Automation and Decision Support in Nephrology: An Expert System Based on AI and ML for the Assessment, Treatment, and Management of Focal Segmental Glomerulosclerosis
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 s...
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MDPI AG
2025-01-01
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| Online Access: | https://www.mdpi.com/2076-3417/15/3/1044 |
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| author | Dawid Pawuś Tomasz Porażko Szczepan Paszkiel |
| author_facet | Dawid Pawuś Tomasz Porażko Szczepan Paszkiel |
| author_sort | Dawid Pawuś |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-00fa12f5e7e64075b80da7cddcc97556 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-00fa12f5e7e64075b80da7cddcc975562025-08-20T02:48:01ZengMDPI AGApplied Sciences2076-34172025-01-01153104410.3390/app15031044Automation and Decision Support in Nephrology: An Expert System Based on AI and ML for the Assessment, Treatment, and Management of Focal Segmental GlomerulosclerosisDawid Pawuś0Tomasz Porażko1Szczepan Paszkiel2Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Prószkowska 76 Street, 45-758 Opole, PolandDepartament of Internal Medicine and Nephrology, Institute of Medical Sciences, University of Opole, Oleska 48 Street, 45-052 Opole, PolandFaculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Prószkowska 76 Street, 45-758 Opole, PolandFocal 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.https://www.mdpi.com/2076-3417/15/3/1044expert systemFSGSautomation systempractical approachkidney insufficiencynumerical algorithms |
| spellingShingle | Dawid Pawuś Tomasz Porażko Szczepan Paszkiel Automation and Decision Support in Nephrology: An Expert System Based on AI and ML for the Assessment, Treatment, and Management of Focal Segmental Glomerulosclerosis Applied Sciences expert system FSGS automation system practical approach kidney insufficiency numerical algorithms |
| title | Automation and Decision Support in Nephrology: An Expert System Based on AI and ML for the Assessment, Treatment, and Management of Focal Segmental Glomerulosclerosis |
| title_full | Automation and Decision Support in Nephrology: An Expert System Based on AI and ML for the Assessment, Treatment, and Management of Focal Segmental Glomerulosclerosis |
| title_fullStr | Automation and Decision Support in Nephrology: An Expert System Based on AI and ML for the Assessment, Treatment, and Management of Focal Segmental Glomerulosclerosis |
| title_full_unstemmed | Automation and Decision Support in Nephrology: An Expert System Based on AI and ML for the Assessment, Treatment, and Management of Focal Segmental Glomerulosclerosis |
| title_short | Automation and Decision Support in Nephrology: An Expert System Based on AI and ML for the Assessment, Treatment, and Management of Focal Segmental Glomerulosclerosis |
| title_sort | automation and decision support in nephrology an expert system based on ai and ml for the assessment treatment and management of focal segmental glomerulosclerosis |
| topic | expert system FSGS automation system practical approach kidney insufficiency numerical algorithms |
| url | https://www.mdpi.com/2076-3417/15/3/1044 |
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