An accurate deep learning model for wheezing in children using real world data.
An accurate deep learning model for wheezing in children using real world data.
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DOI：
10.1038/s41598-022-25953-1
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发表时间：
2022-12-28
影响因子：
4.6
通讯作者：
Kim, Kyung Hoon
中科院分区：
文献类型：
--
作者：
Kim, Beom Joon;Kim, Baek Seung;Mun, Jeong Hyeon;Lim, Changwon;Kim, Kyung Hoon
听诊是肺部疾病的重要诊断方法。然而，这是一种主观的方式，需要高度的专业知识。为了克服这一限制，正在开发人工智能模型。然而，这些模型需要性能改进，并且不能反映实际的临床情况。我们的目标是开发一种改进的深度学习模型，根据真实的临床实践数据学习检测儿童喘息。在这项前瞻性研究中，儿科肺科医生记录并验证了76名在韩国一所大学医院就诊的儿科患者的呼吸音。此外，还收集了结构化数据，如性别、年龄和听诊位置。使用我们的数据集，我们通过基于卷积神经网络模型对其进行转换来实现最佳模型。最后，我们提出了一个使用34层残差网络的模型，其中卷积块注意模块用于音频数据，多层感知器层用于表格数据。该模型的准确率为91.2%，曲线下面积为89.1%，精确率为94.4%，召回率为81%，F1得分为87.2%。提出的深度学习模型在检测喘息声方面具有很高的准确性。该高性能模型将有助于在实际的临床实践中对呼吸系统疾病进行准确的诊断。
Auscultation is an important diagnostic method for lung diseases. However, it is a subjective modality and requires a high degree of expertise. To overcome this constraint, artificial intelligence models are being developed. However, these models require performance improvements and do not reflect the actual clinical situation. We aimed to develop an improved deep-learning model learning to detect wheezing in children, based on data from real clinical practice. In this prospective study, pediatric pulmonologists recorded and verified respiratory sounds in 76 pediatric patients who visited a university hospital in South Korea. In addition, structured data, such as sex, age, and auscultation location, were collected. Using our dataset, we implemented an optimal model by transforming it based on the convolutional neural network model. Finally, we proposed a model using a 34-layer residual network with the convolutional block attention module for audio data and multilayer perceptron layers for tabular data. The proposed model had an accuracy of 91.2%, area under the curve of 89.1%, precision of 94.4%, recall of 81%, and F1-score of 87.2%. The deep-learning model proposed had a high accuracy for detecting wheeze sounds. This high-performance model will be helpful for the accurate diagnosis of respiratory diseases in actual clinical practice.
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影响因子：
0.9
作者：
通讯作者：
--
影响因子：
3.7
作者：
Pramono RXA;Bowyer S;Rodriguez-Villegas E
通讯作者：
Rodriguez-Villegas E
影响因子：
3.1
作者：
Kim Y;Hyon Y;Lee S;Woo SD;Ha T;Chung C
通讯作者：
Chung C
DOI：
10.3390/s22041521
发表时间：
2022-02-16
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
Tariq Z;Shah SK;Lee Y
通讯作者：
Lee Y
影响因子：
3.9
作者：
Teresa Garcia-Ordas, Maria;Alberto Benitez-Andrades, Jose;Alaiz-Moreton, Hector
通讯作者：
Alaiz-Moreton, Hector