ArticleBioengineering (Basel, Switzerland)2025
An Artificial Intelligence-Based Fuzzy Logic System for Periodontitis Risk Assessment in Patients with Type 2 Diabetes Mellitus.
Article in Bioengineering (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- An interdisciplinary framework for artificial intelligence, precision medicine, and ethical governance in periodontal care: a systematic review.BMC oral health · 2026Pooled it
- Fuzzy Logic and Fuzzy-Based Artificial Intelligence in Dentistry: A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- Review
- [Applications and perspectives of artificial intelligence in periodontology].Hua xi kou qiang yi xue za zhi = Huaxi kouqiang yixue zazhi = West China journal of stomatology · 2025Review
- The future of periodontal medicine.Journal of Indian Society of PeriodontologyArticle
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSince periodontitis prevalence has increased globally and there is a bidirectional relationship between periodontitis and diabetes mellitus (DM), new methods of preventing and screening involving DM biomarkers could impact periodontitis management. We aimed to develop a fuzzy system to estimate the risk of periodontitis in patients with DM.
methodsBody mass index (BMI), glycemia (G), total cholesterol (C), and triglyceride (T) measurements were collected from 87 patients diagnosed with DM. Oral examinations were performed, and the number of the periodontal pockets (nrPPs) was determined. A fuzzy system was developed: BMI and G as inputs resulted in Periodontitis Risk 1 (PRisk1) output; C and T as inputs resulted in Periodontitis Risk 2 (PRisk2) output. From PRisk1 and PRisk2, the cumulative periodontitis risk (PCRisk) was assessed. Linguistic terms and linguistic grades (very small, small, medium, big, and very big) were assigned to the numerical variables by using 25 different membership functions. PCRisk and nrPP values were statistically processed.
resultsIn our developed fuzzy system, BMI, G, C, and T as input data resulted in periodontitis risk estimation. PCRisk was correlated with nrPP: when PCRisk increased by 1.881 units, nrPP increased by 1 unit. The fuzzy logic-based system effectively estimated periodontitis risk in type 2 diabetes patients, showing a significant correlation with the number of periodontal pockets. These findings highlight its potential for early diagnosis and improved interdisciplinary care.
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