[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"leadSponsorName\":\"Bangladesh University of Engineering and Technology\",\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:":125},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,4,0,[8,45,74,100],{"id":9,"slug":10,"hasResults":11,"nctId":12,"briefTitle":13,"officialTitle":14,"acronym":4,"eligibilityCriteria":15,"healthyVolunteers":11,"sex":16,"minAge":17,"maxAge":18,"enrollmentInfo":19,"targetDuration":4,"studyType":22,"phases":23,"briefSummary":25,"conditions":26,"keywords":28,"overallStatus":32,"whyStopped":4,"lastUpdateSubmitDate":33,"lastUpdatePostDateStruct":34,"startDateStruct":37,"completionDateStruct":39,"leadSponsor":41,"locationsCount":44},"100595565","spatiotemporal-gait-parameters-of-healthy-and-stroke-patients-during-overground-treadmill-and-body-weight-supported-treadmill-walking-100595565",false,"NCT07034105","Spatiotemporal Gait Parameters of Healthy and Stroke Patients During Overground, Treadmill, and Body Weight Supported Treadmill Walking","Variability in Spatiotemporal Gait Parameters of Stroke Patients Across Overground, Self Paced Treadmill, and Body-Weight Supported Treadmill Walking","Inclusion Criteria:\n\n* Participants must be between 18 to 75 years old.\n* More than 1- month post stroke patient.\n* Participants must be able to walk with or without assistance.\n* Patients including both the male and female.\n* Participants must be able to provide informed consent to participate in the study.\n\nExclusion Criteria:\n\n* Severe cognitive or communicative disorders.\n* Significant joint malposition.\n* Psychological, cognitive dysfunction, and any other neuromuscular problem.\n* Pregnant women will be excluded to avoid any potential risks to the mother and fetus.\n* Unstable cardiovascular disease like congenital heart disease, deep vein thrombosis, coronary heart disease, previous history of heart attack or heart failure.","ALL","18 Years","75 Years",{"count":20,"type":21},25,"ESTIMATED","INTERVENTIONAL",[24],"NA","Gait impairments following a stroke significantly hinder mobility and quality of life, emphasizing the need for precise assessment methods to guide effective rehabilitation strategies. This study evaluates the variability and reliability of spatiotemporal gait parameters across three walking modalities: overground walking, treadmill walking, and body-weight-supported treadmill walking. Using a counterbalanced design, all participants undergo gait analysis in each modality to ensure unbiased and reliable comparisons.\n\nThe study also incorporates a locally developed, cost-effective Body Weight Support System (BWSS) to address the limitations of accessibility in resource-constrained settings. By identifying how different modalities influence gait variability and reliability, this research aims to optimize rehabilitation outcomes and demonstrate the feasibility of implementing affordable gait analysis tools in clinical practice.",[27],"Gait Impairment in Stroke Patients",[29,30,31],"Gait Analysis","Spatiotemporal Parameters","Body Weight Support System (BWSS)","RECRUITING","2025-06-14",{"date":35,"type":36},"2025-06-24","ACTUAL",{"date":38,"type":36},"2024-06-12",{"date":40,"type":21},"2026-02-11",{"name":42,"class":43},"Bangladesh University of Engineering and Technology","OTHER",1,{"id":46,"slug":47,"hasResults":11,"nctId":48,"briefTitle":49,"officialTitle":50,"acronym":4,"eligibilityCriteria":51,"healthyVolunteers":52,"sex":16,"minAge":53,"maxAge":54,"enrollmentInfo":55,"targetDuration":4,"studyType":57,"phases":4,"briefSummary":58,"conditions":59,"keywords":61,"overallStatus":32,"whyStopped":4,"lastUpdateSubmitDate":66,"lastUpdatePostDateStruct":67,"startDateStruct":69,"completionDateStruct":71,"leadSponsor":73,"locationsCount":44},"100593150","geometrical-influences-on-atherosclerosis-and-blood-flow-100593150","NCT07002697","Geometrical Influences on Atherosclerosis and Blood Flow","High-Risk Plaques Identification Using Coronary Computed Tomography and Computational Fluid Dynamics","Inclusion Criteria:\n\n* Adults aged 40-70 years.\n* Presenting with symptoms of CAD (e.g., chest pain, shortness of breath) or having multiple risk factors (e.g., hypertension, diabetes, smoking).\n* Able to provide informed consent.\n\nExclusion Criteria:\n\n* Severe renal impairment (due to contrast media risk).\n* Previous allergic reaction to iodinated contrast media.\n* Pregnant or lactating women.",true,"40 Years","70 Years",{"count":56,"type":21},30,"OBSERVATIONAL","The study investigates the use of advanced imaging techniques and computational methods to identify high-risk plaques in coronary arteries. These plaques are significant because they have the potential to cause acute coronary syndrome (ACS), a condition that includes heart attacks and unstable angina. The research focuses on integrating Coronary Computed Tomography (CCT) with Computational Fluid Dynamics (CFD) to provide detailed insights into plaque characteristics and their hemodynamic environment.\n\nThe study's primary aim is to enhance the early detection and characterization of high-risk coronary plaques that could lead to ACS. By combining CCT, a non-invasive imaging technique, with CFD, which stimulates blood flow dynamics, the study seeks to: Identify High-Risk Plaques, Apply CFD to analyze the blood flow around these plaques, Improve Prediction of ACS, Inform Clinical Decision-Making.\n\nComputational fluid dynamics (CFD) analysis of CCT data can also provide a non-invasive hemodynamic assessment to identify high-risk plaques destined to cause acute coronary syndrome. Patients with adverse plaque characteristics like positive remodeling or low-attenuation plaque have a greater risk of future coronary events.",[60],"Atherosclerosis and Blood Flow in Coronary Artery",[62,63,64,65],"Computational Fluid Dynamics (CFD)","Design Of Experiment (DOE)","Computed Tomography (CT)","Acute Coronary Syndrome (ACS)","2025-05-25",{"date":68,"type":36},"2025-06-03",{"date":70,"type":36},"2024-08-10",{"date":72,"type":21},"2026-04-11",{"name":42,"class":43},{"id":75,"slug":76,"hasResults":11,"nctId":77,"briefTitle":78,"officialTitle":79,"acronym":4,"eligibilityCriteria":80,"healthyVolunteers":52,"sex":16,"minAge":17,"maxAge":4,"enrollmentInfo":81,"targetDuration":4,"studyType":57,"phases":4,"briefSummary":83,"conditions":84,"keywords":86,"overallStatus":32,"whyStopped":4,"lastUpdateSubmitDate":92,"lastUpdatePostDateStruct":93,"startDateStruct":95,"completionDateStruct":97,"leadSponsor":99,"locationsCount":44},"100566198","ai-model-for-bone-mineral-density-prediction-from-x-ray-images-100566198","NCT06652061","AI Model for Bone Mineral Density Prediction From X-Ray Images","Development and Evaluation of an Artificial Intelligence Model for Bone Mineral Density Prediction From X-Ray Images","▪ Inclusion criteria:\n\n* Female and male patients aged 18 and above\n* Individuals willing to participate and who have provided informed consent for the use of their X-ray images and clinical data for research purposes.\n* Subjects with both X-ray images of hip and spine and DEXA scan results.\n* Accessibility to supplementary medical records that may contribute to the model's predictive accuracy, such as historical data on fractures, pregnancies, relevant medical conditions and other osteoporosis-related factors.\n* Exclusion criteria:\n\n  * Subjects for whom X-ray images or clinical data are incomplete or of insufficient quality for analysis.\n  * Individuals with medical conditions that could significantly alter bone density independently of osteoporosis, such as bone cancers or certain metabolic diseases.\n  * Subjects who have undergone treatments or procedures that might significantly impact bone density measurement, such as long-term steroid use or recent orthopaedic surgeries.\n  * Pregnant women, given the potential impact on screening results and the need for special considerations during pregnancy.\n  * Patients with implant in hip or spine",{"count":82,"type":21},600,"Osteoporosis, a pervasive skeletal disorder characterized by diminished bone strength predisposing individuals to an increased risk of fractures, presents a substantial public health challenge globally. It's estimated that osteoporosis and its consequent increase in fracture risk significantly contribute to morbidity, mortality, and economic costs. Despite the availability of effective treatments, the condition often remains undiagnosed and untreated until a fracture occurs, underscoring the critical need for early detection and intervention.\n\nDual-energy X-ray absorptiometry (DEXA) is the gold standard for assessing bone mineral density (BMD) and fracture risk. However, its utility is hampered by limited availability, especially in rural and low-resource settings, such as Bangladesh, where osteoporosis prevalence is notably high. The scarcity of DEXA units exacerbates the challenge of osteoporosis screening and management, leaving a significant portion of the population at risk In this context, plain X-ray imaging, widely available even in resource-constrained settings, emerges as a promising alternative for osteoporosis screening. Recent advancements in deep learning and computer vision offer the potential to automate the analysis of X-ray images for BMD estimation.\n\nThe primary objective is to curate a comprehensive dataset of X-ray images of hip and spine as well as BMD reports and relevant clinical information sourced from local health facilities in Bangladesh encompassing diverse demographic data. The objective of this thesis is to develop and evaluate an Artificial Intelligence (AI)-based model that predicts BMD from plain X-ray images of the lumbar spine and pelvis. The proposed AI model processes X-ray images to detect subtle changes in bone texture and density, potentially offering a rapid, non-invasive, and cost-effective tool for large-scale osteoporosis screening, particularly beneficial in regions like Bangladesh where DEXA is scarcely available. This research addresses the critical gap in osteoporosis screening and diagnosis, aiming to contribute significantly to public health by enabling earlier detection and management of osteoporosis, thereby reducing the incidence of fractures and associated healthcare costs.",[85],"Osteoporosis",[85,87,88,89,90,91],"Bone Mineral Density","AI","Radiography","Deep Learning","Hip and Spine X-Rays","2024-10-18",{"date":94,"type":36},"2024-10-22",{"date":96,"type":36},"2024-09-12",{"date":98,"type":21},"2025-03-12",{"name":42,"class":43},{"id":101,"slug":102,"hasResults":11,"nctId":103,"briefTitle":104,"officialTitle":105,"acronym":4,"eligibilityCriteria":106,"healthyVolunteers":52,"sex":107,"minAge":17,"maxAge":4,"enrollmentInfo":108,"targetDuration":4,"studyType":57,"phases":4,"briefSummary":110,"conditions":111,"keywords":113,"overallStatus":32,"whyStopped":4,"lastUpdateSubmitDate":117,"lastUpdatePostDateStruct":118,"startDateStruct":120,"completionDateStruct":122,"leadSponsor":124,"locationsCount":44},"100565597","ai-model-for-cervical-cancer-detection-from-colposcopy-images-100565597","NCT06644248","AI Model for Cervical Cancer Detection From Colposcopy Images","Development and Evaluation of an Artificial Intelligence Model for Cervical Cancer Detection From Colposcopic Images","Inclusion Criteria:\n\n* Female patients of age 18 years or older can be selectedas subjects.\n* Individuals willing to participate in cervical cancerscreening.\n* Availability for colposcopic examination.\n* Women with no history of hysterectomy (total removalof the uterus).\n* Women with no current or prior diagnosis of cervicalcancer.\n* Availability of relevant medical records forconfirmation and comparison purposes.\n\nExclusion Criteria:\n\n* Pregnant women, given the potential impact onscreening results and the need for specialconsiderations during pregnancy.\n* Individuals with severe medical conditions orcircumstances that may make colposcopic examinationinappropriate or unsafe.\n* Patients with conditions that could interfere with theaccuracy of the screening results, such as severevaginal bleeding.\n* Follow-up screenings.","FEMALE",{"count":109,"type":21},500,"Cervical cancer is a significant health issue, particularly in low-income countries, where late diagnosis and limited access to screenings contribute to high mortality rates. This study aims to develop and evaluate an artificial intelligence (AI) model to analyze colposcopic images for detecting cervical cancer more accurately and efficiently. Colposcopy, a procedure used to examine the cervix for signs of cancer, relies heavily on doctors' expertise, leading to inconsistent results. The current gold standard, colposcopy-directed biopsy, is invasive and can cause complications. The hypothesis is that an AI model can outperform traditional methods in identifying cervical abnormalities, providing a reliable and scalable solution for early detection, especially in underserved areas. By automating the analysis process, the AI model aims to reduce reliance on trained personnel, making cervical cancer screening more accessible and improving early diagnosis and treatment outcomes. The study will create a diverse dataset of colposcopy images from various sources and develop the AI model. The model's performance will be validated in clinical settings, assessing its accuracy in classifying cancer stages and identifying transformation zones. The impact on early detection, patient outcomes, and model usability will be evaluated, as well as its generalizability across different healthcare environments. The goal is to enhance the accuracy and efficiency of cervical cancer screening, ultimately reducing mortality rates and improving patient care.",[112],"Uterine Cervical Neoplasms",[114,115,90,116],"Cervical Cancer","Colposcopy","Transformation Zones","2024-10-14",{"date":119,"type":36},"2024-10-16",{"date":121,"type":36},"2024-01-11",{"date":123,"type":21},"2025-02-11",{"name":42,"class":43},""]