Tej Trivedi,
Tanish Shah,
Akhilesh Soni,
Dr. Madhura Phadke,
- Student, Department of Computer Engineering, K. J. Somaiya Institute of Engineering, Sion, Mumbai, Maharashtra, India.
- Student, Department of Computer Engineering, K. J. Somaiya Institute of Engineering, Sion, Mumbai, Maharashtra, India
- Student, Department of Computer Engineering, K. J. Somaiya Institute of Engineering, Sion, Mumbai, Maharashtra, India
- Assistant Professor, Department of Computer Engineering, K. J. Somaiya Institute of Engineering, Sion, Mumbai, Maharashtra, India
Abstract
Dental diseases, also known as oral diseases or dental conditions, encompass a range of health problems affecting the teeth, gums, mouth, and associated structures. These conditions can lead to pain, discomfort, and severe complications if left untreated. Early detection and accurate diagnosis are crucial for effective treatment and prevention of further complications. This comprehensive literature review aims to identify common dental problems such as Tooth Decay (Cavities), Gingivitis, Periodontitis, and other oral diseases using advanced machine learning and deep learning approaches in dental image analysis. Machine learning and deep learning techniques provide significant assistance to dentists in making informed decisions during the treatment process. X-rays and radiographic images are the primary tools used for detecting dental diseases, and advancements in imaging technologies have enhanced the quality and precision of these diagnostics. Machine learning algorithms, such as Convolutional Neural Networks (CNNs), Graph Neural Networks (GNNs), and Region-based Convolutional Neural Networks (R-CNNs), have shown remarkable efficacy in automating the diagnostic process, reducing the reliance on manual interpretation, and minimizing human error. Deep learning, in particular, offers substantial promise for dental disease detection and tooth identification. Techniques such as transfer learning have enabled models to achieve high accuracy even with limited datasets, enhancing the potential for early and accurate diagnosis. The application of CNNs allows for the effective analysis of complex image data, facilitating the identification of minute details that may indicate the onset of dental conditions. GNNs and R-CNNs further contribute by enabling the analysis of relational data and improving the detection of abnormalities within dental structures. The evolution of artificial intelligence in dental healthcare signifies a transformative shift towards early disease detection, personalized treatment planning, and improved patient outcomes. This survey explores the current technological advancements in AI-driven dental diagnostics, highlighting the integration of these cutting-edge tools into routine clinical practice. By leveraging the power of AI, dental professionals can enhance diagnostic accuracy, streamline treatment processes, and ultimately provide better care to patients.
Keywords: Dental Diseases, Panoramic Images, CNN Algorithm, Faster R-CNN Algorithm, GNN Algorithm, Transfer Learning, YOLO (You Only Look Once).
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Current Trends in Signal Processing
Volume | |
Received | August 2, 2024 |
Accepted | August 8, 2024 |
Published | August 20, 2024 |