@article{nokey,

title = {Nasoalveolar Molding in Unilateral Cleft Lip and Palate: A Three-Dimensional Analysis},

author = {Yoon Weon Suh and Yong Jong Park and Olivia Yue and Marilia Sayako Yatabe and Heesoo Oh and Lucia Cevidanes and Meghan Hansen and Sophia Dipietro and Jonas Bianchi},

url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/ocr.70051?getft_integrator=scopus&utm_source=scopus&utm_source=scopus},

doi = {https://doi.org/10.1111/ocr.70051},

year  = {2025},

date = {2025-10-28},

journal = {Orthodontics and Craniofacial Research},

issue = {29},

pages = {83-92},

abstract = {Objectives: This study aimed to three-dimensionally analyze the effects of pre-surgical nasoalveolar molding (NAM) in patients with unilateral cleft lip and palate. Methods: The sample consisted of 66 patients (with pre- and post-NAM casts), collected from three centers. Landmarks and measurements were identified on each cast, and the changes from pre- to post-NAM were recorded and analyzed using a paired t-test. Results: Post-NAM, we observed a mean decrease of 5.18 mm in 3D cleft width, a 1.78 mm decrease in the sagittal cleft gap, a 1.34 mm decrease in the anterior arch width and a 2.27 mm increase in the posterior arch width. There was a greater inward rotation of the greater segment (8.50°) compared to the lesser segment (3.09°), an increase in arch depth (1.74 mm) and internal flexion (9.20°) of the lesser segment. No statistically significant changes in the greater segment's arch depth and internal flexion were observed. The morphological changes between pre- and post-NAM therapy were visualized by the closest-distance color maps and 3D superimposition assessments. Conclusions: This study demonstrated the significant morphological changes induced by pre-surgical nasoalveolar molding (NAM) in patients with unilateral cleft lip and palate through a three-dimensional analysis. There was a notable reduction in cleft width, inward rotation of the greater segment and changes in arch dimensions. },

keywords = {3D analysis, cleft lip and palate, Nasoalveolar, soft tissue morphology},

pubstate = {published},

tppubtype = {article}

}

@article{Bianchi2023j,

title = {Interpretable artificial intelligence for classification of alveolar bone defect in patients with cleft lip and palate. },

author = {Miranda F and Choudhari V and Barone S and Anchling L and Hutin N and Gurgel M and et al},

url = {https://doi.org/10.1038/s41598-023-43125-7},

doi = {10.1038/s41598-023-43125-7},

year  = {2023},

date = {2023-09-22},

journal = {Scientific Reports},

volume = {15861},

abstract = {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.},

keywords = {3D landmark identification, alveolar bone defect, artificial intelligence, cleft lip, cleft lip and palate},

pubstate = {published},

tppubtype = {article}

}