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Artificial intelligence in glomerular diseases

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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.

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Acknowledgements

The work has been financially supported by a grant of the Italian Ministry of University (PON RI ARS01_00876). The funder had no role in the design of the study.

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Authors and Affiliations

  1. Department of Emergency and Organ Transplantation, University of Bari, Bari, Italy

    Francesco P. Schena

  2. Department of Nephrology, University of Modena, Modena, Italy

    Riccardo Magistroni

  3. Department of Electrical and Information Engineering, Polytechnic of Bari, Bari, Italy

    Fedelucio Narducci, Vito W. Anelli & Tommaso Di Noia

  4. Schena Foundation, Bari, Italy

    Daniela I. Abbrescia

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  1. Francesco P. Schena
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  3. Fedelucio Narducci
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Correspondence to Francesco P. Schena.

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Schena, F.P., Magistroni, R., Narducci, F. et al. Artificial intelligence in glomerular diseases. Pediatr Nephrol 37, 2533–2545 (2022). https://doi.org/10.1007/s00467-021-05419-8

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