Automated Machine Learning to Develop Predictive Models of Metabolic Syndrome in Patients with Periodontal Disease.

Metabolic syndrome is experiencing a concerning and escalating rise in prevalence today. The link between metabolic syndrome and periodontal disease is a highly relevant area of research. Some studies have suggested a bidirectional relationship between metabolic syndrome and periodontal disease, where one condition may exacerbate the other. Furthermore, the existence of periodontal disease among these individuals significantly impacts overall health management. This research focuses on the relationship between periodontal disease and metabolic syndrome, while also incorporating data on general health status and overall well-being. We aimed to develop advanced machine learning models that efficiently identify key predictors of metabolic syndrome, a significant emphasis being placed on thoroughly explaining the predictions generated by the models. We studied a group of 296 patients, hospitalized in SCJU Sibiu, aged between 45-79 years, of which 57% had metabolic syndrome. The patients underwent dental consultations and subsequently responded to a dedicated questionnaire, along with a standard EuroQol 5-Dimensions 5-Levels (EQ-5D-5L) questionnaire. The following data were recorded: DMFT (Decayed, Missing due to caries, and Filled Teeth), CPI (Community Periodontal Index), periodontal pockets depth, loss of epithelial insertion, bleeding after probing, frequency of tooth brushing, regular dental control, cardiovascular risk, carotid atherosclerosis, and EQ-5D-5L score. We used Automated Machine Learning (AutoML) frameworks to build predictive models in order to determine which of these risk factors exhibits the most robust association with metabolic syndrome. To gain confidence in the results provided by the machine learning models provided by the AutoML pipelines, we used SHapley Additive exPlanations (SHAP) values for the interpretability of these models, from a global and local perspective. The obtained results confirm that the severity of periodontal disease, high cardiovascular risk, and low EQ-5D-5L score have the greatest impact in the occurrence of metabolic syndrome.

Total Psoas Area and Psoas Density Assessment in COVID-19 Patients Using CT Imaging - Could Muscle Mass Alteration During Intensive Care Hospitalization be Determined?

Background: Since its debut, as reported by the first published studies, COVID-19 has been linked to life-threatening conditions that needed vital assistance and admission to the intensive care unit. Skeletal muscle is a core element in an organism's health due to its ability to keep energy balance and homeostasis. Many patients with prolonged hospitalization are characterized by a greater probability prone to critical illness myopathy or intensive care unit-acquired weakness. Objective: The main aim of this study was to assess the skeletal muscle in a COVID-19 cohort of critically ill patients by measuring the psoas area and density. Material and methods: This is a retrospective study that included critically ill adult patients, COVID-19 positive, mechanically ventilated, with an ICU stay of over 24 hours, and who had 2 CT scans eligible for psoas muscle evaluation. In these patients, correlations between different severity scores and psoas CT scans were sought, along with correlations with the outcome of the patients. Results: Twenty-two patients met the inclusion criteria. No statistically significant differences were noticed regarding the psoas analysis by two blinded radiologists. Significant correlations were found between LOS in the hospital and in ICU with psoas area and Hounsfield Units for the first CT scan performed. With reference to AUC-ROC and outcome, it is underlined that AUC-ROC is close to 0.5 values, for both the psoas area and HU, indicating that the model had no class separation capacity. Conclusion: The study suggested that over a short period, the psoas muscle area, and the psoas HU decline, for both the left and the right sight, in adult COVID-19 patients in ICU conditions, yet not statistically significant. Although more than two-thirds of the patients had a negative outcome, it was not possible to demonstrate an association between the SARS-COV2 infection and psoas muscle impairment. These findings highlight the need for further larger investigations.

Prediction of the mesiodistal size of unerupted canines and premolars for a group of Romanian children: a comparative study.

OBJECTIVES: The aim of the present study was to develop an optimization method of multiple linear regression equation (MLRE), using a genetic algorithm to determine a set of coefficients that minimize the prediction error for the sum of permanent premolars and canine dimensions in a group of young people from a central area of Romania represented by a city called Sibiu. MATERIAL AND METHODS: To test the proposed method, we used a multiple linear regression equation derived from the estimation method proposed by Mojers, to which we adjusted regression coefficients using the Breeder genetic algorithm. A total of 92 children were selected with complete permanent teeth with no clinically visible dental caries, proximal restorations or orthodontic treatment. A hard dental stone was made for each of these models, which was then measured with a digital caliper. The Dahlberg analyses of variance had been performed to determine the error of method, then the Correlation t Test was applied, and finally the MLRE equations were obtained using the version 16 for Windows of the SPSS program. RESULTS: The correlation coefficient of MLRE was between 51-67% and the significance level was set at α=0.05. Comparing predictions provided by the new and respectively old method, we can conclude that the Breeder genetic algorithm is capable of providing the best values for parameters of multiple linear regression equations, and thus our equations are optimized for the best performance. CONCLUSION: The prediction error rates of the optimized equations using the Breeder genetic algorithm are smaller than those provided by the multiple linear regression equations proposed in the recent study.

Prediction of the mesiodistal size of unerupted canines and premolars for a group of Romanian children: a comparative study

Objectives The aim of the present study was to develop an optimization method of multiple linear regression equation (MLRE), using a genetic algorithm to determine a set of coefficients that minimize the prediction error for the sum of permanent premolars and canine dimensions in a group of young people from a central area of Romania represented by a city called Sibiu. Material and Methods To test the proposed method, we used a multiple linear regression equation derived from the estimation method proposed by Mojers, to which we adjusted regression coefficients using the Breeder genetic algorithm. A total of 92 children were selected with complete permanent teeth with no clinically visible dental caries, proximal restorations or orthodontic treatment. A hard dental stone was made for each of these models, which was then measured with a digital calliper. The Dahlberg analyses of variance had been performed to determine the error of method, then the Correlation t Test was applied, and finally the MLRE equations were obtained using the version 16 for Windows of the SPSS program. Results The correlation coefficient of MLRE was between 51-67% and the significance level was set at α=0.05. Comparing predictions provided by the new and respectively old method, we can conclude that the Breeder genetic algorithm is capable of providing the best values for parameters of multiple linear regression equations, and thus our equations are optimized for the best performance. Conclusion The prediction error rates of the optimized equations using the Breeder genetic algorithm are smaller than those provided by the multiple linear regression equations proposed in the recent study. .