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| http://dx.doi.org/10.3389/fpls.2023.1145458 | DOI Listing |
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Deep Learning to Simulate Contrast-Enhanced MRI for Evaluating Suspected Prostate Cancer.
Radiology
January 2025
From the Department of Radiology, Shenzhen Nanshan People's Hospital, Shenzhen University, Taoyuan Rd No. 89, Nanshan District, Shenzhen 518000, Guangdong, China (H.H., Z.D., Y.Q.); Medical AI Laboratory and Guangdong Key Laboratory of Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China (J.M., R.L., B.H.); Department of Medical Imaging, People's Hospital of Longhua, Shenzhen, Guangdong, China (X.P., Y.Z.); and Department of Radiology, Shenzhen People's Hospital, Shenzhen, Guangdong, China (D.Z., G.H.).
Background Multiparametric MRI, including contrast-enhanced sequences, is recommended for evaluating suspected prostate cancer, but concerns have been raised regarding potential contrast agent accumulation and toxicity. Purpose To evaluate the feasibility of generating simulated contrast-enhanced MRI from noncontrast MRI sequences using deep learning and to explore their potential value for assessing clinically significant prostate cancer using Prostate Imaging Reporting and Data System (PI-RADS) version 2.1.
View Article and Find Full Text PDFOver-resection, or under-resection, of the femoral cam lesion in patients having hip arthroscopy for femoroacetabular impingement syndrome indicate different possible solutions.
Arthroscopy
January 2025
Kansas City Orthopedic Alliance, 10777 Nall Avenue, Overland Park, KS 66224. Electronic address:
As surgeons, we strive to recognize and correct any mistakes that may occur before completing an operation, and importantly, do our best to avoid irreversible mistakes. Over-resection of the femoral cam lesion in patients having hip arthroscopy for femoroacetabular impingement syndrome has been considered irreversible. While cam under-resection is a technical complication of femoroacetabular impingement surgery to be avoided, avoiding this at the expense of over-resection of the proximal femur is of great concern.
View Article and Find Full Text PDFToo Much of a Good Thing: Updated Current Management and Perinatal Outcomes of Polyhydramnios.
J Med Ultrasound
November 2024
Department of Obstetrics and Gynecology, Mackay Memorial Hospital, Taipei, Taiwan.
Amniotic fluid assessment is crucial in prenatal ultrasound to monitor fetal conditions, with polyhydramnios, characterized by excessive amniotic fluid, affecting 1%-2% of pregnancies. Polyhydramnios is linked to complications such as placental abruption, preterm labor, congenital anomalies, and postpartum hemorrhage, emphasizing the need for early detection and management. While idiopathic causes account for 60%-70% of cases, other causes include impaired fetal swallowing and increased urine production due to maternal, fetal, and placental conditions.
View Article and Find Full Text PDFClinical validation of artificial intelligence-based single-subject morphometry without normative reference database.
J Alzheimers Dis
January 2025
Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
Background: Single-subject voxel-based morphometry (VBM) is a powerful technique for reader-independent detection of brain atrophy in structural magnetic resonance imaging (MRI) to support the (differential) diagnosis and staging of neurodegenerative diseases in individual patients. However, VBM is sensitive to the MRI scanner platform and details of the acquisition sequence. To mitigate this limitation, we recently proposed and validated a convolutional neural network (CNN)-based VBM which does not rely on a normative reference database.
View Article and Find Full Text PDFConsiderations for adapting digital competencies and training approaches to the public health workforce: an interpretive description of practitioners' perspectives in Canada.
BMC Public Health
January 2025
School of Population and Public Health (SPPH), University of British Columbia (UBC), 2206 East Mall, Vancouver, BC, V6T 1Z3, Canada.
Background: Widespread digital transformation necessitates developing digital competencies for public health practice. Given work in 2024 to update Canada's public health core competencies, there are opportunities to consider digital competencies. In our previous research, we identified digital competency and training recommendations within the literature.
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