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Description

Objectives:

The goal of this systematic review is to examine the impact of using artificial intelligence (AI) to predict a patient’s risk level for periodontal disease by analyzing proven systemic health diseases linked to periodontitis. This is being done by primarily focusing on prevention and early diagnosis using machine learning programs that have been proven effective in other fields of periodontitis research.

Methods:

To conduct this study, a comprehensive literature review of journals published after 2020 was performed through four databases: PubMed, Scopus, Web of Science and Dentistry and Oral Science Source. The search was completed in adherence to PRISMA guidelines, and inclusion criteria comprised of peer-reviewed journals which assessed AI applications in periodontitis and its impact on preventing periodontal disease in patients with linked systemic diseases. Risk of bias assessment is in progress by using Cochrane RoB tools, and data will be synthesized narratively and quantitatively where applicable.

Preliminary Results:

An initial search across four databases yielded over 200 potentially relevant studies. To date, 10 studies have been selected that met both the predefined inclusion and exclusion criteria. Early data analysis shows artificial intelligence as a promising tool for assessing patients’ risk of periodontitis by creating a fuzzy logic system based on significant biomarkers of systemic health diseases.

Conclusion:

Artificial Intelligence is an extremely powerful and rapidly advancing technology that has seen increasing use in healthcare and patient care. This review highlights one important application of AI in assessing a patient’s risk for periodontitis, enabling quicker and more accurate diagnosis and treatment of the disease. Further research is needed to improve the models currently being used for risk assessment and to ensure they are being used in an ethical and beneficial manner.

Disciplines

Computer and Systems Architecture | Periodontics and Periodontology

Keywords

Periodontal Disease, Artificial Intelligence, Risk Assessment, Diabetes Mellitus, Cardiovascular Disease, Decision Trees, Machine Learning, Systematic Review

Document Type

Poster

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AI Applications in Assessing Risk for Periodontal Disease: A Systematic Review

Objectives:

The goal of this systematic review is to examine the impact of using artificial intelligence (AI) to predict a patient’s risk level for periodontal disease by analyzing proven systemic health diseases linked to periodontitis. This is being done by primarily focusing on prevention and early diagnosis using machine learning programs that have been proven effective in other fields of periodontitis research.

Methods:

To conduct this study, a comprehensive literature review of journals published after 2020 was performed through four databases: PubMed, Scopus, Web of Science and Dentistry and Oral Science Source. The search was completed in adherence to PRISMA guidelines, and inclusion criteria comprised of peer-reviewed journals which assessed AI applications in periodontitis and its impact on preventing periodontal disease in patients with linked systemic diseases. Risk of bias assessment is in progress by using Cochrane RoB tools, and data will be synthesized narratively and quantitatively where applicable.

Preliminary Results:

An initial search across four databases yielded over 200 potentially relevant studies. To date, 10 studies have been selected that met both the predefined inclusion and exclusion criteria. Early data analysis shows artificial intelligence as a promising tool for assessing patients’ risk of periodontitis by creating a fuzzy logic system based on significant biomarkers of systemic health diseases.

Conclusion:

Artificial Intelligence is an extremely powerful and rapidly advancing technology that has seen increasing use in healthcare and patient care. This review highlights one important application of AI in assessing a patient’s risk for periodontitis, enabling quicker and more accurate diagnosis and treatment of the disease. Further research is needed to improve the models currently being used for risk assessment and to ensure they are being used in an ethical and beneficial manner.