PDF(Pubmed)
Abstract：
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.
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.
摘要：
背景：肥胖的全球患病率不断上升，需要探索新的诊断方法。最近的科学调查表明，与肥胖相关的语音特征可能发生变化，提示使用语音作为肥胖检测的非侵入性生物标志物的可行性。
目的：本研究旨在通过对短录音的分析，使用深度神经网络来预测肥胖状态，研究声乐特征与肥胖的关系。
方法：对696名参与者进行了一项初步研究，使用自我报告的BMI将个体分为肥胖和非肥胖组。参与者阅读简短脚本的录音被转换为频谱图，并使用改编的YOLOv8模型（Ultralytics）进行分析。使用准确性对模型性能进行了评估，召回，精度,和F1分数。
结果：适应的YOLOv8模型显示出0.70的全局准确性和0.65的宏F1评分。在识别非肥胖（F1评分为0.77）方面比肥胖（F1评分为0.53）更有效。这种中等水平的准确性凸显了使用声乐生物标志物进行肥胖检测的潜力和挑战。
结论：虽然该研究在基于语音的肥胖医学诊断领域显示出希望，它面临着一些限制，比如依赖自我报告的BMI数据，均匀的样本量。这些因素,再加上录音质量的可变性，需要使用更强大的方法和不同的样本进行进一步的研究，以增强这种新颖方法的有效性。这些发现为将来使用语音作为肥胖检测的非侵入性生物标志物的研究奠定了基础。
目的：本研究旨在通过对短录音的分析，使用深度神经网络来预测肥胖状态，研究声乐特征与肥胖的关系。
方法：对696名参与者进行了一项初步研究，使用自我报告的BMI将个体分为肥胖和非肥胖组。参与者阅读简短脚本的录音被转换为频谱图，并使用改编的YOLOv8模型（Ultralytics）进行分析。使用准确性对模型性能进行了评估，召回，精度,和F1分数。
结果：适应的YOLOv8模型显示出0.70的全局准确性和0.65的宏F1评分。在识别非肥胖（F1评分为0.77）方面比肥胖（F1评分为0.53）更有效。这种中等水平的准确性凸显了使用声乐生物标志物进行肥胖检测的潜力和挑战。
结论：虽然该研究在基于语音的肥胖医学诊断领域显示出希望，它面临着一些限制，比如依赖自我报告的BMI数据，均匀的样本量。这些因素,再加上录音质量的可变性，需要使用更强大的方法和不同的样本进行进一步的研究，以增强这种新颖方法的有效性。这些发现为将来使用语音作为肥胖检测的非侵入性生物标志物的研究奠定了基础。
