Department of Orthodontics and Pediatric Dentistry, University of Michigan School of Dentistry, Ann Arbor, MI, USA.
Department of Orthodontics, Bauru Dental School, University of São Paulo, Bauru, SP, Brazil.
Sci Rep. 2023 Sep 22;13(1):15861. doi: 10.1038/s41598-023-43125-7.
Cleft lip and/or palate (CLP) is the most common congenital craniofacial anomaly and requires bone grafting of the alveolar cleft. This study aimed to develop a novel classification algorithm to assess the severity of alveolar bone defects in patients with CLP using three-dimensional (3D) surface models and to demonstrate through an interpretable artificial intelligence (AI)-based algorithm the decisions provided by the classifier. Cone-beam computed tomography scans of 194 patients with CLP were used to train and test the performance of an automatic classification of the severity of alveolar bone defect. The shape, height, and width of the alveolar bone defect were assessed in automatically segmented maxillary 3D surface models to determine the ground truth classification index of its severity. The novel classifier algorithm renders the 3D surface models from different viewpoints and captures 2D image snapshots fed into a 2D Convolutional Neural Network. An interpretable AI algorithm was developed that uses features from each view and aggregated via Attention Layers to explain the classification. The precision, recall and F-1 score were 0.823, 0.816, and 0.817, respectively, with agreement ranging from 97.4 to 100% on the severity index within 1 group difference. The new classifier and interpretable AI algorithm presented satisfactory accuracy to classify the severity of alveolar bone defect morphology using 3D surface models of patients with CLP and graphically displaying the features that were considered during the deep learning model's classification decision.
唇腭裂（CLP）是最常见的颅面先天畸形，需要进行牙槽裂植骨。本研究旨在开发一种新的分类算法，使用三维（3D）表面模型评估 CLP 患者牙槽骨缺损的严重程度，并通过可解释的人工智能（AI）算法展示分类器提供的决策。使用 194 例 CLP 患者的锥形束 CT 扫描来训练和测试自动分类牙槽骨缺损严重程度的性能。在自动分割的上颌 3D 表面模型中评估牙槽骨缺损的形状、高度和宽度，以确定其严重程度的地面实况分类指数。新的分类器算法从不同角度呈现 3D 表面模型，并捕获输入到二维卷积神经网络的二维图像快照。开发了一种可解释的 AI 算法，该算法使用来自每个视图的特征，并通过注意力层进行聚合，以解释分类。在 1 个分组差异内，严重指数的精度、召回率和 F1 评分分别为 0.823、0.816 和 0.817，一致性范围为 97.4%至 100%。新的分类器和可解释的 AI 算法使用 CLP 患者的 3D 表面模型对牙槽骨缺损形态的严重程度进行分类，具有令人满意的准确性，并以图形方式显示了在深度学习模型分类决策中考虑的特征。
