Huang Jingyi, Guo Peiqi, Zhang Sheng, Ji Mengmeng, An Ruopeng
School of Economics and Management, Shanghai University of Sport, Shanghai, China.
Brown School, Washington University in St. Louis, St. Louis, MO, United States.
JMIR AI. 2024 Jul 25;3:e54885. doi: 10.2196/54885.
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.
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.
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 F-scores.
The adapted YOLOv8 model demonstrated a global accuracy of 0.70 and a macro F-score of 0.65. It was more effective in identifying nonobesity (F-score of 0.77) than obesity (F-score of 0.53). This moderate level of accuracy highlights the potential and challenges in using vocal biomarkers for obesity detection.
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.
全球肥胖患病率不断上升，因此有必要探索新的诊断方法。最近的科学研究表明，与肥胖相关的声音特征可能存在变化，这表明将声音用作肥胖检测的非侵入性生物标志物具有可行性。
本研究旨在通过分析短音频记录，利用深度神经网络预测肥胖状况，研究声音特征与肥胖之间的关系。
对696名参与者进行了一项试点研究，使用自我报告的体重指数将个体分为肥胖组和非肥胖组。参与者阅读简短脚本的音频记录被转换为频谱图，并使用经过改编的YOLOv8模型（Ultralytics）进行分析。使用准确率、召回率、精确率和F分数评估模型性能。
经过改编的YOLOv8模型的整体准确率为0.70，宏观F分数为0.65。在识别非肥胖（F分数为0.77）方面比识别肥胖（F分数为0.53）更有效。这种中等水平的准确率凸显了使用声音生物标志物进行肥胖检测的潜力和挑战。
虽然该研究在基于声音的肥胖医学诊断领域显示出前景，但它面临着一些局限性，如依赖自我报告的体重指数数据以及样本量小且同质化。这些因素，再加上录音质量的差异，需要采用更稳健的方法和多样的样本进行进一步研究，以提高这种新方法的有效性。这些发现为未来将声音用作肥胖检测的非侵入性生物标志物的研究奠定了基础。
