Dental midline localization: development and validation of an automated deep learning pipeline
European Journal of Orthodontics, cilt.48, sa.4, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 48 Sayı: 4
- Basım Tarihi: 2026
- Doi Numarası: 10.1093/ejo/cjag056
- Dergi Adı: European Journal of Orthodontics
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, EMBASE, MEDLINE, Natural Science Collection (ProQuest), Biological Science Database (ProQuest), Biomedical Reference Collection: Corporate Edition (EBSCO), Health Research Premium Collection (ProQuest), Pharma Collection (ProQuest)
- Anahtar Kelimeler: automated detection, deep learning, dental midline, dental midline asymmetry
- Çanakkale Onsekiz Mart Üniversitesi Adresli: Evet
Özet
Objectives: To develop and validate an automated pipeline for maxillary dental midline localization on posed smile photographs and compare the performance of four convolutional neural network (CNN) backbones. Materials and Methods: A total of 221 standardized frontal posed smile photographs were collected. The maxillary dental midline was annotated by two orthodontists across two sessions, and mean coordinates served as the orthodontist-derived reference standard. An automated pipeline including face detection, interpupillary-line rotational normalization, mouth region extraction, and CNN-based keypoint regression was developed. Four CNN models (EfficientNet-B0, ResNet-18, MobileNetV3-Small, and HRNet-W18) were trained and compared using 5-fold stratified cross-validation. Deviation was expressed as the angle between the predicted dental midline and the interpupillary-derived photographic reference line (T-line). Results: HRNet-W18 achieved the lowest mean angular error (0.63° ± 0.63°) and the highest ≤1° error-threshold accuracy (81.0%). All 4 models exceeded 90% accuracy at the ≤2° threshold. No significant effect of tooth visibility grade on angular error was observed. HRNet-W18 also showed the highest agreement with the reference standard [intraclass correlation coefficient (ICC) = 0.721] and minimal Bland–Altman bias (+0.03°). Post hoc pairwise comparisons showed that HRNet-W18 outperformed all other models across all error metrics. Limitations: The sample was limited to standardized posed smile photographs from a single center, and generalizability to diverse populations and less controlled conditions remains to be established. Conclusions and Implications: The automated pipeline showed high accuracy for dental midline localization on posed smile photographs relative to an orthodontist-derived reference standard. Further validation on larger and more diverse datasets is needed before clinical application.