A novel deep learning-based model for automated tooth detection and numbering in mixed and permanent dentition in occlusal photographs

BackgroundWhile artificial intelligence-driven approaches have shown great promise in dental diagnosis and treatment planning, most research focuses on dental radiographs. Only three studies have explored automated tooth numbering in oral photographs, all focusing on permanent dentition. Our study aimed to introduce an automated system for detection and numbering of teeth across mixed and permanent dentitions in occlusal photographs.MethodsA total of 3215 occlusal view images of maxilla and mandible were included. Five senior dental students, trained under the guidance of an associate professor in dental public health, annotated the dataset. Samples were distributed across the training, validation, and test sets using a ratio of 7:1.5:1.5, respectively. We employed two separate convolutional neural network (CNN) models working in conjunction. The first model detected tooth presence and position, generating bounding boxes, while the second model localized these boxes, conducted classification, and assigned tooth numbers. Python and YOLOv8 were utilized in model development. Overall performance was assessed using sensitivity, precision, and F1 score.ResultsThe model demonstrated a sensitivity of 99.89% and an overall precision of 95.72% across all tooth types, with an F1 score of 97.76%. Misclassifications were primarily observed in underrepresented teeth, including primary incisors and permanent third molars. Among primary teeth, maxillary molars showed the highest performance, with precisions above 94%, 100% sensitivities, and F1 scores exceeding 97%. The mandibular primary canine showed the lowest results, with a precision of 88.52% and an F1 score of 93.91%.ConclusionOur study advances dental diagnostics by developing a highly precise artificial intelligence model for detecting and numbering primary and permanent teeth on occlusal photographs. The model's performance, highlights its potential for real-world applications, including tele-dentistry and epidemiological studies in underserved areas. The model could be integrated with other systems to identify dental problems such as caries and orthodontic issues.