该手术中手术器械的实例分割。利用这项技术,可以无偏见地、更容易地掌握 RDG 手术技能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Evaluating surgical expertise with AI-based automated instrument recognition for robotic distal gastrectomy

Evaluating surgical expertise with AI-based automated instrument recognition for robotic distal gastrectomy

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.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Annals of Gastroenterological Surgery
Annals of Gastroenterological Surgery GASTROENTEROLOGY & HEPATOLOGY-
CiteScore
5.30
自引率
11.10%
发文量
98
审稿时长
11 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信