基于深度学习的上呼吸道阻塞自动评估。
Automated Evaluation of Upper Airway Obstruction Based on Deep Learning.
机构信息
State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, Department of Orthodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan 610041, China.
出版信息
Biomed Res Int. 2023 Feb 18;2023:8231425. doi: 10.1155/2023/8231425. eCollection 2023.
OBJECTIVES
This study is aimed at developing a screening tool that could evaluate the upper airway obstruction on lateral cephalograms based on deep learning.
METHODS
We developed a novel and practical convolutional neural network model to automatically evaluate upper airway obstruction based on ResNet backbone using the lateral cephalogram. A total of 1219 X-ray images were collected for model training and testing.
RESULTS
In comparison with VGG16, our model showed a better performance with sensitivity of 0.86, specificity of 0.89, PPV of 0.90, NPV of 0.85, and F1-score of 0.88, respectively. The heat maps of cephalograms showed a deeper understanding of features learned by deep learning model.
CONCLUSION
This study demonstrated that deep learning could learn effective features from cephalograms and automated evaluate upper airway obstruction according to X-ray images. . A novel and practical deep convolutional neural network model has been established to relieve dentists' workload of screening and improve accuracy in upper airway obstruction.
目的
本研究旨在开发一种基于深度学习的侧位头颅 X 光片评估上气道阻塞的筛选工具。
方法
我们开发了一种新颖而实用的卷积神经网络模型，使用 ResNet 骨干网络基于侧位头颅 X 光片自动评估上气道阻塞。共收集了 1219 张 X 光图像进行模型训练和测试。
结果
与 VGG16 相比，我们的模型具有更高的性能，其敏感性为 0.86，特异性为 0.89，PPV 为 0.90，NPV 为 0.85，F1 得分为 0.88。头颅 X 光片的热图更深入地了解了深度学习模型所学习到的特征。
结论
本研究表明，深度学习可以从头颅 X 光片中学习到有效的特征，并根据 X 光图像自动评估上气道阻塞。已经建立了一种新颖而实用的深度卷积神经网络模型，以减轻牙医在筛选中的工作量并提高上气道阻塞的准确性。
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