Summary of A Review Of Deep Learning Approaches For Non-invasive Cognitive Impairment Detection, by Muath Alsuhaibani et al.
A Review of Deep Learning Approaches for Non-Invasive Cognitive Impairment Detection
by Muath Alsuhaibani, Ali Pourramezan Fard, Jian Sun, Farida Far Poor, Peter S. Pressman, Mohammad H. Mahoor
First submitted to arxiv on: 25 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper reviews recent advances in deep learning approaches for non-invasive cognitive impairment detection. It explores various indicators of cognitive decline, including speech and language, facial, and motoric mobility. The review provides an overview of relevant datasets, feature-extracting techniques, and deep-learning architectures applied to this domain. Key findings include the highest detection performance achieved by speech and language-based methods, with studies combining acoustic and linguistic features outperforming those using a single modality. Facial analysis methods showed promise for visual modalities. The paper also highlights challenges such as data standardization and accessibility, model explainability, longitudinal analysis limitations, and clinical adaptation. The authors propose future research directions, including investigating language-agnostic speech analysis methods, developing multi-modal diagnostic systems, and addressing ethical considerations in AI-assisted healthcare. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how computers can help diagnose cognitive impairment without taking a person’s blood or doing other invasive tests. It talks about different ways to do this, like looking at someone’s face or listening to their speech. The study found that using both speech and language together was the most accurate way to detect impairments. The researchers also said that there are some problems with current approaches, like not having enough data or not being able to explain why a computer made a certain decision. To fix these problems, the authors suggest studying new ways to analyze speech and combining different types of data. |
Keywords
» Artificial intelligence » Deep learning » Multi modal
