AI-Enabled Periodontal Disease Diagnosis and Management
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Keywords

Artificial Intelligence
Periodontal Disease
Diagnosis
Management

How to Cite

[1]
Leila Rahman, “AI-Enabled Periodontal Disease Diagnosis and Management”, Journal of AI in Healthcare and Medicine, vol. 4, no. 1, pp. 10–18, May 2024, Accessed: Nov. 22, 2024. [Online]. Available: https://healthsciencepub.com/index.php/jaihm/article/view/3

Abstract

This paper explores the application of artificial intelligence (AI) in diagnosing and managing periodontal diseases, a significant oral health concern globally. Periodontal diseases, including gingivitis and periodontitis, are primarily caused by bacterial infection and can lead to tooth loss if left untreated. Traditional diagnostic methods rely heavily on clinical assessment and radiographic imaging, which can be subjective and time-consuming. AI-based approaches offer the potential to enhance the accuracy and efficiency of diagnosis and management.

The paper reviews the current landscape of AI applications in periodontal disease diagnosis and management, including machine learning algorithms, deep learning models, and image analysis techniques. It discusses the advantages and challenges of implementing AI in dentistry, particularly in periodontal care. Additionally, the paper examines the role of AI in personalized treatment planning and patient education, highlighting its potential to improve outcomes and patient satisfaction.

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References

Pillai, Aravind Sasidharan. "Multi-label chest X-ray classification via deep learning." arXiv preprint arXiv:2211.14929 (2022).

Venigandla, Kamala. "Integrating RPA with AI and ML for Enhanced Diagnostic Accuracy in Healthcare." Power System Technology 46.4 (2022).

Nalluri, Mounika, et al. "MACHINE LEARNING AND IMMERSIVE TECHNOLOGIES FOR USER- CENTERED DIGITAL HEALTHCARE INNOVATION." Pakistan Heart Journal 57.1 (2024): 61-68.

Dutta, Ashit Kumar, et al. "Deep learning-based multi-head self-attention model for human epilepsy identification from EEG signal for biomedical traits." Multimedia Tools and Applications (2024): 1-23.

Pargaonkar, Shravan. "Bridging the Gap: Methodological Insights from Cognitive Science for Enhanced Requirement Gathering." Journal of Science & Technology 1.1 (2020): 61-66.

Jahangir, Zeib, et al. "Applications of ML and DL Algorithms in The Prediction, Diagnosis, and Prognosis of Alzheimer’s Disease." American Journal of Biomedical Science & Research 22.6 (2024): 779-786.

Ahmad, Ahsan, et al. "Prediction of Fetal Brain and Heart Abnormalties using Artificial Intelligence Algorithms: A Review." American Journal of Biomedical Science & Research 22.3 (2024): 456-466.

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.

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