A Smartphone-Based System for Real-Time Early Childhood Caries Diagnosis

• Published in: Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis Vol. 12437; pp. 233 - 242
• Main Authors: Zhang, Yipeng, Liao, Haofu, Xiao, Jin, Jallad, Nisreen Al, Ly-Mapes, Oriana, Luo, Jiebo
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2020; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Cavity diagnosis; Deep learning; Mobile deployment
• ISBN: 9783030603335; 3030603334
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-60334-2_23

Early childhood caries (ECC) is the most common, yet preventable chronic disease in children under the age of 6. Treatments on severe ECC are extremely expensive and unaffordable for socioeconomically disadvantaged families. The identification of ECC in an early stage usually requires expertise in the field, and hence is often ignored by parents. Therefore, early prevention strategies and easy-to-adopt diagnosis techniques are desired. In this study, we propose a multistage deep learning-based system for cavity detection. We create a dataset containing RGB oral images labeled manually by dental practitioners. We then investigate the effectiveness of different deep learning models on the dataset. Furthermore, we integrate the deep learning system into an easy-to-use mobile application that can diagnose ECC from an early stage and provide real-time results to untrained users.