According to a new study presented at the 52nd Annual Meeting & Exhibition of the AADOCR, held in conjunction with the 47th Annual Meeting of the CADR, the researchers aim to develop a machine learning model for predicting cardiovascular disease using indicators of oral infections. The researchers believe that by using Machine learning, the relationship between periodontal disease and cardiovascular disease can be identified. The new machine learning model will help develop new prevention strategies and treatment options for cardiovascular disease. The research is currently ongoing, and the findings are yet to be published.
The study by Dylan Joseph Baxter group from the University of Pittsburgh investigated the link between markers of oral infections and self-reported cardiovascular disease in 5,188 individuals enrolled in the Dental Registry and DNA Repository project at the University of Pittsburgh School of Dental Medicine. The study investigated several indicators of cardiovascular disease, such as heart surgery, heart valve, heart murmur, irregular heartbeat, and congenital heart disease. The analysis involved comparing decayed, missing, or filled teeth and surfaces (DMFT and DMFS) data from 5,010 participants to the periodontal screening and recording data (PSR) from 740 participants.
The results suggest a notable association between DMFT and DMFS with cardiovascular disease, which is not influenced by gender or tobacco use. The covariance analysis between DMFS and cardiovascular disease remained substantial (p-value = 0.0027) even after accounting for the participant’s age. The machine learning model accurately predicted whether a person had heart disease with 84.3% accuracy using DMFS scores in the registry.
The study underlined the potential for machine learning techniques to enhance heart disease prediction using indicators of oral infections and confirmed the link between dental caries and heart disease. The management of dental caries will be combined with an assessment of whether artificial intelligence techniques can help forecast improvements in cardiovascular disease markers.
Conclusion
The development of a machine learning model to predict cardiovascular disease using indicators of oral infections has the potential to revolutionize preventive medicine. The researchers found that there is a significant relationship between oral health and cardiovascular disease. Thus, oral health may be used as an indicator of heart disease. However, more research is required to validate and improve the model before it can be used in clinical settings. This study emphasizes the potential for novel applications of machine learning in the healthcare and highlights the significance of maintaining good oral hygiene for general health and well-being.
Story Source: The study was presented as part of the Interactive Talk presentation, “Machine Learning Model for Cardiovascular Disease Prediction Using Indicators of Oral Infections,” held on Wednesday, March 15, 2023, at 2:20 p.m. Pacific Daylight Time (UTC-07:00) during the “Clinical and Translational Science Network III: Advances in Oral Disease Mechanisms and the Connection with Systemic Condition” session from 1:30 p.m. – 3 p.m.
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Dr. Tamanna Anwar is a Scientist and Co-founder of the Centre of Bioinformatics Research and Technology (CBIRT). She is a passionate bioinformatics scientist and a visionary entrepreneur. Dr. Tamanna has worked as a Young Scientist at Jawaharlal Nehru University, New Delhi. She has also worked as a Postdoctoral Fellow at the University of Saskatchewan, Canada. She has several scientific research publications in high-impact research journals. Her latest endeavor is the development of a platform that acts as a one-stop solution for all bioinformatics related information as well as developing a bioinformatics news portal to report cutting-edge bioinformatics breakthroughs.
