Application of deep learning artificial intelligence technique to the classification of clinical orthodontic photos
Abstract Background Taking facial and intraoral clinical photos is one of the essential parts of orthodontic diagnosis and treatment planning. Among the diagnostic procedures, classification of the shuffled clinical photos with their orientations will be the initial step while it was not easy for a...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-10-01
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| Series: | BMC Oral Health |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12903-022-02466-x |
| _version_ | 1811335117780549632 |
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| author | Jiho Ryu Yoo-Sun Lee Seong-Pil Mo Keunoh Lim Seok-Ki Jung Tae-Woo Kim |
| author_facet | Jiho Ryu Yoo-Sun Lee Seong-Pil Mo Keunoh Lim Seok-Ki Jung Tae-Woo Kim |
| author_sort | Jiho Ryu |
| collection | DOAJ |
| description | Abstract Background Taking facial and intraoral clinical photos is one of the essential parts of orthodontic diagnosis and treatment planning. Among the diagnostic procedures, classification of the shuffled clinical photos with their orientations will be the initial step while it was not easy for a machine to classify photos with a variety of facial and dental situations. This article presents a convolutional neural networks (CNNs) deep learning technique to classify orthodontic clinical photos according to their orientations. Methods To build an automated classification system, CNNs models of facial and intraoral categories were constructed, and the clinical photos that are routinely taken for orthodontic diagnosis were used to train the models with data augmentation. Prediction procedures were evaluated with separate photos whose purpose was only for prediction. Results Overall, a 98.0% valid prediction rate resulted for both facial and intraoral photo classification. The highest prediction rate was 100% for facial lateral profile, intraoral upper, and lower photos. Conclusion An artificial intelligence system that utilizes deep learning with proper training models can successfully classify orthodontic facial and intraoral photos automatically. This technique can be used for the first step of a fully automated orthodontic diagnostic system in the future. |
| first_indexed | 2024-04-13T17:20:19Z |
| format | Article |
| id | doaj.art-5346ca524c6e46deb94ee89bb0f08652 |
| institution | Directory Open Access Journal |
| issn | 1472-6831 |
| language | English |
| last_indexed | 2024-04-13T17:20:19Z |
| publishDate | 2022-10-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Oral Health |
| spelling | doaj.art-5346ca524c6e46deb94ee89bb0f086522022-12-22T02:38:01ZengBMCBMC Oral Health1472-68312022-10-012211710.1186/s12903-022-02466-xApplication of deep learning artificial intelligence technique to the classification of clinical orthodontic photosJiho Ryu0Yoo-Sun Lee1Seong-Pil Mo2Keunoh Lim3Seok-Ki Jung4Tae-Woo Kim5Department of Orthodontics, School of Dentistry, Dental Research Institute, Seoul National UniversityDepartment of Orthodontics, School of Dentistry, Dental Research Institute, Seoul National UniversityDepartment of Orthodontics, School of Dentistry, Dental Research Institute, Seoul National UniversityDepartment of Orthodontics, School of Dentistry, Dental Research Institute, Seoul National UniversityDepartment of Orthodontics, Korea University Guro HospitalDepartment of Orthodontics, School of Dentistry, Dental Research Institute, Seoul National UniversityAbstract Background Taking facial and intraoral clinical photos is one of the essential parts of orthodontic diagnosis and treatment planning. Among the diagnostic procedures, classification of the shuffled clinical photos with their orientations will be the initial step while it was not easy for a machine to classify photos with a variety of facial and dental situations. This article presents a convolutional neural networks (CNNs) deep learning technique to classify orthodontic clinical photos according to their orientations. Methods To build an automated classification system, CNNs models of facial and intraoral categories were constructed, and the clinical photos that are routinely taken for orthodontic diagnosis were used to train the models with data augmentation. Prediction procedures were evaluated with separate photos whose purpose was only for prediction. Results Overall, a 98.0% valid prediction rate resulted for both facial and intraoral photo classification. The highest prediction rate was 100% for facial lateral profile, intraoral upper, and lower photos. Conclusion An artificial intelligence system that utilizes deep learning with proper training models can successfully classify orthodontic facial and intraoral photos automatically. This technique can be used for the first step of a fully automated orthodontic diagnostic system in the future.https://doi.org/10.1186/s12903-022-02466-xClinical photographsArtificial intelligenceDeep learningOrthodontics |
| spellingShingle | Jiho Ryu Yoo-Sun Lee Seong-Pil Mo Keunoh Lim Seok-Ki Jung Tae-Woo Kim Application of deep learning artificial intelligence technique to the classification of clinical orthodontic photos BMC Oral Health Clinical photographs Artificial intelligence Deep learning Orthodontics |
| title | Application of deep learning artificial intelligence technique to the classification of clinical orthodontic photos |
| title_full | Application of deep learning artificial intelligence technique to the classification of clinical orthodontic photos |
| title_fullStr | Application of deep learning artificial intelligence technique to the classification of clinical orthodontic photos |
| title_full_unstemmed | Application of deep learning artificial intelligence technique to the classification of clinical orthodontic photos |
| title_short | Application of deep learning artificial intelligence technique to the classification of clinical orthodontic photos |
| title_sort | application of deep learning artificial intelligence technique to the classification of clinical orthodontic photos |
| topic | Clinical photographs Artificial intelligence Deep learning Orthodontics |
| url | https://doi.org/10.1186/s12903-022-02466-x |
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