Artificial intelligence in glomerular diseases.
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 (CKD). With the development of natural language pr...
| Publicado en: | Pediatric Nephrology Vol. 37; no. 11; pp. 2533 - 2546 |
|---|---|
| Autores principales: | , , , , , |
| Formato: | review tables/charts Journal Article |
| Publicado: |
Springer Nature
Nov2022
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=159213092&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 159213092 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 0931041X EF1 jtl: Pediatric Nephrology issn: 0931041X maglogo: N pubinfo: dt: Nov2022 vid: 37 iid: 11 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 159213092 155652127 159213092 159213092 10.1007/s00467-021-05419-8 159213092 ppf: 2533 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Artificial intelligence in glomerular diseases. aug: au: Schena, Francesco P. Magistroni, Riccardo Narducci, Fedelucio Abbrescia, Daniela I. Anelli, Vito W. Di Noia, Tommaso affil: Department of Emergency and Organ Transplantation, University of Bari, Bari, Italy sug: subj: Artificial Intelligence Glomerulonephritis Pathology Glomerulonephritis Diagnosis Kidney Ultrasonography Biopsy Prediction Models Fluorescent Antibody Technique Lupus Nephritis Diagnosis Diabetic Nephropathies Diagnosis Natural Language Processing Deep Learning Machine Learning Algorithms Early Diagnosis Disease Progression ab: 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 (CKD). With the development of natural language processing, the clinical history of a patient can be used to identify a computable phenotype. In kidney pathology, digital imaging has adopted innovative deep learning algorithms (DLAs) that can improve the predictive capability of the examined lesions. However, at this time, these applications can only be used in research because there is no recognized validation to replace the conventional diagnostic applications. Kidney ultrasonography, used in the clinical examination of patients, provides information about the progression of kidney damage. Machine learning algorithms (MLAs) with promising results for the early detection of CKD have been proposed, but, still, they are not solid enough to be incorporated into the clinical practice. A few tools for glomerulonephritis, based on MLAs, are available in clinical practice. They can be downloaded on computers and cellular phones but can only be applied to uniracial cohorts of patients. To improve their performance, it is necessary to organize large consortia with multiracial cohorts. Finally, in many studies MLA development has been carried out using retrospective cohorts. The performance of the models might differ in retrospective cohorts compared to real-world data. Therefore, the models should be validated in prospective external large cohorts. pubtype: Academic Journal doctype: review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|