Deep learning based prediction of necessity for orthognathic surgery of skeletal malocclusion using cephalogram in Korean individuals
Abstract Background Posteroanterior and lateral cephalogram have been widely used for evaluating the necessity of orthognathic surgery. The purpose of this study was to develop a deep learning network to automatically predict the need for orthodontic surgery using cephalogram. Methods The cephalogra...
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| Format: | Article |
| Language: | English |
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BMC
2021-03-01
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| Series: | BMC Oral Health |
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| Online Access: | https://doi.org/10.1186/s12903-021-01513-3 |
| _version_ | 1818416271134294016 |
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| author | WooSang Shin Han-Gyeol Yeom Ga Hyung Lee Jong Pil Yun Seung Hyun Jeong Jong Hyun Lee Hwi Kang Kim Bong Chul Kim |
| author_facet | WooSang Shin Han-Gyeol Yeom Ga Hyung Lee Jong Pil Yun Seung Hyun Jeong Jong Hyun Lee Hwi Kang Kim Bong Chul Kim |
| author_sort | WooSang Shin |
| collection | DOAJ |
| description | Abstract Background Posteroanterior and lateral cephalogram have been widely used for evaluating the necessity of orthognathic surgery. The purpose of this study was to develop a deep learning network to automatically predict the need for orthodontic surgery using cephalogram. Methods The cephalograms of 840 patients (Class ll: 244, Class lll: 447, Facial asymmetry: 149) complaining about dentofacial dysmorphosis and/or a malocclusion were included. Patients who did not require orthognathic surgery were classified as Group I (622 patients—Class ll: 221, Class lll: 312, Facial asymmetry: 89). Group II (218 patients—Class ll: 23, Class lll: 135, Facial asymmetry: 60) was set for cases requiring surgery. A dataset was extracted using random sampling and was composed of training, validation, and test sets. The ratio of the sets was 4:1:5. PyTorch was used as the framework for the experiment. Results Subsequently, 394 out of a total of 413 test data were properly classified. The accuracy, sensitivity, and specificity were 0.954, 0.844, and 0.993, respectively. Conclusion It was found that a convolutional neural network can determine the need for orthognathic surgery with relative accuracy when using cephalogram. |
| first_indexed | 2024-12-14T11:48:14Z |
| format | Article |
| id | doaj.art-d071e29b844a4fad83981b75ebeb7dcf |
| institution | Directory Open Access Journal |
| issn | 1472-6831 |
| language | English |
| last_indexed | 2024-12-14T11:48:14Z |
| publishDate | 2021-03-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Oral Health |
| spelling | doaj.art-d071e29b844a4fad83981b75ebeb7dcf2022-12-21T23:02:28ZengBMCBMC Oral Health1472-68312021-03-012111710.1186/s12903-021-01513-3Deep learning based prediction of necessity for orthognathic surgery of skeletal malocclusion using cephalogram in Korean individualsWooSang Shin0Han-Gyeol Yeom1Ga Hyung Lee2Jong Pil Yun3Seung Hyun Jeong4Jong Hyun Lee5Hwi Kang Kim6Bong Chul Kim7Safety System Research Group, Korea Institute of Industrial Technology (KITECH)Department of Oral and Maxillofacial Radiology, Daejeon Dental Hospital, Wonkwang University College of DentistryDepartment of Oral and Maxillofacial Surgery, Daejeon Dental Hospital, Wonkwang University College of DentistrySafety System Research Group, Korea Institute of Industrial Technology (KITECH)Safety System Research Group, Korea Institute of Industrial Technology (KITECH)Safety System Research Group, Korea Institute of Industrial Technology (KITECH)Department of Oral and Maxillofacial Surgery, Daejeon Dental Hospital, Wonkwang University College of DentistryDepartment of Oral and Maxillofacial Surgery, Daejeon Dental Hospital, Wonkwang University College of DentistryAbstract Background Posteroanterior and lateral cephalogram have been widely used for evaluating the necessity of orthognathic surgery. The purpose of this study was to develop a deep learning network to automatically predict the need for orthodontic surgery using cephalogram. Methods The cephalograms of 840 patients (Class ll: 244, Class lll: 447, Facial asymmetry: 149) complaining about dentofacial dysmorphosis and/or a malocclusion were included. Patients who did not require orthognathic surgery were classified as Group I (622 patients—Class ll: 221, Class lll: 312, Facial asymmetry: 89). Group II (218 patients—Class ll: 23, Class lll: 135, Facial asymmetry: 60) was set for cases requiring surgery. A dataset was extracted using random sampling and was composed of training, validation, and test sets. The ratio of the sets was 4:1:5. PyTorch was used as the framework for the experiment. Results Subsequently, 394 out of a total of 413 test data were properly classified. The accuracy, sensitivity, and specificity were 0.954, 0.844, and 0.993, respectively. Conclusion It was found that a convolutional neural network can determine the need for orthognathic surgery with relative accuracy when using cephalogram.https://doi.org/10.1186/s12903-021-01513-3CephalogramMachine learningMachine intelligenceOrthognathic surgery |
| spellingShingle | WooSang Shin Han-Gyeol Yeom Ga Hyung Lee Jong Pil Yun Seung Hyun Jeong Jong Hyun Lee Hwi Kang Kim Bong Chul Kim Deep learning based prediction of necessity for orthognathic surgery of skeletal malocclusion using cephalogram in Korean individuals BMC Oral Health Cephalogram Machine learning Machine intelligence Orthognathic surgery |
| title | Deep learning based prediction of necessity for orthognathic surgery of skeletal malocclusion using cephalogram in Korean individuals |
| title_full | Deep learning based prediction of necessity for orthognathic surgery of skeletal malocclusion using cephalogram in Korean individuals |
| title_fullStr | Deep learning based prediction of necessity for orthognathic surgery of skeletal malocclusion using cephalogram in Korean individuals |
| title_full_unstemmed | Deep learning based prediction of necessity for orthognathic surgery of skeletal malocclusion using cephalogram in Korean individuals |
| title_short | Deep learning based prediction of necessity for orthognathic surgery of skeletal malocclusion using cephalogram in Korean individuals |
| title_sort | deep learning based prediction of necessity for orthognathic surgery of skeletal malocclusion using cephalogram in korean individuals |
| topic | Cephalogram Machine learning Machine intelligence Orthognathic surgery |
| url | https://doi.org/10.1186/s12903-021-01513-3 |
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