Clinical Scorecard: A Clinically Relevant and Understandable Approach to Developing Digital Speech Biomarkers for Neurodegenerative Disorders

At a Glance

Key Highlights

• Automated extraction of interpretable acoustic speech markers enables objective assessment and phenotyping of communication impairments.
• Markers detect subtle subclinical changes before functional communication declines and differentiate disease-specific speech patterns with high accuracy (AUC > 0.90).
• Unsupervised clustering identifies distinct speech profiles within diseases, supporting personalized, measurement-based care.

Guideline-Based Recommendations

Diagnosis

• Utilize objective, automated speech analysis tools to detect early subclinical communicative changes.
• Apply disease-specific speech markers to differentiate neurodegenerative disorders such as ALS and Parkinson's disease.

Management

• Implement personalized interventions tailored to evolving communication profiles based on longitudinal speech marker monitoring.
• Incorporate behavioral strategies and assistive technologies (e.g., AAC devices) alongside speech biomarker assessments.

Monitoring & Follow-up

• Conduct systematic longitudinal reevaluations using speech biomarkers to track progression and adjust interventions accordingly.
• Leverage interpretable acoustic markers for ongoing phenotyping and stratification within disease populations.

Risks

• Be aware of limitations in traditional subjective assessments that may miss subtle subclinical changes.
• Consider challenges related to data collection and model interpretability when applying AI-based speech analysis.

Patient & Prescribing Data

Individuals with neurodegenerative diseases affecting speech, including ALS and Parkinson's disease

Speech biomarkers facilitate early detection and personalized management, enabling timely behavioral interventions and assistive technology use to preserve communication function.

Clinical Best Practices

• Adopt explainable, interpretable speech markers rather than black-box AI models to enhance clinician and patient trust.
• Integrate speech biomarker assessments into routine clinical workflows for early diagnosis and monitoring.
• Use standardized oral reading tasks to obtain consistent speech samples for analysis.
• Combine speech biomarker data with cognitive and motor assessments for comprehensive evaluation.

References

• Berisha and Liss - Explainable Speech Markers for AI Modeling