AI Medical Compendium Journal:
Pediatric nephrology (Berlin, Germany)

Showing 1 to 7 of 7 articles

Serum sclerostin as a marker of microvascular and macrovascular complications among children and adolescents with type 1 diabetes mellitus.

Pediatric nephrology (Berlin, Germany)
BACKGROUND: Uncontrolled diabetes mellitus (DM) accelerates atherosclerosis and vascular diseases, leading to micro- and macrovascular complications. Early cardiac and kidney involvement necessitates an early biomarker. Sclerostin is a Wnt-signaling ...

Predicting graft survival in paediatric kidney transplant recipients using machine learning.

Pediatric nephrology (Berlin, Germany)
BACKGROUND: Identification of factors that affect graft survival in kidney transplantation can increase graft survival and reduce mortality. Artificial intelligence modelling enables impartial evaluation of clinician bias. This study aimed to examine...

Predicting renal damage in children with IgA vasculitis by machine learning.

Pediatric nephrology (Berlin, Germany)
BACKGROUND: Children with IgA Vasculitis (IgAV) may develop renal complications, which can impact their long-term prognosis. This study aimed to build a machine learning model to predict renal damage in children with IgAV and analyze risk factors for...

Artificial intelligence in early detection and prediction of pediatric/neonatal acute kidney injury: current status and future directions.

Pediatric nephrology (Berlin, Germany)
Acute kidney injury (AKI) has a significant impact on the short-term and long-term clinical outcomes of pediatric and neonatal patients, and it is imperative in these populations to mitigate the pathways leading to AKI and be prepared for early diagn...

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...

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 ...

Artificial intelligence outperforms experienced nephrologists to assess dry weight in pediatric patients on chronic hemodialysis.

Pediatric nephrology (Berlin, Germany)
BACKGROUND: Dry weight is the lowest weight patients on hemodialysis can tolerate; correct dry weight estimation is necessary to minimize morbi-mortality, but is difficult to achieve. Here, we used artificial intelligence to improve the accuracy of d...