1*Research Scholar, School of Computer Science and IT, JAIN (Deemed-to-be-University), Bangalore, India
Email: seeamos@gmail.com
2Professor, School of Computer Science and IT, JAIN (Deemed-to-be-University), Bangalore, India
Email: profdrkamalraj@gmail.com
Diabetes is a chronic metabolic disorder with profound systemic and oral health implications, including an increased risk of periodontal disease and other dental disorders. Understanding the complex relationship between diabetes and dental health requires analysis of multidimensional clinical and imaging data, which is challenging using conventional methods. This study proposes the development of an automated analytical model that integrates dental imaging and periodontal clinical parameters to systematically assess the association between diabetes and oral disorders. Using machine learning techniques, including convolutional neural networks (CNNs), random forests, and support vector machines (SVMs), the model aims to classify and quantify dental disease severity in both diabetic and non-diabetic populations, enabling early detection, risk stratification, and personalized intervention strategies. By combining advanced computational methods with clinical data, this approach has the potential to improve diagnostic accuracy, uncover patterns linking metabolic control and oral health outcomes, and facilitate interdisciplinary patient management. The proposed framework represents a step toward data-driven, precision dentistry in the context of disease.
Keywords: Dental Disorders, Machine Learning, Artificial Intelligence, Dental Imaging, Predictive Modeling, Clinical Decision Support.
How to cite this article: Amos R, Kamalraj R. Understanding the Relationship between Diabetes and Dental Disorders Using an Automation Tool. Int J Drug Deliv Technol. 2026;16(7s): 944-949; DOI: 10.25258/ijddt.16.7s.100
Source of support: None
Conflict of interest: None