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Predicting occlusal force distribution from intraoral scans using deep learning: a pilot study
Cengiz Han Şahin1, S Ezgi Bostancıoğlu2, İbrahim Ediz3
1Department of Prosthodontics, Faculty of Dentistry, Gazi University, Ankara, Türkiye.
Objectives:
This pilot study investigated whether occlusal force distribution measured by T-Scan can be predicted from intraoral scan images using deep learning.
Methods:
Paired intraoral scan and T-Scan images from 26 subjects were analyzed. Following quality control, data were randomly partitioned at the subject level into training (n = 18), validation (n = 4), and independent test (n = 4; 8 image pairs) sets. Five deep learning architectures (U-Net, ResNet-UNet, Vision Transformer, CNN-Attention, and Pix2Pix) were trained to predict T-Scan force maps from occlusal scan images. Structural Similarity Index (SSIM) was predefined as the primary endpoint. Nonparametric statistical comparisons (Friedman test with Holm-corrected Wilcoxon post hoc analysis) and bootstrap confidence intervals were used.
Results:
A significant difference in SSIM was observed among models (χ² = 19.3, p = 0.00069). CNN-Attention achieved the highest structural similarity (mean SSIM 0.501 ± 0.030), significantly outperforming U-Net, Vision Transformer, and ResNet-UNet (adjusted p < 0.05), while not differing significantly from Pix2Pix. Pix2Pix demonstrated the lowest mean absolute error (0.237 ± 0.018), indicating superior pixel-level intensity approximation. Validation curves revealed distinct optimization behaviors, with adversarial training showing late-stage improvements in SSIM.
Conclusions:
Deep learning models approximated T-Scan occlusal force maps from intraoral scan images in this pilot dataset. These findings suggest that artificial intelligence may help estimate functional occlusal force distribution from morphological intraoral scan data. Further studies with larger datasets are required to validate clinical applicability.
Clinical Significance:
Computational prediction of functional occlusal force maps from intraoral scans may represent a future step toward integrating morphological and functional data within digital dentistry workflows; however, larger validation studies are required before clinical application.
