Deep Learning Approaches for Automated Diagnosis and Treatment Planning in Dentistry
Keywords:
Deep Learning, Dentistry, Diagnosis, Treatment Planning, Automation, Image Recognition, Pattern Analysis, Healthcare, Technology, Artificial IntelligenceAbstract
This paper explores the potential of deep learning approaches in revolutionizing the field of dentistry by automating the processes of diagnosis and treatment planning. Deep learning has shown remarkable capabilities in image recognition and pattern analysis, making it a promising technology for enhancing the efficiency and accuracy of dental practices. By analyzing a vast amount of dental data, including images, patient records, and treatment outcomes, deep learning models can assist dentists in diagnosing oral conditions and planning treatments tailored to individual patient needs. This paper reviews recent advancements in deep learning for dentistry, discusses challenges and future directions, and highlights the benefits of integrating these technologies into dental healthcare.
Downloads
References
Jha, Rajesh K., et al. "An appropriate and cost-effective hospital recommender system for a patient of rural area using deep reinforcement learning." Intelligent Systems with Applications 18 (2023): 200218.
Pargaonkar, Shravan. "Bridging the Gap: Methodological Insights from Cognitive Science for Enhanced Requirement Gathering." Journal of Science & Technology 1.1 (2020): 61-66.
Pulimamidi, Rahul. "To enhance customer (or patient) experience based on IoT analytical study through technology (IT) transformation for E-healthcare." Measurement: Sensors (2024): 101087.
Sasidharan Pillai, Aravind. “Utilizing Deep Learning in Medical Image Analysis for Enhanced Diagnostic Accuracy and Patient Care: Challenges, Opportunities, and Ethical Implications”. Journal of Deep Learning in Genomic Data Analysis 1.1 (2021): 1-17.
Raparthi, Mohan. "AI Integration in Precision Health-Advancements, Challenges, and Future Prospects." Asian Journal of Multidisciplinary Research & Review 1.1 (2020): 90-96.
Raparthi, Mohan. "Deep Learning for Personalized Medicine-Enhancing Precision Health With AI." Journal of Science & Technology 1.1 (2020): 82-90.
Raparthi, Mohan. "AI-Driven Decision Support Systems for Precision Medicine: Examining the Development and Implementation of AI-Driven Decision Support Systems in Precision Medicine." Journal of Artificial Intelligence Research 1.1 (2021): 11-20.
Raparthi, Mohan. "Precision Health Informatics-Big Data and AI for Personalized Healthcare Solutions: Analyzing Their Roles in Generating Insights and Facilitating Personalized Healthcare Solutions." Human-Computer Interaction Perspectives 1.2 (2021): 1-8.
Raparthi, Mohan. "AI Assisted Drug Discovery: Emphasizing Its Role in Accelerating Precision Medicine Initiatives and Improving Treatment Outcomes." Human-Computer Interaction Perspectives 2.2 (2022): 1-10.
Raparthi, Mohan. "Robotic Process Automation in Healthcare-Streamlining Precision Medicine Workflows With AI." Journal of Science & Technology 1.1 (2020): 91-99.
Raparthi, Mohan. "Harnessing Quantum Computing for Drug Discovery and Molecular Modelling in Precision Medicine: Exploring Its Applications and Implications for Precision Medicine Advancement." Advances in Deep Learning Techniques 2.1 (2022): 27-36.
Shiwlani, Ashish, et al. "Synergies of AI and Smart Technology: Revolutionizing Cancer Medicine, Vaccine Development, and Patient Care." International Journal of Social, Humanities and Life Sciences 1.1 (2023): 10-18.
Raparthi, Mohan. "Quantum Cryptography and Secure Health Data Transmission: Emphasizing Quantum Cryptography’s Role in Ensuring Privacy and Confidentiality in Healthcare Systems." Blockchain Technology and Distributed Systems 2.2 (2022): 1-10.
Raparthi, Mohan. "Quantum Sensing Technologies for Biomedical Applications: Investigating the Advancements and Challenges." Journal of Computational Intelligence and Robotics 2.1 (2022): 21-32.
Raparthi, Mohan. "Quantum-Inspired Optimization Techniques for IoT Networks: Focusing on Resource Allocation and Network Efficiency Enhancement for Improved IoT Functionality." Advances in Deep Learning Techniques 2.2 (2022): 1-9.
Raparthi, Mohan. "Quantum-Inspired Neural Networks for Advanced AI Applications-A Scholarly Review of Quantum Computing Techniques in Neural Network Design." Journal of Computational Intelligence and Robotics 2.2 (2022): 1-8.
Raparthi, Mohan. "Privacy-Preserving IoT Data Management with Blockchain and AI-A Scholarly Examination of Decentralized Data Ownership and Access Control Mechanisms." Internet of Things and Edge Computing Journal 1.2 (2021): 1-10.
Raparthi, Mohan. "Real-Time AI Decision Making in IoT with Quantum Computing: Investigating & Exploring the Development and Implementation of Quantum-Supported AI Inference Systems for IoT Applications." Internet of Things and Edge Computing Journal 1.1 (2021): 18-27.
Raparthi, Mohan. "Blockchain-Based Supply Chain Management Using Machine Learning: Analyzing Decentralized Traceability and Transparency Solutions for Optimized Supply Chain Operations." Blockchain Technology and Distributed Systems 1.2 (2021): 1-9.
Tatineni, Sumanth. "Applying DevOps Practices for Quality and Reliability Improvement in Cloud-Based Systems." Technix international journal for engineering research (TIJER)10.11 (2023): 374-380.
