Introduction: Policymakers wish to extend access to medical records, including medical imaging. Appreciating how patients might review radiographs could be key to establishing future training needs for healthcare professionals and how image sharing could be integrated into practice.
Method: A pilot study in the UK using a survey was distributed to adult participants via the online research platform Prolific. All subjects were without prior professional healthcare experience. Participants reviewed ten radiographs (single projection only) and were asked a two-stage question. Firstly, if the radiograph was 'normal' or 'abnormal' and secondly, if they had answered 'abnormal', to identify the abnormality from a pre-determined list featuring generic terms for pathologies.
Results: Fifty participants completed the survey. A mean of 65.8 % of participants were able to correctly identify if radiographs were normal or abnormal. Results in relation to the identification of a pathology were not as positive, but still notable with a mean of 46.4 % correctly identifying abnormalities. Qualitative data demonstrated that members of the public are enthralled with reviewing radiographs and intrigued to understand their performance in identifying abnormalities.
Conclusion: In the pilot, members of the public could identify if a radiograph is normal or abnormal to a reasonable standard. Further detailed interpretation of images requires supportive intervention. This pilot study suggests that patients can participate in image sharing as part of their care. Image sharing may be beneficial to the therapeutic relationship, aiding patient understanding and enhancing consultations between healthcare professional and patient. Further research is indicated.
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http://dx.doi.org/10.1016/j.jmir.2024.04.016 | DOI Listing |
J Neurotrauma
January 2025
Division of Neuroscience, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health (NIH), Bethesda, Maryland, USA.
Effective team science requires procedural harmonization for rigor and reproducibility. Multicenter studies across experimental modalities (domains) can help accelerate translation. The Translational Outcomes Project in NeuroTrauma (TOP-NT) is a pre-clinical traumatic brain injury (TBI) consortium charged with establishing and validating noninvasive TBI assessment tools through team science.
View Article and Find Full Text PDFCardiovasc Ther
January 2025
Department of Cardiology, Tripoli University Hospital, Tripoli, Libya.
Coronary artery disease (CAD) is the leading cause of death worldwide in both men and women. Accordingly, we retrospectively reviewed the effects of various risk factors on coronary angiographic outcomes. Data were collected from the catheter lab through Tripoli University Hospital records, whereas the team reviewed clinical data and coronary artery diagrams for 1 year from 01/04/2019 to 31/03/2020.
View Article and Find Full Text PDFTransl Lung Cancer Res
December 2024
Department of Pathology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.
Background: Differences in the immune microenvironment and responses to immunotherapy may exist between primary non-small cell lung cancer (NSCLC) and brain metastases (BMs). This study aimed to investigate discrepancies in programmed death-ligand 1 (PD-L1) expression, tumor-infiltrating lymphocytes (TILs), tertiary lymphoid structures (TLS), and tumor mutational burden (TMB) between matched BMs and primary tumors (PTs) in NSCLC.
Methods: Twenty-six pairs of surgically resected BMs and corresponding PTs from NSCLC patients were collected.
Front Bioeng Biotechnol
January 2025
Institute for Neuroradiology, TUM University Hospital, School of Medicine and Health, Technical University of Munich, Munich, Germany.
Introduction: Biomechanical simulations can enhance our understanding of spinal disorders. Applied to large cohorts, they can reveal complex mechanisms beyond conventional imaging. Therefore, automating the patient-specific modeling process is essential.
View Article and Find Full Text PDFFunctional magnetic resonance imaging (fMRI) of the spinal cord is relevant for studying sensation, movement, and autonomic function. Preprocessing of spinal cord fMRI data involves segmentation of the spinal cord on gradient-echo echo planar imaging (EPI) images. Current automated segmentation methods do not work well on these data, due to the low spatial resolution, susceptibility artifacts causing distortions and signal drop-out, ghosting, and motion-related artifacts.
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