Characteristics and Validity of Commercially Available Technologies Analyzing Voice Features to Assess Parkinson's Disease
Bibliographic record
Abstract
BACKGROUND: Interest in technologies for quantitative assessment of Parkinson's disease (PD) is growing, particularly those enabling voice and speech analysis. However, clinical validation varies widely, much algorithm data remain unpublished, and real-world use is still limited. OBJECTIVE: The aim was to provide an overview of the characteristics and validity of commercially available tools that utilize machine learning to assess voice and speech features for identifying PD or assessing disease severity. METHODS: The literature was reviewed to (1) identify, (2) compare, and (3) group commercially available tools deployed in smartphone apps and other scalable platforms based on their (1) previous use in PD, (2) feasibility, and (3) successful clinimetric testing. The International Parkinson and Movement Disorder Society Digital Tools and Technologies Repository; online databases, for example, PubMed; and web pages related to the entities associated with different technologies were searched for tools that use voice assessment. Identified devices were grouped into 3 categories: (1) "recommended," (2) "suggested," or (3) "listed." RESULTS: Twelve relevant technologies were identified. One was placed in the "recommended" group ("Dysarthria Analyzer"), 6 in the "suggested" group, ("Audeering," "Aural Analytics," Canary Speech," "KI:Elements," "Modality.ai," and "Redenlab"), and 5 in the "listed" group ("i-Prognosis," "No Pa," and "PdAssist," as well as "mPower" and "HopkinsPD"). CONCLUSIONS: Despite their potential for clinical and research applications, their suitability in real-world environments remains to be determined.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.160 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".