人工智能在内淋巴积水影像学诊断中的应用进展OA

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.