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Clinical Decision Support Using Speech Signal Analysis: Systematic Scoping Review of Neurological Disorders.

Upeka De Silva¹, Samaneh Madanian¹, Sharon Olsen²

¹Department of Computer Science and Software Engineering, Auckland University of Technology, Auckland, New Zealand.

Journal of Medical Internet Research

|January 13, 2025

Summary

This summary is machine-generated.

Speech analysis using digital biomarkers offers insights into physiological processes for clinical decision support. This review highlights data science

Keywords:

artificial intelligence digital biomarker digital health health informatics machine learning speech analytics

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Area of Science:

Speech signal analysis
Digital biomarkers
Clinical decision support

Background:

Digital biomarkers, including speech features, are increasingly vital for clinical decision support.
Speech production complexity offers insights into physiological processes and motor system deficits.
Developing speech-based clinical decision support systems is a growing area of research.

Purpose of the Study:

To systematically review technological advancements and trends in digital clinical speech signal analysis.
To understand key concepts and research processes from both clinical and technical viewpoints.

Main Methods:

A systematic scoping review of 6 databases guided by research questions.
Quantitative analysis of articles on speech signal analysis for clinical decision-making.
Qualitative content analysis of studies focusing on neurological diseases.

Main Results:

72 studies on neurological diseases (Parkinson's, Alzheimer's, cognitive disorders) were analyzed.
Speech features show potential for diagnosis, differentiation, severity assessment, and treatment monitoring.
A research gap exists in structured processes for clinical technology intervention; a design science research framework is proposed.

Conclusions:

Data science techniques significantly enhance speech signal analysis for clinical decision-making.
Integrating clinical practice, speech science, and data science within a structured framework is crucial for future research relevance.