ArticleMachine learning in medical imaging. MLMI (Workshop)2022
Understanding Clinical Progression of Late-Life Depression to Alzheimer's Disease Over 5 Years with Structural MRI.
Article in Machine learning in medical imaging. MLMI (Workshop), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 5 citations in OpenAlex.
- Predictive factors for dementia among older adults in South Korea: an interpretable machine learning analysis.Alzheimer's research & therapy · 2025Article
- Hybrid representation learning for cognitive diagnosis in late-life depression over 5 years with structural MRI.Medical image analysis · 2024Article
- Brain morphometric features predict depression symptom phenotypes in late-life depression using a deep learning model.Frontiers in neuroscience · 2023Article
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Authors and funding
7 authors at 6 institutions in 2 countries.
Funding
Abstract
Previous studies have shown that late-life depression (LLD) may be a precursor of neurodegenerative diseases and may increase the risk of dementia. At present, the pathological relationship between LLD and dementia, in particularly Alzheimer's disease (AD) is unclear. Structural MRI (sMRI) can provide objective biomarkers for the computer-aided diagnosis of LLD and AD, providing a promising solution to understand the clinical progression of brain disorders. But few studies have focused on sMRI-based predictive analysis of clinical progression from LLD to AD. In this paper, we develop a deep learning method to predict the clinical progression of LLD to AD up to 5 years after baseline time using T1-weighted structural MRIs. We also analyze several important factors that limit the diagnostic performance of learning-based methods, including data imbalance, small-sample-size, and multi-site data heterogeneity, by leveraging a relatively large-scale database to aid model training. Experimental results on 308 subjects with sMRIs acquired from 2 imaging sites and the publicly available ADNI database demonstrate the potential of deep learning in predicting the clinical progression of LLD to AD. To the best of our knowledge, this is among the first attempts to explore the complex pathophysiological relationship between LLD and AD based on structural MRI using a deep learning method.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.