AIMC Topic: Kidney Glomerulus

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AI-driven glomerular morphology quantification: a novel pipeline for assessing basement membrane thickness and podocyte foot process effacement in kidney diseases.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Measuring the thickness of the glomerular basement membrane (GBM) and assessing the percentage of podocyte foot process effacement (%PFPE) are important for diagnosing non-neoplastic kidney diseases. However, when performed ...

Application of cloud server-based machine learning for assisting pathological structure recognition in IgA nephropathy.

Journal of clinical pathology
BACKGROUND: Machine learning (ML) models can help assisting diagnosis by rapidly localising and classifying regions of interest (ROIs) within whole slide images (WSIs). Effective ML models for clinical decision support require a substantial dataset o...

Exploring the subtle and novel renal pathological changes in diabetic nephropathy using clustering analysis with deep learning.

Scientific reports
To decrease the number of chronic kidney disease (CKD), early diagnosis of diabetic kidney disease is required. We performed invariant information clustering (IIC)-based clustering on glomerular images obtained from nephrectomized kidneys of patients...

Glo-net: A dual task branch based neural network for multi-class glomeruli segmentation.

Computers in biology and medicine
Accurate segmentation and classification of glomeruli are fundamental to histopathology slide analysis in renal pathology, which helps to characterize individual kidney disease. Accurate segmentation of glomeruli of different types faces two main cha...

Unveiling pathology-related predictive uncertainty of glomerular lesion recognition using prototype learning.

Journal of biomedical informatics
OBJECTIVE: Recognizing glomerular lesions is essential in diagnosing chronic kidney disease. However, deep learning faces challenges due to the lesion heterogeneity, superposition, progression, and tissue incompleteness, leading to uncertainty in mod...

Self-Supervised Learning for Feature Extraction from Glomerular Images and Disease Classification with Minimal Annotations.

Journal of the American Society of Nephrology : JASN
BACKGROUND: Deep learning has great potential in digital kidney pathology. However, its effectiveness depends heavily on the availability of extensively labeled datasets, which are often limited because of the specialized knowledge and time required ...

Accurate classification of glomerular diseases by hyperspectral imaging and transformer.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: In renal disease research, precise glomerular disease diagnosis is crucial for treatment and prognosis. Currently reliant on invasive biopsies, this method bears risks and pathologist-dependent variability, yielding inconsis...

Unsupervised stain augmentation enhanced glomerular instance segmentation on pathology images.

International journal of computer assisted radiology and surgery
PURPOSE: In pathology images, different stains highlight different glomerular structures, so a supervised deep learning-based glomerular instance segmentation model trained on individual stains performs poorly on other stains. However, it is difficul...

Artificial intelligence assists identification and pathologic classification of glomerular lesions in patients with diabetic nephropathy.

Journal of translational medicine
BACKGROUND: Glomerular lesions are the main injuries of diabetic nephropathy (DN) and are used as a crucial index for pathologic classification. Manual quantification of these morphologic features currently used is semi-quantitative and time-consumin...

Deep learning-based glomerulus detection and classification with generative morphology augmentation in renal pathology images.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Glomerulus morphology on renal pathology images provides valuable diagnosis and outcome prediction information. To provide better care, an efficient, standardized, and scalable method is urgently needed to optimize the time-consuming and labor-intens...