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Kidney

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A U-Net based framework to quantify glomerulosclerosis in digitized PAS and H&E stained human tissues.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Reliable counting of glomeruli and evaluation of glomerulosclerosis in renal specimens are essential steps to assess morphological changes in kidney and identify individuals requiring treatment. Because microscopic identification of sclerosed glomeru...

An Interpretable Machine Learning Survival Model for Predicting Long-term Kidney Outcomes in IgA Nephropathy.

AMIA ... Annual Symposium proceedings. AMIA Symposium
IgA nephropathy (IgAN) is common worldwide and has heterogeneous phenotypes. Predicting long-term outcomes is important for clinical decision-making. As right-censored patients become common during the long-term follow-up, either excluding these pati...

Quantitative evaluation of chronically obstructed kidneys from noncontrast computed tomography based on deep learning.

European journal of radiology
OBJECTIVE: To quantitatively report renal parenchymal volume (RPV), renal sinus volume (RSV), and renal parenchymal density (RPD) for chronically obstructed kidneys from noncontrast computed tomography (NCCT).

Long-Term Functional and Oncologic Outcomes of Robot-Assisted Partial Nephrectomy for Cystic Renal Tumors: A Single-Center Retrospective Study.

Journal of endourology
To evaluate the outcomes of robot-assisted partial nephrectomy (RAPN) in cystic renal tumors. We retrospectively analyzed patients who underwent RAPN for either cystic ( = 46) or solid ( = 271) renal tumors at Fujita Health University between 2010 ...

Development and Validation of a Deep Learning Model to Quantify Glomerulosclerosis in Kidney Biopsy Specimens.

JAMA network open
IMPORTANCE: A chronic shortage of donor kidneys is compounded by a high discard rate, and this rate is directly associated with biopsy specimen evaluation, which shows poor reproducibility among pathologists. A deep learning algorithm for measuring p...

Reducing scan time of paediatric Tc-DMSA SPECT via deep learning.

Clinical radiology
AIM: To investigate the feasibility of reducing the scan time of paediatric technetium 99m (Tc) dimercaptosuccinic acid (DMSA) single-photon-emission computed tomographic (SPECT) using a deep learning (DL) method.

In Situ Classification of Cell Types in Human Kidney Tissue Using 3D Nuclear Staining.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
To understand the physiology and pathology of disease, capturing the heterogeneity of cell types within their tissue environment is fundamental. In such an endeavor, the human kidney presents a formidable challenge because its complex organizational ...

CT-ORG, a new dataset for multiple organ segmentation in computed tomography.

Scientific data
Despite the relative ease of locating organs in the human body, automated organ segmentation has been hindered by the scarcity of labeled training data. Due to the tedium of labeling organ boundaries, most datasets are limited to either a small numbe...

Deep learning-enabled multi-organ segmentation in whole-body mouse scans.

Nature communications
Whole-body imaging of mice is a key source of information for research. Organ segmentation is a prerequisite for quantitative analysis but is a tedious and error-prone task if done manually. Here, we present a deep learning solution called AIMOS that...

Deep Learning-Based Segmentation and Quantification in Experimental Kidney Histopathology.

Journal of the American Society of Nephrology : JASN
BACKGROUND: Nephropathologic analyses provide important outcomes-related data in experiments with the animal models that are essential for understanding kidney disease pathophysiology. Precision medicine increases the demand for quantitative, unbiase...