AUGUR-AIM: Clinical validation of an artificial intelligence indocyanine green fluorescence angiography expert representer

IF 2.9 3区医学 Q2 GASTROENTEROLOGY & HEPATOLOGY

Colorectal Disease Pub Date : 2025-04-15 DOI:10.1111/codi.70097

Philip D. Mc Entee, Patrick A. Boland, Ronan A. Cahill

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引用次数: 0

Abstract

Aim

Recent randomized controlled trials and meta-analyses have demonstrated a reduction in the anastomotic leak rate when indocyanine green fluorescence angiography (ICGFA) is used versus when it is not in colorectal resections. We have previously demonstrated that an artificial intelligence (AI) model, AUGUR-AI, can digitally represent in real time where experienced ICGFA users would place their surgical stapler based on their interpretation of the fluorescence imagery. The aim of this study, called AUGUR-AIM, is to validate this method across multiple clinical sites with regard to generalizability, usability and accuracy while generating new algorithms for testing and determining the optimal mode of deployment for the software device.

Method

This is a prospective, observational, multicentre European study involving patients undergoing resectional colorectal surgery with ICGFA as part of their standard clinical care enrolled over a 1-year period. Video recordings of the ICGFA imagery will be computationally analysed both in real time and post hoc by AUGUR-AI, with the operating surgeon blinded to the results, testing developed algorithms iteratively versus the actual surgeon's ICGFA interpretation. AI-based interpretation of the fluorescence signal will be compared with the actual transection site selected by the operating surgeon and usability optimized.

Conclusion

AUGUR-AIM will validate the use of AUGUR-AI to interpret ICGFA imagery in real time to the level of an expert ICGFA user, building on our previous work to include a larger, more diverse patient and surgeon population. This could allow future progression to develop the AI model into a usable clinical tool that could provide decision support, including to new/infrequent ICGFA users, and documentary support of the decision made by experienced users.

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AUGUR-AIM：人工智能吲哚菁绿荧光血管造影专家代表的临床验证

最近的随机对照试验和荟萃分析表明，在结直肠切除术中使用吲哚菁绿荧光血管造影（ICGFA）与不使用ICGFA相比，吻合口漏率降低。我们之前已经证明，人工智能（AI）模型AUGUR-AI可以实时数字表示有经验的ICGFA用户根据他们对荧光图像的解释放置手术订书机的位置。这项名为AUGUR-AIM的研究的目的是在多个临床地点验证该方法的通用性、可用性和准确性，同时生成用于测试和确定软件设备最佳部署模式的新算法。方法：这是一项前瞻性、观察性、多中心的欧洲研究，研究对象是接受结肠直肠癌切除手术并将ICGFA作为其标准临床治疗的一部分的患者，研究时间超过1年。AUGUR-AI将对ICGFA图像的视频记录进行实时和事后的计算分析，操作外科医生对结果不知情，迭代测试开发的算法与实际外科医生的ICGFA解释。基于人工智能的荧光信号解释将与手术医生选择的实际横断部位进行比较，并优化可用性。AUGUR-AIM将验证AUGUR-AI的使用，以实时解释ICGFA图像，达到ICGFA专家用户的水平，建立在我们之前的工作基础上，包括更大，更多样化的患者和外科医生群体。这可以使AI模型在未来发展成为一种可用的临床工具，可以提供决策支持，包括向新的/不常用的ICGFA用户提供决策支持，并为经验丰富的用户所做的决策提供文件支持。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Colorectal Disease 医学-胃肠肝病学

CiteScore

6.10

自引率

11.80%

发文量

406

审稿时长

1.5 months

期刊介绍： Diseases of the colon and rectum are common and offer a number of exciting challenges. Clinical, diagnostic and basic science research is expanding rapidly. There is increasing demand from purchasers of health care and patients for clinicians to keep abreast of the latest research and developments, and to translate these into routine practice. Technological advances in diagnosis, surgical technique, new pharmaceuticals, molecular genetics and other basic sciences have transformed many aspects of how these diseases are managed. Such progress will accelerate. Colorectal Disease offers a real benefit to subscribers and authors. It is first and foremost a vehicle for publishing original research relating to the demanding, rapidly expanding field of colorectal diseases. Essential for surgeons, pathologists, oncologists, gastroenterologists and health professionals caring for patients with a disease of the lower GI tract, Colorectal Disease furthers education and inter-professional development by including regular review articles and discussions of current controversies. Note that the journal does not usually accept paediatric surgical papers.