Role of Artificial Intelligence in Periodontology
This study focuses into the possible links between patient demographics, smoking habits, treatment received, and periodontal disease severity before and after treatment. A new dataset of 1,000 patients was created, with information on age, smoking status, periodontal disease severity before and after therapy, and whether or not treatment was received. To obtain insight into the correlations between the variables, descriptive and inferential statistical analyses were performed using SPSS. A machine learning model was also created and trained on the information to predict the severity of periodontal disease following treatment. Despite the apparent complexity of the disease process, the machine learning model was discovered to be a reliable tool for forecasting disease development. The findings demonstrate an insignificant relationship between age and post-treatment severity, implying that age may not be a significant role in the progression of periodontal disease after treatment. The performance of the machine learning model, its implications for clinical practice, and prospective applications of AI in periodontology are also examined. The findings have important implications for periodontal disease patient management and treatment decisions. Furthermore, they lay the door for future AI implementations in periodontal disease prediction and management that are more sophisticated. More research is needed, however, to corroborate these findings and include more different parameters into the machine learning model.
Keywords: Artificial Intelligence, Periodontology, Dental healthcare, Diagnosis,Treatment