Short- and Long-Term Prediction of the Post-Pubertal Mandibular Length and Y-Axis in Females Utilizing Machine Learning
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ABSTRACT:
SUBMITTER: Parrish M
PROVIDER: S-EPMC10486405 | biostudies-literature | 2023 Aug
REPOSITORIES: biostudies-literature
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Project description:The aim was to predict the post-pubertal mandibular length and Y axis of growth in males by using various machine learning (ML) techniques. Cephalometric data obtained from 163 males with Class I Angle malocclusion, were used to train various ML algorithms. Analysis of variances (ANOVA) was used to compare the differences between predicted and actual measurements among methods and between time points. All the algorithms revealed an accuracy range from 95.80% to 97.64% while predicting post-pubertal mandibular length. When predicting the Y axis of growth, accuracies ranged from 96.60% to 98.34%. There was no significant interaction between methods and time points used for predicting the mandibular length (p = 0.235) and Y axis of growth (p = 0.549). All tested ML algorithms accurately predicted the post-pubertal mandibular length and Y axis of growth. The best predictors for the mandibular length were mandibular and maxillary lengths, and lower face height, while they were Y axis of growth, lower face height, and mandibular plane angle for the post-pubertal Y axis of growth. No significant difference was found among the accuracies of the techniques, except the least squares method had a significantly larger error than all others in predicting the Y axis of growth.
| S-EPMC10178146 | biostudies-literature
Project description:The goal of this study was to create a novel machine learning (ML) model that can predict the magnitude and direction of pubertal mandibular growth in males with Class II malocclusion. Lateral cephalometric radiographs of 123 males at three time points (T1: 12; T2: 14; T3: 16 years old) were collected from an online database of longitudinal growth studies. Each radiograph was traced, and seven different ML models were trained using 38 data points obtained from 92 subjects. Thirty-one subjects were used as the test group to predict the post-pubertal mandibular length and y-axis, using input data from T1 and T2 combined (2 year prediction), and T1 alone (4 year prediction). Mean absolute errors (MAEs) were used to evaluate the accuracy of each model. For all ML methods tested using the 2 year prediction, the MAEs for post-pubertal mandibular length ranged from 2.11-6.07 mm to 0.85-2.74° for the y-axis. For all ML methods tested with 4 year prediction, the MAEs for post-pubertal mandibular length ranged from 2.32-5.28 mm to 1.25-1.72° for the y-axis. Besides its initial length, the most predictive factors for mandibular length were found to be chronological age, upper and lower face heights, upper and lower incisor positions, and inclinations. For the y-axis, the most predictive factors were found to be y-axis at earlier time points, SN-MP, SN-Pog, SNB, and SNA. Although the potential of ML techniques to accurately forecast future mandibular growth in Class II cases is promising, a requirement for more substantial sample sizes exists to further enhance the precision of these predictions.
| S-EPMC10453460 | biostudies-literature
Project description:Autism spectrum disorder (ASD) is characterized by impaired social communication and poor adaptation to change; thus, the onset of puberty may be a pivotal transition. This cross-sectional study measured pubertal timing to examine hypothesized differences for sex (female vs. male) and group (ASD vs. typical development [TD]). Participants included 239 children (137 ASD, 102 TD) between 10 and 13 years. The ASD group included 35 females and 102 males; the TDs included 44 females and 58 males. Pubertal onset measured by genital or pubic stage was investigated with linear regression using main effects of sex and age-by-sex interactions in TD and ASD groups and main effects of diagnosis and diagnosis-by-age interactions in males and females, controlling for body mass index, socioeconomic status, and race. In TD, examination of main effects for genital (penis/breast) stage showed no difference for male and female children (t = 1.33, P = 0.187, rdf = 92); however, there were significant differences in ASD (t = 2.70, P = 0.008, rdf = 121). For diagnosis modeled separately by sex, there was significantly earlier pubertal development in females with ASD (t = 1.97, P = 0.053, rdf = 70, but not males (t = 1.329, P = 0.186, rdf = 143). In addition, analysis of menses revealed females with ASD had significantly earlier onset than TD (t = -2.56, P = 0.018, rdf = 21). Examination of pubic stage revealed expected sex differences for TD (t = 2,674, P = 0.009, rdf = 91) and ASD (t = 3.482, P = 0.001, rdf = 121). Females with ASD evidence advanced pubertal onset relative to ASD males and TD females. Findings underscore the need for enhanced understanding of pubertal development in ASD, as differences may have significant psychological, social, physiological, and developmental consequences. LAY SUMMARY: Children with autism spectrum disorder (ASD) have difficulty with social communication and respond poorly to change, which may include the onset and course of puberty. The study measured the timing of puberty in 239 children (137 ASD and 102 typical development [TD]) between 10 and 13 years based on pubertal stage of genital (breast/penis) and pubic hair development. Females with ASD evidence advanced pubertal onset relative to ASD males and TD females. Findings underscore the need for an enhanced understanding of pubertal development in ASD.
| S-EPMC8021204 | biostudies-literature
Project description:This work incorporates machine learning (ML) techniques, such as multivariate regression, the multi-layer perceptron, and random forest to predict the slip length at the nanoscale. Data points are collected both from our simulation data and data from the literature, and comprise Molecular Dynamics simulations of simple monoatomic, polar, and molecular liquids. Training and test points cover a wide range of input parameters which have been found to affect the slip length value, concerning dynamical and geometrical characteristics of the model, along with simulation parameters that constitute the simulation conditions. The aim of this work is to suggest an accurate and efficient procedure capable of reproducing physical properties, such as the slip length, acting parallel to simulation methods. Non-linear models, based on neural networks and decision trees, have been found to achieve better performance compared to linear regression methods. After the model is trained on representative simulation data, it is capable of accurately predicting the slip length values in regions between or in close proximity to the input data range, at the nanoscale. Results also reveal that, as channel dimensions increase, the slip length turns into a size-independent material property, affected mainly by wall roughness and wettability.
| S-EPMC8206074 | biostudies-literature
Project description:BackgroundRett syndrome is a unique neurodevelopmental disorder, affecting approximately one in 10,000 live female births, most experiencing reduced growth. We characterized pubertal trajectories in females with Rett syndrome. We hypothesized that pubertal trajectory deviates from the general female population with early pubertal onset and delayed menarche.MethodsParticipants were individuals enrolled in the Rett Syndrome Natural History Study with clinical diagnosis of Rett syndrome or mutations in MECP2. Intervals to thelarche, adrenarche, and menarche were assessed by survival analysis; body mass index, mutation type, clinical severity, and pubertal milestone relationships were assessed by log-likelihood test; pathway synchrony (relationship between thelarche, adrenarche, and menarche) was assessed by chi-squared analysis.ResultsCompared with the general female population, more than 25% initiated puberty early, yet entered menarche later (median age 13.0 years). A total of 19% experienced delayed menarche. Median length of puberty, from thelarche to menarche, was 3.9 years. Higher body mass index correlated with earlier thelarche and adrenarche but not menarche; milder mutations correlated with earlier menarche; and milder clinical presentation correlated with earlier thelarche and menarche. Fifty-two percent entered puberty in synchrony, but different from the general population, 15% led with thelarche and 32% with adrenarche.ConclusionsPubertal trajectories in Rett syndrome differ from general population, entering puberty early and reaching menarche later. Body mass index affects pubertal timing, but the relationship between specific mutations, clinical presentation, and underlying neuroendocrine pathology is less clear.
| S-EPMC4254166 | biostudies-literature
Project description:The aims of this study were to assess bone mass in children and adolescent soccer players and to evaluate the influence of both gender and pubertal status on bone mass. A total of 110 soccer players (75 males / 35 females; 12.73 ± 0.65 / 12.76 ± 0.59 years) participated in this cross-sectional study. They were divided into two groups according to their pubertal status. Bone and lean masses were measured with Dual-energy X-ray Absorptiometry. An independent t-test and an adjusted by subtotal lean and training experience multivariate analysis of covariance were used to analyse the differences in bone mass values between genders and maturity status. Female soccer players presented higher bone mass values than their male counterparts in most of the measured weight-bearing sites. Moreover, when stratifying by pubertal status, peripubertal and postpubertal females had higher subtotal body and lumbar spine bone mass than males. Comparing between pubertal status groups before adjustment, both male and female postpubertal players showed higher bone mass than their pubertal counterparts. After adjusting, these differences disappeared and, in fact results were inverted as bone mass at the femoral neck was higher in both male and female peripubertal soccer players than in postpubertal players. Bone mass seems to be more intensely stimulated by playing soccer in female than male players, particularly in the lumbar spine. The results of peripubertal players showing higher bone mass at the femoral neck after adjusting suggest that playing soccer during the peripubertal stage could be an effective activity to achieve optimal bone mass values.
| S-EPMC6714372 | biostudies-literature
Project description:Purpose: To develop and validate machine learning models for predicting the length of stay (LOS) in the Pediatric Intensive Care Unit (PICU) using data from the Virtual Pediatric Systems (VPS) database. Methods: A retrospective study was conducted utilizing machine learning (ML) algorithms to analyze and predict PICU LOS based on historical patient data from the VPS database. The study included data from over 100 North American PICUs spanning the years 2015-2020. After excluding entries with missing variables and those indicating recovery from cardiac surgery, the dataset comprised 123,354 patient encounters. Various ML models, including Support Vector Machine, Stochastic Gradient Descent Classifier, K-Nearest Neighbors, Decision Tree, Gradient Boosting, CatBoost, and Recurrent Neural Networks (RNNs), were evaluated for their accuracy in predicting PICU LOS at thresholds of 24 h, 36 h, 48 h, 72 h, 5 days, and 7 days. Results: Gradient Boosting, CatBoost, and RNN models demonstrated the highest accuracy, particularly at the 36 h and 48 h thresholds, with accuracy rates between 70 and 73%. These results far outperform traditional statistical and existing prediction methods that report accuracy of only around 50%, which is effectively unusable in the practical setting. These models also exhibited balanced performance between sensitivity (up to 74%) and specificity (up to 82%) at these thresholds. Conclusions: ML models, particularly Gradient Boosting, CatBoost, and RNNs, show moderate effectiveness in predicting PICU LOS with accuracy slightly over 70%, outperforming previously reported human predictions. This suggests potential utility in enhancing resource and staffing management in PICUs. However, further improvements through training on specialized databases can potentially achieve better accuracy and clinical applicability.
| S-EPMC11504172 | biostudies-literature
Project description:BackgroundAcute kidney injury (AKI) is a common complication associated with significant morbidity and mortality in high-energy trauma patients. Given the poor efficacy of interventions after AKI development, it is important to predict AKI before its diagnosis. Therefore, this study aimed to develop models using machine learning algorithms to predict the risk of AKI in patients with femoral neck fractures.MethodsWe developed machine-learning models using the Medical Information Mart from Intensive Care (MIMIC)-IV database. AKI was predicted using 10 predictive models in three-time windows, 24, 48, and 72 h. Three optimal models were selected according to the accuracy and area under the receiver operating characteristic curve (AUROC), and the hyperparameters were adjusted using a random search algorithm. The Shapley additive explanation (SHAP) analysis was used to determine the impact and importance of each feature on the prediction. Compact models were developed using important features chosen based on their SHAP values and clinical availability. Finally, we evaluated the models using metrics such as accuracy, precision, AUROC, recall, F1 scores, and kappa values on the test set after hyperparameter tuning.ResultsA total of 1,596 patients in MIMIC-IV were included in the final cohort, and 402 (25%) patients developed AKI after surgery. The light gradient boosting machine (LightGBM) model showed the best overall performance for predicting AKI before 24, 48, and 72 h. AUROCs were 0.929, 0.862, and 0.904. The SHAP value was used to interpret the prediction models. Renal function markers and perioperative blood transfusions are the most critical features for predicting AKI. In compact models, LightGBM still performs the best. AUROCs were 0.930, 0.859, and 0.901.ConclusionsIn our analysis, we discovered that LightGBM had the best metrics among all algorithms used. Our study identified the LightGBM as a solid first-choice algorithm for early AKI prediction in patients after femoral neck fracture surgery.
| S-EPMC9360500 | biostudies-literature
Project description:BackgroundWhile prior research has shown differences in the risk of malaria infection and sickness between males and females, little is known about sex differences in vaccine-induced immunity to malaria. Identifying such differences could elucidate important aspects of malaria biology and facilitate development of improved approaches to malaria vaccination.MethodsUsing a standardized enzyme-linked immunosorbent assay, IgG antibodies to the major surface protein on Plasmodium falciparum (Pf) sporozoites (SPZ), the Pf circumsporozoite protein (PfCSP), were measured before and two weeks after administration of a PfSPZ-based malaria vaccine (PfSPZ Vaccine) to 5-month to 61-year-olds in 11 clinical trials in Germany, the US and five countries in Africa, to determine if there were differences in vaccine elicited antibody response between males and females and if these differences were associated with differential protection against naturally transmitted Pf malaria (Africa) or controlled human malaria infection (Germany, the US and Africa).ResultsFemales ≥ 11 years of age made significantly higher levels of antibodies to PfCSP than did males in most trials, while there was no indication of such differences in infants or children. Although adult females had higher levels of antibodies, there was no evidence of improved protection compared to males. In 2 of the 7 trials with sufficient data, protected males had significantly higher levels of antibodies than unprotected males, and in 3 other trials protected females had higher levels of antibodies than did unprotected females.ConclusionImmunization with PfSPZ Vaccine induced higher levels of antibodies in post-pubertal females but showed equivalent protection in males and females. We conclude that the increased antibody levels in post-pubertal females did not contribute substantially to improved protection. We hypothesize that while antibodies to PfCSP (and PfSPZ) may potentially contribute directly to protection, they primarily correlate with other, potentially protective immune mechanisms, such as antibody dependent and antibody independent cellular responses in the liver.
| S-EPMC9641621 | biostudies-literature
Project description:Gene expression profiles were generated from 199 primary breast cancer patients. Samples 1-176 were used in another study, GEO Series GSE22820, and form the training data set in this study. Sample numbers 200-222 form a validation set. This data is used to model a machine learning classifier for Estrogen Receptor Status. RNA was isolated from 199 primary breast cancer patients. A machine learning classifier was built to predict ER status using only three gene features.
2013-01-01 | E-GEOD-29210 | biostudies-arrayexpress