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http://dx.doi.org/10.3758/bf03196951 | DOI Listing |
Gout Urate Cryst Depos Dis
December 2024
Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095, USA.
Background: The gold standard for crystal arthritis diagnosis relies on the identification of either monosodium urate (MSU) or calcium pyrophosphate (CPP) crystals in synovial fluid. With the goal of enhanced crystal detection, we adapted a standard compensated polarized light microscope (CPLM) with a polarized digital camera and multi-focal depth imaging capabilities to create digital images from synovial fluid mounted on microscope slides. Using this single-shot computational polarized light microscopy (SCPLM) method, we compared rates of crystal detection and raters' preference for image.
View Article and Find Full Text PDFHeliyon
January 2025
Department of Physics and Astronomy, University of Bologna, Bologna, 40127, Italy.
Background: The modern approach to treating rectal cancer, which involves total mesorectal excision directed by imaging assessments, has significantly enhanced patient outcomes. However, locally recurrent rectal cancer (LRRC) continues to be a significant clinical issue. Identifying LRRC through imaging is complex, due to the mismatch between fibrosis and inflammatory pelvic tissue.
View Article and Find Full Text PDFQuant Imaging Med Surg
January 2025
School of Computing, Mathematics and Engineering, Charles Sturt University, Albury, Australia.
Background: The limitation in spatial resolution of bone scintigraphy, combined with the vast variations in size, location, and intensity of bone metastasis (BM) lesions, poses challenges for accurate diagnosis by human experts. Deep learning-based analysis has emerged as a preferred approach for automating the identification and delineation of BM lesions. This study aims to develop a deep learning-based approach to automatically segment bone scintigrams for improving diagnostic accuracy.
View Article and Find Full Text PDFZhongguo Xue Xi Chong Bing Fang Zhi Za Zhi
January 2025
School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China.
Objective: To construct a visual intelligent recognition model for in Yunnan Province based on the EfficientNet-B4 model, and to evaluate the impact of data augmentation methods and model hyperparameters on the recognition of .
Methods: A total of 400 and 400 snails were collected from Yongsheng County, Yunnan Province in June 2024, and snail images were captured following identification and classification of 300 and 300 snails. A total of 925 images and 1 062 snail images were collected as a dataset and divided into a training set and a validation set at a ratio of 8:2, while 352 images captured from the remaining 100 and 354 images from the remaining 100 snails served as an external test set.
J Eat Disord
January 2025
Faculty of Medicine, Department of Endocrinology and Metabolism, Gazi University, Ankara, Turkey.
Purpose: Adults with type 1 diabetes (T1D) are reported to be at higher risk for clinical eating disorders (ED) and other disordered eating behaviors (DEB) than their peers without diabetes. On the other hand, there is insufficient data on DEB in adults with type 2 diabetes (T2D). Our study aimed to investigate the prevalence of DEB in patients with T1D and T2D on intensive insulin therapy followed in our outpatient clinic.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!