# A digital tool for monitoring speech decline in ALS

> **NIH NIH R42** · MODALITY.AI , INC · 2022 · $332,831

## Abstract

PROJECT SUMMARY
 Amyotrophic Lateral Sclerosis (ALS) is a fatal neurodegenerative disease resulting in the progressive
loss of limb, trunk, and head and neck (bulbar) motor function and is the most common adult-onset motor neuron
disease. The disease is characterized by significant across-patient heterogeneity in the onset region and in
pattern and progression rate, making early and accurate diagnosis difficult. Most individuals with ALS will
eventually acquire bulbar symptoms, including a loss of speech and swallowing. The social and psychological
impact of communication loss on the quality of life of patients with ALS is significant and a diagnostic tool to
detect these changes early, accurately, and easily remains unmet.
 Moreover, improved speech testing tools are critically needed in ALS to (1) improve diagnostic testing
and monitoring, and (2) expediting clinical trials of behavioral and pharmacologic interventions. Although speech
testing is a core component of many neurological assessment batteries, few commercial digital speech
monitoring tools have been developed or scientifically validated, especially for ALS. This project aims to develop
and scientifically validate a digital speech assessment tool – the Modality Digital Speech Monitoring Tool (M-
DSMT) - for monitoring speech decline due to ALS.
 The M-DSMT is fully automated, online, objective, scalable, accessible, and is used in real-time. The tool
is operating-system and device-agnostic, running on commercially available laptops, tablets, or smartphones.
Therefore, if demonstrated to be effective, the proposed tool can be rapidly commercialized and deployed into
standard care and clinical trials for ALS. Our specific aims are to (1) optimize the quality and yield of remote
audio and video recordings for automated measurement, (2) determine the analytical validity of the M-DSMT,
(3) determine the clinical validity of the M-DSMT, and (4) determine the usability of the M-DSMT. This project's
objectives are to improve diagnosis for patients with ALS, decrease costs of speech assessment, and allow
patients who have difficulty traveling to continue to have access to regular speech assessments. Ultimately, we
aim to improve the quality of care in persons with ALS and facilitate ongoing efforts to discover a cure for this
fatal disease through the development of this innovative technology.

## Key facts

- **NIH application ID:** 10482581
- **Project number:** 1R42DC019877-01A1
- **Recipient organization:** MODALITY.AI , INC
- **Principal Investigator:** JORDAN R GREEN
- **Activity code:** R42 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $332,831
- **Award type:** 1
- **Project period:** 2022-06-01 → 2023-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10482581

## Citation

> US National Institutes of Health, RePORTER application 10482581, A digital tool for monitoring speech decline in ALS (1R42DC019877-01A1). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10482581. Licensed CC0.

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