## 2 State of the Art and Related Works  
Recent advancements in nephrology have demonstrated the increasing role of expert-guided systems, machine learning models, and artificial intelligence in enhancing diagnostic accuracy, predicting outcomes, and supporting clinical decision-making. A notable application of rule-based AI systems, particularly those utilizing fuzzy logic, is in predicting chronic kidney disease (CKD). For instance, one such system achieved high accuracy (92.13%) and sensitivity (95.37%) in predicting CKD, showcasing the potential of AI to improve early diagnosis and patient outcomes [1]. Other studies have leveraged machine learning to uncover key biomarkers and predictors in focal segmental glomerulosclerosis. One study applied ML to plasma metabolomic profiling, identifying dysmetabolism in the sphingomyelin-ceramide axis and plasmalogen metabolites as markers for FSGS. ML models, including logistic regression and random forests, were used to stratify these biomarkers based on CKD causes [2]. Another study combined clinical, genetic, and pathology data using ridge regression to predict FSGS outcomes. ML models showed excellent discrimination (iAUC = 0.95) and identified risk and protective factors, such as high-risk APOL1 genotype and serum albumin levels [3]. Further research identified NR4A1 and DUSPI as immune-related biomarkers through gene expression profiling and ML, revealing insights into the immune mechanisms behind FSGS [4]. A separate study utilized ML to analyze histopathologic features from biopsies, uncovering novel descriptors predictive of outcomes in MCD/FSGS. This highlights ML's role in improving biopsy reporting and prognosis prediction [5]. Finally, a study discovered ApoA-Ib, a misprocessed form of ApoA-I, as a potential urinary biomarker for recurrent FSGS, offering new molecular insights [6]. Machine learning models have also been employed to predict the progression of kidney diseases such as diabetic kidney disease (DKD) and membranous nephropathy. For instance, a machine learning risk score derived from biomarker data and electronic health records demonstrated strong predictive power for the progression of DKD, aiding in early intervention and personalized treatment plans [7]. Similarly, a fuzzy expert system for diagnosing primary membranous nephropathy showed a high sensitivity of 98%, accuracy of 97.8%, and an area under the curve (AUC) of 0.93, suggesting its robustness in clinical settings [8]. The integration of ML with expert systems (AL) has proven particularly effective in predicting kidney disease progression. For example, a study combining fuzzy logic with AI algorithms to predict CKD using clinical indicators such as age, blood pressure, and serum creatinine levels yielded favorable diagnostic outcomes [9]. In addition, fuzzy logic-based clinical decision support systems (CDSSs) have been explored in post-transplant renal function monitoring, achieving over 90% accuracy in assessing renal health and optimizing drug dosages [10]. The application of such expert-guided AI (AL) solutions in analyzing complex medical data is further demonstrated by a predictive model for kidney disease based on symptoms, where a fuzzy soft expert system (AL) showed high reliability and efficiency, emphasizing its potential for early detection and treatment optimization [11]. Meanwhile, advancements in deep learning (DL) have led to automated systems for detecting kidney stones and other nephrological conditions from medical imaging, providing clinicians with a powerful diagnostic tool [12]. Similarly, Ref. [13] discusses AI applications in dialysis, covering areas such as intradialytic hypotension prediction, anemia management, and treatment optimization. Nevertheless, challenges such as data privacy and model interpretability must be addressed for successful implementation. In CKD management, Ref. [14] explores AI's applications in continuous kidney replacement therapy (CKRT), emphasizing the importance of accurate data handling, ethical considerations, and prospective validation.