Deep learning-based dental plaque detection on primary teeth: a comparison with clinical assessments

Abstract

Background: Dental plaque causes many common oral diseases (e.g., caries, gingivitis, and periodontitis). Therefore, plaque detection and control are extremely important for children's oral health. The objectives of this study were to design a deep learning-based artificial intelligence (AI) model to detect plaque on primary teeth and to evaluate the diagnostic accuracy of the model. Methods: A conventional neural network (CNN) framework was adopted, and 886 intraoral photos of primary teeth were used for training. To validate clinical feasibility, 98 intraoral photos of primary teeth were assessed by the AI model. Additionally, tooth photos were acquired using a digital camera. One experienced pediatric dentist examined the photos and marked the regions containing plaque. Then, a plaque-disclosing agent was applied, and the areas with plaque were identified. After 1 week, the dentist drew the plaque area on the 98 photos taken by the digital camera again to evaluate the consistency of manual diagnosis. Additionally, 102 intraoral photos of primary teeth were marked to denote the plaque areas obtained by the AI model and the dentist to evaluate the diagnostic capacity of each approach based on lower-resolution photos. The mean intersection-over-union (MIoU) metric was employed to indicate detection accuracy. Results: The MIoU for detecting plaque on the tested tooth photos was 0.726 ± 0.165. The dentist's MIoU was 0.695 ± 0.269 when first diagnosing the 98 photos taken by the digital camera and 0.689 ± 0.253 after 1 week. Compared to the dentist, the AI model demonstrated a higher MIoU (0.736 ± 0.174), and the results did not change after 1 week. When the dentist and the AI model assessed the 102 intraoral photos, the MIoU was 0.652 ± 0.195 for the dentist and 0.724 ± 0.159 for the model. The results of a paired t-test found no significant difference between the AI model and human specialist (P > .05) in diagnosing dental plaque on primary teeth. Conclusions: The AI model showed clinically acceptable performance in detecting dental plaque on primary teeth compared with an experienced pediatric dentist. This finding illustrates the potential of such AI technology to help improve pediatric oral health.

Benefícios da inteligência artificial na identificação de cárie dentária: revisão integrativa

“…Segundo You et al (2020), esse modelo demonstra um desempenho clinicamente aceitável nadetecção de placa dentária em dentes decíduos, contemplando a odontopediatria. Outros procedimentos podem ser derivados do Deep Learning, como apresenta F. Casalegno et al (2019), sobre o uso de uma abordagem de deep learning automático para segmentação semântica de exames odontológicos obtidos com o sistema DIAGNOcam.…”

“…O algoritmo resultante inclui a metodologia de seleção de recursos com base em suas propriedades estatísticas. You et al(2020) Estudo pioneiro que usa redes para detectar placa dentária com base em um conjunto de dados de fotos de dentes decíduos. Além disso, avaliam o diagnóstico de desempenho de um sistema de IA que usa deep learning para detectar placa dentária Uma CNN foi adotada e 886 fotos intraorais de dentes decíduos foram usados para treinamento.…”

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