Automation and Decision Support in Nephrology: An Expert System Based on AI and ML for the Assessment, Treatment, and Management of Focal Segmental Glomerulosclerosis
Source abstract
Abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Insurance, Mortality, Demography, Risk Management · Artificial Intelligence in Healthcare
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Prószkowska 76 Street, 45-758 Opole, Poland
Departament of IT, Opole University Hospital, Witosa 26 Street, 46-020 Opole, Poland
Departament of Internal Medicine and Nephrology, Institute of Medical Sciences, University of Opole, Oleska 48 Street, 45-052 Opole, Poland
Departament of Nephrology with Dialysis Unit, Opole University Hospital, Witosa 26 Street, 46-020 Opole, Poland
References (66)
Show source-provided references
1. Clinical decision support system to predict chronic kidney disease: A fuzzy expert system approach10.1016/j.ijmedinf.2020.104134 ↗
2. Using machine learning to identify metabolomic signatures of pediatric chronic kidney disease etiology10.1681/ASN.2021040538 ↗
3. Machine Learning-Derived Predictors of Clinical Outcomes in CureGN Participants with FSGS: TH-PO61210.1681/ASN.2024rmgbhzsb ↗
4. Renal tubular gene biomarkers identification based on immune infiltrates in focal segmental glomerulosclerosis10.1080/0886022X.2022.2081579 ↗
5. Kidney biopsy features most predictive of clinical outcomes in the spectrum of minimal change disease and focal segmental glomerulosclerosis10.1681/ASN.2021101396 ↗
6. Jacobs-Cachá, C., Puig-Gay, N., Helm, D., Rettel, M., Sellarès, J., Meseguer, A., Savitski, M.M., Moreso, F.J., Soler, M.J., and Seron, D. (2020). A misprocessed form of Apolipoprotein AI is specifically associated with recurrent Focal Segmental Glomerulosclerosis. Sci. Rep., 10.10.1038/s41598-020-58197-y ↗
7. Derivation and validation of a machine learning risk score using biomarker and electronic patient data to predict progression of diabetic kidney disease10.1007/s00125-021-05444-0 ↗
8. Gao, J., Wang, S., Xu, L., Wang, J., Guo, J., Wang, H., and Sun, J. (2023). Computer-aided diagnosis of primary membranous nephropathy using expert system. Biomed. Eng. OnLine, 22.10.1186/s12938-023-01063-5 ↗
9. Pareek, N.K., Soni, D., and Degadwala, S. (2023, January 23–25). Prediction of CKD Using Expert System Fuzzy Logic & AI. Proceedings of the 2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS), Trichy, India.10.1109/ICAISS58487.2023.10250477 ↗
10. Fuzzy logic–based clinical decision support system for the evaluation of renal function in post-Transplant Patients10.1111/jep.13302 ↗
11. A novel diagnosis system for detection of kidney disease by a fuzzy soft decision-making problem10.1016/j.matcom.2022.06.014 ↗
12. Yildirim, K., Bozdag, P.G., Talo, M., Yildirim, O., Karabatak, M., and Acharya, U.R. (2021). Deep learning model for automated kidney stone detection using coronal CT images. Comput. Biol. Med., 135.10.1016/j.compbiomed.2021.104569 ↗
13. Artificial Intelligence and Machine Learning in Dialysis: Ready for Prime Time?10.2215/CJN.0000000000000089 ↗
14. Artificial Intelligence in Continuous Kidney Replacement Therapy10.2215/CJN.0000000000000099 ↗
15. Cautious optimism: Artificial intelligence and acute kidney injury10.2215/CJN.0000000000000088 ↗
16. Risk Prediction and Machine Learning: A Case-Based Overview10.2215/CJN.0000000000000083 ↗
17. Bias in Artificial Intelligence: Basic Primer10.2215/CJN.0000000000000078 ↗
18. Bolt, H., Suffel, A., Matthewman, J., Sandmann, F., Tomlinson, L., and Eggo, R. (2023). Seasonality of acute kidney injury phenotypes in England: An unsupervised machine learning classification study of electronic health records. BMC Nephrol., 24.10.1186/s12882-023-03269-0 ↗
19. Yuan, W., Sui, L., Xin, H., Liu, M., and Shi, H. (2022). Discussion on machine learning technology to predict tacrolimus blood concentration in patients with nephrotic syndrome and membranous nephropathy in real-world settings. BMC Med. Inform. Decis. Mak., 22.10.1186/s12911-022-02089-w ↗
20. Ghosh, P., Shamrat, F.J.M., Shultana, S., Afrin, S., Anjum, A.A., and Khan, A.A. (2020, January 18–20). Optimization of prediction method of chronic kidney disease using machine learning algorithm. Proceedings of the 2020 15th international joint symposium on artificial intelligence and natural language processing (iSAI-NLP), Bangkok, Thailand.10.1109/iSAI-NLP51646.2020.9376787 ↗
21. An Empirical Study of Various Machine Learning Approaches in Prediction of Chronic Kidney Disease
22. Automation and decision support in the area of nephrology using numerical algorithms, artificial intelligence and expert approach-review of the current state of knowledge10.1109/ACCESS.2024.3413595 ↗
23. Baidya, D., Umaima, U., Islam, M.N., Shamrat, F.J.M., Pramanik, A., and Rahman, M.S. (2022, January 28–30). A deep prediction of chronic kidney disease by employing machine learning method. Proceedings of the 2022 6th International Conference on Trends in Electronics and Informatics (ICOEI), Tirunelveli, India.10.1109/ICOEI53556.2022.9776876 ↗
24. Development of smart cardiovascular measurement system using feature selection and machine learning models for prediction of sleep deprivation, cold hands and feet, and Shanghuo syndrome10.1016/j.measurement.2023.113441 ↗
25. Interpretable filter based convolutional neural network (IF-CNN) for glucose prediction and classification using PD-SS algorithm10.1016/j.measurement.2021.109804 ↗
26. An Evolutionary Random Forest to measure the Dworak tumor regression grade applied to colorectal cancer10.1016/j.measurement.2022.112131 ↗
27. Spatio-temporal MLP network for seizure prediction using EEG signals10.1016/j.measurement.2022.112278 ↗
28. EEG-based affective state recognition from human brain signals by using Hjorth-activity10.1016/j.measurement.2022.111738 ↗
29. EEG-based seizure prediction via Transformer guided CNN10.1016/j.measurement.2022.111948 ↗
30. Interstitial fibrosis and tubular atrophy measurement via hierarchical extractions of kidney and atrophy regions with deep learning method10.1016/j.measurement.2022.111885 ↗
31. Kidney disease detection and segmentation using artificial neural network and multi-kernel k-means clustering for ultrasound images10.1016/j.measurement.2019.106952 ↗
32. Sensitive electrochemical detection of creatinine based on electrodeposited molecular imprinting polymer modified screen printed carbon electrode10.1016/j.measurement.2023.112502 ↗
33. Double active thermographic inspection of additive manufacturing composites: Numerical modelling and validation10.1016/j.measurement.2023.113212 ↗
34. Pseudo label estimation based on label distribution optimization for industrial semi-supervised soft sensor10.1016/j.measurement.2023.113036 ↗
35. Fuzzy-assisted machine learning framework for the fog-computing system in remote healthcare monitoring10.1016/j.measurement.2022.111085 ↗
36. Wei, W., Cai, Z., Chen, L., Yuan, W., Fan, Y., and Rong, S. (2023). Short-term prognostic models for severe acute kidney injury patients receiving prolonged intermittent renal replacement therapy based on machine learning. BMC Med. Inform. Decis. Mak., 23.10.1186/s12911-023-02231-2 ↗
37. Fuzzy linear regression based on a hybrid of fuzzy C-means and the fuzzy inference system for predicting serum iron levels in patients with chronic kidney disease10.1016/j.eswa.2023.120314 ↗
38. Nakamura, K., Uchino, E., Sato, N., Araki, A., Terayama, K., Kojima, R., Murashita, K., Itoh, K., Mikami, T., and Tamada, Y. (2023). Individual health-disease phase diagrams for disease prevention based on machine learning. J. Biomed. Inform., 144.10.1016/j.jbi.2023.104448 ↗
39. CKD. Net: A novel deep learning hybrid model for effective, real-time, automated screening tool towards prediction of multi stages of CKD along with eGFR and creatinine10.1016/j.eswa.2023.119851 ↗
40. Kim, D.W., Ahn, H.G., Kim, J., Yoon, C.S., Kim, J.H., and Yang, S. (2021). Advanced kidney volume measurement method using ultrasonography with artificial intelligence-based hybrid learning in children. Sensors, 21.10.3390/s21206846 ↗
41. Machine learning models to predict the progression from early to late stages of papillary renal cell carcinoma10.1016/j.compbiomed.2018.06.030 ↗
42. Machine Learning Models for Prediction of Diabetic Microvascular Complications10.1177/19322968231223726 ↗
43. Transforming Disease Diagnosis and Management: A Comprehensive Review of AI-Driven Urine Analysis in Clinical Medicine
44. Kamel Rahimi, A., Ghadimi, M., van der Vegt, A.H., Canfell, O.J., Pole, J.D., Sullivan, C., and Shrapnel, S. (2023). Machine learning clinical prediction models for acute kidney injury: The impact of baseline creatinine on prediction efficacy. BMC Med. Inform. Decis. Mak., 23.10.1186/s12911-023-02306-0 ↗
45. Karar, M.E., El-Fishawy, N., and Radad, M. (2023). Automated classification of urine biomarkers to diagnose pancreatic cancer using 1-D convolutional neural networks. J. Biol. Eng., 17.10.1186/s13036-023-00340-0 ↗
46. Ou, S.M., Tsai, M.T., Lee, K.H., Tseng, W.C., Yang, C.Y., Chen, T.H., Bin, P.J., Chen, T.J., Lin, Y.P., and Sheu, W.H.H. (2023). Prediction of the risk of developing end-stage renal diseases in newly diagnosed type 2 diabetes mellitus using artificial intelligence algorithms. BioData Min., 16.10.1186/s13040-023-00324-2 ↗
47. Deep-learning-based real-time prediction of acute kidney injury outperforms human predictive performance10.1038/s41746-020-00346-8 ↗
48. Mencacci, A., De Socio, G.V., Pirelli, E., Bondi, P., and Cenci, E. (2023). Laboratory Automation, Informatics and Artificial Intelligence: Current and future perspectives in clinical microbiology. Front. Cell. Infect. Microbiol., 13.10.3389/fcimb.2023.1188684 ↗
49. Badrouchi, S., Bacha, M.M., Ahmed, A., Ben Abdallah, T., and Abderrahim, E. (2023). Predicting long-term outcomes of kidney transplantation in the era of artificial intelligence. Sci. Rep., 13.10.1038/s41598-023-48645-w ↗
50. Prediction of kidney stones using machine learning10.22214/ijraset.2022.42416 ↗
51. Smartphone-based, automated detection of urine albumin using deep learning approach10.1016/j.measurement.2022.110948 ↗
52. A mobile phone-based analyzer for quantitative determination of urinary albumin using self-calibration approach10.1016/j.snb.2016.11.057 ↗
53. A machine learning model for improving healthcare services on cloud computing environment10.1016/j.measurement.2018.01.022 ↗
54. Giles, S.N., and Fiori, S. (2019). Glomerular filtration rate estimation by a novel numerical binning-less isotonic statistical bivariate numerical modeling method. Information, 10.10.3390/info10030100 ↗
55. Early detection of kidney problem through voltammetry, potentiometry, amperometry, and impedance electrochemical techniques: A comprehensive review10.1016/j.measurement.2024.114475 ↗
56. da Silva, A.Q.B., de Almeida, J.B., and Kirsztajn, G.M. (2022). Artificial Intelligence in Nephrology. Innovations in Nephrology: Breakthrough Technologies in Kidney Disease Care, Springer.10.1007/978-3-031-11570-7_15 ↗
57. Machine learning models for predicting acute kidney injury: A systematic review and critical appraisal10.1093/ckj/sfac181 ↗
58. M, F.B., and Narayan, S. (2022, January 29–31). A Machine Learning Model for Acute Kidney Injury Prediction with Novel Kidney Biomarkers. Proceedings of the 2022 Second International Conference on Next Generation Intelligent Systems (ICNGIS), Kottayam, India.10.1109/ICNGIS54955.2022.10079856 ↗
59. Ilyas, H., Ali, S., Ponum, M., Hasan, O., Mahmood, M.T., Iftikhar, M., and Malik, M.H. (2021). Chronic kidney disease diagnosis using decision tree algorithms. BMC Nephrol., 22.10.1186/s12882-021-02474-z ↗
60. Prediction of Chronic Kidney Disease—A Machine Learning Perspective10.1109/ACCESS.2021.3053763 ↗
61. Identification and Expert Approach to Controlling the Cement Grinding Process Using Artificial Neural Networks and Other Non-Linear Models10.1109/ACCESS.2024.3366703 ↗
62. Sokół, S., Pawuś, D., Majewski, P., and Krok, M. (2022). The Study of the Effectiveness of Advanced Algorithms for Learning Neural Networks Based on FPGA in the Musical Notation Classification Task. Appl. Sci., 12.10.3390/app12199829 ↗
63. Pawuś, D., and Paszkiel, S. (2022). Application of EEG signals integration to proprietary classification algorithms in the implementation of mobile robot control with the use of motor imagery supported by EMG measurements. Appl. Sci., 12.10.3390/app12115762 ↗
64. Pawuś, D., and Paszkiel, S. (2022). The application of integration of EEG signals for authorial classification algorithms in implementation for a mobile robot control using movement imagery—Pilot study. Appl. Sci., 12.10.3390/app12042161 ↗
65. Classical vs. neural network-based PCA approaches for lossy image compression: Similarities and differences10.1016/j.asoc.2024.111721 ↗
66. KDIGO 2021 clinical practice guideline for the management of glomerular diseases10.1016/j.kint.2021.05.021 ↗