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Automated extraction and evaluation of anterior esthetic parameters: A computer vision approach
Wenjia Chen1, Tian Zhou2, Mengyu Liang3
1Graduate student, Department of Prosthodontics, Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Clinical Research Center for Oral Diseases, Key Laboratory of OralBiomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Engineering Research Center of Oral Biomaterials and Devices of Zhejiang Province, Hangzhou, PR China.
Statement Of Problem:
Assessment of anterior dental and gingival esthetics has been commonly based on visual judgment and manual measurements. These approaches are time-consuming and show considerable examiner-dependent variation.
Purpose:
The purpose of this study was to develop and validate a computer vision-based approach for automated extraction of anterior tooth morphologic parameters and the Pink Esthetic Score (PES) and the White Esthetic Score (WES) from intraoral scan data.
Material And Methods:
Intraoral scans from 490 maxillary anterior teeth (245 pairs) of orthodontic patients were analyzed. An automated approach extracted dental and gingival contours, length-to-width ratios, and geometric and relative color differences (ΔE00, ΔL, Δa) compared with corresponding contralateral homologous teeth. Agreement between automated and manual measurements was evaluated using Bland-Altman analysis. The 245 pairs were randomly divided into exploration (n=175 pairs) and validation (n=70 pairs) sets. In the exploration set, logistic regression models combined with descriptive statistics established quantitative grading thresholds based on expert scores. In the validation set, agreement and reliability between automated and expert consensus scores were assessed using weighted Cohen kappa (κ) and intraclass correlation coefficients (ICC). Bland-Altman analysis and the Wilcoxon signed-rank test evaluated systematic bias. Furthermore, a Z test was performed to compare inter-examiner agreement with and without visual contour assistance across 245 pairs (α=.05).
Results:
Expert evaluations demonstrated moderate to almost perfect intra-examiner and inter-examiner agreement. The Bland-Altman analysis demonstrated excellent agreement between automated and manual measurements for tooth length-to-width ratios, indicating negligible systematic bias (mean difference=-0.001, 95% LoA: -0.061 to 0.058). Reliability between automated PES and WES scores and expert consensus scores was excellent for total PES (ICC=0.926, 95% CI: 0.881-0.954) and total WES (ICC=0.960, 95% CI: 0.936-0.975). Across all esthetic subcategories (95% CIs ranging from 0.639 to 1.000), agreement was almost perfect for 7 parameters (κ>0.890) and substantial for soft tissue contour (κ=0.790). No significant differences were found between automated and expert consensus scores (Wilcoxon P>.05). Furthermore, visual contour assistance improved inter-examiner agreement.
Conclusions:
The developed approach demonstrated excellent agreement with expert consensus and provided stable, reproducible esthetic measurements from intraoral scan data. By integrating objective metrics with visual annotations, it offered a practical tool for standardized esthetic assessment and may support clinical decision-making in digitally assisted esthetic dentistry. Further validation in broader clinical settings is warranted.
