View clinical trials related to Skeletal Dysplasia.
Filter by:The study titled "Reliability Of Artificial Intelligence for Treatment Decision Recommendation of Adult Skeletal Class III Patients" aims to assess the accuracy and dependability of artificial intelligence (AI) in providing treatment decision recommendations for adult patients with skeletal Class III malocclusion. Skeletal Class III malocclusion is characterized by an underdeveloped upper jaw or an overdeveloped lower jaw, leading to facial and dental irregularities. The study focuses on evaluating whether AI-based recommendations can reliably guide orthodontic treatment planning for this specific patient group. This diagnostic test accuracy study involves collecting a diverse dataset of adult patients diagnosed with skeletal Class III malocclusion. AI algorithms will be trained on this dataset using various clinical and radiographic parameters to learn patterns and make treatment recommendations. The study will then compare the AI-generated treatment recommendations to those provided by experienced orthodontists. Key aspects of the study include: AI Reliability: The primary objective is to assess how consistently and accurately the AI system can recommend appropriate treatment decisions for adult skeletal Class III patients. Diagnostic Test Accuracy: The study will determine the sensitivity, specificity, positive predictive value, and negative predictive value of the AI-generated treatment recommendations. This analysis will highlight the AI's ability to correctly identify patients who require specific treatment interventions. Clinical Validity: Researchers will investigate whether the AI recommendations align with the decisions made by experienced orthodontists. This assessment is crucial to establish the AI system's clinical applicability. Potential Benefits: If the AI system proves reliable and accurate, it could offer a time-efficient and standardized method for treatment decision support, aiding orthodontists in providing personalized care to adult skeletal Class III patients. By conducting this study, researchers aim to contribute to the advancement of AI-assisted medical decision-making within the field of orthodontics. Successful outcomes would have the potential to revolutionize treatment planning processes, improve patient outcomes, and provide a valuable tool for orthodontists to make informed treatment decisions for adult skeletal Class III patients
Researchers at the University of Bath are investigating the metabolism of individuals with extreme body size, including those with skeletal dysplasia (commonly known as dwarfism), to manage health risks such as heart disease. By better understanding how body size could change how the body processes food, or how being bigger or smaller may influence eating habits, healthy living guidelines to tackle issues such as obesity and overweight in these populations can be improved. With help from expertise in the psychology field, this research will also investigate whether the mental health of these individuals has been affected by their size. On the whole, this study will involve one 24-hour visit to a research laboratory at the University of Bath, followed by a 2-week monitoring period to capture 'normal' physical activity and eating routines.
This 5-year project aims to (1) search for genetic causes for yet unsolved congenital skeletal disorders (GSDs); (2) study consequences of the newly identified pathogenic variants in cells and in transgenic mice, (3) summarize data on natural course and complications for different GSD groups. For patients with unsolved GSD, the investigators search for molecular causes of GSDs using whole genome sequencing (WGS) and total ribonucleic acid (RNA) sequencing. Candidate gene variants are selected using genome or transcriptome sequencing data, clinical findings and screening of omics databases. Causality of the new variants is studied in cells and in transgenic mice models. Molecular and clinical findings are summarized for different GSD groups.
RD-DATA is a retrospective and prospective data collection, finalized to care and research. It is articulated in main sections - strongly related and mutually dependent on each other - corresponding to different data domains: personal information, clinical data, genetic data, genealogical data, surgeries, etc.. This approach has been individuated in order to corroborate and integrate data from different resources and aspects of the diseases and to correlate genetic background and phenotypic outcomes, in order to better investigate diseases pathophysiology.
The "North Carolina Clinical Genomic Evaluation by Next-gen Exome Sequencing, 2 (NCGENES 2)" study is part of a larger consortium project investigating the clinical utility, or net benefit of an intervention on patient and family well-being as well as diagnostic efficacy, management planning, and medical outcomes. A clinical trial will be implemented to compare (1) first-line exome sequencing to usual care and (2) participant pre-visit preparation to no pre-visit preparation. The study will use a randomized controlled design, with 2x2 factorial design, coupled with patient-reported outcomes and comprehensive clinical data collection addressing key outcomes, to determine the net impact of diagnostic results and secondary findings.
Identify novel genetic variants causing skeletal dysplasia in Qatari populations and to additionally develop an understanding of how those variants influence skeletal biology at a molecular and cellular level.