AIMC Topic: Kidney

Clear Filters Showing 131 to 140 of 492 articles

Deep learning-based automated kidney and cyst segmentation of autosomal dominant polycystic kidney disease using single vs. multi-institutional data.

Clinical imaging
PURPOSE: This study aimed to investigate if a deep learning model trained with a single institution's data has comparable accuracy to that trained with multi-institutional data for segmenting kidney and cyst regions in magnetic resonance (MR) images ...

Comparison of Percutaneous Renal Access Between Robot-Assisted Fluoroscopy Guidance Using the Bi-Plane Method and Ultrasound Guidance: A Multicenter Randomized Control Benchtop Study.

Journal of endourology
To evaluate the efficacy of supine percutaneous renal access by robot-assisted (RA) fluoroscopy and ultrasound (US) guidance in terms of procedural outcomes and surgeon workload. We conducted a multicenter, randomized, controlled benchtop study inv...

Dynamic parametric MRI and deep learning: Unveiling renal pathophysiology through accurate kidney size quantification.

NMR in biomedicine
Renal pathologies often manifest as alterations in kidney size, providing a valuable avenue for employing dynamic parametric MRI as a means to derive kidney size measurements for the diagnosis, treatment, and monitoring of renal disease. Furthermore,...

FastCellpose: A Fast and Accurate Deep-Learning Framework for Segmentation of All Glomeruli in Mouse Whole-Kidney Microscopic Optical Images.

Cells
Automated evaluation of all glomeruli throughout the whole kidney is essential for the comprehensive study of kidney function as well as understanding the mechanisms of kidney disease and development. The emerging large-volume microscopic optical ima...

Convolutional neural network-based kidney volume estimation from low-dose unenhanced computed tomography scans.

BMC medical imaging
PURPOSE: Kidney volume is important in the management of renal diseases. Unfortunately, the currently available, semi-automated kidney volume determination is time-consuming and prone to errors. Recent advances in its automation are promising but mos...

Correlating Deep Learning-Based Automated Reference Kidney Histomorphometry with Patient Demographics and Creatinine.

Kidney360
KEY POINTS: The authors leverage the unique benefits of panoptic segmentation to perform the largest ever quantitation of reference kidney morphometry. Kidney features vary with age and sex; and glomeruli size may intricately link to creatinine, defy...

Combined colour deconvolution and artificial intelligence approach for region-selective immunohistochemical labelling quantification: The example of alpha smooth muscle actin in mouse kidney.

Journal of biophotonics
Immunohistochemical (IHC) localisation of protein expression is a widely used tool in pathology. This is semi-quantitative and exhibits substantial intra- and inter-observer variability. Digital approaches based on stain quantification applied to IHC...

Accurate exclusion of kidney regions affected by susceptibility artifact in blood oxygenation level-dependent (BOLD) images using deep-learning-based segmentation.

Scientific reports
Susceptibility artifact (SA) is common in renal blood oxygenation level-dependent (BOLD) images, and including the SA-affected region could induce much error in renal oxygenation quantification. In this paper, we propose to exclude kidney regions aff...

A large-scale retrospective study enabled deep-learning based pathological assessment of frozen procurement kidney biopsies to predict graft loss and guide organ utilization.

Kidney international
Lesion scores on procurement donor biopsies are commonly used to guide organ utilization for deceased-donor kidneys. However, frozen sections present challenges for histological scoring, leading to inter- and intra-observer variability and inappropri...