ArticleDento maxillo facial radiology2023
Detection of unilateral and bilateral cleft alveolus on panoramic radiographs using a deep-learning system.
Article in Dento maxillo facial radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Artificial intelligence applications in the diagnosis and management of cleft lip and palate: An updated review.Journal of dental sciences · 2026Review
- Artificial Intelligence Applications in Pediatric Craniofacial Surgery.Diagnostics (Basel, Switzerland) · 2025Review
- Differences in the panoramic appearance of cleft alveolus patients with or without a cleft palate.Imaging science in dentistry · 2024Article
- Deep learning and artificial intelligence in dental diagnostic imaging.The Japanese dental science review · 2023Review
- Deep-learning systems for diagnosing cleft palate on panoramic radiographs in patients with cleft alveolus.Oral radiology · 2023Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThe purpose of this study was to evaluate the difference in performance of deep-learning (DL) models with respect to the image classes and amount of training data to create an effective DL model for detecting both unilateral cleft alveoli (UCAs) and bilateral cleft alveoli (BCAs) on panoramic radiographs.
methodsModel U was created using UCA and normal images, and Model B was created using BCA and normal images. Models C1 and C2 were created using the combined data of UCA, BCA, and normal images. The same number of CAs was used for training Models U, B, and C1, whereas Model C2 was created with a larger amount of data. The performance of all four models was evaluated with the same test data and compared with those of two human observers.
resultsThe recall values were 0.60, 0.73, 0.80, and 0.88 for Models A, B, C1, and C2, respectively. The results of Model C2 were highest in precision and F-measure (0.98 and 0.92) and almost the same as those of human observers. Significant differences were found in the ratios of detected to undetected CAs of Models U and C1 (
conclusionsThe DL models trained using both UCA and BCA data (Models C1 and C2) achieved high detection performance. Moreover, the performance of a DL model may depend on the amount of training data.
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