Introduction: We estimated the diagnostic accuracy of Abrams-Griffiths number (AG), urethral resistance factor (URA) and detrusor-adjusted mean passive urethral resistance relation factor (DAMPF) for bladder outlet obstruction (BOO) in benign prostate hyperplasia (BPH) patients.
Materials And Methods: AG, URA and DAMPF were obtained by pressure-flow studies from BPH patients. Receiver operating characteristic (ROC) curves were used to analyze the diagnostic accuracy of the AG, URA and DAMPF in the diagnosis of BOO.
Results: Among the 172 cases there were 154 classified as obstructed (89.5%) and 18 as unobstructed (10.5%). There were statistically significant differences in AG, URA and DAMPF between the obstructed and the unobstructed cases. The ROC curve demonstrated a similar diagnostic accuracy in the diagnosis of BOO for AG and URA values, and the least was seen for the DAMPF value. An AG cutoff of >33 provided a sensitivity of 89.61% and a specificity of 100%. A URA cutoff of >28 provided a sensitivity of 91.56% and a specificity of 100%. A sensitivity of 93.51% and the weakest specificity of 77.78% were recorded for DAMPF values of >52. AG and URA had a similar accuracy, while the efficacy of DAMPF is significantly lower in the diagnosis of BOO.
Conclusions: AG or URA appeared to be the best discriminating parameters of BOO in BPH patients. The DAMPF could be used to aid the BOO diagnosis. Lower cutoff values were suggested for these BOO parameters.
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http://dx.doi.org/10.1159/000319968 | DOI Listing |
Pol J Vet Sci
December 2024
Nicolaus Copernicus University Veterinary Clinic, Szosa Bydgoska 13, 87-100 Toruń, Poland.
Proper management of cattle reproduction has a major impact on the efficiency and profitability of dairy production. Ultrasound examination and transrectal palpation or the pregnancy-associated glycoprotein (PAG) test are currently the most commonly used methods for pregnancy diagnosis. However, alternative methods to those mentioned above are constantly being sought in order to minimise stress during the examination, the cost of veterinary services and to reduce the rate of errors in pregnancy diagnosis.
View Article and Find Full Text PDFPorcine circovirus type 2 (PCV2) is the major causative agent of postweaning multisystemic wasting syndrome which leads to significant economic losses in the global swine industry. In China, there is a widespread dissemination of PCV2 infection in the pig population. Serological diagnosis of the disease is considered as an effective control measure.
View Article and Find Full Text PDFJ Integr Neurosci
December 2024
Department of Computer Science and Engineering, Shaoxing University, 312000 Shaoxing, Zhejiang, China.
Background: Motor imagery (MI) plays an important role in brain-computer interfaces, especially in evoking event-related desynchronization and synchronization (ERD/S) rhythms in electroencephalogram (EEG) signals. However, the procedure for performing a MI task for a single subject is subjective, making it difficult to determine the actual situation of an individual's MI task and resulting in significant individual EEG response variations during motion cognitive decoding.
Methods: To explore this issue, we designed three visual stimuli (arrow, human, and robot), each of which was used to present three MI tasks (left arm, right arm, and feet), and evaluated differences in brain response in terms of ERD/S rhythms.
Indian J Orthop
January 2025
Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, 150001 China.
Introduction: The Steinberg classification system is commonly used by orthopedic surgeons to stage the severity of patients with osteonecrosis of the femoral head (ONFH), and it includes mild, moderate, and severe grading of each stage based on the area of the femoral head affected. However, clinicians mostly grade approximately by visual assessment or not at all. To accurately distinguish the mild, moderate, or severe grade of early stage ONFH, we propose a convolutional neural network (CNN) based on magnetic resonance imaging (MRI) of the hip joint of patients to accurately grade and aid diagnosis of ONFH.
View Article and Find Full Text PDFFront Physiol
December 2024
Department of Oral & Maxillofacial Surgery, Shenzhen Stomatology Hospital, Affiliated to Shenzhen University, Shenzhen, Guangdong Province, China.
Introduction: This study aimed to develop a deep learning-based method for interpreting magnetic resonance imaging (MRI) scans of temporomandibular joint (TMJ) anterior disc displacement (ADD) and to formulate an automated diagnostic system for clinical practice.
Methods: The deep learning models were utilized to identify regions of interest (ROI), segment TMJ structures including the articular disc, condyle, glenoid fossa, and articular tubercle, and classify TMJ ADD. The models employed Grad-CAM heatmaps and segmentation annotation diagrams for visual diagnostic predictions and were deployed for clinical application.
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