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基于深度学习的卷积神经网络在肠胃疾病内镜检查中的应用
Yang-Yang Wang1, Bin Liu2, Ji-Han Wang3
1School of Physics and Electronic Information, Yan'an University, Yan'an 716000, Shaanxi Province, China.
World journal of gastroenterology
|September 30, 2025
概括
使用卷积神经网络 (CNN) 的深度学习显示出从内镜图像改善胃肠道 (GI) 疾病诊断的前景. 这些人工智能系统可以提高准确性并支持实时决策,有可能彻底改变患者护理.
科学领域:
- 胃肠病学和人工智能的人工智能
- 医学成像分析 医学成像分析
背景情况:
- 胃肠道疾病,包括癌症,对全球健康构成重大挑战.
- 内镜诊断至关重要,但由于操作员的依赖性和可变性而受到限制.
- 深度学习,特别是CNN,有可能提高内镜诊断的准确性.
研究的目的:
- 审查CNN在分析肠道疾病检测和分类的内镜图像中的应用.
- 将CNN的性能与传统的诊断方法进行比较.
- 确定GI内镜中AI的挑战和未来方向.
主要方法:
- 对将CNN应用于内镜成像的研究进行审查.
- 专注于多和瘤检测,疾病分类,内镜超声波和囊内镜.
- 对CNN模型与传统技术的性能分析.
主要成果:
- 在肠道内镜检查中,CNN显示了提高精度和实时决策支持的潜力.
- 应用包括多/瘤检测和疾病分类.
- 在分析内镜超声波和囊内镜数据方面取得了有希望的结果.
结论:
- 基于CNN的人工智能可以显著改善早期胃肠道疾病的检测,并减少诊断错误.
- 挑战包括数据可用性,模型解释性和临床整合.
- 未来的研究应该专注于模型的概括性,可解释性和临床验证.
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