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Combining Multifrequency Magnetic Resonance Elastography With Automatic Segmentation to Assess Renal Function in Patients With Chronic Kidney Disease.
J Magn Reson Imaging
January 2025
Department of Radiology, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine (Shenzhen Traditional Chinese Medicine Hospital), Shenzhen, China.
Background: Multifrequency MR elastography (mMRE) enables noninvasive quantification of renal stiffness in patients with chronic kidney disease (CKD). Manual segmentation of the kidneys on mMRE is time-consuming and prone to increased interobserver variability.
Purpose: To evaluate the performance of mMRE combined with automatic segmentation in assessing CKD severity.
The Added Effect of Artificial Intelligence in CT Assessment of Abdominal Lymphadenopathy.
Lymphology
January 2024
Medical Biophysics Department, Medical Research Institute, Alexandria University, Alexandria, Egypt.
Lymphadenopathy is associated with lymph node abnormal size or consistency due to many causes. We employed the deep convolutional neural network ResNet-34 to detect and classify CT images from patients with abdominal lymphadenopathy and healthy controls. We created a single database containing 1400 source CT images for patients with abdominal lymphadenopathy (n = 700) and healthy controls (n = 700).
View Article and Find Full Text PDFEditorial for "Combining Multifrequency Magnetic Resonance Elastography with Automatic Segmentation to Assess Renal Function in Patients With Chronic Kidney Disease".
J Magn Reson Imaging
January 2025
School of Medicine, Department of Radiology, Hacettepe University, Ankara, Turkey.
A novel artificial intelligence-powered tool for automated root canal segmentation in single-rooted teeth on cone-beam computed tomography.
Int Endod J
January 2025
OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, University of Leuven, Leuven, Belgium.
Aim: To develop and validate an artificial intelligence (AI)-powered tool based on convolutional neural network (CNN) for automatic segmentation of root canals in single-rooted teeth using cone-beam computed tomography (CBCT).
Methodology: A total of 69 CBCT scans were retrospectively recruited from a hospital database and acquired from two devices with varying protocols. These scans were randomly assigned to the training (n = 31, 88 teeth), validation (n = 8, 15 teeth) and testing (n = 30, 120 teeth) sets.
Ingredient-based method to create medication lists and support granular data segmentation.
Health Informatics J
January 2025
College of Health Solutions, Arizona State University, Phoenix, AZ, USA.
Show the generalizability of an ingredient-based method to automatically create an up-to-date, error-free, complete list of medication codes (e.g., opioid medications with at least one opioid ingredient) from an ingredient list (e.
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