So Stephen, Tadj Timothy, Schwerin Belinda, Chang Anne B, Frakking Thuy T
School of Engineering and Built Environment, Griffith University, Parklands Dr, Southport, QLD, 4215, Australia.
Department of Respiratory Medicine, Queensland Children's Hospital, 501 Stanley St, South Brisbane, QLD, 4101, Australia.
Dysphagia. 2025 Jun 3. doi: 10.1007/s00455-025-10833-3.
Digital cervical auscultation (CA) has high diagnostic test accuracy in the detection of aspiration in children. However, the clinical application of digital CA is limited because swallow sound recordings require manual segmentation by trained experts, which is time consuming and not feasible in clinical practice. The automated detection of swallowing sounds in adults from sound recordings have reported accuracies between 76 and 95%. No equivalent literature exists for the automated detection of swallowing sounds in children. This study aimed to establish whether automated machine learning using a transfer learning approach can accurately detect and segment swallows from digital CA recordings in children. Swallow sounds were collected from 16 typically developing children, median age 18 months (range 4-35 months, 50% males); and 19 videofluoroscopic swallow studies of children with pediatric feeding disorders, median age 9 months (range 3-71 months, males 36.8% males). All swallowing sounds were on thin fluids. A deep convolutional neural network (DCNN) that was pre-trained for the task of audio event classification was used as the base machine learning model. Using the raw swallow audio data as input, embedding vectors from the base DCNN were computed and used to train a feedforward neural network to identify whether an audio segment was a swallow or not. A high overall accuracy of 91% was achieved using our model, with a sensitivity (or recall) and positive predictability (or precision) of 81% and 79%, respectively. Interestingly, the model was also able to detect saliva swallows in the clinical feeding evaluation test set, even though these non-nutritive swallows were not part of the training set. This indicates a level of generalizability of the model, where it was able to recognize swallowing events that it had not "seen" before. Our study provides the highest accuracy reported to date on the automatic segmentation and detection of swallowing sounds in children.
数字式颈部听诊（CA）在检测儿童误吸方面具有较高的诊断测试准确性。然而，数字式CA的临床应用受到限制，因为吞咽声音记录需要由训练有素的专家进行手动分割，这既耗时又在临床实践中不可行。从录音中自动检测成人吞咽声音的准确率在76%至95%之间。目前尚无关于自动检测儿童吞咽声音的等效文献。本研究旨在确定使用迁移学习方法的自动机器学习是否能够准确地从儿童数字式CA记录中检测和分割吞咽声音。从16名发育正常的儿童（中位年龄18个月，范围4 - 35个月，50%为男性）以及19名患有儿科喂养障碍儿童的视频荧光吞咽研究中收集吞咽声音，这些儿童的中位年龄为9个月（范围3 - 71个月，36.8%为男性）。所有吞咽声音均针对稀薄液体。一个针对音频事件分类任务进行预训练的深度卷积神经网络（DCNN）被用作基础机器学习模型。以原始吞咽音频数据作为输入，计算基础DCNN的嵌入向量，并用于训练前馈神经网络，以识别音频片段是否为吞咽声音。我们的模型实现了91%的高总体准确率，灵敏度（或召回率）和阳性预测值（或精确率）分别为81%和79%。有趣的是，该模型在临床喂养评估测试集中也能够检测到唾液吞咽，尽管这些非营养性吞咽并非训练集的一部分。这表明该模型具有一定程度的泛化能力，即它能够识别之前未“见过”的吞咽事件。我们的研究提供了迄今为止关于儿童吞咽声音自动分割和检测的最高准确率报告。
