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This paper is in the following e-collection/theme issue:

Development of Novel Medical Devices and Innovations for Existing Devices (143) New Methods (1742) Diabetes (335) Diabetes Technology Around the World (43) New Technologies (157) Digital Biomarkers and Digital Phenotyping (420) Computer-Aided Diagnosis (406) Voice Phenotyping and Vocal Biomarkers (32)

Published on in Vol 10 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/64357, first published .
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Optimizing Voice Sample Quantity and Recording Settings for the Prediction of Type 2 Diabetes Mellitus: Retrospective Study

Optimizing Voice Sample Quantity and Recording Settings for the Prediction of Type 2 Diabetes Mellitus: Retrospective Study

Authors of this article:

Atousa Assadi1 Author Orcid Image ;   Jessica Oreskovic1 Author Orcid Image ;   Jaycee Kaufman1 Author Orcid Image ;   Yan Fossat1 Author Orcid Image
Article Authors Cited by (3) Tweetations (1) Metrics

Journals

  1. Guo J, Peng W, Hu S, Lu D, Chen S. A Novel Machine Learning-Driven Voice and Clinical Biomarkers Framework for Robust Prediction of Type 2 Diabetes Mellitus. Journal of Voice 2025 View
  2. Oreskovic J, Fazli G, Varma V, Malik K, Kaufman J, Fossat Y. Voice-based prediction of prediabetes using classical machine learning models. Frontiers in Clinical Diabetes and Healthcare 2025;6 View

Conference Proceedings

  1. Veluri S, Goel S, Jadala V. 2026 International Conference on Computing Theory and Wireless Communications (ICCTWC). Vo-DiGen: A Generative AI and NLP-Driven Framework for Voice-Based Early Detection of Diabetes Through Multimodal Vocal Biomarkers View