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Bülow RD, Droste P, Boor P. [Advances in computational quantitative nephropathology]. PATHOLOGIE (HEIDELBERG, GERMANY) 2024; 45:140-145. [PMID: 38308066 DOI: 10.1007/s00292-024-01300-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 01/05/2024] [Indexed: 02/04/2024]
Abstract
BACKGROUND Semiquantitative histological scoring systems are frequently used in nephropathology. In computational nephropathology, the focus is on generating quantitative data from histology (so-called pathomics). Several recent studies have collected such data using next-generation morphometry (NGM) based on segmentations by artificial neural networks and investigated their usability for various clinical or diagnostic purposes. AIM To present an overview of the current state of studies regarding renal pathomics and to identify current challenges and potential solutions. MATERIALS AND METHODS Due to the literature restriction (maximum of 30 references), studies were selected based on a database search that processed as much data as possible, used innovative methodologies, and/or were ideally multicentric in design. RESULTS AND DISCUSSION Pathomics studies in the kidney have impressively demonstrated that morphometric data are useful clinically (for example, for prognosis assessment) and translationally. Further development of NGM requires overcoming some challenges, including better standardization and generation of prospective evidence.
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Affiliation(s)
- Roman D Bülow
- Institut für Pathologie, Sektion Nephropathologie, Universitätsklinikum RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Deutschland
| | - Patrick Droste
- Institut für Pathologie, Sektion Nephropathologie, Universitätsklinikum RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Deutschland
- Medizinische Klinik II, Universitätsklinikum RWTH Aachen, Aachen, Deutschland
| | - Peter Boor
- Institut für Pathologie, Sektion Nephropathologie, Universitätsklinikum RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Deutschland.
- Medizinische Klinik II, Universitätsklinikum RWTH Aachen, Aachen, Deutschland.
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Fayos De Arizón L, Viera ER, Pilco M, Perera A, De Maeztu G, Nicolau A, Furlano M, Torra R. Artificial intelligence: a new field of knowledge for nephrologists? Clin Kidney J 2023; 16:2314-2326. [PMID: 38046016 PMCID: PMC10689169 DOI: 10.1093/ckj/sfad182] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Indexed: 12/05/2023] Open
Abstract
Artificial intelligence (AI) is a science that involves creating machines that can imitate human intelligence and learn. AI is ubiquitous in our daily lives, from search engines like Google to home assistants like Alexa and, more recently, OpenAI with its chatbot. AI can improve clinical care and research, but its use requires a solid understanding of its fundamentals, the promises and perils of algorithmic fairness, the barriers and solutions to its clinical implementation, and the pathways to developing an AI-competent workforce. The potential of AI in the field of nephrology is vast, particularly in the areas of diagnosis, treatment and prediction. One of the most significant advantages of AI is the ability to improve diagnostic accuracy. Machine learning algorithms can be trained to recognize patterns in patient data, including lab results, imaging and medical history, in order to identify early signs of kidney disease and thereby allow timely diagnoses and prompt initiation of treatment plans that can improve outcomes for patients. In short, AI holds the promise of advancing personalized medicine to new levels. While AI has tremendous potential, there are also significant challenges to its implementation, including data access and quality, data privacy and security, bias, trustworthiness, computing power, AI integration and legal issues. The European Commission's proposed regulatory framework for AI technology will play a significant role in ensuring the safe and ethical implementation of these technologies in the healthcare industry. Training nephrologists in the fundamentals of AI is imperative because traditionally, decision-making pertaining to the diagnosis, prognosis and treatment of renal patients has relied on ingrained practices, whereas AI serves as a powerful tool for swiftly and confidently synthesizing this information.
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Affiliation(s)
- Leonor Fayos De Arizón
- Nephrology Department, Fundació Puigvert; Institut d'Investigacions Biomèdiques Sant Pau (IIB-Sant Pau); Departament de Medicina, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Elizabeth R Viera
- Nephrology Department, Fundació Puigvert; Institut d'Investigacions Biomèdiques Sant Pau (IIB-Sant Pau); Departament de Medicina, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Melissa Pilco
- Nephrology Department, Fundació Puigvert; Institut d'Investigacions Biomèdiques Sant Pau (IIB-Sant Pau); Departament de Medicina, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Alexandre Perera
- Center for Biomedical Engineering Research (CREB), Universitat Politècnica de Barcelona (UPC), Barcelona, Spain; Networking Biomedical Research Centre in the subject area of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain; Institut de Recerca Sant Joan de Déu, Esplugues de Llobregat, Barcelona, Spain
| | | | | | - Monica Furlano
- Nephrology Department, Fundació Puigvert; Institut d'Investigacions Biomèdiques Sant Pau (IIB-Sant Pau); Departament de Medicina, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Roser Torra
- Nephrology Department, Fundació Puigvert; Institut d'Investigacions Biomèdiques Sant Pau (IIB-Sant Pau); Departament de Medicina, Universitat Autònoma de Barcelona, Barcelona, Spain
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De Bruyne S, De Kesel P, Oyaert M. Applications of Artificial Intelligence in Urinalysis: Is the Future Already Here? Clin Chem 2023; 69:1348-1360. [PMID: 37708293 DOI: 10.1093/clinchem/hvad136] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 08/16/2023] [Indexed: 09/16/2023]
Abstract
BACKGROUND Artificial intelligence (AI) has emerged as a promising and transformative tool in the field of urinalysis, offering substantial potential for advancements in disease diagnosis and the development of predictive models for monitoring medical treatment responses. CONTENT Through an extensive examination of relevant literature, this narrative review illustrates the significance and applicability of AI models across the diverse application area of urinalysis. It encompasses automated urine test strip and sediment analysis, urinary tract infection screening, and the interpretation of complex biochemical signatures in urine, including the utilization of cutting-edge techniques such as mass spectrometry and molecular-based profiles. SUMMARY Retrospective studies consistently demonstrate good performance of AI models in urinalysis, showcasing their potential to revolutionize clinical practice. However, to comprehensively evaluate the real clinical value and efficacy of AI models, large-scale prospective studies are essential. Such studies hold the potential to enhance diagnostic accuracy, improve patient outcomes, and optimize medical treatment strategies. By bridging the gap between research and clinical implementation, AI can reshape the landscape of urinalysis, paving the way for more personalized and effective patient care.
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Affiliation(s)
- Sander De Bruyne
- Department of Laboratory Medicine, Ghent University Hospital, Ghent, Belgium
| | - Pieter De Kesel
- Department of Laboratory Medicine, Ghent University Hospital, Ghent, Belgium
| | - Matthijs Oyaert
- Department of Laboratory Medicine, Ghent University Hospital, Ghent, Belgium
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Filler G, Gipson DS, Iyamuremye D, Díaz González de Ferris ME. Artificial Intelligence in Pediatric Nephrology-A Call for Action. ADVANCES IN KIDNEY DISEASE AND HEALTH 2023; 30:17-24. [PMID: 36723276 DOI: 10.1053/j.akdh.2022.11.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Revised: 10/24/2022] [Accepted: 11/07/2022] [Indexed: 12/24/2022]
Abstract
Artificial intelligence is playing an increasingly important role in many fields of clinical care to assist health care providers in patient management. In adult-focused nephrology, artificial intelligence is beginning to be used to improve clinical care, hemodialysis prescriptions, and follow-up of transplant recipients. This article provides an overview of medical artificial intelligence applications relevant to pediatric nephrology. We describe the core concepts of artificial intelligence and machine learning and cover the basics of neural networks and deep learning. We also discuss some examples for clinical applications of artificial intelligence in pediatric nephrology, including neonatal kidney function, early recognition of acute kidney injury, renally cleared drug dosing, intrapatient variability, urinary tract infection workup in infancy, and longitudinal disease progression. Furthermore, we consider the future of artificial intelligence in clinical pediatric nephrology and its potential impact on medical practice and address the ethical issues artificial intelligence raises in terms of clinical decision-making, health care provider-patient relationship, patient privacy, and data collection. This article also represents a call for action involving those of us striving to provide optimal services for children, adolescents, and young adults with chronic conditions.
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Affiliation(s)
- Guido Filler
- Division of Pediatric Nephrology, Departments of Paediatrics, Western University, London, Ontario, Canada; Departments of Medicine, Western University, London, Ontario, Canada; Department of Pathology and Laboratory Medicine, Western University, London, Ontario, Canada.
| | - Debbie S Gipson
- Department of Pediatrics, University of Michigan, Ann Arbor, Michigan
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Natural Language Processing in Diagnostic Texts from Nephropathology. Diagnostics (Basel) 2022; 12:diagnostics12071726. [PMID: 35885630 PMCID: PMC9325286 DOI: 10.3390/diagnostics12071726] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 07/11/2022] [Accepted: 07/12/2022] [Indexed: 11/23/2022] Open
Abstract
Introduction: This study investigates whether it is possible to predict a final diagnosis based on a written nephropathological description—as a surrogate for image analysis—using various NLP methods. Methods: For this work, 1107 unlabelled nephropathological reports were included. (i) First, after separating each report into its microscopic description and diagnosis section, the diagnosis sections were clustered unsupervised to less than 20 diagnostic groups using different clustering techniques. (ii) Second, different text classification methods were used to predict the diagnostic group based on the microscopic description section. Results: The best clustering results (i) could be achieved with HDBSCAN, using BoW-based feature extraction methods. Based on keywords, these clusters can be mapped to certain diagnostic groups. A transformer encoder-based approach as well as an SVM worked best regarding diagnosis prediction based on the histomorphological description (ii). Certain diagnosis groups reached F1-scores of up to 0.892 while others achieved weak classification metrics. Conclusion: While textual morphological description alone enables retrieving the correct diagnosis for some entities, it does not work sufficiently for other entities. This is in accordance with a previous image analysis study on glomerular change patterns, where some diagnoses are associated with one pattern, but for others, there exists a complex pattern combination.
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