A voice-based algorithm can predict type 2 diabetes status in USA adults: Findings from the Colive Voice study.
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....
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2024-12-01
|
| Series: | PLOS Digital Health |
| Online Access: | https://doi.org/10.1371/journal.pdig.0000679 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841555398440517632 |
|---|---|
| author | Abir Elbéji Mégane Pizzimenti Gloria Aguayo Aurélie Fischer Hanin Ayadi Franck Mauvais-Jarvis Jean-Pierre Riveline Vladimir Despotovic Guy Fagherazzi |
| author_facet | Abir Elbéji Mégane Pizzimenti Gloria Aguayo Aurélie Fischer Hanin Ayadi Franck Mauvais-Jarvis Jean-Pierre Riveline Vladimir Despotovic Guy Fagherazzi |
| author_sort | Abir Elbéji |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-949064a1d87c43309fba4901d3efedd2 |
| institution | Kabale University |
| issn | 2767-3170 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLOS Digital Health |
| spelling | doaj-art-949064a1d87c43309fba4901d3efedd22025-01-08T05:34:12ZengPublic Library of Science (PLoS)PLOS Digital Health2767-31702024-12-01312e000067910.1371/journal.pdig.0000679A voice-based algorithm can predict type 2 diabetes status in USA adults: Findings from the Colive Voice study.Abir ElbéjiMégane PizzimentiGloria AguayoAurélie FischerHanin AyadiFranck Mauvais-JarvisJean-Pierre RivelineVladimir DespotovicGuy FagherazziThe 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.https://doi.org/10.1371/journal.pdig.0000679 |
| spellingShingle | Abir Elbéji Mégane Pizzimenti Gloria Aguayo Aurélie Fischer Hanin Ayadi Franck Mauvais-Jarvis Jean-Pierre Riveline Vladimir Despotovic Guy Fagherazzi A voice-based algorithm can predict type 2 diabetes status in USA adults: Findings from the Colive Voice study. PLOS Digital Health |
| title | A voice-based algorithm can predict type 2 diabetes status in USA adults: Findings from the Colive Voice study. |
| title_full | A voice-based algorithm can predict type 2 diabetes status in USA adults: Findings from the Colive Voice study. |
| title_fullStr | A voice-based algorithm can predict type 2 diabetes status in USA adults: Findings from the Colive Voice study. |
| title_full_unstemmed | A voice-based algorithm can predict type 2 diabetes status in USA adults: Findings from the Colive Voice study. |
| title_short | A voice-based algorithm can predict type 2 diabetes status in USA adults: Findings from the Colive Voice study. |
| title_sort | voice based algorithm can predict type 2 diabetes status in usa adults findings from the colive voice study |
| url | https://doi.org/10.1371/journal.pdig.0000679 |
| work_keys_str_mv | AT abirelbeji avoicebasedalgorithmcanpredicttype2diabetesstatusinusaadultsfindingsfromthecolivevoicestudy AT meganepizzimenti avoicebasedalgorithmcanpredicttype2diabetesstatusinusaadultsfindingsfromthecolivevoicestudy AT gloriaaguayo avoicebasedalgorithmcanpredicttype2diabetesstatusinusaadultsfindingsfromthecolivevoicestudy AT aureliefischer avoicebasedalgorithmcanpredicttype2diabetesstatusinusaadultsfindingsfromthecolivevoicestudy AT haninayadi avoicebasedalgorithmcanpredicttype2diabetesstatusinusaadultsfindingsfromthecolivevoicestudy AT franckmauvaisjarvis avoicebasedalgorithmcanpredicttype2diabetesstatusinusaadultsfindingsfromthecolivevoicestudy AT jeanpierreriveline avoicebasedalgorithmcanpredicttype2diabetesstatusinusaadultsfindingsfromthecolivevoicestudy AT vladimirdespotovic avoicebasedalgorithmcanpredicttype2diabetesstatusinusaadultsfindingsfromthecolivevoicestudy AT guyfagherazzi avoicebasedalgorithmcanpredicttype2diabetesstatusinusaadultsfindingsfromthecolivevoicestudy AT abirelbeji voicebasedalgorithmcanpredicttype2diabetesstatusinusaadultsfindingsfromthecolivevoicestudy AT meganepizzimenti voicebasedalgorithmcanpredicttype2diabetesstatusinusaadultsfindingsfromthecolivevoicestudy AT gloriaaguayo voicebasedalgorithmcanpredicttype2diabetesstatusinusaadultsfindingsfromthecolivevoicestudy AT aureliefischer voicebasedalgorithmcanpredicttype2diabetesstatusinusaadultsfindingsfromthecolivevoicestudy AT haninayadi voicebasedalgorithmcanpredicttype2diabetesstatusinusaadultsfindingsfromthecolivevoicestudy AT franckmauvaisjarvis voicebasedalgorithmcanpredicttype2diabetesstatusinusaadultsfindingsfromthecolivevoicestudy AT jeanpierreriveline voicebasedalgorithmcanpredicttype2diabetesstatusinusaadultsfindingsfromthecolivevoicestudy AT vladimirdespotovic voicebasedalgorithmcanpredicttype2diabetesstatusinusaadultsfindingsfromthecolivevoicestudy AT guyfagherazzi voicebasedalgorithmcanpredicttype2diabetesstatusinusaadultsfindingsfromthecolivevoicestudy |