内淋巴积水（endolymphatic hydrops，EH）是多种前庭疾病的重要病理基础，尤其在梅尼埃病诊断中具
有关键意义。随着磁共振成像技术的不断进步，特别是钆剂延迟增强MRI的应用，EH的影像诊断能力显
著提升。临床上，内耳积水影像学的判读常常依赖经验丰富的专家，但主观性强且耗时长，给外周前庭病变
的精准诊断带来了巨大的挑战。近年来，随着基于人工智能尤其深度学习图像分析方法的应用，为EH快
速、客观判读提供了新的解决方案。本文综述了EH诊断影像组学和人工智能的基本原理、内耳影像积水
判读中的应用现状、存在的挑战及未来发展方向，旨在为内耳疾病的精准诊断和个性化治疗提供理论支持
和技术参考。
Endolymphatic hydrops (EH) serves as a crucial pathological basis for various vestibular disorders
and holds particular significance in the diagnosis of Meniere's disease. With the continuous advancement of mag
netic resonance imaging (MRI) technology, especially the application of delayed gadolinium-enhanced delayed
3D-FLAIR sequences, the capability for imaging diagnosis of EH has been significantly enhanced. Clinically,
the interpretation of inner ear hydrops imaging often relies on experienced experts; however, this process is high
ly subjective and time-consuming, posing a substantial challenge to the precise diagnosis of peripheral vestibular
disorders. In recent years, the application of artificial intelligence (AI), particularly deep learning-based image
analysis methods, has provided a novel solution for the rapid and objective assessment of EH. This article re
views the fundamental principles of radiomics and AI in EH diagnosis, the current status of their application in
interpreting inner ear hydrops imaging, existing challenges, and future development directions. It aims to pro
vide theoretical support and technical reference for the precise diagnosis and personalized treatment of inner ear
diseases.
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