# DCP: Prediction of Dental Caries Using Machine Learning in Personalized Medicine
## 2 Related Work  
Artificial intelligence (AI) technology can be applied in a variety of ways in the medical field, from early detection of disease, patient condition monitoring using software, clinical decision making using big data, and wearable devices and sensors that use IoT technology to monitor a patient's health status in real time. In addition, ML can improve diagnosis and treatment effectiveness by allowing medical experts to analyze real-time data to identify changes in patients. The field of medical image analysis and diagnosis has the highest annual growth rate in the AI-based healthcare industry, and a large amount of medical image data available facilitate the research venture in this field. Based on this paradigm, many AI methods have been introduced in the medical area to alleviate the time-consuming and cost-related aspect of performing some diagnoses. VUNO, a representative case, helps perform bone age assessment using DL by integrating and analyzing medical image information such as X-ray and CT [15]. Lunit improves the early diagnosis rate and reduces the false diagnosis rate by developing an AI-based solution for chest X-rays and mammography images, using DL technology [16]. Consent agreement related to medical data has restricted some research's vulgarization and is currently heavily performed only by medical doctors. With the entry into an aging society, oral health is one of the essential factors determining the quality of life. Periodontitis is one of the most common diseases afflicted by humankind and is the sixth most prevalent disease globally. Loss of alveolar bone is one of the main symptoms of periodontitis and causes tooth loss, edentulous jaw, and masticatory dysfunction [17]. A classification system for periodontitis was needed to provide a standard for research on the etiology and treatment of periodontitis [18]. Since then, the classification system for periodontitis has been continuously revised according to the newly identified scientific and clinical evidence [19]. In general, clinical attachment loss (CAL) evaluates periodontal health using a periodontal probe. This method has limitations in terms of reliability; thus, a computer-aided diagnosis (CAD) system can help clinicians make decisions by extracting important features from medical images taken in various environments [20]. However, the existing CAD approach has limitations in that feature extraction is not easy and takes much time due to the diversity of disease patterns [21]. Nevertheless, current methods based on DL, a subset of ML, have the advantage of automatically extracting critical features from images through learning [22]. Convolutional neural network (CNN) is the most used method to segment, classify, and detect organs or related diseases in medical images [23; 24; 25]. In the field of oral and maxillofacial surgery, the CAD approach integrated with CNN has been used to detect landmarks in cephalograms [26], tooth detection and classification [27; 28], caries diagnosis [29], and maxillary sinusitis detection [30]. A dentist can provide a more accurate and rapid diagnosis result by adding a second opinion to the results obtained through this method. Recently, studies on RBL detection using CNN in dental panoramic images have also been conducted [31; 32]. Research on dental caries classification by image interpretation mainly controls the contrast of the X-ray image. The authors of [33] present a novel approach that detects the presence of dental caries-hybridized negative transformation and statistical analysis for the dental image containing dental caries along with cysts. Radon transform and discrete cosine transform were applied to cavities, and some studies diagnose by classification using various classification techniques such as decision tree, random forest, and naive Bayes. This technique improved the diagnostic accuracy of caries by adjusting the image contrast, but the limitation of the study was revealed due to the generation of image noise.