What question did this study set out to answer?

This research aims to improve early diagnosis of Alzheimer's disease using machine learning and MRI data.

March 3, 2026

Early Diagnosis of Alzheimer’s: Machine Learning Analysis Leveraging Structural MRI

SDSuhail Ahmad DarSaint Joseph's College NINasheed ImtiazAligarh Muslim University RDRameez Ahmad DarTezpur University

Key Points

This research aims to improve early diagnosis of Alzheimer's disease using machine learning and MRI data.
Analyzed subcortical atrophy patterns derived from structural MRI images.
Utilized machine learning algorithms for predictive analysis.
Compared various metrics, notably cortical thickness, for distinguishing stages of Alzheimer's.
Cortical thickness showed the most significant decline in individuals with mild cognitive impairment progressing to Alzheimer's.
Specific subcortical regions exhibited progressive deterioration, particularly as diagnosis approached.
Classifiers improved accuracy in differentiating stages of Alzheimer's, especially between mild cognitive impairment and healthy controls.

Abstract

SBM-derived subcortical atrophy patterns aid early AD detection and, when combined with ML, offer a scalable predictive framework. Among metrics, CTh showed the greatest decline in MCI-AD, followed by SD and GI. Progressive deterioration was observed in specific subcortical regions, accelerating near diagnosis. Classifiers achieved higher accuracy in distinguishing MCI-AD from HC at later time points, highlighting stage-specific shifts in feature importance.

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