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...

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Vydané v:	Pediatric nephrology (Berlin, West) Ročník 37; číslo 11; s. 2533 - 2545
Hlavní autori:	Schena, Francesco P., Magistroni, Riccardo, Narducci, Fedelucio, Abbrescia, Daniela I., Anelli, Vito W., Di Noia, Tommaso
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2022 Springer Nature B.V
Predmet:	Algorithms Artificial intelligence Biopsy Cellular telephones Clinical medicine Computers Deep learning Glomerulonephritis Kidney diseases Learning algorithms Medicine Medicine & Public Health Nephrology Pathology Patients Pediatrics Phenotypes Review Ultrasonic imaging Urology Glomerulonephritis Machine Learning Algorithm Clinical Outcome Deep Learning Algorithm Artificial Intelligence Natural Language Processing
ISSN:	0931-041X, 1432-198X, 1432-198X
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Abstract	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.
AbstractList	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.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. 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.
Author	Abbrescia, Daniela I. Di Noia, Tommaso Narducci, Fedelucio Schena, Francesco P. Magistroni, Riccardo Anelli, Vito W.
Author_xml	– sequence: 1 givenname: Francesco P. surname: Schena fullname: Schena, Francesco P. email: paolo.schena@uniba.it organization: Department of Emergency and Organ Transplantation, University of Bari – sequence: 2 givenname: Riccardo surname: Magistroni fullname: Magistroni, Riccardo organization: Department of Nephrology, University of Modena – sequence: 3 givenname: Fedelucio surname: Narducci fullname: Narducci, Fedelucio organization: Department of Electrical and Information Engineering, Polytechnic of Bari – sequence: 4 givenname: Daniela I. surname: Abbrescia fullname: Abbrescia, Daniela I. organization: Schena Foundation – sequence: 5 givenname: Vito W. surname: Anelli fullname: Anelli, Vito W. organization: Department of Electrical and Information Engineering, Polytechnic of Bari – sequence: 6 givenname: Tommaso surname: Di Noia fullname: Di Noia, Tommaso organization: Department of Electrical and Information Engineering, Polytechnic of Bari
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Keywords	Glomerulonephritis Machine Learning Algorithm Clinical Outcome Deep Learning Algorithm Artificial Intelligence Natural Language Processing
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Snippet	In this narrative review, we focus on the application of artificial intelligence in the clinical history of patients with glomerular disease, digital pathology...
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SubjectTerms	Algorithms Artificial intelligence Biopsy Cellular telephones Clinical medicine Computers Deep learning Glomerulonephritis Kidney diseases Learning algorithms Medicine Medicine & Public Health Nephrology Pathology Patients Pediatrics Phenotypes Review Ultrasonic imaging Urology
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Title	Artificial intelligence in glomerular diseases
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