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基于深度学习的虚拟染色内镜在胃瘤的诊断性能

Sho Suzuki¹, Yusuke Monno², Ryo Arai²

¹Department of Gastroenterology, International University of Health and Welfare Ichikawa Hospital, 6-1-14, Konodai, Ichikawa-Shi, Chiba, 272-0827, Japan. s.sho.salubriter.mail@gmail.com.

Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association

|January 19, 2024

概括

由CycleGAN生成的虚拟蓝色卡明 (IC) 图像显示了胃瘤诊断的潜力,与白光内镜 (WLE) 相比,改善了特定病变类型的检测. 为了更广泛的优越性,需要进一步的细化.

科学领域:

医疗成像医学成像
人工智能在医学中的应用
胃肠病学胃肠病学

背景情况:

循环一致的生成对抗网络 (CycleGAN) 是用于图像对图像翻译的深度学习模型.
使用CycleGAN用于胃瘤,生成了虚拟色红 (IC) 染色内镜图像.

研究的目的:

与白光内镜 (WLE) 相比,评估CycleGAN生成的虚拟IC图像的诊断性能.
评估CycleGAN生成的虚拟IC图像在识别胃瘤中的实用性.

主要方法:

来自146名患者的1,633张白光内镜 (WLE) 和红色卡明 (IC) 图像的数据集被用于训练CycleGAN.
经过训练的CycleGAN将30个WLE图像转换为虚拟IC图像进行验证.
118名内镜医生评估了60张配对的虚拟IC和WLE图像,以确定病变区域的一致性和整个病变的诊断.

主要成果:

虚拟IC显示的病变区域一致率低于WLE (44.1%vs48.5%).
全损伤诊断在虚拟IC中较高,但没有显著差异 (28.2%与26.4%).
亚组分析显示,虚拟IC在抑郁形态学,差异化组织学,较小的病变和专家内镜师的诊断率显著更高.

结论:

虚拟IC成像显示了针对特定胃瘤亚型的改善诊断能力.
虽然不是普遍优于WLE,虚拟IC显示承诺,需要进一步的系统优化.

关键词:

染色内镜检查技术 (Chromoendoscopy) 是一种使用染色内镜检查技术.循环一致的生成对抗网络.深度学习是一种深度学习.深度神经网络是一个神经网络.胃癌胃癌是指胃癌.

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