Deep Learning Models for Classification of Deciduous and Permanent Teeth From Digital Panoramic Images

• Published in: Curēus (Palo Alto, CA) CA), 2023-12, Vol.15 (12), p.e49937-e49937
• Main Authors: Jaiswal, Manoj, Sharma, Megha, Khandnor, Padmavati, Goyal, Ashima, Belokar, Rajendra, Harit, Sandeep, Sood, Tamanna, Goyal, Kanav, Dua, Pallavi
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Collaboration; Datasets; Deep learning; Dental surgery; Dentistry; Neural networks; Pediatrics; Radiography; Radiology; Teeth
• ISSN: 2168-8184; 2168-8184
• DOI: 10.7759/cureus.49937

Dental radiographs are essential in the diagnostic process in dentistry. They serve various purposes, including determining age, analyzing patterns of tooth eruption/shedding, and treatment planning and prognosis. The emergence of digital radiography technology has piqued interest in using artificial intelligence systems to assist and guide dental professionals. These cutting-edge technologies assist in streamlining decision-making processes by enabling entity classification and localization tasks. With the integration of artificial Intelligence algorithms tailored for pediatric dentistry applications and utilizing automated tools, there is an optimistic outlook on improving diagnostic capabilities while reducing stress and fatigue among clinicians. The dataset comprised 620 images (mixed dentition: 314, permanent dentition: 306). Panoramic radiographs taken were within the age range of 4-16 years. The classification of deciduous and permanent teeth involved training CNN-based models using different architectures such as Resnet, AlexNet, and EfficientNet, among others. A ratio of 70:15:15 was utilized for training, validation, and testing, respectively. The findings indicated that among the models proposed, EfficientNetB0 and EfficientNetB3 exhibited superior performance. Both EfficientNetB0 and EfficientNetB3 achieved an accuracy rate, precision, recall, and F1 scores of 98% in classifying teeth as either deciduous or permanent. This implies that these models were highly accurate in identifying patterns/features within the dataset used for evaluation.