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Dataset results
31 results for “Radiologists”
The impact of deep learning aid on the workload and interpretation accuracy of radiologists on chest computed tomography: a cross-over reader study.
<p>Data used to perform statistical analysis in "The impact of deep learning aid on the workload and interpretation accuracy of radiologists on chest computed tomography: a cross-over reader study.". </p>
Intrahepatic cholangiocarcinoma and its Differential Diagnosis at MRI: How Radiologist Should Assess MR features.
<p>We uploaded the figures of the manuscript Intrahepatic cholangiocarcinoma and its Differential Diagnosis at MRI: How Radiologist Should Assess MR features submitted on Radiologia Medica.</p> <p> </p> <p><strong>Figure Legend</strong>:</p> <p>Figure 1: mass-forming ICC; the lesion shows a targetoid appearance, with central fibrous stroma in T2-W sequence (A, arrow), DWI (B: b800s/mm<sup>2</sup>, arrow) and ADC map (C, arrow). During contrast study (arterial phase (D, arrow), portal (E, arrow), transitional (F, arrow), the lesion shows a progressive contrast enhancement.</p> <p>Figure 2: mass-forming ICC on left lobe, with diffuse metastases. The lesions show in T2-W sequences (A, B and C, arrows) targetoid appearance with central fibrosis in ICC and necrosis in metastases; TA is also present in DWI (D: b800 s/mm<sup>2</sup>, arrow) and ADC map (E, arrow). In arterial phase (F, arrow) the lesions show rim APHE with progressive contrast enhancement during portal phase (G, arrow).</p> <p>Figure 3: periductal infiltrating ICC; in T2-W sequence the lesion shows hypeintense SI (A, arrow); in DWI (B, arrow) and ADC map (C, arrow), biliary stent causes a dysomogeneous SI. During arterial (D, arrow) and portal phase (E, arrow) the lesion shows a progressive contrast enhancement.</p> <p>Figure 4: intraductal growing ICC. In T2-W sequence (A, arrow) the lesion shows hyperintense SI, with biliary dilatation and restricted signal in DWI (B, arrow) and ADC map (C, arrow) during arterial (D, arrow), portal (E, arrow) and late phase (F, arrow) the lesion shows a progressive contrast enhancement.</p> <p>Figure 5: mCRC patient. The lesions show targetoid appearance in T2-W sequence (A, arrow), DWI (B, arrow) and ADC map (C, arrow). In arterial phase (D, arrow) the lesions show rim APHE and hypointense SI in portal (E and F, arrows) phase. In F the arrow shows a pheripheral rim enhancement.</p> <p>Figure 6: Peribiliary metastases in pancreatic cancer patient. In T2-W the lesion shows hyperintense SI (A, arrow), with restricted diffusion (B, arrow) and iso-hypointense SI in ADC map (C, arrow). During arterial (D, arrow), portal (E, arrow) and transitional (F, arrow) phase the lesion shows a progressive contrast enhancement.</p> <p>Figure 7: HCC patient. In T2-W the lesion shows iso-hypeintense SI (A, arrow), with hypointense SI in T1-W (B: in phase; C: out phase, arrows). During arterial phase the lesion shows APHE (D, arrow), with washout appearance (E, arrow) and capsule appearance (F, arrow). The Diffusion is restricted (G: b500 s/mm<sup>2</sup>; H: b800 s/mm<sup>2</sup> and I: ADC map, arrows).</p> <p>Figure 8: cHCC-ICC patient. The lesion shows targetoid appearance in T2-W (A, arrow) and T1-w sequences (B: in phase and C: out of phase, arrows). During contrast study, the lesion shows progressive contrast enhancement (D: arterial phase; E: portal phase and F: equilibrium phase, arrows). In DWI sequences, the lesion shows targetoid appearance (G= b50 s/mm<sup>2</sup>; H: b800s/mm<sup>2</sup> and I: ADC map, arrows)</p> <p>Figure 9: hepatic hemangioma; in T2 (A, arrow) sequence the lesion shows hyperintense SI, with progressive contrast enhancement in arterial (B, arrow), portal (C, arrow) and equilibrium (D: coronal plane, arrow) phase.</p>
Pulmonary lymphangitis pose a major challenge for radiologists in an oncological setting during the COVID-19 pandemic
<p>I uploaded the images of the manuscript "Pulmonary lymphangitis pose a major challenge for radioLogists in an oncological setting during the COVID-19 pandemic".</p>
Magnetic Resonance Features of Liver Mucinous Colorectal Metastases: what the radiologist should know
<p>I uploaded the images of the manuscript "Magnetic Resonance Features of Liver Mucinous Colorectal Metastases: what the radiologist should know" with the aim to asssess MRI features of mucinous liver metastases compared to non-mucinous metastases and hepatic hemangioma.</p>
Immunotherapy Assessment: A New Paradigm for Radiologists
<p>I uploaded the original images about the manuscript "Immunotherapy Assessment: A New Paradigm for Radiologists"</p>
CT and Radiologist RCT
ClinicalTrials.gov study NCT03121131. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Artificial Intelligence and Radiologists at Prostate Cancer Detection in MRI: The PI-CAI Challenge
ClinicalTrials.gov study NCT05489341. IPD Sharing: YES. Countries: 1. Publications: 1.
Generative AI Radiologist's Workstation
ClinicalTrials.gov study NCT07057830. IPD Sharing: NO. Countries: 1. Publications: 0.
Screening Mammography: Single Reading by One Radiologist With AI vs. Double Reading by Two Radiologists (AI-BCSQ)
ClinicalTrials.gov study NCT07075679. IPD Sharing: NO. Countries: 0. Publications: 12.
Clinical applicability of COVID-19 detection in chest radiography with deep learning - radiologist annotations
<p>Chest radiography (CXR) can be used as a complementary method for diagnosing/following COVID-19 patients. However, experience level and workload of technicians and radiologists may affect the decision process. In order to evaluate the performance of radiologists in the detection of COVID-19 radiological features in CXR, manual annotation of a subset of random CXR images was performed by two radiologists using an in-house software. The software presented CXRs from a randomly selected subset and allowed for window center/width adjustment, zooming and panning. Radiologists were asked to label CXRs into one of 4 classes: Normal, Not indicative of COVID-19 (pathological), Indicative of COVID-19 and Undetermined. The Indicative of COVID-19 class was defined as CXRs where the patient presented findings indicative of COVID-19, namely bilateral pulmonary opacities of low/medium density. The Undetermined class was defined as CXRs where the patient presented findings that could be indicative of COVID-19 but which could also be indicative of another condition, namely unilateral lung opacities, diffuse bilateral opacities of ARDS pattern or diffuse reticular opacities. The Not indicative of COVID-19 (pathological) class was defined as CXRs where the patient presented findings indicative of any other pathology except for COVID-19. CXRs where the patient presented medical devices were classified as Normal if the underlying pathology was not visible. Additionally, CXRs without sufficient quality for visual assessment by the radiologists due to bad image quality, patient positioning or any other factors could be labelled as Compromised for exclusion. A total of 1,845 CXRs were selected for annotation by two radiologists. Of these, 1,256 belong to the Mixed dataset(a combination of multiple public sources), 289 belong to BIMCV and 300 belong to COVIDGR. Selection of CXRs for annotation was performed randomly: for the Mixed dataset, a balanced selection strategy was used during image selection, whereas for BIMCV and COVIDGR, the dataset class distribution was maintained in the subset selected for annotation. Manual labelling of CXRs was performed in two stages. First, both radiologists independently classified each CXR. CXRs where the two radiologists disagreed were then selected for the second stage where the two radiologists assessed the CXRs together to achieve consensus. At no point were radiologists given access to the ground truth label, RT-PCR results or any other information besides the CXR image. To ensure that written information present in the CXR image (such as hospital system, health service, laterality markers, patient positioning, etc.) did not bias the annotation, all written labels were blacked out during before annotation. This dataset contains the information regarding each annotated CXR and the resulting radiologist labels for each image before and after consensus.</p> <p>The public datasets used in this study are available in the following repositories:</p> <p>CheXpert: <a href="https://stanfordmlgroup.github.io/competitions/chexpert/">https://stanfordmlgroup.github.io/competitions/chexpert/</a></p> <p>ChestXRay-8: <a href="https://nihcc.app.box.com/v/ChestXray-NIHCC">https://nihcc.app.box.com/v/ChestXray-NIHCC</a></p> <p>COVID-19 IDC: <a href="https://github.com/ieee8023/covid-chestxray-dataset">https://github.com/ieee8023/covid-chestxray-dataset</a></p> <p>COVIDx: <a href="https://github.com/lindawangg/COVID-Net">https://github.com/lindawangg/COVID-Net</a></p> <p>RSNA-PDC: <a href="https://www.kaggle.com/c/rsna-pneumonia-detection-challenge">https://www.kaggle.com/c/rsna-pneumonia-detection-challenge</a></p> <p>SAVE LIVES: <a href="https://www.hmhospitales.com/coronavirus/covid-data-save-lives">https://www.hmhospitales.com/coronavirus/covid-data-save-lives</a></p> <p>SERAM: <a href="https://seram.es/images/site/TUTORIAL_CSI_RX_TORAX_COVID-19_vs_4.0.pdf">https://seram.es/images/site/TUTORIAL_CSI_RX_TORAX_COVID-19_vs_4.0.pdf</a></p> <p>Twitter: <a href="https://twitter.com/ChestImaging">https://twitter.com/ChestImaging</a></p> <p>BIMCV PADCHEST: <a href="https://bimcv.cipf.es/bimcv-projects/padchest/">https://bimcv.cipf.es/bimcv-projects/padchest/</a></p> <p>BIMCV COVID-19-PADCHEST: <a href="https://bimcv.cipf.es/bimcv-projects/bimcv-covid19/">https://bimcv.cipf.es/bimcv-projects/bimcv-covid19/</a></p> <p>COVIDGR: <a href="https://dasci.es/transferencia/open-data/covidgr-2/">https://dasci.es/transferencia/open-data/covidgr-2/</a></p>
Online supplement to manuscript: "ChatGPT may free time needed by the interventional radiologist for administration / documentation : A study on the RSNA PICC line reporting template."
<p>Online supplement to manuscript: </p> <p>Senge JF, Mc Murray MT, Haupt F, Breiding PS, Beisbart C, Daneshvar K, et al. ChatGPT may free time needed by the interventional radiologist for administration / documentation : A study on the RSNA PICC line reporting template. Swiss J Radiol Nucl Med. 2024;7: 1–14. doi:10.59667/sjoranm.v7i2.12</p>
Role of the Radiologist in Management of Pulsatile Tinnitus
ClinicalTrials.gov study NCT05338684. IPD Sharing: Not stated. Countries: 0. Publications: 12.
The Discrepancy in Radiologists' Practice of Intravenous Contrast Imaging Studies Among Chronic Kidney Disease Patients
ClinicalTrials.gov study NCT05729126. IPD Sharing: NO. Countries: 1. Publications: 0.
Efficacy of Hypnoanalgesia by a Radiologist Technologist in Children With Cutaneous Angioma Treated With Sclerosis in Interventional Radiology
ClinicalTrials.gov study NCT03674346. IPD Sharing: NO. Countries: 1. Publications: 0.
Individual Dosimetric Monitoring of Workers During Interventional Radiology and Cardiology Procedures for Cardiologists and Radiologists in France
ClinicalTrials.gov study NCT05561439. IPD Sharing: Not stated. Countries: 1. Publications: 0.
The Impact of Oral Contrast in Multidetector CT of the Abdomen and Pelvis on Radiologist Confidence
ClinicalTrials.gov study NCT01655550. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Handheld Ultrasound Device to Triage Women With a Positive Clinical Breast Examination by Trained Non-radiologists
ClinicalTrials.gov study NCT06056843. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Radiologist Diagnostic Performance Versus Artificial Intelligence (AI)
ClinicalTrials.gov study NCT04304742. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Peripherally Inserted Central Catheter Placement by Interventional Radiologists Using the Sonic Flashlight
ClinicalTrials.gov study NCT00226213. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Tele-ultrasound Experimentation: Cooperation Protocol Between Electro-radiology Assistant and Remote Radiologist
ClinicalTrials.gov study NCT03555006. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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
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DANDI Archive for NWB datasets
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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.