Department of Diagnostic Pathology, Kinki Central Hospital of Mutual Aid Association of Public School Teachers, Itami, Hyogo, Japan.
Faculty of Medicine Division of Medicine, Department of Pathology, Osaka University Graduate School of Medicine, Suita, Japan.
Int J Surg Pathol. 2024 Aug;32(5):890-894. doi: 10.1177/10668969231204955. Epub 2023 Oct 25.
Ulcerative colitis (UC) is an intractable disease that affects young adults. Histological findings are essential for its diagnosis; however, the number of diagnostic pathologists is limited. Herein, we used a no-code artificial intelligence (AI) platform "Teachable Machine" to train a model that could distinguish between histological images of UC, non-UC coloproctitis, adenocarcinoma, and control. A total of 5100 histological images for training and 900 histological images for testing were prepared by pathologists. Our model showed accuracies of 0.99, 1.00, 0.99, and 0.99, for UC, non-UC coloproctitis, adenocarcinoma, and control, respectively. This is the first report in which a no-code easy AI platform has been able to comprehensively recognize the distinctive histologic patterns of UC.
溃疡性结肠炎（UC）是一种影响年轻人的难治性疾病。组织学发现对其诊断至关重要；然而，诊断病理学家的数量有限。在此，我们使用无代码人工智能（AI）平台“Teachable Machine”来训练一个能够区分 UC、非 UC 结肠炎、腺癌和对照组织学图像的模型。由病理学家准备了 5100 张用于训练的组织学图像和 900 张用于测试的组织学图像。我们的模型对 UC、非 UC 结肠炎、腺癌和对照的准确率分别为 0.99、1.00、0.99 和 0.99。这是第一个报告无代码简易 AI 平台能够全面识别 UC 独特组织学模式的报告。
