Diagnostic systems designed to detect possibly multiple lesions per patient (e.g. multiple polyps during CT colonoscopy) are often evaluated in "free-response" studies that allow for diagnostic responses unconstrained in their number and locations. Analysis of free-response studies requires extensions of the traditional receiver operating characteristic (ROC) analysis, which are termed free-response ROC (FROC) methodology. Despite substantial developments in this area, FROC tools and approaches are much more cumbersome than traditional ROC methods. Alternative approaches that use well-known ROC tools (e.g. ROI-ROC) require defining and physically delineating regions of interest (ROI) and combine FROC data within ROIs. We propose an approach that allows analyzing FROC data using conventional ROC tools without delineating the actual ROIs or reducing data. The design parameters of FROC study are used to make FROC data analyzable using ROC tools and to calibrate the corresponding FROC and ROC curves on both conceptual and numerical levels. Differences in the performance indices of the nonparametric FROC and the new approach are shown to be asymptotically negligible and typically rather small in practice. Data from a large multi-reader study of colon cancer detection are used to illustrate the new approach.
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http://dx.doi.org/10.1177/0962280218776683 | DOI Listing |
Pharmacotherapy
January 2025
Department of Pharmacy Services, Medical University of South Carolina Health, Charleston, South Carolina, USA.
Background: Infections caused by extended-spectrum β-lactamase-producing Enterobacterales (ESBL-E) are increasing in the United States. Although many risk factor scoring tools exist, many are specific to bloodstream isolates and may not represent all patient populations. The purpose of this study was to create and validate an institution-specific scoring tool for select ESBL-E of non-urinary origin based on previously identified risk factors.
View Article and Find Full Text PDFDiagnostics (Basel)
January 2025
Department of Advanced Medical and Surgical Sciences, University of Campania Luigi Vanvitelli, 80138 Naples, Italy.
: Long-term work-related musculoskeletal disorders are predominantly influenced by factors such as the duration, intensity, and repetitive nature of load lifting. Although traditional ergonomic assessment tools can be effective, they are often challenging and complex to apply due to the absence of a streamlined, standardized framework. Recently, integrating wearable sensors with artificial intelligence has emerged as a promising approach to effectively monitor and mitigate biomechanical risks.
View Article and Find Full Text PDFClin Biochem
January 2025
Department of Microbial Biotechnology, Biotechnology Research Institute, National Research Centre, Dokki, P.O. 12622, Giza, Egypt. Electronic address:
Background: The incidence of Breast cancer (BC) is currently augmented and it has become the most common malignant cancer in females. Phosphatase and tensin homolog (PTEN) is a tumor suppressor gene as a result of blocking the phosphorylation of PIP3 in PI3K pathway.
Methods: The computational bioinformatics tools were performed to determine the link between PTEN rs701848T/C genetic variants and breast cancer progression.
J Am Nutr Assoc
January 2025
First Hospital of Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China.
Background: Diabetes is closely related to hypertension, and insulin resistance-related indices are novel metrics used to evaluate the risk of diabetes and cardiovascular diseases. This study aims to explore the relationships between the TyG index, METS-IR, TG/HDL-C, and HOMA-IR with hypertension.
Methods: Data from the NHANES spanning ten consecutive survey cycles from 1998 to 2018 were utilized, focusing on adults with complete blood pressure data and comprehensive information for calculating the TyG index, METS-IR, TG/HDL-C, and HOMA-IR.
Cardiovasc Diagn Ther
December 2024
Operational Research Center in Healthcare, Near East University, Nicosia, Turkey.
Background: Cardiovascular diseases (CVDs) continue to be the world's greatest cause of death. To evaluate heart function and diagnose coronary artery disease (CAD), myocardial perfusion imaging (MPI) has become essential. Artificial intelligence (AI) methods have been incorporated into diagnostic methods such as MPI to improve patient outcomes in recent years.
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