# Deep Learning Application in Dental Caries Detection Using Intraoral Photos Taken by Smartphones  
[PERSON]  
1 Hadong Medical College, Hanoi 100000, Vietnam; [EMAIL_ADDRESS]  
1 School of Dentistry, Hanoi Medical University, Hanoi 100000, Vietnam; [EMAIL_ADDRESS] (V.T.N.N.); [EMAIL_ADDRESS] (N.T.T)  
2 School of Dentistry, Hanoi Medical University, Hanoi 100000, Vietnam; [EMAIL_ADDRESS] (V.T.N.N.); [EMAIL_ADDRESS] (N.T.T)  
3 Institute for Preventive Medicine and Public Health, Hanoi Medical University, Hanoi 100000, Vietnam; [EMAIL_ADDRESS] (N.T.N.)  
4 Hanoi Medical University Hospital, Hanoi 100000, Vietnam  
5 Graduate School of Medicine, Nagoya University, Nagoya 466-8560, Japan  
6 Department of Informatics, Thuongmai University, Hanoi 100000, Vietnam  
7 Division of Research and Treatment for Oral Maxillofacial Congenital Anomalies, Aichi Gakuin University, Nagoya 64-8651, Japan  
8 Correspondence:  
Deep learning with image input has been explosively growing and promising to become an important platform in medical images. One of its most popular applications in the medical field is classification [5]. Applications of deep learning in dentistry are remarkable in a variety of fields such as teeth-related diseases, dental plaque, and periodontium [6].  
In terms of dental caries, currently, different approaches exist for building automatic diagnosis tools, such as the application of common data-mining algorithms on the factors from annual oral checkups [7] or the classification algorithms used two separate steps: image segmentation and classification [7; 8]. However, the current prominent approach is building an object detector via deep learning models, such as CNN, deep neural network (DNN), Region-Based CNN (R-CNN), Fast R-CNN, Faster R-CNN, Mask R-CNN, You Only Look Once version 3 (YOLOv3), RetinaNet, and Single-Shot Multi-Box Detector (SSD) [6; 9; 10; 11]. Research by [PERSON] showed that the YOLOv3 algorithm has a potential capability for caries detection [12]. [PERSON] built a home dental care system using the RetinaNet model and reported that the system allowed users to effectively manage their dental problems by providing needed dental treatment information [13]. [PERSON] conducted a study using the Faster R-CNN for automatic detection of caries on bitewing radiographs. This study demonstrated a promising performance for detecting proximal surface caries on dental bitewings [14]. A study by [PERSON] et al. using DNN Mask R-CNN to detect caries on occlusal surfaces showed an accuracy of 0.889 [10]. Another study applied CNN to detect white spots in dental photography, reporting a mean accuracy from 0.81 to 0.84 [11]. Several completed commercial software to detect dental caries are also available, such as Logicon Caries Detector for dental monitoring [15].  
Previous studies were mainly conducted in the laboratory; data on the potential of AI in vivo have been limited [9]. Recently, [PERSON] et al. [16] used photos taken by smartphones of the occlusal surface of molars and premolars to develop an automated caries detection software. Both the training and testing data were dried teeth. [PERSON] et al. detected caries lesions in vivo on occlusal and proximal surfaces on posterior teeth. However, images were taken by the near-infrared transillumination device, which is rarely used in clinical practice [17]. A study by [PERSON] used intracoral photos applying deep learning to diagnose tooth decay. It should be noted that photographs were taken by professional cameras (Nikon D300, D7100, and D7200), with a Nikon Micro 105 mm lens [18].  
The aim of our study was to develop a deep learning model for dental caries diagnostic that can be used to build a smartphone application using input as smartphone intraoral photos.