Summary of Learning the Irreversible Progression Trajectory Of Alzheimer’s Disease, by Yipei Wang et al.
Learning the irreversible progression trajectory of Alzheimer’s disease
by Yipei Wang, Bing He, Shannon Risacher, Andrew Saykin, Jingwen Yan, Xiaoqian Wang
First submitted to arxiv on: 10 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Image and Video Processing (eess.IV)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposed novel regularization approach aims to predict Alzheimer’s disease longitudinally while maintaining the expected monotonicity of increasing disease risk during progression. The technique introduces a monotonicity constraint that encourages the model to predict disease risk in a consistent and ordered manner across follow-up visits, unlike existing techniques that only focus on accurate group assignment. The proposed method is evaluated using longitudinal structural MRI and amyloid-PET imaging data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The results show that the model outperforms existing techniques in capturing the progressiveness of disease risk, while preserving prediction accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Alzheimer’s disease is a serious brain disorder that gets worse over time. Scientists are trying to develop new ways to predict when someone will get Alzheimer’s so they can start treating it early. Right now, some methods for predicting Alzheimer’s only look at whether someone has the disease or not, without considering how bad it gets as time goes on. This makes it hard to understand how the disease progresses and how to treat it effectively. The researchers in this study came up with a new way to predict Alzheimer’s that takes into account how the risk of getting the disease changes over time. They tested their method using brain scans from people who participated in a research study. Their results show that their method is better than existing methods at capturing the progression of Alzheimer’s, which could lead to more effective treatments. |
Keywords
* Artificial intelligence * Regularization
