With the advent and increased accessibility of deep neural networks (DNNs), complex
properties of histologic images can be rigorously and reproducibly quantified. We used DNN …

Background Compilation of different morphological lesion signatures is characteristic of
renal pathology. Previous studies have documented the potential value of artificial …

Identification of glomerular lesions and structures is a key point for pathological diagnosis,
treatment instructions, and prognosis evaluation in kidney diseases. These time‐consuming …

Artificial intelligence (AI) is a new frontier and often enigmatic for medical professionals.
Cloud computing could open up the field of computer vision to a wider medical audience …

Reliable counting of glomeruli and evaluation of glomerulosclerosis in renal specimens are
essential steps to assess morphological changes in kidney and identify individuals requiring …

Background Nephropathologic analyses provide important outcomes-related data in
experiments with the animal models that are essential for understanding kidney disease …

Current methods of scoring histological kidney samples, specifically glomeruli, do not allow
for collection of quantitative data in a high-throughput and consistent manner. Neither …

Background The development of deep neural networks is facilitating more advanced digital
analysis of histopathologic images. We trained a convolutional neural network for multiclass …

Renal biopsy pathology is an essential gold standard for the diagnosis of most kidney
diseases. With the increase in the incidence rate of kidney diseases, the lack of renal …

Renal pathology is essential for diagnosing and assessing the severity and prognosis of
kidney diseases. Deep learning-based approaches have developed rapidly and have been …