CONVOLUTIONAL NEURAL NETWORK (CNN) BASED ON ARTIFICIAL INTELLIGENCE IN PERIODONTAL DISEASES DIAGNOSIS

Abstract

Introduction: The main problem by many clinicians is the correct diagnosis of periodontal disease. Usually, conventional clinical measurements such as measuring probing depth, attachment loss, presence of plaque and calculus are the way to diagnose and classify periodontal disease. However, clinical examination has limited reliability for periodontitis screening. Likewise, the ability of dentists to read radiographs using conventional methods increases the risk of misdiagnosis. Due to the diversity of existing clinical criteria and the increase in knowledge about human health, changes in the diagnostic criteria for periodontal disease that have occurred in recent decades have led to several updates. Recent research has focused on developing artificial intelligence tools to assist in diagnostic and therapeutic roles. This literature review aims to determine the use of artificial intelligence-based convolutional neural network (CNN) in diagnosing periodontal disease. Review: Artificial intelligence (AI) can make more accurate and efficient diagnoses, thereby reducing the workload of dentists. The use of the convolutional neural network (CNN) system in diagnosis and treatment planning allows dentists to reduce diagnostic errors that arise. Several studies have found that the CNN algorithm can assist in detecting alveolar bone loss, gingival abnormalities, and assisting early intervention in implantology. The CNN system can also capture details that dentists miss in diagnosis, especially radiographic diagnosis. Conclusion: Implementing an AI system is effective in helping to analyze periodontal disease. The CNN algorithm outperforms other AI techniques that can be used to facilitate diagnosis and treatment planning by dentists in the future.