Kwangbeom Park, Jisup Lim, Seung Hwan Shin, Minkyeong Ryu, Hyungeun Shin, Minyoung Lee, Seung Wook Hong, Sung Wook Hwang, Sang Hyoung Park, Dong-Hoon Yang, Byong Duk Ye, Seung-Jae Myung, Suk-Kyun Yang, Namkug Kim, Jeong-Sik Byeon
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Additionally, a crossover trial involving three expert endoscopists and three trainee endoscopists compared AI-assisted and unassisted human interpretations.</p><p><strong>Results: </strong>In the internal dataset, the sensitivity, specificity, and accuracy of the AI model in distinguishing between CD and GITB were 95.3%, 100.0%, and 97.7%, respectively, with an area under the ROC curve of 0.997. In the external dataset, the AI model exhibited a sensitivity, specificity, and accuracy of 77.8%, 85.1%, and 81.5%, respectively, with an area under the ROC curve of 0.877. In the human endoscopist trial, AI assistance increased the pooled accuracy of the six endoscopists from 86.2% to 88.8% (P = 0.010). While AI did not significantly enhance diagnostic accuracy for the experts (96.7% with AI vs 95.6% without, P = 0.360), it significantly improved accuracy for the trainees (81.0% vs 76.7%, P = 0.002).</p><p><strong>Conclusions: </strong>This AI model shows potential in aiding the accurate differential diagnosis between CD and GITB, particularly benefiting less experienced endoscopists.</p>","PeriodicalId":15877,"journal":{"name":"Journal of Gastroenterology and Hepatology","volume":null,"pages":null},"PeriodicalIF":3.7000,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Artificial intelligence-aided colonoscopic differential diagnosis between Crohn's disease and gastrointestinal tuberculosis.\",\"authors\":\"Kwangbeom Park, Jisup Lim, Seung Hwan Shin, Minkyeong Ryu, Hyungeun Shin, Minyoung Lee, Seung Wook Hong, Sung Wook Hwang, Sang Hyoung Park, Dong-Hoon Yang, Byong Duk Ye, Seung-Jae Myung, Suk-Kyun Yang, Namkug Kim, Jeong-Sik Byeon\",\"doi\":\"10.1111/jgh.16788\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background and aim: </strong>Differentiating between Crohn's disease (CD) and gastrointestinal tuberculosis (GITB) is challenging. 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引用次数: 0
摘要
背景和目的:区分克罗恩病(CD)和胃肠道结核(GITB)具有挑战性。我们旨在评估人工智能(AI)模型在临床上的适用性:人工智能模型是通过一个内部数据集开发和评估的,该数据集包括一个三级转诊中心的 1,132 张 CD 结肠镜图像和 1,045 张 GITB 结肠镜图像。该模型的独立性能还通过外部数据集进行了进一步评估,外部数据集包括来自其他机构的 17 名 CD 患者的 67 张结肠镜检查图像和 14 名 GITB 患者的 63 张结肠镜检查图像。此外,由三位内镜专家和三位内镜实习医生参与的交叉试验还比较了人工智能辅助和非辅助人工判读:在内部数据集中,人工智能模型区分 CD 和 GITB 的灵敏度、特异度和准确度分别为 95.3%、100.0% 和 97.7%,ROC 曲线下面积为 0.997。在外部数据集中,人工智能模型的灵敏度、特异度和准确度分别为 77.8%、85.1% 和 81.5%,ROC 曲线下面积为 0.877。在人类内镜医师试验中,人工智能辅助将六位内镜医师的综合准确率从 86.2% 提高到 88.8%(P = 0.010)。虽然人工智能没有明显提高专家的诊断准确率(有人工智能时为 96.7% vs 无人工智能时为 95.6%,P = 0.360),但却显著提高了受训者的准确率(81.0% vs 76.7%,P = 0.002):该人工智能模型在帮助准确鉴别诊断 CD 和 GITB 方面显示出潜力,尤其对经验不足的内镜医师大有裨益。
Artificial intelligence-aided colonoscopic differential diagnosis between Crohn's disease and gastrointestinal tuberculosis.
Background and aim: Differentiating between Crohn's disease (CD) and gastrointestinal tuberculosis (GITB) is challenging. We aimed to evaluate the clinical applicability of an artificial intelligence (AI) model for this purpose.
Methods: The AI model was developed and assessed using an internal dataset comprising 1,132 colonoscopy images of CD and 1,045 colonoscopy images of GITB at a tertiary referral center. Its stand-alone performance was further evaluated in an external dataset comprising 67 colonoscopy images of 17 CD patients and 63 colonoscopy images of 14 GITB patients from other institutions. Additionally, a crossover trial involving three expert endoscopists and three trainee endoscopists compared AI-assisted and unassisted human interpretations.
Results: In the internal dataset, the sensitivity, specificity, and accuracy of the AI model in distinguishing between CD and GITB were 95.3%, 100.0%, and 97.7%, respectively, with an area under the ROC curve of 0.997. In the external dataset, the AI model exhibited a sensitivity, specificity, and accuracy of 77.8%, 85.1%, and 81.5%, respectively, with an area under the ROC curve of 0.877. In the human endoscopist trial, AI assistance increased the pooled accuracy of the six endoscopists from 86.2% to 88.8% (P = 0.010). While AI did not significantly enhance diagnostic accuracy for the experts (96.7% with AI vs 95.6% without, P = 0.360), it significantly improved accuracy for the trainees (81.0% vs 76.7%, P = 0.002).
Conclusions: This AI model shows potential in aiding the accurate differential diagnosis between CD and GITB, particularly benefiting less experienced endoscopists.
期刊介绍:
Journal of Gastroenterology and Hepatology is produced 12 times per year and publishes peer-reviewed original papers, reviews and editorials concerned with clinical practice and research in the fields of hepatology, gastroenterology and endoscopy. Papers cover the medical, radiological, pathological, biochemical, physiological and historical aspects of the subject areas. All submitted papers are reviewed by at least two referees expert in the field of the submitted paper.