ABSTRACT
Periodontal diseases are prevalent worldwide and pose a significant public health burden. With the advent of artificial intelligence (AI), there has been growing interest in leveraging AI technologies to improve diagnosis, treatment planning, and management of periodontal conditions. This review aims to provide a comprehensive overview of the applications of AI in periodontics, including its potential benefits, challenges, and future directions. Fifteen relevant studies were analyzed to explore the role of AI in periodontal disease detection, risk assessment, treatment planning, and patient management. The findings highlight the promising role of AI in enhancing the accuracy, efficiency, and personalized care delivery in periodontics.
KEYWORDS: Artificial intelligence, periodontics, recent advances
INTRODUCTION
Periodontal diseases, including gingivitis and periodontitis, are among the most prevalent chronic conditions globally, affecting millions of people worldwide. Despite advances in periodontal treatment, the accurate diagnosis, risk assessment, and management of these diseases remain challenging. Artificial intelligence (AI) offers new opportunities to address these challenges by leveraging machine learning algorithms and big data analytics to improve decision-making processes in periodontics.
Introduction to AI in periodontics
Definition: AI refers to the simulation of human intelligence in machines that are programmed to think and learn like humans. In periodontics, AI applications are designed to assist dental professionals in treatment planning, diagnosis, and prediction of treatment outcomes.
Key Applications: AI in periodontics involves the use of algorithms, machine learning, and data analytics to analyze patient data, radiographs, and clinical parameters for more accurate treatment planning.
AI in periodontal disease detection
Several studies have explored the use of AI algorithms for the automated detection of periodontal diseases from clinical images, such as intraoral photographs and radiographs. For example, Li et al. (2019)[1] developed a deep learning model that achieved high accuracy in classifying periodontal bone loss from panoramic radiographs. Similarly, Arora et al. (2020)[2] demonstrated the efficacy of convolutional neural networks (CNNs) in detecting gingival bleeding from intraoral images. These findings suggest that AI-based systems have the potential to assist clinicians in early detection and diagnosis of periodontal diseases.
AI for periodontal risk assessment
Assessing the risk of periodontal disease progression is crucial for developing effective treatment plans and preventive strategies. AI tools can analyze multiple risk factors, including demographic data, medical history, and clinical parameters, to predict the likelihood of future periodontal complications.
AI-driven treatment planning
Personalized treatment planning is essential for optimizing therapeutic outcomes and patient satisfaction in periodontics. AI algorithms can analyze patient-specific data, such as periodontal measurements, genetic markers, and treatment preferences, to generate customized treatment plans tailored to individual needs.[3] Allahverdi and Akcan (2019)[4] proposed a decision support system based on fuzzy logic and genetic algorithms to optimize treatment planning in periodontal therapy. By incorporating patient preferences and clinical guidelines, AI-driven treatment planning tools can enhance treatment adherence and improve long-term oral health outcomes.
AI in patient management and follow-up
Monitoring patients’ oral health status and treatment responses over time is critical for disease management and prevention of recurrence. AI technologies, such as natural language processing (NLP) and predictive analytics, can analyze electronic health records (EHRs) and patient-reported outcomes to track disease progression and treatment efficacy. For example, Krois J et al. (2019)[5] developed an AI-based platform for real-time monitoring of periodontal parameters and patient-reported symptoms. By integrating AI-driven monitoring systems into routine clinical practice, clinicians can identify early signs of disease recurrence and intervene promptly to prevent complications.
Natural language processing (NLP) for clinical notes
NLP algorithms can analyze unstructured clinical notes and reports, extracting relevant information about a patient’s periodontal health.
This facilitates efficient retrieval of patient history and aids in treatment planning by providing a comprehensive overview of the patient’s periodontal status (Khanagar SB).[6]
Smart diagnostic tools
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AI-powered diagnostic tools, such as mobile applications and web platforms, can assist both patients and healthcare professionals in assessing and monitoring periodontal health.
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These tools may include self-assessment features, real-time monitoring, and personalized recommendations for oral care based on individual risk profiles.[7]
Integration with electronic health records (EHR)
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AI can integrate with EHR systems to provide seamless access to patient information and facilitate interdisciplinary communication among healthcare providers.
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This ensures that periodontal health data is readily available for comprehensive patient care and enables a more holistic approach to oral health management (Kruse CS, 2017).[8]
Challenges and limitations
Despite the potential benefits of AI in periodontics, several challenges and limitations need to be addressed to facilitate its widespread adoption in clinical settings. These include the need for large-scale, high-quality datasets for training AI algorithms, concerns regarding data privacy and security, and the lack of regulatory guidelines for AI-based diagnostic and treatment systems. Additionally, there may be barriers related to cost, infrastructure, and clinician acceptance of AI technologies in periodontal practice.
Future directions
Future research in AI and periodontics should focus on addressing the aforementioned challenges and exploring innovative applications of AI in oral healthcare delivery. This includes the development of interoperable AI platforms that can integrate seamlessly with existing clinical workflows, the validation of AI algorithms in diverse patient populations, and the implementation of robust quality assurance mechanisms to ensure the accuracy and reliability of AI-based diagnostic and treatment systems. Collaborative efforts between researchers, clinicians, industry partners, and regulatory agencies are essential to realize the full potential of AI in transforming periodontal care.
CONCLUSION
In conclusion, AI holds great promise for revolutionizing the field of periodontics by enhancing disease detection, risk assessment, treatment planning, and patient management. Despite the challenges and limitations, ongoing advancements in AI technologies offer unprecedented opportunities to improve the accuracy, efficiency, and personalized care delivery in periodontal practice. By embracing AI-driven innovations and fostering interdisciplinary collaboration, the dental community can usher in a new era of precision periodontal care that benefits patients, clinicians, and healthcare systems alike.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
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