AIMC Topic: Renal Insufficiency, Chronic

Clear Filters Showing 111 to 120 of 262 articles

Deep learning imaging features derived from kidney ultrasounds predict chronic kidney disease progression in children with posterior urethral valves.

Pediatric nephrology (Berlin, Germany)
BACKGROUND: We sought to use deep learning to extract anatomic features from postnatal kidney ultrasounds and evaluate their performance in predicting the risk and timing of chronic kidney disease (CKD) progression for boys with posterior urethral va...

Prediction of chronic kidney disease and its progression by artificial intelligence algorithms.

Journal of nephrology
BACKGROUND AND OBJECTIVE: Aim of nephrologists is to delay the outcome and reduce the number of patients undergoing renal failure (RF) by applying prevention protocols and accurately monitoring chronic kidney disease (CKD) patients. General practitio...

Monitoring Approaches for a Pediatric Chronic Kidney Disease Machine Learning Model.

Applied clinical informatics
OBJECTIVE: The purpose of this study is to evaluate the ability of three metrics to monitor for a reduction in performance of a chronic kidney disease (CKD) model deployed at a pediatric hospital.

Machine Learning-Aided Chronic Kidney Disease Diagnosis Based on Ultrasound Imaging Integrated with Computer-Extracted Measurable Features.

Journal of digital imaging
Although ultrasound plays an important role in the diagnosis of chronic kidney disease (CKD), image interpretation requires extensive training. High operator variability and limited image quality control of ultrasound images have made the application...

Implementation of Hospital-to-Home Model for Nutritional Nursing Management of Patients with Chronic Kidney Disease Using Artificial Intelligence Algorithm Combined with CT Internet.

Contrast media & molecular imaging
The objective of this study was to evaluate the application value of "Internet + hospital-to-home (H2H)" nutritional care model using the improved wavelet transform algorithm based on computed tomography (CT) images in the nutritional care management...

Unsupervised machine learning for identifying important visual features through bag-of-words using histopathology data from chronic kidney disease.

Scientific reports
Pathologists use visual classification to assess patient kidney biopsy samples when diagnosing the underlying cause of kidney disease. However, the assessment is qualitative, or semi-quantitative at best, and reproducibility is challenging. To discov...

Artificial intelligence in glomerular diseases.

Pediatric nephrology (Berlin, Germany)
In this narrative review, we focus on the application of artificial intelligence in the clinical history of patients with glomerular disease, digital pathology in kidney biopsy, renal ultrasonography imaging, and prediction of chronic kidney disease ...

Predicting Chronic Kidney Disease Using Hybrid Machine Learning Based on Apache Spark.

Computational intelligence and neuroscience
Chronic kidney disease (CKD) has become a widespread disease among people. It is related to various serious risks like cardiovascular disease, heightened risk, and end-stage renal disease, which can be feasibly avoidable by early detection and treatm...

Predicting factor analysis of postoperative complications after robot-assisted radical cystectomy: Multicenter KORARC database study.

International journal of urology : official journal of the Japanese Urological Association
OBJECTIVES: To evaluate postoperative complications following robot-assisted radical cystectomy in patients diagnosed with bladder cancer and reveal if there are predictors for postoperative complications.