Artificial intelligence (AI) in restorative dentistry: Performance of AI models designed for detection of interproximal carious lesions on primary and permanent dentition

• Published in: Digital health 2023-01, Vol.9, p.20552076231216681-20552076231216681
• Main Authors: Azhari, Amr Ahmed, Helal, Narmin, Sabri, Leena M, Abduljawad, Abeer
• Format: Article
• Language: English
• Subjects: Deep learning; Original Research; Pediatrics; Semantics
• ISSN: 2055-2076; 2055-2076
• DOI: 10.1177/20552076231216681

Objective The objective of this study was to evaluate the effectiveness of deep learning methods in detecting dental caries from radiographic images. Methods A total of 771 bitewing radiographs were divided into two groups: adult (n = 554) and pediatric (n = 217). Two distinct semantic segmentation models were constructed for each group. They were manually labeled by general dentists for semantic segmentation. The inter-examiner reliability of the two examiners was also measured. Finally, the models were trained using transfer learning methodology along with computer science advanced tools, such as ensemble U-Nets with ResNet50, ResNext101, and Vgg19 as the encoders, which were all pretrained on ImageNet weights using a training dataset. Results Intersection over union (IoU) score was used to evaluate the outcomes of the deep learning model. For the adult dataset, the IoU averaged 98%, 23%, 19%, and 51% for zero, primary, moderate, and advanced carious lesions, respectively. For pediatric bitewings, the IoU averaged 97%, 8%, 17%, and 25% for zero, primary, moderate, and advanced caries, respectively. Advanced caries was more accurately detected than primary caries on adults and pediatric bitewings P