Screening obstructive sleep apnea patients via deep learning of knowledge distillation in the lateral cephalogram
Abstract 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 l...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-10-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-42880-x |
| _version_ | 1827711144388722688 |
|---|---|
| author | Min-Jung Kim Jiheon Jeong Jung-Wook Lee In-Hwan Kim Jae-Woo Park Jae-Yon Roh Namkug Kim Su-Jung Kim |
| author_facet | Min-Jung Kim Jiheon Jeong Jung-Wook Lee In-Hwan Kim Jae-Woo Park Jae-Yon Roh Namkug Kim Su-Jung Kim |
| author_sort | Min-Jung Kim |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-03-10T17:49:38Z |
| format | Article |
| id | doaj.art-2c852cefb3dd478892908bac9fa21e08 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-10T17:49:38Z |
| publishDate | 2023-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-2c852cefb3dd478892908bac9fa21e082023-11-20T09:23:50ZengNature PortfolioScientific Reports2045-23222023-10-011311810.1038/s41598-023-42880-xScreening obstructive sleep apnea patients via deep learning of knowledge distillation in the lateral cephalogramMin-Jung Kim0Jiheon Jeong1Jung-Wook Lee2In-Hwan Kim3Jae-Woo Park4Jae-Yon Roh5Namkug Kim6Su-Jung Kim7Department of Convergence Medicine, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of MedicineDepartment of Convergence Medicine, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of MedicineDepartment of Convergence Medicine, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of MedicineDepartment of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, College of Medicine, University of UlsanDepartment of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, College of Medicine, University of UlsanDepartment of Orthodontics, Kyung Hee University Dental HospitalDepartment of Convergence Medicine, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of MedicineDepartment of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, College of Medicine, University of UlsanAbstract 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.https://doi.org/10.1038/s41598-023-42880-x |
| spellingShingle | Min-Jung Kim Jiheon Jeong Jung-Wook Lee In-Hwan Kim Jae-Woo Park Jae-Yon Roh Namkug Kim Su-Jung Kim Screening obstructive sleep apnea patients via deep learning of knowledge distillation in the lateral cephalogram Scientific Reports |
| title | Screening obstructive sleep apnea patients via deep learning of knowledge distillation in the lateral cephalogram |
| title_full | Screening obstructive sleep apnea patients via deep learning of knowledge distillation in the lateral cephalogram |
| title_fullStr | Screening obstructive sleep apnea patients via deep learning of knowledge distillation in the lateral cephalogram |
| title_full_unstemmed | Screening obstructive sleep apnea patients via deep learning of knowledge distillation in the lateral cephalogram |
| title_short | Screening obstructive sleep apnea patients via deep learning of knowledge distillation in the lateral cephalogram |
| title_sort | screening obstructive sleep apnea patients via deep learning of knowledge distillation in the lateral cephalogram |
| url | https://doi.org/10.1038/s41598-023-42880-x |
| work_keys_str_mv | AT minjungkim screeningobstructivesleepapneapatientsviadeeplearningofknowledgedistillationinthelateralcephalogram AT jiheonjeong screeningobstructivesleepapneapatientsviadeeplearningofknowledgedistillationinthelateralcephalogram AT jungwooklee screeningobstructivesleepapneapatientsviadeeplearningofknowledgedistillationinthelateralcephalogram AT inhwankim screeningobstructivesleepapneapatientsviadeeplearningofknowledgedistillationinthelateralcephalogram AT jaewoopark screeningobstructivesleepapneapatientsviadeeplearningofknowledgedistillationinthelateralcephalogram AT jaeyonroh screeningobstructivesleepapneapatientsviadeeplearningofknowledgedistillationinthelateralcephalogram AT namkugkim screeningobstructivesleepapneapatientsviadeeplearningofknowledgedistillationinthelateralcephalogram AT sujungkim screeningobstructivesleepapneapatientsviadeeplearningofknowledgedistillationinthelateralcephalogram |