Deep learning analysis of urine-derived stem cell mitochondrial morphology as a non-invasive Alzheimer's disease biomarker.

Authors

Ran Yan, Wenhua Zhang, Wenjing Wang, Jiaqi Wu, Jun Zhang, Yingjie Xu, Wei Xu, Wen Yang

Published in

Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics. Volume 23. Issue 1. Pages e00813. Epub Dec 16, 2025.

Abstract

Alzheimer's disease (AD), closely associated with mitochondrial dysfunction, currently lacks convenient and non-invasive biomarkers for mitochondrial assessment. In this study, we developed an artificial intelligence framework leveraging live urine-derived stem cell (USC) mitochondrial fluorescence imaging to investigate differences between cognitively impaired individuals (AD and mild cognitive impairment (MCI)) and cognitively normal (CN) subjects. Mitochondrial fluorescence images from living HeLa cells were first segmented, and two binary classification models based on the ResNet-18 convolutional neural network were trained to identify mitochondrial hyperfission and hyperfusion relative to normal morphology. The models demonstrated robust performance in detecting intermediate mitochondrial states during validation. When applied to USCs, the system effectively distinguished mitochondrial patterns associated with cognitive impairment, highlighting its potential for the early detection of Alzheimer's disease and merits further validation in larger, independent cohorts.

PMID:
41407593
Bibliographic data and abstract were imported from PubMed on 17 Sep 2026.

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