在机器人辅助胃切除术中通过语义分割精确标出胰腺:利用人工智能为外科医生提供视觉辅助

IF 6 1区 医学 Q1 GASTROENTEROLOGY & HEPATOLOGY Gastric Cancer Pub Date : 2024-04-04 DOI:10.1007/s10120-024-01495-5
Tatsuro Nakamura, Nao Kobayashi, Yuta Kumazu, Kyohei Fukata, Motoki Murakami, Shugo Kohno, Yudai Hojo, Eiichiro Nakao, Yasunori Kurahashi, Yoshinori Ishida, Hisashi Shinohara
{"title":"在机器人辅助胃切除术中通过语义分割精确标出胰腺:利用人工智能为外科医生提供视觉辅助","authors":"Tatsuro Nakamura, Nao Kobayashi, Yuta Kumazu, Kyohei Fukata, Motoki Murakami, Shugo Kohno, Yudai Hojo, Eiichiro Nakao, Yasunori Kurahashi, Yoshinori Ishida, Hisashi Shinohara","doi":"10.1007/s10120-024-01495-5","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Background</h3><p>A postoperative pancreatic fistula (POPF) is a critical complication of radical gastrectomy for gastric cancer, mainly because surgeons occasionally misrecognize the pancreas and fat during lymphadenectomy. Therefore, this study aimed to develop an artificial intelligence (AI) system capable of identifying and highlighting the pancreas during robot-assisted gastrectomy.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>A pancreas recognition algorithm was developed using HRNet, with 926 training images and 232 validation images extracted from 62 scenes of robot-assisted gastrectomy videos. During quantitative evaluation, the precision, recall, intersection over union (IoU), and Dice coefficients were calculated based on the surgeons’ ground truth and the AI-inferred image from 80 test images. During the qualitative evaluation, 10 surgeons answered two questions related to sensitivity and similarity for assessing clinical usefulness.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>The precision, recall, IoU, and Dice coefficients were 0.70, 0.59, 0.46, and 0.61, respectively. Regarding sensitivity, the average score for pancreas recognition by AI was 4.18 out of 5 points (1 = lowest recognition [less than 50%]; 5 = highest recognition [more than 90%]). Regarding similarity, only 54% of the AI-inferred images were correctly differentiated from the ground truth.</p><h3 data-test=\"abstract-sub-heading\">Conclusions</h3><p>Our surgical AI system precisely highlighted the pancreas during robot-assisted gastrectomy at a level that was convincing to surgeons. This technology may prevent misrecognition of the pancreas by surgeons, thus leading to fewer POPFs.</p>","PeriodicalId":12684,"journal":{"name":"Gastric Cancer","volume":null,"pages":null},"PeriodicalIF":6.0000,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Precise highlighting of the pancreas by semantic segmentation during robot-assisted gastrectomy: visual assistance with artificial intelligence for surgeons\",\"authors\":\"Tatsuro Nakamura, Nao Kobayashi, Yuta Kumazu, Kyohei Fukata, Motoki Murakami, Shugo Kohno, Yudai Hojo, Eiichiro Nakao, Yasunori Kurahashi, Yoshinori Ishida, Hisashi Shinohara\",\"doi\":\"10.1007/s10120-024-01495-5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<h3 data-test=\\\"abstract-sub-heading\\\">Background</h3><p>A postoperative pancreatic fistula (POPF) is a critical complication of radical gastrectomy for gastric cancer, mainly because surgeons occasionally misrecognize the pancreas and fat during lymphadenectomy. Therefore, this study aimed to develop an artificial intelligence (AI) system capable of identifying and highlighting the pancreas during robot-assisted gastrectomy.</p><h3 data-test=\\\"abstract-sub-heading\\\">Methods</h3><p>A pancreas recognition algorithm was developed using HRNet, with 926 training images and 232 validation images extracted from 62 scenes of robot-assisted gastrectomy videos. During quantitative evaluation, the precision, recall, intersection over union (IoU), and Dice coefficients were calculated based on the surgeons’ ground truth and the AI-inferred image from 80 test images. During the qualitative evaluation, 10 surgeons answered two questions related to sensitivity and similarity for assessing clinical usefulness.</p><h3 data-test=\\\"abstract-sub-heading\\\">Results</h3><p>The precision, recall, IoU, and Dice coefficients were 0.70, 0.59, 0.46, and 0.61, respectively. Regarding sensitivity, the average score for pancreas recognition by AI was 4.18 out of 5 points (1 = lowest recognition [less than 50%]; 5 = highest recognition [more than 90%]). Regarding similarity, only 54% of the AI-inferred images were correctly differentiated from the ground truth.</p><h3 data-test=\\\"abstract-sub-heading\\\">Conclusions</h3><p>Our surgical AI system precisely highlighted the pancreas during robot-assisted gastrectomy at a level that was convincing to surgeons. This technology may prevent misrecognition of the pancreas by surgeons, thus leading to fewer POPFs.</p>\",\"PeriodicalId\":12684,\"journal\":{\"name\":\"Gastric Cancer\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":6.0000,\"publicationDate\":\"2024-04-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Gastric Cancer\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s10120-024-01495-5\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"GASTROENTEROLOGY & HEPATOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Gastric Cancer","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s10120-024-01495-5","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GASTROENTEROLOGY & HEPATOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

背景术后胰瘘(POPF)是胃癌根治性胃切除术的一种严重并发症,主要原因是外科医生在淋巴腺切除术中偶尔会误识胰腺和脂肪。因此,本研究旨在开发一种人工智能(AI)系统,该系统能够在机器人辅助胃切除术过程中识别并突出显示胰腺。方法使用 HRNet 开发了一种胰腺识别算法,从机器人辅助胃切除术视频的 62 个场景中提取了 926 幅训练图像和 232 幅验证图像。在定量评估中,根据外科医生的基本事实和人工智能从80张测试图像中推断出的图像,计算了精确度、召回率、交集大于联合(IoU)和骰子系数。在定性评估中,10 名外科医生回答了与灵敏度和相似度有关的两个问题,以评估临床实用性。结果精确度、召回率、IoU 和 Dice 系数分别为 0.70、0.59、0.46 和 0.61。在灵敏度方面,人工智能识别胰腺的平均得分为 4.18 分(满分 5 分,1 分=识别率最低[低于 50%];5 分=识别率最高[高于 90%])。结论我们的手术人工智能系统在机器人辅助胃切除术中精确地突出了胰腺,令外科医生信服。这项技术可以防止外科医生错误识别胰腺,从而减少 POPF。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Precise highlighting of the pancreas by semantic segmentation during robot-assisted gastrectomy: visual assistance with artificial intelligence for surgeons

Background

A postoperative pancreatic fistula (POPF) is a critical complication of radical gastrectomy for gastric cancer, mainly because surgeons occasionally misrecognize the pancreas and fat during lymphadenectomy. Therefore, this study aimed to develop an artificial intelligence (AI) system capable of identifying and highlighting the pancreas during robot-assisted gastrectomy.

Methods

A pancreas recognition algorithm was developed using HRNet, with 926 training images and 232 validation images extracted from 62 scenes of robot-assisted gastrectomy videos. During quantitative evaluation, the precision, recall, intersection over union (IoU), and Dice coefficients were calculated based on the surgeons’ ground truth and the AI-inferred image from 80 test images. During the qualitative evaluation, 10 surgeons answered two questions related to sensitivity and similarity for assessing clinical usefulness.

Results

The precision, recall, IoU, and Dice coefficients were 0.70, 0.59, 0.46, and 0.61, respectively. Regarding sensitivity, the average score for pancreas recognition by AI was 4.18 out of 5 points (1 = lowest recognition [less than 50%]; 5 = highest recognition [more than 90%]). Regarding similarity, only 54% of the AI-inferred images were correctly differentiated from the ground truth.

Conclusions

Our surgical AI system precisely highlighted the pancreas during robot-assisted gastrectomy at a level that was convincing to surgeons. This technology may prevent misrecognition of the pancreas by surgeons, thus leading to fewer POPFs.

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
Gastric Cancer
Gastric Cancer 医学-胃肠肝病学
CiteScore
14.70
自引率
2.70%
发文量
80
审稿时长
6-12 weeks
期刊介绍: Gastric Cancer is an esteemed global forum that focuses on various aspects of gastric cancer research, treatment, and biology worldwide. The journal promotes a diverse range of content, including original articles, case reports, short communications, and technical notes. It also welcomes Letters to the Editor discussing published articles or sharing viewpoints on gastric cancer topics. Review articles are predominantly sought after by the Editor, ensuring comprehensive coverage of the field. With a dedicated and knowledgeable editorial team, the journal is committed to providing exceptional support and ensuring high levels of author satisfaction. In fact, over 90% of published authors have expressed their intent to publish again in our esteemed journal.
期刊最新文献
Morbidity and quality of life of totally laparoscopic versus laparoscopy-assisted distal gastrectomy for early gastric cancer: a multi-center prospective randomized controlled trial (CKLASS01). Volume of hepatoid component and intratumor M2 macrophages predict prognosis in patients with hepatoid adenocarcinoma of the stomach. Prognostic and predictive factors for the efficacy and safety of trastuzumab deruxtecan in HER2-positive gastric or gastroesophageal junction cancer. Comment on "Comparative effectiveness of sodium-glucose cotransporter-2 inhibitors for new-onset gastric cancer and gastric diseases in patients with type 2 diabetes mellitus: a population-based cohort study". Comment on "a machine learning model for predicting the lymph node metastasis of early gastric cancer not meeting the endoscopic curability criteria".
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1