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Automated detection of traumatic bleeding in CT images using 3D U-Net# and multi-organ segmentation.
Biomed Phys Eng Express
January 2025
Chiba University Center for Frontier Medical Engineering, 1-33 Yayoi-cho, Inage-ku, Chiba, Chiba, 263-8522, JAPAN.
Traumatic injury remains a leading cause of death worldwide, with traumatic bleeding being one of its most critical and fatal consequences. The use of whole-body computed tomography (WBCT) in trauma management has rapidly expanded. However, interpreting WBCT images within the limited time available before treatment is particularly challenging for acute care physicians.
View Article and Find Full Text PDFParental Mental Health and Child Maltreatment in the COVID-19 Pandemic: Importance of Sampling in a Quantitative Statistical Study.
J Med Internet Res
January 2025
Clinical Psychology and Psychotherapy, Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany.
Background: Results on parental burden during the COVID-19 pandemic are predominantly available from nonrepresentative samples. Although sample selection can significantly influence results, the effects of sampling strategies have been largely underexplored.
Objective: This study aimed to investigate how sampling strategy may impact study results.
Intra- and interobserver agreement of proposed objective transvaginal ultrasound image-quality scoring system for use in artificial intelligence algorithm development.
Ultrasound Obstet Gynecol
January 2025
Robinson Research Institute, University of Adelaide, Adelaide, Australia.
Objectives: The development of valuable artificial intelligence (AI) tools to assist with ultrasound diagnosis depends on algorithms developed using high-quality data. This study aimed to test the intra- and interobserver agreement of a proposed image-quality scoring system to quantify the quality of gynecological transvaginal ultrasound (TVS) images, which could be used in clinical practice and AI tool development.
Methods: A proposed scoring system to quantify TVS image quality was created following a review of the literature.
Automated mold defects classification in paintings: A comparison of machine learning and rule-based techniques.
PLoS One
January 2025
Faculty of Science and Engineering, School of Computer Science, University of Hull, Hull, United Kingdom.
Mold defects pose a significant risk to the preservation of valuable fine art paintings, typically arising from fungal growth in humid environments. This paper presents a novel approach for detecting and categorizing mold defects in fine art paintings. The technique leverages a feature extraction method called Derivative Level Thresholding to pinpoint suspicious regions within an image.
View Article and Find Full Text PDFAI-assisted radiologists vs. standard double reading for rib fracture detection on CT images: A real-world clinical study.
PLoS One
January 2025
Department of Radiology, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China.
To evaluate the diagnostic accuracy of artificial intelligence (AI) assisted radiologists and standard double-reading in real-world clinical settings for rib fractures (RFs) detection on CT images. This study included 243 consecutive chest trauma patients (mean age, 58.1 years; female, 166) with rib CT scans.
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