ArticleScientific reports2023
Interpretable artificial intelligence for classification of alveolar bone defect in patients with cleft lip and palate.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.
- Explainable artificial intelligence in dental imaging: a systematic review of interpretability and the current state of trust evidence.Frontiers in dental medicine · 2026Pooled it
- Proceedings from the VIII International Symposium on Orofacial Clefts and Related Anomalies - Bauru Medical School, University of São Paulo (FMBRU-USP).BMC proceedings · 2026Article
- From pixels to precision: a narrative review of AI-driven 3D morphological analysis and digital twinning in alveolar cleft management.Translational pediatrics · 2026Review
- The Use of Artificial Intelligence in Cleft Care: A Scoping Review.Plastic surgery (Oakville, Ont.) · 2026Review
- Artificial intelligence applications in the diagnosis and management of cleft lip and palate: An updated review.Journal of dental sciences · 2026Review
- Explainable artificial intelligence to quantify adenoid hypertrophy-related upper airway obstruction using 3D Shape Analysis.Journal of dentistry · 2025Article
- Revolutionizing cleft lip and palate management through artificial intelligence: a scoping review.Oral and maxillofacial surgery · 2025Article
- Artificial Intelligence Applications in Pediatric Craniofacial Surgery.Diagnostics (Basel, Switzerland) · 2025Review
- Exploring the Utility of ChatGPT in Cleft Lip Repair Education.Journal of clinical medicine · 2025Article
- Strategies for Craniofacial Tissue Engineering: Innovations for Scalable Bone Regeneration.Plastic and aesthetic research · 2025Article
- Management of orofacial clefts in times of artificial intelligence: advances and challenges.European archives of paediatric dentistry : official journal of the European Academy of Paediatric Dentistry · 2024Article
- Artificial intelligence for maxillofacial prosthodontics: A technological shift in craniofacial rehabilitation- a scoping review.Journal of oral biology and craniofacial researchReview
Corrections and comments
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Authors and funding
15 authors at 5 institutions in 5 countries.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.