Summary of Clustering Alzheimer’s Disease Subtypes Via Similarity Learning and Graph Diffusion, by Tianyi Wei et al.
Clustering Alzheimer’s Disease Subtypes via Similarity Learning and Graph Diffusion
by Tianyi Wei, Shu Yang, Davoud Ataee Tarzanagh, Jingxuan Bao, Jia Xu, Patryk Orzechowski, Joost B. Wagenaar, Qi Long, Li Shen
First submitted to arxiv on: 4 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The study aims to identify homogeneous subtypes of Alzheimer’s disease (AD) that can aid in diagnosis and treatment. By using unsupervised clustering with graph diffusion and similarity learning, the researchers identified five distinct subtypes based on cortical thickness measurements from magnetic resonance imaging (MRI) scans of 829 patients with AD and mild cognitive impairment (MCI). The proposed approach demonstrated better performance compared to traditional clustering methods, and the subtypes differed significantly in biomarkers, cognitive status, and clinical features. A genetic association study was also conducted to identify potential genetic underpinnings of different AD subtypes. The results have implications for personalized treatment approaches and improving diagnosis of AD. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The research aims to find groups of people with Alzheimer’s disease that are similar in certain ways. They used a special kind of computer analysis to look at brain scans from 829 people with Alzheimer’s or mild cognitive impairment. The approach worked better than others did, and the study found five different groups that were unique in their characteristics. These groups could be important for finding new treatments and diagnosing Alzheimer’s more accurately. |
Keywords
» Artificial intelligence » Clustering » Diffusion » Unsupervised
