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Review Paper | Dentistry | Volume 15 Issue 5, May 2026 | Pages: 225 - 231 | India
From Pixels to Precision: A Comprehensive Review of Artificial Neural Networks and Convolutional Neural Networks in Contemporary Endodontics
Abstract: The integration of Artificial Intelligence (AI) into dentistry has significantly transformed diagnostic and therapeutic approaches, particularly in endodontics. Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN), key subsets of machine learning and deep learning, respectively, have demonstrated remarkable potential in analysing complex clinical and radiographic data. This review critically evaluates current literature on ANN and CNN applications in endodontics, focusing on diagnostic accuracy, treatment planning, outcome prediction, and clinical workflow enhancement. CNN-based models have shown superior performance in interpreting periapical radiographs and cone-beam computed tomography (CBCT) scans, enabling early detection of periapical pathologies, root fractures, and anatomical variations. ANN models, on the other hand, have been widely used for predictive analytics and decision support systems. Despite promising outcomes, challenges such as dataset heterogeneity, lack of external validation, ethical concerns, and limited clinical integration persist. Future advancements in explainable AI and real-time clinical applications are expected to bridge the gap between research and practice. This review highlights the transformative potential of ANN and CNN while emphasizing the need for standardized protocols and robust clinical validation.
Keywords: Artificial Intelligence, Artificial Neural Network, Convolutional Neural Network, Deep Learning, Endodontics, CBCT, Periapical Lesions
How to Cite?: Dr. L Krishna Prasada, Dr. Preetam Khatua, "From Pixels to Precision: A Comprehensive Review of Artificial Neural Networks and Convolutional Neural Networks in Contemporary Endodontics", Volume 15 Issue 5, May 2026, International Journal of Science and Research (IJSR), Pages: 225-231, https://www.ijsr.net/getabstract.php?paperid=SR26428145414, DOI: https://dx.dx.doi.org/10.21275/SR26428145414