Development of a diagnostic classification model for lateral cephalograms based on multitask learning
Abstract Objectives This study aimed to develop a cephalometric classification method based on multitask learning for eight diagnostic classifications. Methods This study was retrospective. A total of 3,310 lateral cephalograms were collected to construct a dataset. Eight clinical classifications we...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-02-01
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| Series: | BMC Oral Health |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12903-025-05588-0 |
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| Summary: | Abstract Objectives This study aimed to develop a cephalometric classification method based on multitask learning for eight diagnostic classifications. Methods This study was retrospective. A total of 3,310 lateral cephalograms were collected to construct a dataset. Eight clinical classifications were employed, including sagittal and vertical skeletal facial patterns, maxillary and mandibular anteroposterior positions, inclinations of upper and lower incisors, as well as their anteroposterior positions. The images were manually annotated for initially classification, which was verified by senior orthodontists. The data were randomly divided into training, validation, and test sets at a ratio of approximately 8:1:1. The multitask learning classification model was constructed based on the ResNeXt50_32 × 4d network and consisted of shared layers and task-specific layers. The performance of the model was evaluated using classification accuracy, precision, sensitivity, specificity and area under the curve (AUC). Results This model could perform eight clinical diagnostic classifications on cephalograms within an average of 0.0096 s. The accuracy of the six classifications was 0.8–0.9, and the accuracy of the two classifications was 0.75-0.8. The overall AUC values for each classification exceeded 0.9. Conclusions An automatic diagnostic classification model for lateral cephalograms was established based on multitask learning to achieve simultaneous classification of eight common clinical diagnostic items. The multitask learning model achieved better classification performance and reduced the computational costs, providing a novel perspective and reference for addressing such problems. |
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| ISSN: | 1472-6831 |