%0 Journal Article
%@ 2817-1705
%I JMIR Publications
%V 3
%N 
%P e54885
%T Use of Deep Neural Networks to Predict Obesity With Short Audio Recordings: Development and Usability Study
%A Huang,Jingyi
%A Guo,Peiqi
%A Zhang,Sheng
%A Ji,Mengmeng
%A An,Ruopeng
%+ School of Journalism and Communication, Shanghai University of Sport, 650 Hengren Road, Yangpu District, Shanghai, 200000, China, 86 18017355353, zhsheng1@126.com
%K obesity
%K obese
%K overweight
%K voice
%K vocal
%K vocal cord
%K vocal cords
%K voice-based
%K machine learning
%K ML
%K artificial intelligence
%K AI
%K algorithm
%K algorithms
%K predictive model
%K predictive models
%K predictive analytics
%K predictive system
%K practical model
%K practical models
%K early warning
%K early detection
%K deep neural network
%K deep neural networks
%K DNN
%K artificial neural network
%K artificial neural networks
%K deep learning
%D 2024
%7 25.7.2024
%9 Original Paper
%J JMIR AI
%G English
%X Background: The escalating global prevalence of obesity has necessitated the exploration of novel diagnostic approaches. Recent scientific inquiries have indicated potential alterations in voice characteristics associated with obesity, suggesting the feasibility of using voice as a noninvasive biomarker for obesity detection. Objective: This study aims to use deep neural networks to predict obesity status through the analysis of short audio recordings, investigating the relationship between vocal characteristics and obesity. Methods: A pilot study was conducted with 696 participants, using self-reported BMI to classify individuals into obesity and nonobesity groups. Audio recordings of participants reading a short script were transformed into spectrograms and analyzed using an adapted YOLOv8 model (Ultralytics). The model performance was evaluated using accuracy, recall, precision, and F1-scores. Results: The adapted YOLOv8 model demonstrated a global accuracy of 0.70 and a macro F1-score of 0.65. It was more effective in identifying nonobesity (F1-score of 0.77) than obesity (F1-score of 0.53). This moderate level of accuracy highlights the potential and challenges in using vocal biomarkers for obesity detection. Conclusions: While the study shows promise in the field of voice-based medical diagnostics for obesity, it faces limitations such as reliance on self-reported BMI data and a small, homogenous sample size. These factors, coupled with variability in recording quality, necessitate further research with more robust methodologies and diverse samples to enhance the validity of this novel approach. The findings lay a foundational step for future investigations in using voice as a noninvasive biomarker for obesity detection. 
%R 10.2196/54885
%U https://ai.jmir.org/2024/1/e54885
%U https://doi.org/10.2196/54885