Dawid Pawuś

Purpose: Glomerulopathies are a heterogeneous group of diseases characterized by complex immunopathogenesis and wide clinical variability. A wide range of laboratory and histopathological tests is used to assess glomerular diseases. However, the volume of data is often too great to analyze and to provide a clear diagnosis. The rapid development of artificial intelligence (AI) methodologies, including machine learning (ML) and deep learning, facilitates the integration of histopathological, clinical, and molecular data. This foundation enables the creation of more precise and objective diagnostic models. Method: Modular expert systems based on ML algorithms (XGBoost, Random Forest, and natural language processing were implemented. A retrospective comparative analysis was performed comparing physician decisions with algorithmic recommendations in a patient cohort from the Opole University Clinical Hospital and the Wroclaw Medical University Hospital (2018–2025), comprising idiopathic membranous glomerulonephritis (iMN, n = 56), focal segmental glomerulosclerosis (FSGS, n = 127), lupus nephritis (LN, n = 31), and minimal change disease (MCD, n = 187). Results: The iMN module achieved 100% accuracy in risk categorization. Retrospective analysis revealed significant gaps in clinical practice: 13.4% of decisions were suboptimal in the FSGS group, 25.8% of diagnoses were irregular in the LN cohort, and 10.8% of diagnoses were irregular in the MCD group. Conclusion: The integration of AI into clinical practice is a key element in the evolution of “precision nephrology,” supporting the detection of subtle disease patterns and the standardization of management.

The management of spinal infections, such as spondylodiscitis, remains a complex challenge. With advancements in technology, spinal endoscopy has emerged as a promising minimally invasive approach. This systematic review and case series aims to evaluate the efficacy of endoscopic techniques in treating primary and post-operative spinal infections. A comprehensive literature review was conducted using databases such as PubMed, MEDLINE, and Cochrane Library, focusing on studies from 2000 to 2023. Our case series included patients treated with endoscopy for spinal infections in our institution between 2020 and 2023. The findings suggest that endoscopic techniques are associated with reduced tissue trauma, shorter hospital stays, and faster recovery, while maintaining comparable clinical outcomes to traditional open surgery. Despite the relatively high pathogen identification rates and favorable clinical outcomes, complications such as residual infection and the need for re-operations were noted. These results emphasize the need for further research to optimize endoscopic procedures in spinal infection management, potentially paving the way for this technique to become a standard treatment option.

Focal segmental glomerulosclerosis (FSGS) presents significant challenges in diagnosis, treatment, and management due to its complex etiology and clinical variability. Traditional approaches often rely on clinician judgment and are prone to inconsistencies. This study introduces an advanced expert system integrating Artificial Intelligence (AI) with Machine Learning (ML) to support nephrologists in assessing, treating, and managing FSGS. The proposed system features a modular design comprising diagnostic workflows, risk stratification, treatment guidance, and outcome monitoring modules. By leveraging ML algorithms and clinical data, the system offers personalized, data-driven recommendations, enhancing decision-making and patient care. The evaluation demonstrates the system’s efficacy in reducing diagnostic errors and optimizing treatment pathways. These findings underscore the potential of AI-driven tools in transforming nephrology practice and improving clinical outcomes for FSGS patients.

Background: The integration of AI, numerical algorithms, and expert approaches in nephrology shows promise for enhancing patient care. Traditionally, nephrology relied on clinician expertise, but the growing volume of data requires innovative methods. AI, fueled by vast datasets, can uncover patterns and improve diagnostic accuracy and treatment optimization. Numerical algorithms enable computational modeling of renal physiology and disease progression, aiding in treatment prediction and optimization. Expert systems codify nephrologists' knowledge into decision support tools, aiding in diagnosis and treatment selection. Embracing these technologies can revolutionize nephrological practice, improving patient outcomes.Aim of the study: The aim of the research is to review scientific works on the use of artificial intelligence (AI), numerical algorithms, and expert algorithms in diagnosis, decision support and prediction of selected disease states and issues related to nephrology.Material and methods: The study rigorously retrieved relevant publications from esteemed academic databases, Scopus and PubMed, and screened them based on predefined criteria. Through keyword-based searches, it analyzed the intersection of nephrology and artificial intelligence, offering insights into emerging trends and advancements. This approach facilitated a comprehensive exploration of the integration of artificial intelligence in nephrology research, paving the way for future developments in the field.Results: Artificial intelligence in nephrology and medicine uses various technologies such as machine learning, deep learning and natural language processing to improve diagnosis, treatment and patient care. Analyzes clinical, laboratory, imaging and genetic data to accelerate the identification of kidney diseases. By integrating health information systems and research data, artificial intelligence enables pattern recognition, outcome prediction and treatment optimization, ultimately having the potential to permanently improve patient care.Conclusions: Numerical algorithms, artificial intelligence, and expert algorithms show promise in nephrology, improving patient care and research.