Application of artificial intelligence imaging in common non-tumorous lesions of the central nervous system:a review
ZHANG Ting
SU Ning
CHEN Guoqiang
Abstract：The non-neoplastic lesions of the central nervous system are characterized by complicated pathology and diverse imaging manifestations,which pose great challenges to clinical diagnosis and treatment.Endowed with powerful feature extraction and pattern recognition capabilities,artificial intelligence(AI)offers a novel approach for the precise diagnosis and treatment of these lesions.This paper aims to systematically review the current application status and prospects of AI imaging technology in this field,focusing on three categories of lesions:firstly,cerebrovascular diseases,including the detection of stroke lesions and intracranial aneurysms;secondly,degenerative diseases,involving the imaging analysis and progression prediction of Alzheimer's disease and Parkinson's disease;thirdly,other lesions,covering the AI-based evaluation of minimal hepatic encephalopathy,multiple sclerosis and epilepsy.By integrating relevant research findings,this review clarifies the advantages and existing limitations of AI applications,provides a reference for clinical practice,promotes the translation of AI from scientific research to clinical application,facilitates the early diagnosis and precise treatment of lesions,and ultimately improves the prognosis of patients.
Keywords：artificial intelligencedeep learningradiomicsnon-tumorous central nervous system diseasesminimal hepatic encephalopathy
Publication Date：2026-02-20
Online Publishing Date：2026-03-25(First online date of this platform, not the publication date of the document)
Pages：7( 270-276 )