Clinical applications of artificial intelligence—an updated overview

Ș Busnatu, AG Niculescu, A Bolocan… - Journal of clinical …, 2022 - mdpi.com
Artificial intelligence has the potential to revolutionize modern society in all its aspects.
Encouraged by the variety and vast amount of data that can be gathered from patients (eg …

KDIGO 2021 clinical practice guideline for the management of glomerular diseases

BH Rovin, SG Adler, J Barratt, F Bridoux… - Kidney …, 2021 - kidney-international.org
Glomerular disease, be it primary or secondary, occurring in the setting of systemic
autoimmune diseases, infections, drugs, or malignancy, affects individuals of all ages. In …

Machine learning algorithms' accuracy in predicting kidney disease progression: a systematic review and meta-analysis

N Lei, X Zhang, M Wei, B Lao, X Xu, M Zhang… - BMC Medical Informatics …, 2022 - Springer
Background Kidney disease progression rates vary among patients. Rapid and accurate
prediction of kidney disease outcomes is crucial for disease management. In recent years …

Machine learning explainability in nasopharyngeal cancer survival using LIME and SHAP

RO Alabi, M Elmusrati, I Leivo, A Almangush… - Scientific Reports, 2023 - nature.com
Nasopharyngeal cancer (NPC) has a unique histopathology compared with other head and
neck cancers. Individual NPC patients may attain different outcomes. This study aims to …

[HTML][HTML] Application of the International IgA Nephropathy Prediction Tool one or two years post-biopsy

SJ Barbour, R Coppo, H Zhang, ZH Liu, Y Suzuki… - Kidney international, 2022 - Elsevier
The International IgA Nephropathy (IgAN) Prediction Tool is the preferred method in the
2021 KDIGO guidelines to predict, at the time of kidney biopsy, the risk of a 50% drop in …

A prognostic predictive system based on deep learning for locoregionally advanced nasopharyngeal carcinoma

M Qiang, C Li, Y Sun, Y Sun, L Ke, C **e… - JNCI: Journal of the …, 2021 - academic.oup.com
Background Images from magnetic resonance imaging (MRI) are crucial unstructured data
for prognostic evaluation in nasopharyngeal carcinoma (NPC). We developed and validated …

Promises of big data and artificial intelligence in nephrology and transplantation

C Thongprayoon, W Kaewput, K Kovvuru… - Journal of clinical …, 2020 - mdpi.com
Kidney diseases form part of the major health burdens experienced all over the world.
Kidney diseases are linked to high economic burden, deaths, and morbidity rates. The great …

[HTML][HTML] Glomerular disease classification and lesion identification by machine learning

CK Yang, CY Lee, HS Wang, SC Huang, PI Liang… - biomedical …, 2022 - Elsevier
Background Classification of glomerular diseases and identification of glomerular lesions
require careful morphological examination by experienced nephropathologists, which is …

[HTML][HTML] Role of artificial intelligence in kidney disease

Q Yuan, H Zhang, T Deng, S Tang, X Yuan… - … Journal of Medical …, 2020 - ncbi.nlm.nih.gov
Artificial intelligence (AI), as an advanced science technology, has been widely used in
medical fields to promote medical development, mainly applied to early detections, disease …

Machine learning for prediction and risk stratification of lupus nephritis renal flare

Y Chen, S Huang, T Chen, D Liang, J Yang… - American Journal of …, 2021 - karger.com
Background: Renal flare of lupus nephritis (LN) is strongly associated with poor kidney
outcomes, and predicting renal flare and stratifying its risk are important for clinical decision …