Artificial Intelligence Techniques: Predicting Necessity for Biopsy in Renal Transplant Recipients Suspected of Acute Cellular Rejection or Nephrotoxicity

Hummel, Anderson Diniz; Maciel, Rafael Fábio; Sousa, Fernando Sequeira; Cohrs, Frederico Molina; Falcão, Alex Esteves Jaccoud; Teixeira, Fábio Oliveira; Baptista, Roberto Silva; Mancini, Félipe; Costa, Thiago Martini da; Alves, Domingos; Rodrigues, R.; Miranda, Rafael N; Pisa, Ivan Torres

doi:10.1016/j.transproceed.2011.02.029

Cited by 4 publications

(3 citation statements)

References 9 publications

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“…Another intriguing perspective is the application of artificial intelligence (AI) models which allows computational analysis and interpretation of large-scale molecular data generation by exploiting machine learning algorithms and neural networks [ 196 , 197 ]. For example, classifiers like artificial neural networks, support vector machines and Bayesian inference have already been employed in pilot studies to screen KTx recipients requiring renal biopsy [ 198 ] and AI has proved useful to improve estimation of TAC Area Under the Concentration Over Time Curve [ 199 ].…”

Section: Current Limits and Perspectives Of Biomarkers In Renal Trmentioning

confidence: 99%

Recent Advances on Biomarkers of Early and Late Kidney Graft Dysfunction

Quaglia

Merlotti

Guglielmetti

et al. 2020

IJMS

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New biomarkers of early and late graft dysfunction are needed in renal transplant to improve management of complications and prolong graft survival. A wide range of potential diagnostic and prognostic biomarkers, measured in different biological fluids (serum, plasma, urine) and in renal tissues, have been proposed for post-transplant delayed graft function (DGF), acute rejection (AR), and chronic allograft dysfunction (CAD). This review investigates old and new potential biomarkers for each of these clinical domains, seeking to underline their limits and strengths. OMICs technology has allowed identifying many candidate biomarkers, providing diagnostic and prognostic information at very early stages of pathological processes, such as AR. Donor-derived cell-free DNA (ddcfDNA) and extracellular vesicles (EVs) are further promising tools. Although most of these biomarkers still need to be validated in multiple independent cohorts and standardized, they are paving the way for substantial advances, such as the possibility of accurately predicting risk of DGF before graft is implanted, of making a “molecular” diagnosis of subclinical rejection even before histological lesions develop, or of dissecting etiology of CAD. Identification of “immunoquiescent” or even tolerant patients to guide minimization of immunosuppressive therapy is another area of active research. The parallel progress in imaging techniques, bioinformatics, and artificial intelligence (AI) is helping to fully exploit the wealth of information provided by biomarkers, leading to improved disease nosology of old entities such as transplant glomerulopathy. Prospective studies are needed to assess whether introduction of these new sets of biomarkers into clinical practice could actually reduce the need for renal biopsy, integrate traditional tools, and ultimately improve graft survival compared to current management.

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Section: Current Limits and Perspectives Of Biomarkers In Renal Trmentioning

confidence: 99%

Recent Advances on Biomarkers of Early and Late Kidney Graft Dysfunction

Quaglia

Merlotti

Guglielmetti

et al. 2020

IJMS

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“…However, the researchers asserted that higher rates of sensitivity would be required to apply the classifier in clinical practice. In a separate paper, the same group of authors used the same database to examine the performance of different AI techniques to screen the need for biopsy among patients suspected of having nephrotoxicity or acute cellular rejection during the first year after transplantation[ 48 ]. They used the ANN, SVM, and Bayesian interference (BI) to indicate if the clinical course of the event suggested the need for biopsy.…”

Section: Application Of Ai In Kidney Transplantationmentioning

confidence: 99%

“…The technique that showed the best sensitivity value as an indicator for biopsy was the SVM with an AUC of 0.79. The authors suggested that this technique could be used in clinical practice[ 48 ].…”

Section: Application Of Ai In Kidney Transplantationmentioning

confidence: 99%

Artificial intelligence and kidney transplantation

Seyahi¹,

Özcan²

2021

WJT

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Artificial intelligence and its primary subfield, machine learning, have started to gain widespread use in medicine, including the field of kidney transplantation. We made a review of the literature that used artificial intelligence techniques in kidney transplantation. We located six main areas of kidney transplantation that artificial intelligence studies are focused on: Radiological evaluation of the allograft, pathological evaluation including molecular evaluation of the tissue, prediction of graft survival, optimizing the dose of immunosuppression, diagnosis of rejection, and prediction of early graft function. Machine learning techniques provide increased automation leading to faster evaluation and standardization, and show better performance compared to traditional statistical analysis. Artificial intelligence leads to improved computer-aided diagnostics and quantifiable personalized predictions that will improve personalized patient care.

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