Related Experiment Video
Updated: Aug 2, 2025
09:47
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
Published on: December 15, 2023
1.2K
Sensitive quantification of cerebellar speech abnormalities using deep learning models
Medrxiv : the Preprint Server for Health Sciences
|April 17, 2023
Summary
Deep learning models can detect subtle speech changes in cerebellar ataxia, even in individuals without obvious clinical symptoms. These models accurately assess disease severity and track progression over time, aiding early detection and clinical care.
Area of Science:
- Neurology
- Speech Science
- Artificial Intelligence
Background:
- Objective disease assessment is crucial for clinical trials and care.
- Cerebellar ataxias manifest early and obviously through speech changes.
- Developing accurate models to identify and quantify speech abnormalities in ataxia is essential.
Approach:
- Utilized deep learning models, specifically ResNet 18.
- Input features included time and frequency partial derivatives of log-mel spectrograms of speech.
- Trained models for classification (ataxia vs. healthy) and regression (disease severity estimation).
Key Points:
- The model accurately distinguished individuals with ataxia from healthy controls, including those with subclinical speech deficits.
- Regression models provided precise estimates of disease severity.
- The approach successfully measured subclinical signs of ataxia and captured disease progression over time.
Conclusions:
- Deep learning models analyzing speech signal derivatives can detect subclinical speech changes in ataxias.
- These models offer sensitive measurement of disease progression over time.
- Potential applications include early ataxia detection and low-burden assessment tools for clinical trials and neurological care.
