Clinical Decision Support Using Speech Signal Analysis: Systematic Scoping Review of Neurological Disorders
محفوظ في:
| الحاوية / القاعدة: | Journal of Medical Internet Research vol. 27 (2025), p. e63004 |
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| المؤلف الرئيسي: | |
| مؤلفون آخرون: | , , , , , , |
| منشور في: |
Gunther Eysenbach MD MPH, Associate Professor
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| الموضوعات: | |
| الوصول للمادة أونلاين: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| الوسوم: |
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MARC
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| 003 | UK-CbPIL | ||
| 022 | |a 1438-8871 | ||
| 024 | 7 | |a 10.2196/63004 |2 doi | |
| 035 | |a 3222368331 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 100 | 1 | |a Upeka De Silva | |
| 245 | 1 | |a Clinical Decision Support Using Speech Signal Analysis: Systematic Scoping Review of Neurological Disorders | |
| 260 | |b Gunther Eysenbach MD MPH, Associate Professor |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Background:Digital biomarkers are increasingly used in clinical decision support for various health conditions. Speech features as digital biomarkers can offer insights into underlying physiological processes due to the complexity of speech production. This process involves respiration, phonation, articulation, and resonance, all of which rely on specific motor systems for the preparation and execution of speech. Deficits in any of these systems can cause changes in speech signal patterns. Increasing efforts are being made to develop speech-based clinical decision support systems.Objective:This systematic scoping review investigated the technological revolution and recent digital clinical speech signal analysis trends to understand the key concepts and research processes from clinical and technical perspectives.Methods:A systematic scoping review was undertaken in 6 databases guided by a set of research questions. Articles that focused on speech signal analysis for clinical decision-making were identified, and the included studies were analyzed quantitatively. A narrower scope of studies investigating neurological diseases were analyzed using qualitative content analysis.Results:A total of 389 articles met the initial eligibility criteria, of which 72 (18.5%) that focused on neurological diseases were included in the qualitative analysis. In the included studies, Parkinson disease, Alzheimer disease, and cognitive disorders were the most frequently investigated conditions. The literature explored the potential of speech feature analysis in diagnosis, differentiating between, assessing the severity and monitoring the treatment of neurological conditions. The common speech tasks used were sustained phonations, diadochokinetic tasks, reading tasks, activity-based tasks, picture descriptions, and prompted speech tasks. From these tasks, conventional speech features (such as fundamental frequency, jitter, and shimmer), advanced digital signal processing–based speech features (such as wavelet transformation–based features), and spectrograms in the form of audio images were analyzed. Traditional machine learning and deep learning approaches were used to build predictive models, whereas statistical analysis assessed variable relationships and reliability of speech features. Model evaluations primarily focused on analytical validations. A significant research gap was identified: the need for a structured research process to guide studies toward potential technological intervention in clinical settings. To address this, a research framework was proposed that adapts a design science research methodology to guide research studies systematically.Conclusions:The findings highlight how data science techniques can enhance speech signal analysis to support clinical decision-making. By combining knowledge from clinical practice, speech science, and data science within a structured research framework, future research may achieve greater clinical relevance. | |
| 653 | |a Language | ||
| 653 | |a Physiological processes | ||
| 653 | |a Databases | ||
| 653 | |a Alzheimer's disease | ||
| 653 | |a Citation management software | ||
| 653 | |a Neurological disorders | ||
| 653 | |a Medical diagnosis | ||
| 653 | |a Disease | ||
| 653 | |a Content analysis | ||
| 653 | |a Clinical research | ||
| 653 | |a Data science | ||
| 653 | |a Articulation | ||
| 653 | |a Jitter | ||
| 653 | |a Prediction models | ||
| 653 | |a Qualitative research | ||
| 653 | |a Machine learning | ||
| 653 | |a Statistical analysis | ||
| 653 | |a Cognitive impairment | ||
| 653 | |a Transformation | ||
| 653 | |a Respiration | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Clinical medicine | ||
| 653 | |a Clinical decision making | ||
| 653 | |a Multimedia | ||
| 653 | |a Biological markers | ||
| 653 | |a Research methodology | ||
| 653 | |a Fundamental frequency | ||
| 653 | |a Biomarkers | ||
| 653 | |a Data collection | ||
| 653 | |a Reliability | ||
| 653 | |a Acoustics | ||
| 653 | |a Speech | ||
| 653 | |a Speech production | ||
| 653 | |a Parkinson's disease | ||
| 653 | |a Decision support systems | ||
| 653 | |a Diagnostic tests | ||
| 653 | |a Deep learning | ||
| 653 | |a Scientific technological revolution | ||
| 653 | |a Quantitative analysis | ||
| 653 | |a Models | ||
| 653 | |a Research design | ||
| 653 | |a Disorders | ||
| 653 | |a Anatomical systems | ||
| 653 | |a Support networks | ||
| 653 | |a Phonation | ||
| 653 | |a Brain diseases | ||
| 653 | |a Shimmer | ||
| 653 | |a Signal processing | ||
| 653 | |a Decision making | ||
| 653 | |a Medical decision making | ||
| 653 | |a Respiratory system | ||
| 700 | 1 | |a Samaneh Madanian | |
| 700 | 1 | |a Olsen, Sharon | |
| 700 | 1 | |a Templeton, John Michael | |
| 700 | 1 | |a Poellabauer, Christian | |
| 700 | 1 | |a Schneider, Sandra L | |
| 700 | 1 | |a Narayanan, Ajit | |
| 700 | 1 | |a Rubaiat, Rahmina | |
| 773 | 0 | |t Journal of Medical Internet Research |g vol. 27 (2025), p. e63004 | |
| 786 | 0 | |d ProQuest |t Library Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3222368331/abstract/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3222368331/fulltextwithgraphics/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3222368331/fulltextPDF/embedded/H09TXR3UUZB2ISDL?source=fedsrch |