Voice Biomarkers for Type 2 Diabetes Detection: A Two-Stage Validation Study
试验速览
- 阶段
- 不适用
- 状态
- Enrolling By Invitation
- 发起方
- 入组人数
- 10,000
- 试验地点
- 1
- 主要终点
- Accuracy of AI Model for Type 2 Diabetes Classification as Assessed by Voice Biomarker Analysis
研究概览
简要总结
The goal of this observational study is to learn if computer analysis of voice recordings can detect Type 2 diabetes in adults.
The main questions it aims to answer are:
- Can advanced voice analysis accurately identify participants with Type 2 diabetes or pre-diabetes based on vocal biomarkers?
- How do voice-based predictions compare to HbA1c blood test results for diabetes screening?
- Can machine learning approaches effectively address the challenge of undiagnosed diabetes in population screening?
Participants will:
- Record themselves reading a short passage and answering brief questions out loud in a single online session.
- Complete health questionnaires about diabetes risk factors, medications, and general health status.
- A subset of participants (n=1,000) will provide a blood sample through an at-home HbA1c testing kit to validate voice-based predictions against laboratory results.
- Use their own devices (computer, tablet, or smartphone) to complete all study activities online from home.
详细描述
This study addresses a critical challenge in Type 2 diabetes detection, where approximately 30% of individuals with diabetes remain undiagnosed, equating to roughly 1 million adults in the UK. Current screening methods rely on opportunistic testing with only 40.4% uptake among those offered NHS Health Checks, highlighting a need for innovative, accessible screening approaches that can identify at-risk individuals before complications develop.
STUDY RATIONALE AND INNOVATION:
Recent research demonstrates that diabetes affects voice production through multiple physiological pathways across the spectrum of the disease: peripheral neuropathy impacts vocal cord control and speech articulation, autonomic neuropathy affects breathing patterns and vocal dynamics, xerostomia (dry mouth) from neuropathic damage alters resonance characteristics, and glucose fluctuations modify the elastic properties of the larynx and vocal cords. These findings and early evidence from initial studies suggest that voice analysis could be used to screen for diabetes. The current study leverages these voice-diabetes associations using advanced machine learning to develop a non-invasive, scalable screening solution.
STUDY DESIGN AND METHODOLOGY:
This two-stage observational study combines large-scale data collection with strategic biological validation. Stage 1 involves 10,000 participants completing voice recordings and comprehensive health questionnaires through a secure online platform. Stage 2 selects 1,000 of these participants to provide informative diagnostic results for HbA1c home testing validation.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Cross Sectional
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •18+ years of age
- •English as a first language
- •No language difficulties
- •Geographically based in the UK
- •Normal or corrected to normal eye-sight, i.e., wearing glasses/contact lenses
排除标准
- •No hearing impairments
结局指标
主要结局
Accuracy of AI Model for Type 2 Diabetes Classification as Assessed by Voice Biomarker Analysis
时间窗: Single assessment session at enrolment with HbA1c validation results obtained within 2 months of submission of voice measurement.
Binary classification performance (presence vs. absence of Type 2 diabetes) of the artificial intelligence-based system using voice biomarker analysis, with HbA1c laboratory results (≥48 mmol/mol threshold) serving as ground truth. Performance will be measured using sensitivity (target ≥65%), specificity (target ≥65%), and area under the receiver operating characteristic curve (AUC target \~0.70) through cross-validation methods.
次要结局
- Detection of Pre-diabetes Using Voice Biomarker Analysis(Single assessment session at enrolment with HbA1c validation within 2 months of submitting voice measurement.)