Department of Convergence Medicine, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-Gil Songpa-gu, Seoul, 05505, Republic of Korea.
Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, College of Medicine, University of Ulsan, Seoul, Republic of Korea.
Sci Rep. 2023 Oct 18;13(1):17788. doi: 10.1038/s41598-023-42880-x.
The lateral cephalogram in orthodontics is a valuable screening tool on undetected obstructive sleep apnea (OSA), which can lead to consequences of severe systematic disease. We hypothesized that a deep learning-based classifier might be able to differentiate OSA as anatomical features in lateral cephalogram. Moreover, since the imaging devices used by each hospital could be different, there is a need to overcome modality difference of radiography. Therefore, we proposed a deep learning model with knowledge distillation to classify patients into OSA and non-OSA groups using the lateral cephalogram and to overcome modality differences simultaneously. Lateral cephalograms of 500 OSA patients and 498 non-OSA patients from two different devices were included. ResNet-50 and ResNet-50 with a feature-based knowledge distillation models were trained and their performances of classification were compared. Through the knowledge distillation, area under receiver operating characteristic curve analysis and gradient-weighted class activation mapping of knowledge distillation model exhibits high performance without being deceived by features caused by modality differences. By checking the probability values predicting OSA, an improvement in overcoming the modality differences was observed, which could be applied in the actual clinical situation.
在正畸学中，侧颅面片是一种用于筛查未被发现的阻塞性睡眠呼吸暂停（OSA）的有效筛查工具，而阻塞性睡眠呼吸暂停可能会导致严重的系统性疾病后果。我们假设基于深度学习的分类器可以根据侧颅面片的解剖特征来区分 OSA。此外，由于每家医院使用的成像设备可能不同，因此需要克服射线照相的模态差异。因此，我们提出了一种具有知识蒸馏的深度学习模型，使用侧颅面片对患者进行 OSA 和非 OSA 分组分类，并同时克服模态差异。纳入了来自两种不同设备的 500 名 OSA 患者和 498 名非 OSA 患者的侧颅面片。训练了 ResNet-50 和基于特征的知识蒸馏模型的 ResNet-50，并比较了它们的分类性能。通过知识蒸馏，接收器工作特征曲线分析的曲线下面积和知识蒸馏模型的梯度加权类激活映射表现出高性能，而不会被模态差异引起的特征所欺骗。通过检查预测 OSA 的概率值，可以观察到克服模态差异的能力有所提高，这可以应用于实际的临床情况。
