van Nistelrooij Niels, Maia Haline Cunha de Medeiros, Cao Lingyun, Vinayahalingam Shankeeth, Loomans Bas, Cenci Maximiliano Sergio, Mendes Fausto Medeiros
Department of Oral and Maxillofacial Surgery, Radboud University Medical Center, Nijmegen, the Netherlands; Department of Oral and Maxillofacial Surgery, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
Department of Dentistry, Radboud University Medical Center, Nijmegen, the Netherlands; Department of Pediatric Dentistry, School of Dentistry, University of São Paulo, São Paulo, Brazil.
J Dent. 2025 Oct;161:105976. doi: 10.1016/j.jdent.2025.105976. Epub 2025 Jul 12.
Considering the importance of distinguishing between primary and permanent teeth in children with mixed dentition, this study aimed to develop and evaluate an automated method for segmenting and labelling primary and permanent teeth in digital impressions.
716 digital impressions from 351 patients with primary or mixed dentitions were collected from the Netherlands, Brazil, and the 3DTeethSeg22 challenge dataset. The scans were annotated with tooth segmentations and primary and permanent teeth FDI numbers. A deep learning model was applied that combined large-context predictions for tooth labelling with high-resolution predictions for tooth segmentation. Using the collected scans, the model was trained and evaluated with five-fold cross-validation for tooth detection (F1-score), tooth segmentation (Dice score), and tooth labelling (macro-F1). Additionally, the model was trained and evaluated using the train-test split of the 3DTeethSeg22 challenge dataset.
The developed model achieved highly effective results for tooth detection (F1-score = 0.996), tooth segmentation (Dice = 0.969), and tooth labelling (macro-F1 = 0.989). Moreover, a digital impression was processed in under two seconds on average. Furthermore, the proposed method outperformed the top-ranked 3DTeethSeg22 challenge submission (score = 0.954 vs. 0.976) and was particularly effective for tooth labelling (tooth identification rate = 0.910 vs. 0.955). Failure cases revealed mistakes for unusual dental conditions or ambiguous tooth eruption patterns.
A highly effective algorithm for tooth segmentation was developed to differentiate between primary and permanent teeth in digital impressions. This fast and accurate model can benefit dentists in documenting children's teeth during the mixed dentition stage.
The algorithm provides an accurate and reliable tool for AI-assisted identification and numbering of primary and permanent teeth in digital impressions obtained from children with mixed dentition, thereby enhancing clinical workflow, improving treatment planning accuracy, and facilitating communication with patients and caregivers.
鉴于区分混合牙列期儿童乳牙和恒牙的重要性，本研究旨在开发并评估一种用于在数字印模中分割和标记乳牙和恒牙的自动化方法。
从荷兰、巴西以及3DTeethSeg22挑战数据集收集了351例乳牙或混合牙列患者的716个数字印模。扫描结果标注有牙齿分割以及乳牙和恒牙的FDI编号。应用了一种深度学习模型，该模型将用于牙齿标记的大上下文预测与用于牙齿分割的高分辨率预测相结合。使用收集到的扫描数据，通过五折交叉验证对模型进行训练和评估，以检测牙齿（F1分数）、分割牙齿（Dice分数）和标记牙齿（宏F1）。此外，使用3DTeethSeg22挑战数据集的训练-测试分割对模型进行训练和评估。
所开发的模型在牙齿检测（F1分数=0.996）、牙齿分割（Dice=0.969）和牙齿标记（宏F1=0.989）方面取得了高效的结果。此外，平均每个数字印模的处理时间不到两秒。此外，所提出的方法优于3DTeethSeg22挑战中排名第一的提交结果（分数=0.954对0.976），并且在牙齿标记方面特别有效（牙齿识别率=0.910对0.955）。失败案例揭示了在异常牙齿情况或不明确的牙齿萌出模式下出现的错误。
开发了一种高效的牙齿分割算法，以区分数字印模中的乳牙和恒牙。这种快速准确的模型可以帮助牙医在混合牙列期记录儿童牙齿情况。
该算法为在混合牙列期儿童获得的数字印模中进行人工智能辅助的乳牙和恒牙识别及编号提供了准确可靠的工具，从而优化临床工作流程，提高治疗计划的准确性，并促进与患者及护理人员的沟通。
