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Updated: Feb 28, 2026

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WATCH-SS: Developing a Trustworthy and Explainable Modular Framework for Detecting Cognitive Impairment from

Sydney Pugh¹, Matthew Hill², Sy Hwang²

¹Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA, Sydney.Pugh@PennMedicine.upenn.edu.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing

|February 27, 2026

Summary

Early detection of cognitive impairment (CI) is crucial for Alzheimer's disease intervention. The WATCH-SS tool analyzes speech for CI indicators, achieving 80% accuracy on a key dataset for early screening.

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Area of Science:

Neurology
Computational Linguistics
Artificial Intelligence in Medicine

Background:

Early detection of cognitive impairment (CI) is vital for managing Alzheimer's disease and related dementias.
Current screening methods may lack accessibility or efficiency for widespread primary care use.

Purpose of the Study:

To introduce the Warning Assessment and Alerting Tool for Cognitive Health from Spontaneous Speech (WATCH-SS).
To develop a modular, explainable framework for detecting CI using speech analysis.

Main Methods:

WATCH-SS employs a three-stage framework analyzing linguistic and acoustic speech features.
It incorporates multiple detector approaches, from real-time efficient to high-accuracy offline methods.
A predictive model classifies CI based on aggregated, clinically interpretable summary features.

Main Results:

The WATCH-SS framework demonstrated strong predictive performance on the DementiaBank ADReSS dataset.
Achieved an Area Under the Curve (AUC) of 80% on the test set.
The modular, feature-based approach provided a transparent diagnostic profile.

Conclusions:

WATCH-SS shows potential as a trustworthy and clinically usable screening tool for primary care settings.
This speech-based analysis represents a significant advancement in early CI detection.
The explainable nature of the tool supports clinical adoption and patient trust.