Researchers

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StenoVoice Research Overview

The StenoVoice project is an interdisciplinary initiative aiming to develop and validate vocal biomarkers for the risk assessment of diabetes-related complications. By integrating deep learning methods with multimodal data, including voice, we aim to provide novel tools for predicting and monitoring cardiovascular disease (CVD), neuropathy, and diabetes distress in people with diabetes. Our research spans methodological innovation, clinical application, and collaboration across international partners.

Research Objectives

Study 1: Vocal Biomarkers for Cardiovascular Disease

This study focuses on:

  • Initiating voice data collection nested in an existing population-based cohort, i.e., Health in Central Denmark (HICD), and to identify and validate vocal biomarkers for CVD in the general population.

Study 2: Vocal Biomarkers for Diabetic Neuropathy

This study seeks to:

  • Identify vocal biomarkers of neuropathy in people with type 1 diabetes (T1D) and type 2 diabetes (T2D).

Study 3: Multimodal Assessment of Diabetes Distress

This study aims to:

  • Develop a multimodal approach to detect diabetes distress based on voice recordings and audio transcriptions by people with T1D and T2D.

Methodology

We use a multi-step approach to data processing and analysis:

  • Voice Data: Preprocessing includes filtering, silence removal, noise reduction, framing, windowing, feature extraction, and feature selection, among other things.
  • Text Data: Natural language processing techniques are applied to transcribed responses and extract features and embeddings from the context.
  • Modeling: Predictive models include both classical machine learning and deep learning approaches.

Collaborations

The project is conducted in collaboration with:

  • Luxembourg Institute of Health, with the involvement of Guy Fagherazzi, Director of the Department of Precision Health & Head of the “Deep Digital Phenotyping” Research Unit
  • Steno Diabetes Center Copenhagen, with the involvement of Christian Stevns Hansen

Preliminary Work and Feasibility

  • Updated continuously

Research Team

  • Principal Investigator: Adam Hulman, Associate Professor at the Department of Public Health, Aarhus University & Senior Data Scientist at Steno Diabetes Center Aarhus

  • PhD Student: Manuel Thomasen, Steno Diabetes Center Aarhus & Department of Public Health, Aarhus University

  • Affiliated Lab: Machine Learning and Clinical Prediction HulmanLab, Steno Diabetes Center Aarhus.

Publications and Resources

  • Preprint manuscripts will be available on [medRxiv / arXiv links].
  • Code will be shared via GitHub.

Contact

For questions or collaboration opportunities, please contact:

Adam Hulman, Associate Professor
adahul@rm.dk

Manuel Thomasen
manuth@rm.dk / manuth@ph.au.dk

We welcome researchers interested in digital health, vocal biomarkers, and diabetes epidemiology to reach out for collaboration or discussion.

Supported by:

This work was supported by a research grant from the Danish Diabetes and Endocrine Academy, funded by the Novo Nordisk Foundation (NNF22SA0079901), and the Danish Data Science Academy, funded by the Novo Nordisk Foundation (NNF21SA0069429), and by Steno Diabetes Center Aarhus which is partly funded by a donation from the Novo Nordisk Foundation (NNF17SA0031230), and by a Data Science Emerging Investigator grant (NNF22OC0076725) by the Novo Nordisk Foundation, and funds by Aarhus University, Department of Public Health.