Kim Jung-Min, Kim Min-Seop, Choi Sun-Young, Lee Kyogu, Ryu Ju Seok
Department of Health Science and Technology, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea.
Department of Rehabilitation Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.
Front Bioeng Biotechnol. 2024 Aug 2;12:1433087. doi: 10.3389/fbioe.2024.1433087. eCollection 2024.
This study aimed to identify differences in voice characteristics and changes between patients with dysphagia-aspiration and healthy individuals using a deep learning model, with a focus on under-researched areas of pre- and post-swallowing voice changes in patients with dysphagia. We hypothesized that these variations may be due to weakened muscles and blocked airways in patients with dysphagia.
A prospective cohort study was conducted on 198 participants aged >40 years at the Seoul National University Bundang Hospital from October 2021 to February 2023. Pre- and post-swallowing voice data of the participants were converted to a 64-kbps mp3 format, and all voice data were trimmed to a length of 2 s. The data were divided for 10-fold cross-validation and stored in HDF5 format with anonymized IDs and labels for the normal and aspiration groups. During preprocessing, the data were converted to Mel spectrograms, and the EfficientAT model was modified using the final layer of MobileNetV3 to effectively detect voice changes and analyze pre- and post-swallowing voices. This enabled the model to probabilistically categorize new patient voices as normal or aspirated.
In a study of the machine-learning model for aspiration detection, area under the receiver operating characteristic curve (AUC) values were analyzed across sexes under different configurations. The average AUC values for males ranged from 0.8117 to 0.8319, with the best performance achieved at a learning rate of 3.00e-5 and a batch size of 16. The average AUC values for females improved from 0.6975 to 0.7331, with the best performance observed at a learning rate of 5.00e-5 and a batch size of 32. As there were fewer female participants, a combined model was developed to maintain the sex balance. In the combined model, the average AUC values ranged from 0.7746 to 0.7997, and optimal performance was achieved at a learning rate of 3.00e-5 and a batch size of 16.
This study evaluated a voice analysis-based program to detect pre- and post-swallowing changes in patients with dysphagia, potentially aiding in real-time monitoring. Such a system can provide healthcare professionals with daily insights into the conditions of patients, allowing for personalized interventions.
ClinicalTrials.gov, identifier NCT05149976.
本研究旨在使用深度学习模型识别吞咽困难-误吸患者与健康个体之间的声音特征差异及变化，重点关注吞咽困难患者吞咽前和吞咽后声音变化研究较少的领域。我们假设这些差异可能是由于吞咽困难患者的肌肉无力和气道阻塞所致。
2021年10月至2023年2月，在首尔国立大学盆唐医院对198名年龄大于40岁的参与者进行了一项前瞻性队列研究。参与者吞咽前和吞咽后的声音数据被转换为64 kbps的mp3格式，所有声音数据被修剪至2秒时长。数据被分为10折交叉验证，并以匿名ID和正常组及误吸组标签的形式存储在HDF5格式中。在预处理过程中，数据被转换为梅尔频谱图，并使用MobileNetV3的最后一层对EfficientAT模型进行修改，以有效检测声音变化并分析吞咽前和吞咽后的声音。这使得该模型能够将新患者的声音概率性地分类为正常或误吸。
在一项用于误吸检测的机器学习模型研究中，在不同配置下分析了不同性别的受试者工作特征曲线下面积（AUC）值。男性的平均AUC值在0.8117至0.8319之间，在学习率为3.00e-5和批量大小为16时表现最佳。女性的平均AUC值从0.6975提高到0.7331，在学习率为5.00e-5和批量大小为32时表现最佳。由于女性参与者较少，因此开发了一个组合模型以保持性别平衡。在组合模型中，平均AUC值在0.7746至0.7997之间，在学习率为3.00e-5和批量大小为16时达到最佳性能。
本研究评估了一个基于声音分析的程序，以检测吞咽困难患者吞咽前和吞咽后的变化，这可能有助于实时监测。这样的系统可以为医护人员提供患者状况的日常见解，从而实现个性化干预。
ClinicalTrials.gov，标识符NCT05149976。
