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Zhuang K, Wang W, Xu C, Guo X, Ren X, Liang Y, Duan Z, Song Y, Zhang Y, Cai G. Machine learning-based diagnosis and prognosis of IgAN: A systematic review and meta-analysis. Heliyon 2024; 10:e33090. [PMID: 38988582 PMCID: PMC11234108 DOI: 10.1016/j.heliyon.2024.e33090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 06/04/2024] [Accepted: 06/13/2024] [Indexed: 07/12/2024] Open
Abstract
Purpose Plenty of studies have explored the diagnosis and prognosis of IgA nephropathy (IgAN) based on machine learning (ML), but the accuracy lacks the support of evidence-based medical evidence. We aim at this problem to guide the precision treatment of IgAN. Methods Embase, Pubmed, Cochrane Library, and Web of Science were searched systematically until February 24th, 2024, for publications on ML-based diagnosis and prognosis of IgAN. Subgroup analysis or meta-regression was conducted according to modeling method, follow-up time, endpoint definition, and variable type. Further, the rank sum test was applied to compare the discrimination ability of prognosis. Results A total of 47 studies involving 51,935 patients were eligible. Among the 38 diagnostic models, the pooled C-index was 0.902 (95 % CI: 0.878-0.926) in 27 diagnostic models. Of the 162 prognostic models, the C-index for model discrimination of 144 prognostic models was 0.838 (95 % CI: 0.827-0.850) in training. The overall discrimination ability of prognosis was as follows: COX regression > new ML models (e.g. ANN, DT, RF, SVM, XGBoost) > traditional ML models (logistic regression) > Naïve Bayesian network (P < 0.05). External validation of IIgAN-RPT in 19 models showed a pooled C-index of 0.801 (95 % CI: 0.784-0.817). Conclusions New ML models have shown application values that are as good as traditional ML models, both in diagnosis and prognosis. In addition, future models are desired to use a more sensitive prognostic endpoint (albuminuria), improve predictive ability in moderate progression risk, and ultimately translate into clinically applicable intelligent tools.
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Affiliation(s)
- Kaiting Zhuang
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing 100853, China
| | - Wenjuan Wang
- School of Medicine, Nankai University, Tianjin, 300071, China
| | - Cheng Xu
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing 100853, China
| | - Xinru Guo
- School of Medicine, Nankai University, Tianjin, 300071, China
| | - Xuejing Ren
- Zhengzhou University People's Hospital, Henan Provincial People's Hospital, Henan Key Laboratory of Kidney Disease and Immunology, Academy of Medical Sciences, Zhengzhou University, Zhengzhou, Henan, 450003, China
| | - Yanjun Liang
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing 100853, China
| | - Zhiyu Duan
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing 100853, China
| | - Yanqi Song
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing 100853, China
| | - Yifan Zhang
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing 100853, China
| | - Guangyan Cai
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing 100853, China
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Schena FP, Manno C, Strippoli G. Understanding patient needs and predicting outcomes in IgA nephropathy using data analytics and artificial intelligence: a narrative review. Clin Kidney J 2023; 16:ii55-ii61. [PMID: 38053972 PMCID: PMC10695518 DOI: 10.1093/ckj/sfad206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Indexed: 12/07/2023] Open
Abstract
This narrative review explores two case scenarios related to immunoglobulin A nephropathy (IgAN) and the application of predictive monitoring, big data analysis and artificial intelligence (AI) in improving treatment outcomes. The first scenario discusses how online service providers accurately understand consumer preferences and needs through the use of AI-powered big data analysis. The author, a clinical nephrologist, contemplates the potential application of similar methodologies, including AI, in his medical practice to better understand and meet patient needs. The second scenario presents a case study of a 20-year-old man with IgAN. The patient exhibited recurring symptoms, including gross haematuria and tonsillitis, over a 2-year period. Through histological examination and treatment with renin-angiotensin system blockade and corticosteroids, the patient experienced significant improvement in kidney function and reduced proteinuria over 15 years of follow-up. The case highlights the importance of individualized treatment strategies and the use of predictive tools, such as AI-based predictive models, in assessing treatment response and predicting long-term outcomes in IgAN patients. The article further discusses the collection and analysis of real-world big data, including electronic health records, for studying disease natural history, predicting treatment responses and identifying prognostic biomarkers. Challenges in integrating data from various sources and issues such as missing data and data processing limitations are also addressed. Mathematical models, including logistic regression and Cox regression analysis, are discussed for predicting clinical outcomes and analysing changes in variables over time. Additionally, the application of machine learning algorithms, including AI techniques, in analysing big data and predicting outcomes in IgAN is explored. In conclusion, the article highlights the potential benefits of leveraging AI-powered big data analysis, predictive monitoring and machine learning algorithms to enhance patient care and improve treatment outcomes in IgAN.
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Affiliation(s)
- Francesco Paolo Schena
- Department of Precision and Regenerative Medicine and Ionian Area, University of Bari, Bari, Italy
- Schena Foundation, Policlinic, Bari, Italy
| | - Carlo Manno
- Department of Precision and Regenerative Medicine and Ionian Area, University of Bari, Bari, Italy
| | - Giovanni Strippoli
- Department of Precision and Regenerative Medicine and Ionian Area, University of Bari, Bari, Italy
- School of Public Health, University of Sydney, Sydney, NSW, Australia
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Schena FP, Magistroni R, Narducci F, Abbrescia DI, Anelli VW, Di Noia T. Artificial intelligence in glomerular diseases. Pediatr Nephrol 2022; 37:2533-2545. [PMID: 35266037 DOI: 10.1007/s00467-021-05419-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 12/19/2021] [Accepted: 12/21/2021] [Indexed: 11/30/2022]
Abstract
In this narrative review, we focus on the application of artificial intelligence in the clinical history of patients with glomerular disease, digital pathology in kidney biopsy, renal ultrasonography imaging, and prediction of chronic kidney disease (CKD). With the development of natural language processing, the clinical history of a patient can be used to identify a computable phenotype. In kidney pathology, digital imaging has adopted innovative deep learning algorithms (DLAs) that can improve the predictive capability of the examined lesions. However, at this time, these applications can only be used in research because there is no recognized validation to replace the conventional diagnostic applications. Kidney ultrasonography, used in the clinical examination of patients, provides information about the progression of kidney damage. Machine learning algorithms (MLAs) with promising results for the early detection of CKD have been proposed, but, still, they are not solid enough to be incorporated into the clinical practice. A few tools for glomerulonephritis, based on MLAs, are available in clinical practice. They can be downloaded on computers and cellular phones but can only be applied to uniracial cohorts of patients. To improve their performance, it is necessary to organize large consortia with multiracial cohorts. Finally, in many studies MLA development has been carried out using retrospective cohorts. The performance of the models might differ in retrospective cohorts compared to real-world data. Therefore, the models should be validated in prospective external large cohorts.
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Affiliation(s)
- Francesco P Schena
- Department of Emergency and Organ Transplantation, University of Bari, Bari, Italy.
| | | | - Fedelucio Narducci
- Department of Electrical and Information Engineering, Polytechnic of Bari, Bari, Italy
| | | | - Vito W Anelli
- Department of Electrical and Information Engineering, Polytechnic of Bari, Bari, Italy
| | - Tommaso Di Noia
- Department of Electrical and Information Engineering, Polytechnic of Bari, Bari, Italy
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Siddique A, Herron CB, Valenta J, Garner LJ, Gupta A, Sawyer JT, Morey A. Classification and Feature Extraction Using Supervised and Unsupervised Machine Learning Approach for Broiler Woody Breast Myopathy Detection. Foods 2022. [PMCID: PMC9601423 DOI: 10.3390/foods11203270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Bioelectrical impedance analysis (BIA) was established to quantify diverse cellular characteristics. This technique has been widely used in various species, such as fish, poultry, and humans for compositional analysis. This technology was limited to offline quality assurance/detection of woody breast (WB); however, inline technology that can be retrofitted on the conveyor belt would be more helpful to processors. Freshly deboned (n = 80) chicken breast fillets were collected from a local processor and analyzed by hand-palpation for different WB severity levels. Data collected from both BIA setups were subjected to supervised and unsupervised learning algorithms. The modified BIA showed better detection ability for regular fillets than the probe BIA setup. In the plate BIA setup, fillets were 80.00% for normal, 66.67% for moderate (data for mild and moderate merged), and 85.00% for severe WB. However, hand-held BIA showed 77.78, 85.71, and 88.89% for normal, moderate, and severe WB, respectively. Plate BIA setup is more effective in detecting WB myopathies and could be installed without slowing the processing line. Breast fillet detection on the processing line can be significantly improved using a modified automated plate BIA.
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Affiliation(s)
- Aftab Siddique
- Department of Poultry Science, Auburn University, Auburn, AL 36849, USA
| | - Charles B. Herron
- Department of Poultry Science, Auburn University, Auburn, AL 36849, USA
| | - Jaroslav Valenta
- Department of Animal Science, Czech University of Life Sciences Prague, 16500 Prague, Czech Republic
| | - Laura J. Garner
- Department of Poultry Science, Auburn University, Auburn, AL 36849, USA
| | - Ashish Gupta
- Department of Business Analytics and Information, Auburn University, Auburn, AL 36849, USA
| | - Jason T. Sawyer
- Department of Animal Sciences, Auburn University, Auburn, AL 36849, USA
| | - Amit Morey
- Department of Poultry Science, Auburn University, Auburn, AL 36849, USA
- Correspondence: ; Tel.: +1-229-395-9837
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Education 4.0: Teaching the Basics of KNN, LDA and Simple Perceptron Algorithms for Binary Classification Problems. FUTURE INTERNET 2021. [DOI: 10.3390/fi13080193] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
One of the main focuses of Education 4.0 is to provide students with knowledge on disruptive technologies, such as Machine Learning (ML), as well as the skills to implement this knowledge to solve real-life problems. Therefore, both students and professors require teaching and learning tools that facilitate the introduction to such topics. Consequently, this study looks forward to contributing to the development of those tools by introducing the basic theory behind three machine learning classifying algorithms: K-Nearest-Neighbor (KNN), Linear Discriminant Analysis (LDA), and Simple Perceptron; as well as discussing the diverse advantages and disadvantages of each method. Moreover, it is proposed to analyze how these methods work on different conditions through their implementation over a test bench. Thus, in addition to the description of each algorithm, we discuss their application to solving three different binary classification problems using three different datasets, as well as comparing their performances in these specific case studies. The findings of this study can be used by teachers to provide students the basic knowledge of KNN, LDA, and perceptron algorithms, and, at the same time, it can be used as a guide to learn how to apply them to solve real-life problems that are not limited to the presented datasets.
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