ArticleOral radiology2023
Deep-learning systems for diagnosing cleft palate on panoramic radiographs in patients with cleft alveolus.
Article in Oral radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
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Who cites it
13 citing papers in PubMed, 2 syntheses or guidelines pooled it, 14 citations in OpenAlex.
- Role of Artificial Intelligence in Cleft Lip and/or Cleft Palate in Diagnosis and Detection-An Umbrella Review.Clinical and experimental dental research · 2026Pooled it
- Explainability, Bias and Generalizability of AI Models in Dentistry: A Systematic Review of Model Interpretability and Equity.Clinical and experimental dental research · 2026Pooled it
- Area detection improves the person-based performance of a deep learning system for classifying the presence of carotid artery calcifications on panoramic radiographs.Oral radiology · 2026Article
- Artificial intelligence applications in the diagnosis and management of cleft lip and palate: An updated review.Journal of dental sciences · 2026Review
- 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
- Artificial Intelligence for Tooth Detection in Cleft Lip and Palate Patients.Diagnostics (Basel, Switzerland) · 2024Article
- Tech Bytes-Harnessing Artificial Intelligence for Pediatric Oral Health: A Scoping Review.International journal of clinical pediatric dentistry · 2024Article
- Differences in the panoramic appearance of cleft alveolus patients with or without a cleft palate.Imaging science in dentistry · 2024Article
- Deep learning system for distinguishing between nasopalatine duct cysts and radicular cysts arising in the midline region of the anterior maxilla on panoramic radiographs.Imaging science in dentistry · 2024Article
- Harnessing the Power of Artificial Intelligence in Cleft Lip and Palate: An In-Depth Analysis from Diagnosis to Treatment, a Comprehensive Review.Children (Basel, Switzerland) · 2024Review
- Deep Learning in Diagnosis of Dental Anomalies and Diseases: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- 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
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThe aim of the present study was to create effective deep learning-based models for diagnosing the presence or absence of cleft palate (CP) in patients with unilateral or bilateral cleft alveolus (CA) on panoramic radiographs.
methodsThe panoramic images of 491 patients who had unilateral or bilateral cleft alveolus were used to create two models. Model A, which detects the upper incisor area on panoramic radiographs and classifies the areas into the presence or absence of CP, was created using both object detection and classification functions of DetectNet. Using the same data for developing Model A, Model B, which directly classifies the presence or absence of CP on panoramic radiographs, was created using classification function of VGG-16. The performances of both models were evaluated with the same test data and compared with those of two radiologists.
resultsThe recall, precision, and F-measure were all 1.00 in Model A. The area under the receiver operating characteristic curve (AUC) values were 0.95, 0.93, 0.70, and 0.63 for Model A, Model B, and the radiologists, respectively. The AUCs of the models were significantly higher than those of the radiologists.
conclusionsThe deep learning-based models developed in the present study have potential for use in supporting observer interpretations of the presence of cleft palate on panoramic radiographs.
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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.