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基于深度学习的 Meniere 疾病的严重程度分级使用 2D MRI

Zheng Wang¹, Yang Xue¹, Yongjia Chen^2,3

¹School of Computer Science, Hunan First Normal University, Changsha, China.

Medical physics

|January 6, 2026

概括

一个新的深度学习系统,多阶段严重性评估系统 (MSAS),准确地划分和评分Meniere的细分和等级.

科学领域:

医学成像分析医学成像分析
医疗保健中的深度学习
内耳疾病内耳疾病

背景情况:

梅尼尔氏病 (Meniere's disease,简称MD) 是一种令人衰弱的内耳疾病,难以确诊.
目前MD的诊断方法缺乏准确性和一致性.
需要先进的深度学习细分工具来进行可靠的MD评估.

研究的目的:

开发和评估多阶段严重性评估系统 (MSAS).
MSAS是一个深度学习框架,用于MD的精确细分和严重程度分层.
使用二维磁共振成像 (MRI) 来评估梅尼尔氏病.

主要方法:

在189名患者的MRI扫描 (开发) 上使用像素级手动细分开发的MSAS,并在70名患者 (外部测试组) 上验证.
集成的序列级预测和切片级细分,使用包括YOLO-V5和Grad-CAM在内的技术.
通过交叉与结合 (IoU),子系数,精度,平均平均精度 (mAP) 和曲线下的面积 (AUC) 来评估性能.

主要成果:

MSAS实现了高的整体精度 (0.971) 和AUC (0.995).
显示出强烈的前庭区域检测 (mAP 0.887内部,0.877外部) 和细分 (子0.940内部,0.941外部).
切片注意力图和Grad-CAM为临床决策提供了更好的解释性.

结论:

深度学习技术显示出改善Meniere疾病诊断和严重程度分级的巨大潜力.
在MD管理中,MSAS框架提供了实质性的临床实用性.
这项研究通过先进的成像分析,促进了对梅尼耶氏病病理生理学的理解.

关键词:

梅尼尔氏病是梅尼尔氏病的一种疾病.深度学习是一种深度学习.图像分割图像细分图像细分磁共振成像 (MRI) 的使用.多阶段严重性评估系统 (MSAS)

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