Machine Learning for Renal Pathologies: An Updated Survey

Abstract

Within the literature concerning modern machine learning techniques applied to the medical field, there is a growing interest in the application of these technologies to the nephrological area, especially regarding the study of renal pathologies, because they are very common and widespread in our society, afflicting a high percentage of the population and leading to various complications, up to death in some cases. For these reasons, the authors have considered it appropriate to collect, using one of the major bibliographic databases available, and analyze the studies carried out until February 2022 on the use of machine learning techniques in the nephrological field, grouping them according to the addressed pathologies: renal masses, acute kidney injury, chronic kidney disease, kidney stone, glomerular disease, kidney transplant, and others less widespread. Of a total of 224 studies, 59 were analyzed according to inclusion and exclusion criteria in this review, considering the method used and the type of data available. Based on the study conducted, it is possible to see a growing trend and interest in the use of machine learning applications in nephrology, becoming an additional tool for physicians, which can enable them to make more accurate and faster diagnoses, although there remains a major limitation given the difficulty in creating public databases that can be used by the scientific community to corroborate and eventually make a positive contribution in this area.

An integrated machine learning predictive scheme for longitudinal laboratory data to evaluate the factors determining renal function changes in patients with different chronic kidney disease stages

“…ML has demonstrated promising performance in the nephrological field, including kidney function prediction via ultrasonography ( 54 ), acute kidney injury prediction in critical care ( 55 , 56 ), specific pattern identification on renal pathology slides ( 57 , 58 ), optimal dialysis prescription ( 59 , 60 ), calculation of further eGFRs ( 61 ), mortality risk prediction in patients undergoing dialysis ( 62 ), and ESKD prediction based on clinical data ( 63 – 65 ). In this study, five ML methods were adopted to obtain the 10 most important factors for predicting eGFR changes in different CKD groups.…”

Reinforcement Learning for Clinical Applications

“…Although there have been recent applications of machine learning in nephrology, 6,7 to the best of the authors' knowledge, the application of reinforcement learning to nephrology has been primarily limited to optimizing the erythropoietin dosage in hemodialysis patients. 8,9 However, there are other settings where reinforcement learning has the potential to improve patient care in nephrology.…”