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447 results for “Radiographs”
Study to Evaluate the Effect of Filgotinib on Semen Parameters in Adult Males With Active Rheumatoid Arthritis, Psoriatic Arthritis, Ankylosing Spondylitis, or Non-radiographic Axial Spondyloarthritis
ClinicalTrials.gov study NCT03926195. IPD Sharing: NO. Countries: 8. Publications: 2.
A Study Utilizing Patient-Reported and Radiographic Outcomes and Evaluating the Safety and Efficacy of Lorecivivint (SM04690) for the Treatment of Moderately to Severely Symptomatic Knee Osteoarthriti
ClinicalTrials.gov study NCT03928184. IPD Sharing: NO. Countries: 1. Publications: 1.
Comparison of different Lunit INSIGHT CXR software versions when reading chest radiographs for tuberculosis
Open the record for dataset details and reuse information.
Accuracy of panoramic radiograph for diagnosing periodontitis comparing to clinical examination
<p>Accuracy of panoramic radiograph for diagnosing periodontitis comparing to clinical examination dataset</p>
FIGURE2. Digital radiograph of Siphamia guttulata (CSIRO H 6648-02, 25 mm SL). Blue circle highlighting two supraneurals. Scale bar = 0.5 mm. in Redescription and distributional range extension of the Speckled Siphonfish, Siphamia guttulata (Pisces: Apogonidae)
FIGURE2. Digital radiograph of Siphamia guttulata (CSIRO H 6648-02, 25 mm SL). Blue circle highlighting two supraneurals. Scale bar = 0.5 mm.
Clinical and radiographic course of arrested cerebral adrenoleukodystrophy
<p><b>Objective:</b> To gain insight into the natural history of arrested cerebral adrenoleukodystrophy (CALD), we quantified the change in Neurologic Function Score (NFS) and Loes Score (LS) over time in patients whose cerebral lesions spontaneously stopped progressing.</p> <p><b>Methods: </b>We retrospectively reviewed a series of 22 patients with arrested CALD followed longitudinally over a median time of 2.4 years (0.7 – 17.0). Primary outcomes were change in radiographic disease burden (measured by LS) and clinical symptoms (measured by NFS) between patients who never developed a contrast-enhancing lesion (GdE- subgroup), and those who did (GdE+ subgroup). Secondary analyses comparing patterns of neuroanatomical involvement and lesion number, and prevalence estimates, were performed.</p> <p><b>Results: </b>Cerebral lesions were first detected at a median age of 23.3 years old (8.0 – 67.6), with an initial LS of 4 (0.5 – 9). NFS was 0.5 (0 – 6). Overall change in NFS or LS per year did not differ between subgroups. No patients who remained GdE- converted to a progressive CALD phenotype. The presence of contrast enhancement was associated with disease progression (r<sub>s</sub> = 0.559, <i>p</i> < 0.001). Four patients (18.2%) underwent step-wise progression, followed by spontaneous resolution of contrast enhancement, and re-arrest of disease. Three patients (13.6%) converted to progressive CALD. Nineteen patients (86.4%) have arrested CALD at most recent follow-up. The prevalence of arrested CALD is 12.4%.</p> <p><b>Conclusion: </b>Arrested CALD lesions can begin in childhood, and patients are often asymptomatic early in disease. The majority of patients remain stable. However, clinical and MRI surveillance is recommended as a minority of patients undergo step-wise progression, or conversion to progressive CALD.</p>
Dataset of dental radiographs for the study of periapical lesions
<p>Dataset contains 450 conventional diagnostic periapical radiographs of anterior teeth. These were acquired from the Endodontics Department of the Facultad de Odontología, Universidad Nacional de Asunción. The radiographs were digitized using three cameras, a professional Canon T6 camera with a 100mm macro lens, a smartphone with iOS operating system (iPhone X) and a smartphone with Android operating system (Xiaomi Redmi Note 8). Then, five dental specialists performed the classification of the images by labeling with and without lesion.</p> <p> </p>
EEG abnormalities and their radiographic correlates in a COVID-19 inpatient cohort
<p>Objective: To identify the prevalence of EEG abnormalities in patients with COVID-19 with neurologic changes, their associated neuroimaging abnormalities and rates of mortality.</p> <p>Methods: A retrospective case series of 192 adult COVID-19 positive inpatients with EEG performed between March and June 2020 at 4 hospitals: 161 undergoing continuous, 24 routine, and 7 reduced-montage EEG. Study indication, epilepsy history, intubation status, administration of sedatives or antiseizure medications, metabolic abnormalities, neuroimaging pathology associated with epileptiform abnormalities, and in-hospital mortality were analyzed.</p> <p>Results: EEG indications included encephalopathy (54.7%), seizure (18.2%), coma (17.2%), focal deficit (5.2%), and abnormal movements (4.6%). Epileptiform abnormalities occurred in 39.6% of patients: focal intermittent epileptiform discharges in 25.0%, lateralized periodic discharges in 6.3%, and generalized periodic discharges in 19.3%. Seizures were recorded in 8 patients, 3 with status epilepticus. Antiseizure medication administration, epilepsy history, and older age were associated with epileptiform abnormalities. Only 26.3% of patients with any epileptiform abnormality, 37.5% with electrographic seizures, and 25.7% patients with clinical seizures had known epilepsy. Background findings included generalized slowing (88.5%), focal slowing (15.6%), burst suppression (3.6%), attenuation (3.1%), and normal EEG (3.1%). Neuroimaging pathology was identified in 67.1% of patients with epileptiform abnormalities, over two-thirds acute. In-hospital mortality was 39.5% for patients with epileptiform abnormalities, 36.2% for those without. Risk factors for mortality were coma and ventilator support at time of EEG.</p> <p>Significance: This article highlights the range of EEG abnormalities frequently associated with acute neuroimaging abnormalities in COVID-19. Mortality rates were high, particularly for patients in coma requiring mechanical ventilation. These findings may guide the prognosis and management of patients with COVID-19 and neurologic changes.</p>
Chest Radiograph at Diverse Institutes (CRADI) dataset
<p><strong>Introduction to the Chest Radiograph at Diverse Institutes (CRADI) dataset</strong></p> <p> </p> <p><strong>Background</strong></p> <p> </p> <p>Chest radiography is extensively used to screen and diagnose pulmonary and cardiac diseases. The advantages of its clinical practicality, efficiency, and cost-effectiveness make chest radiography the most accessible imaging test for pulmonary disorders, especially in primary hospitals. Currently, the interpretation of a chest radiograph mainly relies on radiologists.</p> <p> </p> <p>With the development of algorithms, convolutional neural networks (CNNs) have shown the ability to detect a single disorder in chest radiography, e.g., pneumothorax, lung cancer, pneumonia, and tuberculosis. Traditionally, expert annotation is applied to establish a CNN model for classifying medical images. This manual labeling procedure is time-consuming and highly demanding. Importantly, beyond the detection of a single disease, multi-label classification is necessary to interpret a chest radiograph in clinical practice.</p> <p> </p> <p>In order to promote the development of the artificial intelligence-assisted diagnosis of chest radiography, we launched the Chest Radiograph at Diverse Institutes (CRADI) dataset. This dataset is comprised of a large number of chest radiographs. Each radiograph has a 25-label disorder annotation, that was established by the terms adopted from the Fleischner’s glossary, and extracted from the original diagnostic report by natural language processing (NLP) and radiologist expertise.</p> <p> </p> <p>At present, the data of the CRADI dataset comes from two academic hospitals and multiple community clinics in Shanghai. The cases in the CRADI dataset are comprised of in-patients, out-patients, and screening participants.</p> <p> </p> <p>The CRADI dataset provides a better understanding of the multiple and different clinical data sources for chest radiography, which is potentially helpful for the training and test of CNN models.</p> <p> </p> <p>We welcome more data into the CRADI dataset. If you find it helpful to your research work or you want to contribute to this dataset, please feel free to contact us.</p> <p> </p> <p><strong>Data source</strong></p> <p><strong>Number of images</strong></p> <p><strong>Data_source</strong></p> <p>Academic hospital 1</p> <p>74,082</p> <p>0</p> <p>In- and out-patient from Academic hospital 2</p> <p>5,996</p> <p>1</p> <p>Screening participants from Academic hospital 2</p> <p>2,130</p> <p>2</p> <p>Community clinics</p> <p>1,804</p> <p>3</p> <p>Note. Each case includes one posterior-anterior (PA) view chest radiograph and the corresponding text label of disorder findings.</p> <p> </p> <p> </p> <p> </p> <p><strong>Preprocessing methods</strong></p> <p>Images: transformed and resized from DCM format to PNG, changed from 12-bit grayscale to 8-bit. All patient- or institute-related information is de-identified.</p> <p>Label: labels are extracted from the original diagnostic reports. The regular expression is applied by NLP and rules-based extraction methods. In total, 25 labels are extracted for each image.</p> <p><strong>Data</strong></p> <p><strong>Overview: </strong>All images are compressed into one file. All classification labels are listed in one CSV file. Data order is as following way:</p> <p><strong>Data organization</strong></p> <p><strong>Classification result</strong></p> <p>Each image links to the label by an item of ‘patientID’.</p> <p>data_resource<a href="#_msocom_1">[1]</a> stands for the resource of data. Data sources are listed in the previous table.</p> <p>Result table format: |pateintID|data_resource<a href="#_msocom_2">[2]</a> |label1|label2|.......|label25|</p> <p>The order of the 25 labels is as the following:</p> <p>1) pneumothorax, 2) emphysema, 3) pulmonary parenchymal calcification, 4) PICC implant, 5) aortic unfolding, 6) aortic arteriosclerosis, 7) aortic abnormalities, 8) small consolidation, 9) cardiomegaly, 10) patchy consolidation, 11) consolidation, 12) cavity, 13) mass, 14) prominent bronchovascular marking, 15) pulmonary edema, 16) pulmonary nodule, 17) hilar adenopathy, 18) pleural effusion, 19) pleural thickening, 20) pleural adhesion, 21) pleural calcification, 22) pleural abnormalities, 23) scoliosis, 24) pacemaker implant, 25) interstitial involvement.</p> <p>Data_resource or data_source?</p> <p>同上</p> <p><strong>Introduction to the Chest Radiograph at Diverse Institutes (CRADI) dataset</strong></p> <p> </p> <p><strong>Background</strong></p> <p> </p> <p>Chest radiography is extensively used to screen and diagnose pulmonary and cardiac diseases. The advantages of its clinical practicality, efficiency, and cost-effectiveness make chest radiography the most accessible imaging test for pulmonary disorders, especially in primary hospitals. Currently, the interpretation of a chest radiograph mainly relies on radiologists.</p> <p> </p> <p>With the development of algorithms, convolutional neural networks (CNNs) have shown the ability to detect a single disorder in chest radiography, e.g., pneumothorax, lung cancer, pneumonia, and tuberculosis. Traditionally, expert annotation is applied to establish a CNN model for classifying medical images. This manual labeling procedure is time-consuming and highly demanding. Importantly, beyond the detection of a single disease, multi-label classification is necessary to interpret a chest radiograph in clinical practice.</p> <p> </p> <p>In order to promote the development of the artificial intelligence-assisted diagnosis of chest radiography, we launched the Chest Radiograph at Diverse Institutes (CRADI) dataset. This dataset is comprised of a large number of chest radiographs. Each radiograph has a 25-label disorder annotation, that was established by the terms adopted from the Fleischner’s glossary, and extracted from the original diagnostic report by natural language processing (NLP) and radiologist expertise.</p> <p> </p> <p>At present, the data of the CRADI dataset comes from two academic hospitals and multiple community clinics in Shanghai. The cases in the CRADI dataset are comprised of in-patients, out-patients, and screening participants.</p> <p> </p> <p>The CRADI dataset provides a better understanding of the multiple and different clinical data sources for chest radiography, which is potentially helpful for the training and test of CNN models.</p> <p> </p> <p>We welcome more data into the CRADI dataset. If you find it helpful to your research work or you want to contribute to this dataset, please feel free to contact us.</p> <p><strong>Data source </strong></p> <p>training data data source: 0</p> <p>In- and out-patient from external hospital data source: 1</p> <p>Screening participants from external hospital data source : 2</p> <p>Community clinics datasource: 3</p> <p>Note. Each case includes one posterior-anterior (PA) view chest radiograph and the corresponding text label of disorder findings.</p> <p><strong>Preprocessing methods</strong></p> <p>Images: transformed and resized from DCM format to PNG, changed from 12-bit grayscale to 8-bit. All patient- or institute-related information is de-identified.</p> <p>Label: labels are extracted from the original diagnostic reports. The regular expression is applied by NLP and rules-based extraction methods. In total, 25 labels are extracted for each image.</p> <p><strong>Data</strong></p> <p><strong>Overview: </strong>All images are compressed into one file. All classification labels are listed in one CSV file. Data order is as following way:</p> <p><strong>Classification result</strong></p> <p>Each image links to the label by an item of ‘patientID’.</p> <p>data_resource<a href="#_msocom_1">[1]</a> stands for the resource of data. Data sources are listed in the previous table.</p> <p>Result table format: |pateintID|data_resource<a href="#_msocom_2">[2]</a> |label1|label2|.......|label25|</p> <p>The order of the 25 labels is as the following:</p> <p>1) pneumothorax, 2) emphysema, 3) pulmonary parenchymal calcification, 4) PICC implant, 5) aortic unfolding, 6) aortic arteriosclerosis, 7) aortic abnormalities, 8) small consolidation, 9) cardiomegaly, 10) patchy consolidation, 11) consolidation, 12) cavity, 13) mass, 14) prominent bronchovascular marking, 15) pulmonary edema, 16) pulmonary nodule, 17) hilar adenopathy, 18) pleural effusion, 19) pleural thickening, 20) pleural adhesion, 21) pleural calcification, 22) pleural abnormalities, 23) scoliosis, 24) pacemaker implant, 25) interstitial involvement.</p> <p>Data will be released after anonymilization process.</p>
Dataset from "Predicting final results of brace treatment of adolescents with idiopathic scoliosis: first out-of-brace radiograph is better than in-brace radiograph-SOSORT 2020 award winner"
<p>Dataset from the paper published in the European Spine Journal titled "Predicting final results of brace treatment of adolescents with idiopathic scoliosis: first out-of-brace radiograph is better than in-brace radiograph-SOSORT 2020 award winner"</p>
Radiographic outcomes for TheraCal LC group for postoperative pain, tenderness, and neural sensibility at the patient recall period of 21 days, 3 months, and 12 months
<p>Radiographic outcomes for TheraCal LC group for postoperative pain, tenderness, and neural sensibility at the patient recall period of 21 days, 3 months, and 12 months</p>
Clinical Utility to Follow-up Radiographs During the First Year of Knee Replacement Surgery
ClinicalTrials.gov study NCT05944679. IPD Sharing: NO. Countries: 1. Publications: 17.
Comparison of Two Chest Radiograph Prescription Strategies in Intensive Care Unit
ClinicalTrials.gov study NCT00893672. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Clinical, Radiographic, and Digital Evaluation of the Effects of Autogenous Grafts on Peri-implant Mucosa
ClinicalTrials.gov study NCT07330609. IPD Sharing: NO. Countries: 1. Publications: 2.
Radiographic Evaluation of a Star-shaped Incision Technique
ClinicalTrials.gov study NCT05190614. IPD Sharing: NO. Countries: 1. Publications: 1.
Radiographic Contrast To Differentiate Cavitated From Non-cavitated Tooth Decay
ClinicalTrials.gov study NCT02359279. IPD Sharing: NO. Countries: 1. Publications: 1.
Clinical And Radiographic Evaluation Of Zinc Substituted Nanohyrdoxyappatite Bone Graft And Advanced Platelet Rich Fibrin Block In The Treatment Of Periodontal Intrabony Defects
ClinicalTrials.gov study NCT07313254. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Clinical and Radiographic Outcomes of Dental Implant Therapy
ClinicalTrials.gov study NCT01825772. IPD Sharing: Not stated. Countries: 1. Publications: 6.
Clinical and Radiographic Assessment of the Outcomes of Dental Implant Inserted After Xenograft Socket Preservation in Posterior Maxilla
ClinicalTrials.gov study NCT07001813. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Radiographic Prediction of the Nerve Origin of Neck Peripheral Nerve Sheath Tumors
ClinicalTrials.gov study NCT05684835. IPD Sharing: NO. Countries: 1. Publications: 1.
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International Brain Laboratory public data
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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.