A voice-based algorithm can predict type 2 diabetes status in USA adults: Findings from the Colive Voice study Article Swipe
Abir Elbéji
,
Mégane Pizzimenti
,
Gloria Aguayo
,
Aurélie Fischer
,
H. Ayadi
,
Franck Mauvais‐Jarvis
,
Jean‐Pierre Riveline
,
Vladimir Despotović
,
Guy Fagherazzi
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pdig.0000679
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pdig.0000679
The pressing need to reduce undiagnosed type 2 diabetes (T2D) globally calls for innovative screening approaches. This study investigates the potential of using a voice-based algorithm to predict T2D status in adults, as the first step towards developing a non-invasive and scalable screening method. We analyzed pre-specified text recordings from 607 US participants from the Colive Voice study registered on ClinicalTrials.gov (NCT04848623). Using hybrid BYOL-S/CvT embeddings, we constructed gender-specific algorithms to predict T2D status, evaluated through cross-validation based on accuracy, specificity, sensitivity, and Area Under the Curve (AUC). The best models were stratified by key factors such as age, BMI, and hypertension, and compared to the American Diabetes Association (ADA) score for T2D risk assessment using Bland-Altman analysis. The voice-based algorithms demonstrated good predictive capacity (AUC = 75% for males, 71% for females), correctly predicting 71% of male and 66% of female T2D cases. Performance improved in females aged 60 years or older (AUC = 74%) and individuals with hypertension (AUC = 75%), with an overall agreement above 93% with the ADA risk score. Our findings suggest that voice-based algorithms could serve as a more accessible, cost-effective, and noninvasive screening tool for T2D. While these results are promising, further validation is needed, particularly for early-stage T2D cases and more diverse populations.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1371/journal.pdig.0000679
- OA Status
- gold
- Cited By
- 6
- References
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- Related Works
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- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4405614201Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1371/journal.pdig.0000679Digital Object Identifier
- Title
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A voice-based algorithm can predict type 2 diabetes status in USA adults: Findings from the Colive Voice studyWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-12-19Full publication date if available
- Authors
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Abir Elbéji, Mégane Pizzimenti, Gloria Aguayo, Aurélie Fischer, H. Ayadi, Franck Mauvais‐Jarvis, Jean‐Pierre Riveline, Vladimir Despotović, Guy FagherazziList of authors in order
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https://doi.org/10.1371/journal.pdig.0000679Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1371/journal.pdig.0000679Direct OA link when available
- Concepts
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Type 2 diabetes, Algorithm, Medicine, Receiver operating characteristic, Diabetes mellitus, Machine learning, Area under the curve, Scalability, Computer science, Internal medicine, Endocrinology, DatabaseTop concepts (fields/topics) attached by OpenAlex
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6Total citation count in OpenAlex
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2025: 6Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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