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Error mitigation in forensic handwriting examination: the examiner's perspective.
Forensic Sci Res
December 2024
National Forensic Laboratory, Ljubljana, Slovenia.
Like other pattern recognition disciplines, forensic handwriting examination relies on various human factors. Expert opinions in the field are based on visual analysis and comparison, and the evaluation of findings is generally conducted without reference to tabulated data. This high level of subjectivity may contribute to bias and error in the examination process.
View Article and Find Full Text PDFThe Validity of a Smartphone-Based Application for Assessing Cognitive Function in the Elderly.
Diagnostics (Basel)
January 2025
Department of Preventive Medicine, College of Medicine, Seoul National University, 103 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea.
The early detection of individuals at risk of cognitive impairment is a clinical imperative. With the recent advancement of digital devices, smartphone application-based cognitive assessment is considered a promising tool for cognitive screening and monitoring inside and outside the clinic. This study examined whether a smartphone-based cognitive assessment, Brain OK, was valid for evaluating cognitive performance and identifying people at risk of cognitive impairment.
View Article and Find Full Text PDFAutomated Detection and Differentiation of Stanford Type A and Type B Aortic Dissections in CTA Scans Using Deep Learning.
Diagnostics (Basel)
December 2024
Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Taoyuan City 333423, Taiwan.
Background/objectives: To develop and validate a model system using deep learning algorithms for the automatic detection of type A aortic dissection (AD), and differentiate it from normal and type B AD patients.
Methods: In this retrospective study, a deep learning model is developed, based on aortic computed tomography angiography (CTA) scans of 498 patients using training, validation and test sets of 398, 50 and 50 patients, respectively. An independent test set of 316 patients is used to validate and evaluate its performance.
Automated classification of coronary LEsions fRom coronary computed Tomography angiography scans with an updated deep learning model: ALERT study.
Eur Radiol
January 2025
Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, University of Amsterdam, Amsterdam Cardiovascular Sciences, Amsterdam, The Netherlands.
Objectives: The use of deep learning models for quantitative measurements on coronary computed tomography angiography (CCTA) may reduce inter-reader variability and increase efficiency in clinical reporting. This study aimed to investigate the diagnostic performance of a recently updated deep learning model (CorEx-2.0) for quantifying coronary stenosis, compared separately with two expert CCTA readers as references.
View Article and Find Full Text PDFEfficacy of a Dietary Supplement Extracted from Persimmon ( L.f.) in Overweight Healthy Adults: A Randomized, Double-Blind, Controlled Clinical Trial.
Foods
December 2024
Faculty of Medicine, UCAM Universidad Católica San Antonio de Murcia, Carretera de Guadalupe s/n, 30107 Murcia, Spain.
A single-center, randomized, double-blind, and placebo-controlled clinical trial assessed the efficacy in improving body composition and in weight management of a dietary supplement consisting of 400 mg of a standardized extract of the persimmon fruit ( L.f.) in adult subjects with a BMI between 25 and 34.
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