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594
datasets available to search
ShareScore release 0.9.0
Dataset results
594 results for “Virtual patient”
Virtual Reality for Improving Pain and Distress in Patients With Advanced Stage Colorectal Cancer
ClinicalTrials.gov study NCT04069702. IPD Sharing: YES. Countries: 1. Publications: 0.
Virtual Environment Rehabilitation for Patients With Motor Neglect Trial
ClinicalTrials.gov study NCT03887962. IPD Sharing: NO. Countries: 1. Publications: 12.
Effect of Virtual Reality (VR) Therapy on Patients Undergoing Hand Surgery Under Ultrasound-guided Regional Anesthesia
ClinicalTrials.gov study NCT05183412. IPD Sharing: NO. Countries: 1. Publications: 1.
Using Virtual Reality to Prevent Pre-operative Anxiety in Ambulatory Surgery Patients
ClinicalTrials.gov study NCT04711278. IPD Sharing: NO. Countries: 1. Publications: 1.
Virtual Reality Rehabilitation for Patients With Anterior Cruciate Ligament Reconstruction
ClinicalTrials.gov study NCT05080894. IPD Sharing: NO. Countries: 1. Publications: 0.
Patient Satisfaction and Tolerability Using Virtual Reality (VR) as Adjunctive Treatment During Flexible Bronchoscopy
ClinicalTrials.gov study NCT05340907. IPD Sharing: NO. Countries: 1. Publications: 24.
Virtual Reality for Pain Management and Patient Satisfaction During Outpatient Hysteroscopy
ClinicalTrials.gov study NCT06463210. IPD Sharing: UNDECIDED. Countries: 1. Publications: 9.
Effects of Virtual Reality and Stress Ball on Fear, Pain, Anxiety, and Satisfaction in Ureterorenoscopy Patients
ClinicalTrials.gov study NCT07265960. IPD Sharing: NO. Countries: 1. Publications: 2.
Effects of Immersive Virtual Reality on Balance, Mobility, and Fatigue in Patients With Multiple Sclerosis
ClinicalTrials.gov study NCT03501342. IPD Sharing: NO. Countries: 1. Publications: 4.
Virtual Patients - An Interactive Educational Module for Higher Education
ClinicalTrials.gov study NCT06706011. IPD Sharing: NO. Countries: 1. Publications: 0.
Feasibility of Virtual Reality-Based Rehabilitation in Patients With Limited Mobility
ClinicalTrials.gov study NCT06937463. IPD Sharing: NO. Countries: 1. Publications: 0.
Craving Assessment in Patients With Alcohol Use Disorder Using Virtual Reality Exposure
ClinicalTrials.gov study NCT05861843. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Feasibility of Virtual Simulation-Based Diabetes Foot Care Education in Patients With Diabetes in Ethiopia
ClinicalTrials.gov study NCT04841291. IPD Sharing: NO. Countries: 1. Publications: 31.
Virtual Reality to Improve Patient Experience During Endoscopic Mucosal and Submucosal Resection
ClinicalTrials.gov study NCT06766162. IPD Sharing: NO. Countries: 1. Publications: 2.
Data from: A virtual molecular tumor board platform to improve efficiency and scalability of delivering precision oncology to physicians and their patients
OBJECTIVES: Scalable informatics solutions that provide molecularly-tailored treatment recommendations to clinicians are needed to streamline Precision Oncology in care settings. MATERIALS AND METHODS: We developed a cloud-based virtual molecular tumor board (VMTB) platform that included a knowledgebase, scoring model, rules engine, an asynchronous virtual chat room and a reporting tool that generated a treatment plan for each of the 1725 patients based on their molecular profile, previous treatment history, structured trial eligibility criteria, clinically relevant cancer gene-variant assertions, biomarker-treatment associations, and current treatment guidelines. The VMTB systematically allows clinician users to combine expert-curated data and structured data from clinical charts along with molecular testing data to develop consensus on treatments, especially those that require off-label and clinical trial considerations. RESULTS: The VMTB was used as part of the cancer care process for a focused subset of 1725 patients referred by advocacy organizations wherein resultant personalized reports were successfully delivered to treating oncologists. Median turnaround time from data receipt to report delivery decreased from 14 days to 4 days over 4 years while the volume of cases increased nearly twofold each year. Using a novel scoring model for ranking therapy options, oncologists chose to implement the VMTB-derived therapies over others, except when pursuing immunotherapy options without molecular support. DISCUSSION: VMTBs will play an increasingly critical role in precision oncology as the compendium of biomarkers and associated therapy options available to a patient continues to expand. CONCLUSION: Further development of such clinical augmentation tools that systematically combine patient-derived molecular data, real world evidence from electronic health records, and expert curated knowledgebases on biomarkers with computational tools for ranking best treatments can support care pathways at point of care.
3D virtual Histopathology of Cardiac Tissue from Covid-19 Patients based on Phase-Contrast X-ray Tomography
<p>X-ray phase-contrast tomography datasets of formalin fixed paraffin embedded unstained cardiac tissue from Covid-19 patients as well as from control tissue.</p> <p>For each group one overview scan of the entire biopsy (3.5mm cross section) from binning and stitching different tomograms (PB bin2, voxel size: 1.3µm) as well as the respective laboratory data (LJ, voxel size: 2µm, tissue masked) and a cone-beam zoom tomogram recorded with waveguide illumination (WG, voxel size: 159 nm) is uploaded. Further datasets can be provided on request.</p> <p>The data has been recorded at the institute for X-ray physics (Göttingen, Germany) and DESY at the GINIX instrument, beamline P10 of the PETRAIII storage ring (Hamburg, Germany).</p> <p>raw format, voxel numbers given in filename, single (32bit real)</p>
Predicting atrial fibrillation recurrence by combining population data and virtual cohorts of patient-specific left atrial models
<p><strong>Abstract</strong></p> <p><strong>Background: </strong>Current ablation therapy for atrial fibrillation is sub-optimal and long-term response is challenging to predict. Clinical trials identify bedside properties that provide only modest prediction of long-term response in populations, while patient-specific models in small cohorts primarily explain acute response to ablation. We aimed to predict long-term atrial fibrillation recurrence after ablation in large cohorts, by using machine learning to complement biophysical simulations by encoding more inter-individual variability.</p> <p><strong>Methods: </strong>Patient-specific models were constructed for 100 atrial fibrillation patients (43 paroxysmal, 41 persistent, 16 long-standing persistent), undergoing first ablation. Patients were followed for 1-year using ambulatory ECG monitoring. Each patient-specific biophysical model combined differing fibrosis patterns, fibre orientation maps, electrical properties and ablation patterns to capture uncertainty in atrial properties and to test the ability of the tissue to sustain fibrillation. These simulation stress tests of different model variants were post-processed to calculate atrial fibrillation simulation metrics. Machine learning classifiers were trained to predict atrial fibrillation recurrence using features from the patient history, imaging and atrial fibrillation simulation metrics.</p> <p><strong>Results: </strong>We performed 1100 atrial fibrillation ablation simulations across 100 patient-specific models. Models based on simulation stress tests alone showed a maximum accuracy of 0.63 for predicting long-term fibrillation recurrence. Classifiers trained to history, imaging and simulation stress tests (average ten-fold cross-validation area under the curve 0.85 ± 0.09, recall 0.80 ± 0.13, precision 0.74 ± 0.13) outperformed those trained to history and imaging (area under the curve 0.66 ± 0.17), or history alone (area under the curve 0.61 ± 0.14). </p> <p><strong>Conclusion: </strong>A novel computational pipeline accurately predicted long-term atrial fibrillation recurrence in individual patients by combining outcome data with patient-specific acute simulation response. This technique could help to personalise selection for atrial fibrillation ablation.</p> <p><strong>Dataset Description: </strong>We include surface meshes in vtk format, consisting of the nodes, triangular elements, the atrial coordinate fields defined on the nodes, and the endocardial and epicardial fibre fields defined on the elements. </p> <p>We also include universal atrial coordinate fields alpha and beta, which are a lateral-septal coordinate and posterior-anterior coordinate for the LA. More details on the coordinate construction are given in our manuscript and <a href="https://www.ncbi.nlm.nih.gov/pubmed/31026761">https://www.ncbi.nlm.nih.gov/pubmed/31026761</a>. These coordinates can be used for registering datasets. </p> <p><strong>Publication</strong>: https://pubmed.ncbi.nlm.nih.gov/35089057/</p>
Data for: Communicating with deaf patients in the clinical environment: Lessons learned from a virtual patient panel
Open the record for dataset details and reuse information.
Cognition Assessment and REhabilitation in Virtual Reality for Patients With Cancer
ClinicalTrials.gov study NCT07335393. IPD Sharing: YES. Countries: 0. Publications: 2.
Effects of Virtual Glasses, Stress Ball and Music Intervention on Pain, Anxiety and Comfort in Patients Undergoing Inguinal Hernia Surgery
ClinicalTrials.gov study NCT06964984. IPD Sharing: YES. Countries: 0. Publications: 1.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.