Autor(es)

Castro dos Santos, Nidia ; Mangussi, Arthur ; Ribeiro, Tiago ; Silva, Rafael Nascimento de Brito ; Santamaria, Mauro Pedrine ; Feres, Magda ; Van Dyke, Thomas ; Lorena, Ana Carolina

• Tipo de documento: Artigo Científico
• Fonte: Journal of Applied Oral Science; v. 33, p. 11-pg., 2025-01-01.

Resumo

Objective: To evaluate factors influencing the response to periodontal therapy in patients with periodontitis and type 2 diabetes mellitus (DM) using machine learning (ML) techniques, considering periodontal parameters, metabolic status, and demographic characteristics. Methodology: We applied machine learning techniques to perform a post hoc analysis of data collected at baseline and a 6-month follow-up from a randomized clinical trial (RCT). A leave-one-out cross-validation strategy was used for model training and evaluation. We tested seven different algorithms: K-Nearest Neighbors, Decision Tree, Support Vector Machine, Random Forest, Extreme Gradient Boosting, and Logistic Regression. Model performance was assessed using accuracy, specificity, recall, and the area under the Receiver Operating Characteristic (ROC) curve (AUC). Results: a total of 75 patients were included. Using the first exploratory data analysis, we observed three clusters of patients who achieved the clinical endpoint related to HbA1c values. HbA1c <= 9.4% was correlated with lower PD (r=0.2), CAL (r=0.1), and the number of sites with PD >= 5 mm (r=0.1) at baseline. This study induced AI classification models with different biases. The model with the best fit was Random Forest with a 0.83 AUC. The Random Forest AI model has an accuracy of 80%, a sensitivity of 64%, and a specificity of 87%. Our findings demonstrate that PD and CAL were the most important variables contributing to the predictive performance of the Random Forest model. Conclusion: The combination of nine baseline periodontal, metabolic, and demographic factors from patients with periodontitis and type 2 DM may indicate the response to periodontal therapy. Lower levels of full mouth PD, CAL, plaque index, and HbA1c at baseline increased the chances of achieving the endpoint for treatment at 6-month follow-up. However, all nine features included in the model should be considered for treatment outcome predictability. Clinicians may consider the characterization of periodontal therapy response to implement personalized care and treatment decision-making. Clinical trial registration ID: NCT02800252 (AU)

• Processo FAPESP: 22/10553-6 - Uma abordagem unificada para lidar com dados ausentes e de ruído
• Beneficiário: Arthur Dantas Mangussi
• Modalidade de apoio: Bolsas no Brasil - Mestrado
• Processo FAPESP: 21/14439-0 - Ômega-3 e aspirina associados a antibióticos sistêmicos no tratamento periodontal de pacientes com Diabetes Mellitus tipo 2: efeitos locais e sistêmicos, ações imunológicas e impacto na microbiota oral e intestinal
• Beneficiário: Nidia Cristina Castro dos Santos
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 16/02234-7 - ESTUDO DA SUPLEMENTAÇÃO DIÁRIA DE ÁCIDOS GRAXOS POLI-INSATURADOS É-3 E ASPIRINA COMO ADJUVANTES AO DEBRIDAMENTO PERIODONTAL ULTRASSÔNICO PARA O TRATAMENTO DA PERIODONTITE CRÔNICA EM DIABÉTICOS TIPO 2: estudo clínico controlado randomizado de superioridade
• Beneficiário: Nidia Cristina Castro dos Santos
• Modalidade de apoio: Bolsas no Brasil - Doutorado
• Processo FAPESP: 21/06870-3 - Além da seleção de algoritmos: meta-aprendizado para análise e entendimento de dados e algoritmos
• Beneficiário: Ana Carolina Lorena
• Modalidade de apoio: Auxílio à Pesquisa - Jovens Pesquisadores - Fase 2