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Kidney Diseases

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Clinicopathological features for the prediction of immunosuppressive treatment responses in sarcoidosis-related kidney involvement: a single-center retrospective study.

Turkish journal of medical sciences
BACKGROUND/AIM: Sarcoidosis is a multisystem disorder that affects many organs, including the kidneys. This single-center retrospective study investigated the clinical, pathological, and laboratory findings of patients with kidney sarcoidosis who wer...

LLM-based kidney disease diagnostic framework for Pathologists.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Large language models revolutionize the recent paradigm in the medical field and its contributing to various applications, diversified from clinical decision support to information extraction and summarization. The substantial linguistic understandin...

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 ...

Low-Cost Raman Spectroscopy Setup Combined with a Machine Learning Model.

Sensors (Basel, Switzerland)
The diagnosis of kidney diseases presents significant challenges, including the reliance on variable and unstable biomarkers and the necessity for complex and expensive laboratory tests. Raman spectroscopy emerges as a promising technique for analyzi...

Machine learning assisted classification RASAR modeling for the nephrotoxicity potential of a curated set of orally active drugs.

Scientific reports
We have adopted the classification Read-Across Structure-Activity Relationship (c-RASAR) approach in the present study for machine-learning (ML)-based model development from a recently reported curated dataset of nephrotoxicity potential of orally ac...

Fine-tuned deep learning models for early detection and classification of kidney conditions in CT imaging.

Scientific reports
The kidney plays a vital role in maintaining homeostasis, but lifestyle factors and diseases can lead to kidney failures. Early detection of kidney disease is crucial for effective intervention, often challenging due to unnoticeable symptoms in the i...

Multi-level feature fusion network for kidney disease detection.

Computers in biology and medicine
Kidney irregularities pose a significant public health challenge, often leading to severe complications, yet the limited availability of nephrologists makes early detection costly and time-consuming. To address this issue, we propose a deep learning ...

Leveraging ensemble convolutional neural networks and metaheuristic strategies for advanced kidney disease screening and classification.

Scientific reports
To address the public health issue of renal failure and the global shortage of nephrologists, an AI-based system has been developed to automatically identify kidney diseases. Recent advancements in machine learning, deep learning (DL), and artificial...

Recruiting Teacher IF Modality for Nephropathy Diagnosis: A Customized Distillation Method With Attention-Based Diffusion Network.

IEEE transactions on medical imaging
The joint use of multiple modalities for medical image processing has been widely studied in recent years. The fusion of information from different modalities has demonstrated the performance improvement for a lot of medical tasks. For nephropathy di...