Intelligent Decision Support System for Oral Health Risk Assessment and Patient Education Using Hybrid Deep Learning Models
Oral Health Risk Assessment; Intelligent Decision Support System; Hybrid Deep Learning; Patient Education; Dental Informatics.
AuthorsAbstractThe importance of oral health risk assessment is to prevent early, timely clinical referral, and enhance patient awareness. Nevertheless, traditional oral health assessment usually relies on intermittent clinical visits and might not adequately combine behavioral, diet, lifestyle, and symptom risk indicators with individualized patient education. This study designed a smart decision support system to assess the oral health risks and educate patients based on a hybrid deep learning strategy. A 20-item questionnaire was designed and a sample of 50 unique respondents was asked to respond to it using a five-point Likert scale. Risk prediction was done with items Q1-Q19 and Q20 evaluated preference towards digital patient education. Protective oral health behaviors were reverse scored, risk-oriented items were directly scored and respondents were categorized into low, moderate and high risk of oral health. The respondents comprised 14 low-risk, 20 moderate-risk and 16 high-risk, with a total risk score of 23 to 90 and a mean score of 52.96. Interpretation that was based on attention recognized tooth pain, gum bleeding, tobacco consumption, dental cavities, missing or damaged teeth, and medical conditions as important factors in risk classification. Tobacco smoking is a separate, established risk factor for GCA and lung cancer. Long-term smoking stresses the immune system and alters normal immune tolerance, which is central to the pathogenesis of both diseases. The system also connected the predicted levels of risk and the personalized oral health education recommendations. The results indicate the promise of hybrid deep learning to assist in preventive oral care, early risk detection, and patient-centered education, but bigger clinically validated datasets are needed before it can be employed in the real world. 1. Introduction Oral health is a vital part of overall health, functional well-being, nutrition, communication, and quality of life. Dental caries, periodontal disease, tooth loss, oral infections, gingivitis, pain and decreased daily functioning correlate with poor oral health. Even though most oral diseases can be prevented, the late detection of risk factors tends to result in the development of the disease and increased treatment costs. Traditional oral health evaluation usually relies on clinical examination, patient history and professional judgment. Although these approaches continue to play a pivotal role in the dental practice, they can be constrained by differences in the access to dental professionals, patient awareness, time, and the subjective nature of symptom and behavioral risk factor interpretation. Thus, the need to develop early, structured and technology-supported oral health risk assessment systems capable of aiding clinicians and patients in the preventive decision-making process is increasing. AI has now become an important technological resource in the field of dentistry, especially in terms of diagnosis, image analysis, treatment planning, and disease prediction. Systematic reviews revealed that artificial intelligence methods have been implemented into various areas of dentistry, such as caries detection, orthodontics, periodontal assessment, and radiographic ••••••••••••••••••••••••••••••••
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