A voice-based algorithm predicted type 2 diabetes status with an AUC of 75% for males and 71% for females, showing >93% agreement with the ADA risk score.
Observational (n=607)
Does a voice-based algorithm accurately predict type 2 diabetes status in US adults?
A novel voice-based algorithm demonstrated promising predictive capacity for identifying type 2 diabetes in adults, suggesting potential as a non-invasive and scalable screening tool.
Effect estimate: AUC 75% for males, 71% for females
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.
Elbéji et al. (Thu,) conducted a observational in Type 2 diabetes (n=607). Voice-based algorithm vs. American Diabetes Association (ADA) score was evaluated on Prediction of type 2 diabetes status (AUC 75% for males, 71% for females). A voice-based algorithm predicted type 2 diabetes status with an AUC of 75% for males and 71% for females, showing >93% agreement with the ADA risk score.