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The study aimed to evaluate the effectiveness of machine learning in predicting whether orthodontic patients would require extraction or non-extraction treatment using data from two university datasets. A total of 1135 patients, with 297 from University 1 and 838 from University 2, were included during consecutive enrollment periods. The study identified 20 inputs including 9 clinical features and 11 cephalometric measurements based on previous research. Random forest (RF) models were used to make predictions for both institutions. The performance of each model was assessed using sensitivity (SEN), specificity (SPE), accuracy (ACC), and feature ranking. The model trained on the combined data from two universities demonstrated the highest performance, achieving 50% sensitivity, 97% specificity, and 85% accuracy. When cross-predicting, where the University 1 (U1) model was applied to the University 2 (U2) data and vice versa, there was a slight decrease in performance metrics (ranging from 0% to 20%). Maxillary and mandibular crowding were identified as the most significant features influencing extraction decisions in both institutions. This study is among the first to utilize datasets from two United States institutions, marking progress toward developing an artificial intelligence model to support orthodontists in clinical practice.

Objective: To examine the robustness of the published machine learning models in the prediction of extraction vs non-extraction for a diverse US sample population seen by multiple providers., Setting and Sample Population: Diverse group of 838 patients (208 extraction, 630 non-extraction) were consecutively enrolled., Materials and Methods: Two sets of input features (117 and 22) including clinical and cephalometric variables were identified based on previous studies. Random forest (RF) and multilayer perception (MLP) models were trained using these feature sets on the sample population and evaluated using measures including accuracy (ACC) and balanced accuracy (BA). A technique to identify incongruent data was used to explore underlying characteristics of the data set and split all samples into 2 groups (G1 and G2) for further model training., Results: Performance of the models (75%-79% ACC and 72%-76% BA) on the total sample population was lower than in previous research. Models were retrained and evaluated using G1 and G2 separately, and individual group MLP models yielded improved accuracy for G1 (96% ACC and 94% BA) and G2 (88% ACC and 85% BA). RF feature ranking showed differences between top features for G1 (maxillary crowding, mandibular crowding and L1-NB) and G2 (age, mandibular crowding and lower lip to E-plane)., Conclusions: An incongruent data pattern exists in a consecutively enrolled patient population. Future work with incongruent data segregation and advanced artificial intelligence algorithms is needed to improve the generalization ability to make it ready to support clinical decision-making., (© 2021 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Introduction: The aims of this study were to report contemporary orthodontic extraction frequencies at a university center and to investigate what patient-related factors might influence the likelihood of extraction., Methods: The records of 2184 consecutive patients treated at the University of North Carolina from 2000 to 2011 were analyzed. Year-by-year rates for overall orthodontic extractions and for extraction of 4 first premolars were calculated. Logistic regression, adjusting for all recorded patient risk factors for extraction, was used to examine both the changes in extraction frequencies over time and the influence of individual patient factors on the odds of extraction., Results: Small linear decreases in orthodontic extraction frequency overall (OR, 0.91; 95% CI, 0.88-0.95) and in extraction of 4 first premolars (OR, 0.95; 95% CI, 0.90-0.99) were seen. The overall extraction rate was 37.4% in 2000, and it fell just below 25% from 2006 onward. Four first premolar extraction rates ranged from 8.9% to 16.5%. Extractions were significantly more likely as crowding and overjet increased (OR, 1.2; 95% CI, 1.14-1.25; OR, 1.1; 95% CI. 1.07-1.19), as overbite decreased (OR, 0.8; 95% CI, 0.77-0.89), with Class II dental or skeletal relationships (OR, 1.5; 95% CI, 1.12- 2.05; OR, 1.4; 95% CI, 1.04-1.85), and for nonwhite patients (OR, 3.0; 95% CI, 2.2-4.06 for other races; OR, 4.1; 95% CI, 3.03-5.66 for African Americans)., Conclusions: Extractions were just as likely to be associated with Class II dental and skeletal problems and with open-bite problems as with crowding alone., (Copyright © 2016 American Association of Orthodontists. Published by Elsevier Inc. All rights reserved.)

Aims: To investigate the antidermatophytic action of a complementary set imidazolium salts (IMS), determining structure-activity relationships and characterizing the IMS toxicological profiles., Methods and Results: The susceptibility evaluation of 45 dermatophytic clinical isolates, treated in vitro with eleven different IMS (ionic compounds) and commercial antifungals (nonionic compounds), was performed by broth microdilution, following the standard norm of CLSI M38-A2. All dermatophytes were inhibited by IMS, where the lowest minimum inhibitory concentration (MIC) values were observed for salts with n-hexadecyl segment in the cation side chain, containing either the chloride or methanesulfonate anion. 1-n-Hexadecyl-3-methylimidazolium chloride (C16 MImCl) and 1-n-hexadecyl-3-methylimidazolium methanesulfonate (C16 MImMeS) acted as fungicides, even in extremely low concentrations, wherein C16 MImMeS exerted this effect on 100% of the tested dermatophytes. Some of these IMS provoked evident alterations on the fungi cell morphology, causing a total cell damage of ≥ 70%. Importantly, none of the screened IMS were cytotoxic, mutagenic or genotoxic to human leucocyte cells., Conclusions: This report demonstrates for the first time the strong antifungal potential of IMS against multidrug-resistant dermatophytes, without presenting toxicity to human leucocyte cells at MIC., Significance and Impact of the Study: The expressive antifungal activity of IMS, combined with the in vitro nontoxicity, makes them promising compounds for the safe and effective treatment of dermatophytoses, mainly when this skin mycosis is unresponsive to conventional drugs., (© 2015 The Society for Applied Microbiology.)