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人工智能的应用用于通过磁控囊内镜检测Helicobacter pylori感染

Fuying Zheng¹, Qiaoying Zhu¹, Peiwen Yuan¹

¹Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China.

Surgical endoscopy

|November 26, 2025

概括

使用ShuffleNetV2的AI系统在磁控囊内镜 (MCCE) 图像中有效检测到Helicobacter pylori (H. pylori) 感染. 这种人工智能工具显示出高精度和速度,帮助临床医生诊断H. pylori.

科学领域:

医疗成像医学成像
人工智能的人工智能
胃肠病学胃肠病学

背景情况:

人工智能 (AI) 越来越多地应用于磁控囊内镜 (MCCE).
精确检测Helicobacter pylori (H. pylori) 感染对于胃肠道健康至关重要.
在MCCE图像中开发H. pylori的自动诊断工具是一个重要的临床需求.

研究的目的:

开发一种人工智能系统,在MCCE图像中自动检测H. pylori感染.
评估不同AI模型的诊断性能,以检测H. pylori.
在MCCE中确定最佳的AI模型用于临床应用.

主要方法:

对已知H. pylori状态的受试者进行MCCE检查的潜在招生.
从MCCE图像开发一个数据集,分为培训 (80%),验证 (10%) 和测试 (10%) 集.
四个卷积神经网络 (CNN) 模型的应用和评估:MobileNetV2,DenseNet264,ShuffleNetV2和ResNet50.

主要成果:

混合NetV2模型实现了95.7%的准确性,98.5%的灵敏度和93.1%的特异性,平均读取时间为每张图像0.00588秒.
ResNet50模型显示了最高的特异性 (94.5%),准确度为95.1%,灵敏度为95.8%.
所有评估的CNN模型都表现出高的诊断性能,ShuffleNetV2和ResNet50显示出具有竞争力的结果.

结论:

使用ShuffleNetV2模型的AI系统在通过MCCE检测H. pylori感染时表现出卓越的性能.
开发的AI系统显示出有很大的潜力,可以帮助临床医生在MCCE程序期间诊断H. pylori感染.
在MCCE中进一步验证和整合AI可以提高H. pylori的诊断效率和准确性.

关键词:

感染了Helicobacter pylori的病毒.人工智能的人工智能是人工智能.卷积神经网络是一种卷积神经网络.胃肠道疾病是什么磁性控制的囊内镜检查

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