Browsing by Subject "Growth and development"

Now showing 1 - 4 of 4
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    Cortical and trabecular bone benefits of mechanical loading are maintained long term in mice independent of ovariectomy.
    (Wiley, 2014) Warden, Stuart J.; Galley, Matthew R.; Hurd, Andrea L.; Richard, Jeffrey S.; George, Lydia A.; Guildenbecher, Elizabeth A.; Barker, Rick G.; Fuchs, Robyn K.; Health Sciences, School of Health and Rehabilitation Sciences
    Skeletal loading enhances cortical and trabecular bone properties. How long these benefits last after loading cessation remains an unresolved, clinically relevant question. This study investigated long-term maintenance of loading-induced cortical and trabecular bone benefits in female C57BL/6 mice and the influence of a surgically induced menopause on the maintenance. Sixteen-week-old animals had their right tibia extrinsically loaded 3 days/week for 4 weeks using the mouse tibial axial compression loading model. Left tibias were not loaded and served as internal controls. Animals were subsequently detrained (restricted to cage activities) for 0, 4, 8, 26, or 52 weeks, with ovariectomy (OVX) or sham-OVX surgery being performed at 0 weeks detraining. Loading increased midshaft tibia cortical bone mass, size, and strength, and proximal tibia bone volume fraction. The cortical bone mass, area, and thickness benefits of loading were lost by 26 weeks of detraining because of heightened medullary expansion. However, loading-induced benefits on bone total area and strength were maintained at each detraining time point. Similarly, the benefits of loading on bone volume fraction persisted at all detraining time points. The long-term benefits of loading on both cortical and trabecular bone were not influenced by a surgically induced menopause because there were no interactions between loading and surgery. However, OVX had independent effects on cortical bone properties at early (4 and 8 weeks) detraining time points and trabecular bone properties at all detraining time points. These cumulative data indicate loading has long-term benefits on cortical bone size and strength (but not mass) and trabecular bone morphology, which are not influenced by a surgically induced menopause. This suggests skeletal loading associated with physical activity may provide long-term benefits by preparing the skeleton to offset both the cortical and trabecular bone changes associated with aging and menopause.
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    Long-Term Predictive Modelling of the Craniofacial Complex Using Machine Learning on 2D Cephalometric Radiographs
    (Elsevier, 2025) Myers, Michael; Brown, Michael D.; Badirli, Sarkhan; Eckert, George J.; Johnson, Diane Helen-Marie; Turkkahraman, Hakan; Orthodontics and Oral Facial Genetics, School of Dentistry
    Objective: This study aimed to predict long-term growth-related changes in skeletal and dental relationships within the craniofacial complex using machine learning (ML) models. Materials and methods: Cephalometric radiographs from 301 subjects, taken at pre-pubertal (T1, age 11) and post-pubertal stages (T2, age 18), were analysed. Three ML models-Lasso regression, Random Forest, and Support Vector Regression (SVR)-were trained on a subset of 240 subjects, while 61 subjects were used for testing. Model performance was evaluated using mean absolute error (MAE), intraclass correlation coefficients (ICCs), and clinical thresholds (2 mm or 2°). Results: MAEs for skeletal measurements ranged from 1.36° (maxilla to cranial base angle) to 4.12 mm (mandibular length), and for dental measurements from 1.26 mm (lower incisor position) to 5.40° (upper incisor inclination). ICCs indicated moderate to excellent agreement between actual and predicted values. The highest prediction accuracy within the 2 mm or 2° clinical thresholds was achieved for maxilla to cranial base angle (80%), lower incisor position (75%), and maxilla to mandible angle (70%). Pre-pubertal measurements and sex consistently emerged as the most important predictive factors. Conclusions: ML models demonstrated the ability to predict post-pubertal values for maxilla to cranial base, mandible to cranial base, maxilla to mandible angles, upper and lower incisor positions, and upper face height with a clinically acceptable margin of 2 mm or 2°. Prediction accuracy was higher for skeletal relationships compared to dental relationships over the 8-year growth period. Pre-pubertal values of the measurements and sex emerged consistently as the most important predictors of the post-pubertal values.
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    Prediction of the Post-Pubertal Mandibular Length and Y Axis of Growth by Using Various Machine Learning Techniques: A Retrospective Longitudinal Study
    (MDPI, 2023-04-26) Wood, Tyler; Anigbo, Justina O.; Eckert, George; Stewart, Kelton T.; Dundar, Mehmet Murat; Turkkahraman, Hakan; Orthodontics and Oral Facial Genetics, School of Dentistry
    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.
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    Short- and Long-Term Prediction of the Post-Pubertal Mandibular Length and Y-Axis in Females Utilizing Machine Learning
    (MDPI, 2023-08-22) Parrish, Matthew; O’Connell, Ella; Eckert, George; Hughes, Jay; Badirli, Sarkhan; Turkkahraman, Hakan; Orthodontics and Oral Facial Genetics, School of Dentistry
    The aim of this study was to create a novel machine learning (ML) algorithm for predicting the post-pubertal mandibular length and Y-axis in females. Cephalometric data from 176 females with Angle Class I occlusion were used to train and test seven ML algorithms. For all ML methods tested, the mean absolute errors (MAEs) for the 2-year prediction ranged from 2.78 to 5.40 mm and 0.88 to 1.48 degrees, respectively. For the 4-year prediction, MAEs of mandibular length and Y-axis ranged from 3.21 to 4.00 mm and 1.19 to 5.12 degrees, respectively. The most predictive factors for post-pubertal mandibular length were mandibular length at previous timepoints, age, sagittal positions of the maxillary and mandibular skeletal bases, mandibular plane angle, and anterior and posterior face heights. The most predictive factors for post-pubertal Y-axis were Y-axis at previous timepoints, mandibular plane angle, and sagittal positions of the maxillary and mandibular skeletal bases. ML methods were identified as capable of predicting mandibular length within 3 mm and Y-axis within 1 degree. Compared to each other, all of the ML algorithms were similarly accurate, with the exception of multilayer perceptron regressor.