Automated orthodontic diagnosis via self-supervised learning and multi-attribute classification using lateral cephalograms
Abstract Background Malocclusion, characterized by dental misalignment and improper occlusal relationships, significantly impacts oral health and daily functioning, with a global prevalence of 56%. Lateral cephalogram is a crucial diagnostic tool in orthodontic treatment, providing insights into var...
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| Format: | Article |
| Language: | English |
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BMC
2025-02-01
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| Series: | BioMedical Engineering OnLine |
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| Online Access: | https://doi.org/10.1186/s12938-025-01345-0 |
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| _version_ | 1823861710474182656 |
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| author | Qiao Chang Yuxing Bai Shaofeng Wang Fan Wang Shuang Liang Xianju Xie |
| author_facet | Qiao Chang Yuxing Bai Shaofeng Wang Fan Wang Shuang Liang Xianju Xie |
| author_sort | Qiao Chang |
| collection | DOAJ |
| description | Abstract Background Malocclusion, characterized by dental misalignment and improper occlusal relationships, significantly impacts oral health and daily functioning, with a global prevalence of 56%. Lateral cephalogram is a crucial diagnostic tool in orthodontic treatment, providing insights into various structural characteristics. Methods This study introduces a pre-training approach using multi-center lateral cephalograms for self-supervised learning, aimed at improving model generalization across diverse clinical data domains. Additionally, a multi-attribute classification network is proposed, leveraging attribute correlations to optimize parameters and enhance classification performance. Results Comprehensive evaluation on both public and clinical datasets showcases the superiority of the proposed framework, achieving an impressive average accuracy of 90.02%. The developed Self-supervised Pre-training and Multi-Attribute (SPMA) network achieves a best match ratio (MR) score of 71.38% and a low Hamming loss (HL) of 0.0425%, demonstrating its efficacy in orthodontic diagnosis from lateral cephalograms. Conclusions This work contributes significantly to advancing automated diagnostic tools in orthodontics, addressing the critical need for accurate and efficient malocclusion diagnosis. The outcomes not only improve the efficiency and accuracy of diagnosis, but also have the potential to reduce healthcare costs associated with orthodontic treatments. |
| format | Article |
| id | doaj-art-33f57ae12af5495f92ac950c861827cd |
| institution | Kabale University |
| issn | 1475-925X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BioMedical Engineering OnLine |
| spelling | doaj-art-33f57ae12af5495f92ac950c861827cd2025-02-09T12:47:36ZengBMCBioMedical Engineering OnLine1475-925X2025-02-0124111610.1186/s12938-025-01345-0Automated orthodontic diagnosis via self-supervised learning and multi-attribute classification using lateral cephalogramsQiao Chang0Yuxing Bai1Shaofeng Wang2Fan Wang3Shuang Liang4Xianju Xie5Department of Orthodontics, Beijing Stomatological Hospital, Capital Medical UniversityDepartment of Orthodontics, Beijing Stomatological Hospital, Capital Medical UniversityDepartment of Orthodontics, Beijing Stomatological Hospital, Capital Medical UniversityDepartment of Orthodontics, Beijing Stomatological Hospital, Capital Medical UniversitySchool of Biomedical Engineering, Capital Medical UniversityDepartment of Orthodontics, Beijing Stomatological Hospital, Capital Medical UniversityAbstract Background Malocclusion, characterized by dental misalignment and improper occlusal relationships, significantly impacts oral health and daily functioning, with a global prevalence of 56%. Lateral cephalogram is a crucial diagnostic tool in orthodontic treatment, providing insights into various structural characteristics. Methods This study introduces a pre-training approach using multi-center lateral cephalograms for self-supervised learning, aimed at improving model generalization across diverse clinical data domains. Additionally, a multi-attribute classification network is proposed, leveraging attribute correlations to optimize parameters and enhance classification performance. Results Comprehensive evaluation on both public and clinical datasets showcases the superiority of the proposed framework, achieving an impressive average accuracy of 90.02%. The developed Self-supervised Pre-training and Multi-Attribute (SPMA) network achieves a best match ratio (MR) score of 71.38% and a low Hamming loss (HL) of 0.0425%, demonstrating its efficacy in orthodontic diagnosis from lateral cephalograms. Conclusions This work contributes significantly to advancing automated diagnostic tools in orthodontics, addressing the critical need for accurate and efficient malocclusion diagnosis. The outcomes not only improve the efficiency and accuracy of diagnosis, but also have the potential to reduce healthcare costs associated with orthodontic treatments.https://doi.org/10.1186/s12938-025-01345-0MalocclusionSelf-supervised learningMulti-attribute classificationLateral cephalogramsMedical image analysis |
| spellingShingle | Qiao Chang Yuxing Bai Shaofeng Wang Fan Wang Shuang Liang Xianju Xie Automated orthodontic diagnosis via self-supervised learning and multi-attribute classification using lateral cephalograms BioMedical Engineering OnLine Malocclusion Self-supervised learning Multi-attribute classification Lateral cephalograms Medical image analysis |
| title | Automated orthodontic diagnosis via self-supervised learning and multi-attribute classification using lateral cephalograms |
| title_full | Automated orthodontic diagnosis via self-supervised learning and multi-attribute classification using lateral cephalograms |
| title_fullStr | Automated orthodontic diagnosis via self-supervised learning and multi-attribute classification using lateral cephalograms |
| title_full_unstemmed | Automated orthodontic diagnosis via self-supervised learning and multi-attribute classification using lateral cephalograms |
| title_short | Automated orthodontic diagnosis via self-supervised learning and multi-attribute classification using lateral cephalograms |
| title_sort | automated orthodontic diagnosis via self supervised learning and multi attribute classification using lateral cephalograms |
| topic | Malocclusion Self-supervised learning Multi-attribute classification Lateral cephalograms Medical image analysis |
| url | https://doi.org/10.1186/s12938-025-01345-0 |
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