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| http://dx.doi.org/10.3390/diagnostics12112635 | DOI Listing |
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Correction: Sebro et al. Implementation of Antigen-Based Diagnostic Assays for Detection of Histoplasmosis and Cryptococcosis among Patients with Advanced HIV in Trinidad and Tobago: A Cross-Sectional Study. 2024, , 695.
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Department of Communicable Diseases Prevention, Control, and Elimination, Pan American Health Organization, Washington, DC 20037, USA.
In the original publication [...
View Article and Find Full Text PDFDoes NIH funding differ between medical specialties? A longitudinal analysis of NIH grant data by specialty and type of grant, 2011-2020.
BMJ Open
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Center for Augmented Intelligence, Mayo Clinic, Jacksonville, FL, USA
Objectives: Differences in National Institutes of Health (NIH) funding between specialties may affect research and patient outcomes in specialties that are less well funded.The aim of this study is to evaluate how NIH funding has been awarded by medical specialty. This study assesses differences and trends in the amount of funding, by medical specialty, for the years 2011-2020, via a retrospective analysis of data from the NIH RePORTER (Research Portfolio Online Reporting Tools Expenditures and Results).
View Article and Find Full Text PDFCorrection: Sebro, R.; De la Garza-Ramos, C. Machine Learning for Opportunistic Screening for Osteoporosis from CT Scans of the Wrist and Forearm. 2022, , 691.
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Center for Augmented Intelligence, Mayo Clinic Florida, Department of Radiology, Jacksonville, FL 32224, USA.
In the original publication [...
View Article and Find Full Text PDFPre-deployment assessment of an AI model to assist radiologists in chest X-ray detection and identification of lead-less implanted electronic devices for pre-MRI safety screening: realized implementation needs and proposed operational solutions.
J Med Imaging (Bellingham)
September 2022
Mayo Clinic, Department of Radiology, Center for Augmented Intelligence in Imaging, Jacksonville, Florida, United States.
Purpose: Chest X-ray (CXR) use in pre-MRI safety screening, such as for lead-less implanted electronic device (LLIED) recognition, is common. To assist CXR interpretation, we "pre-deployed" an artificial intelligence (AI) model to assess (1) accuracies in LLIED-type (and consequently safety-level) identification, (2) safety implications of LLIED nondetections or misidentifications, (3) infrastructural or workflow requirements, and (4) demands related to model adaptation to real-world conditions.
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Optimized CT Attenuation and SUV Prediction Thresholds for Differentiating Enostoses From Untreated and Treated Metastases on Attenuation-Corrected 18F-FDG PET/CT.
Clin Nucl Med
January 2020
From the Departments of Radiology.
Purpose: The objective of this study was to evaluate the accuracy of using CT attenuation and SUVs to differentiate enostoses from untreated and treated osteoblastic metastases on the attenuation-correction CT component of F-FDG PET/CTs.
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