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@article{175848,
        author = {Harish Ragavendra S and Sumesh B and Sheshathri D and Mr. T. Maria Mahajan},
        title = {DENTAL CARIES DETECTION SYSTEM},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {4565-4570},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175848},
        abstract = {Dental caries, commonly known as tooth decay, is one of the most widespread chronic diseases affecting people of all age groups. Early detection and timely treatment are essential to prevent further oral health complications. This project, the Dental Caries Detection System, is a deep learning-based solution designed to automatically identify dental caries from intraoral or X-ray images, aiding dentists and healthcare professionals in diagnosis.
The system enables users to upload a dental image, which is then analyzed using advanced image processing and convolutional neural network (CNN) techniques to detect the presence and severity of caries. Upon detection, the system visually highlights affected regions and classifies the type of decay. In addition, it generates a diagnostic summary that can be emailed directly to healthcare providers for further action.
This solution is designed to support clinical decision-making, especially in rural or underserved areas where access to dental specialists may be limited. It minimizes the chances of human error in interpretation, accelerates diagnosis, and improves the overall quality of patient care.
Future enhancements may include real-time video analysis for dental screenings, integration with patient management systems, and the use of AI to suggest treatment plans based on the severity and location of decay.
In the field of modern dentistry, early and accurate detection of dental caries plays a crucial role in ensuring effective treatment and preventing further oral health complications. Traditional diagnostic techniques often depend heavily on manual visual inspection and radiographs, which may lead to subjective interpretations and inconsistencies. To address these challenges, this paper proposes the design and development of a Dental Caries Detection System (DCDS) that integrates image processing and machine learning techniques for the automated identification of carious lesions. The system offers a robust, efficient, and user-friendly platform that processes intraoral images, enhances diagnostic precision, and supports real-time clinical decision-making. Using technologies such as Python, OpenCV, and a trained convolutional neural network (CNN), the system evaluates dental imagery for signs of decay with high accuracy. By leveraging data-driven insights and AI capabilities, the DCDS enhances diagnostic workflows, reduces human error, and improves overall patient care outcomes in dental practices.},
        keywords = {Dental caries, Deep learning, Image processing, CNN, Diagnostic system, Email alert, Dental health.},
        month = {April},
        }