As imaging technology plays an important role in the diagnosis and evaluation of the new coronavirus pneumonia (COVID-19), COVID-19 related data sets have been published one after another, but there are relatively few data sets and research progress in related literature. To this end, through COVID-19-related journal papers, reports, and related open-source data set websites, organize and analyze the new coronary pneumonia data set and the deep learning models involved, including computed tomography (CT) image data sets and X-ray (CXR) Image dataset. Analyze the characteristics of the medical images presented in these data sets; focus on open-source data sets, as well as classification and segmentation models that perform well on related data sets. Finally, the future development trend of lung imaging technology is discussed.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9113957 | PMC |
| http://dx.doi.org/10.1016/j.matpr.2022.04.884 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Dosimetry Software for Theranostic Applications: Current Capabilities and Future Prospects.
J Nucl Med
January 2025
Department of Nuclear Medicine, University Hospital Würzburg, Würzburg, Germany
Dosimetry is integral to informed implementation of radiopharmaceutical therapies, enabling personalized treatment planning and ensuring patient safety by calculating absorbed doses to organs and tumors. As the therapeutic radiopharmaceutical field continues to expand, dosimetry software has emerged as a crucial tool for optimization of treatment efficacy. This review discusses key features and capabilities that current dosimetry software solutions have or should have in the future.
View Article and Find Full Text PDFMapping amyloid beta predictors of entorhinal tau in preclinical Alzheimer's disease.
Alzheimers Dement
January 2025
Computational Brain Research and Intervention (C-Brain) Lab, Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford University, Stanford, California, USA.
Introduction: Amyloid beta (Aβ) plaques and hyperphosphorylated tau in the entorhinal regions are key Alzheimer's disease (AD) markers, but the spatial Aβ pathways influencing tau pathology remain unclear.
Methods: We applied predictive modeling to identify Aβ standardized uptake value ratio (SUVR) spatial patterns that predict entorhinal tau levels, future hippocampal volume, and Preclinical Alzheimer's Cognitive Composite (PACC) scores at 5-year follow-up. The model was trained on Alzheimer's Disease Neuroimaging Initiative (ADNI) (N = 237), incorporating amyloid-PET (positron emission tomography), tau-PET, magnetic resonance imaging (MRI), and cognitive data, and validated on Harvard Aging Brain Study (HABS) (N = 276).
Impacts of rapid urbanization on long-term water quality of the peripheral River of Dhaka, Bangladesh.
Water Environ Res
January 2025
Department of Environmental Sciences, Hydrobiogeochemistry and Pollution Control Laboratory, Jahangirnagar University, Dhaka, Bangladesh.
This study was carried out to determine the current state of the physicochemical water quality parameters and the effects of urbanization over 50 years in the peripheral rivers by using primary and secondary data adjacent to Dhaka city. These rivers and waterways had DO levels much below the recommended standard of Bangladesh, and occasionally, they even approached 0. This suggests that the water in these rivers is highly polluted and unfit for aquatic life.
View Article and Find Full Text PDFUtilising causal inference methods to estimate effects and strategise interventions in observational health data.
PLoS One
January 2025
Applied Artificial Intelligence Institute (A2I2), Deakin University, Geelong, Australia.
Randomised controlled trials (RCTs) are the gold standard for evaluating health interventions but often face ethical and practical challenges. When RCTs are not feasible, large observational data sets emerge as a pivotal resource, though these data sets may be subject to bias and unmeasured confounding. Traditional statistical (or non-causal) learning methods, while useful, face limitations in fully uncovering causal effects, i.
View Article and Find Full Text PDFDevelopment and validation of the Iranian Minimum Data Set for Epidermolysis Bullosa: A mixed method approach.
PLoS One
January 2025
Assistant Professor of Health Information Management, Department of Health Information Technology, Ferdows Faculty of Medical Sciences, Birjand University of Medical Sciences, Birjand, Iran.
Minimum Data Set (MDS) enables integration in data collection, uniform data reporting, and data exchange across clinical and research information systems. The current study was conducted to determine a comprehensive national MDS for the Epidermolysis Bullosa (EB) information management system in Iran. This cross-sectional descriptive study consists of three steps: systematic review, focus group discussion, and the Delphi technique.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!