基于基准的临床决策框架,用于多类中耳病的诊断:Swin变压器在准确性和稳定性方面的优越性
Guoping Chen1,2, Haoyi Zhang3, Junbo Zeng4
1The First Clinical Medical College, Jinan University, Guangzhou 510632, China.
Diagnostics (Basel, Switzerland)
|February 13, 2026
概括
这项研究对人工智能 (AI) 模型进行了基准测试,用于使用耳内镜图像诊断中耳病. 斯温变压器实现了最高的准确性,导致了新的AI决策框架,以改善临床支持.
科学领域:
- 医学成像分析 医学成像分析
- 医疗保健中的人工智能
- 耳鼻喉科 诊断 诊断 耳鼻喉科 诊断
背景情况:
- 中耳疾病诊断的耳内镜精度是可变的,需要增强的决策支持系统.
- 目前使用卷积神经网络 (CNN) 的计算机辅助诊断 (CAD) 方法在整合全球特征方面存在局限性.
研究的目的:
- 系统地对最先进的CNN和变压器模型进行基准测试,以诊断中耳疾病.
- 开发一种新的以概率为导向的Top-K临床决策框架,用于实际的AI部署在耳科.
主要方法:
- 使用了多中心数据集,包括五个类别的6361张耳内镜图像.
- 采用了两阶段验证策略 (固定分割和5倍交叉验证).
- 领先的CNN和变压器架构根据准确性和宏观F1分数进行了比较.
主要成果:
- 斯温变压器模型以95.53%的精度和93.37%的宏观F1得分获得了最高的性能.
- 斯温变压器表现出极好的稳定性 (95.61%±0.38%在交叉验证中) 和对类不平衡的稳定性.
- 一个以概率为导向的Top-2决策框架实现了93.25%的准确性,案例覆盖率为100%.
结论:
- 斯温变压器被认为是人工智能辅助中耳病症诊断最有效的架构.
- 开发的决策支持框架提供了一个临床可行的工具,用于在专业耳科中部署人工智能,以诊断慢性中耳病.
关键词:
斯温变压器 变压器人工智能的人工智能是人工智能.计算机辅助诊断是指计算机辅助的诊断.深度学习是一种深度学习.中耳炎 中耳炎.tympanic tympanic tympanic tympanic tympanic tympanic tympanic tympanic tympanic tympanic tympanic tympanic tympanic tympanic tympanic tympanic tympanic tympanic tympanic tympanic tympanic tympanic tympanic tympanic tympanic tympanic tympanic tympanic tympanic tympanic tympanic tympanic tympanic tympanic tympanic tympanic tympanic tympanic tympanic tympanic tympanic tympanic tympanic tympanic tympanic tympanic tympanic tympanic tympanic tympanic tympanic tympanic tympanic tympanic tympanic tympanic tympanic tympanic tympanic相关概念视频
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