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Cazzaniga G, Rossi M, Eccher A, Girolami I, L'Imperio V, Van Nguyen H, Becker JU, Bueno García MG, Sbaraglia M, Dei Tos AP, Gambaro G, Pagni F. Time for a full digital approach in nephropathology: a systematic review of current artificial intelligence applications and future directions. J Nephrol 2024; 37:65-76. [PMID: 37768550 PMCID: PMC10920416 DOI: 10.1007/s40620-023-01775-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 08/22/2023] [Indexed: 09/29/2023]
Abstract
INTRODUCTION Artificial intelligence (AI) integration in nephropathology has been growing rapidly in recent years, facing several challenges including the wide range of histological techniques used, the low occurrence of certain diseases, and the need for data sharing. This narrative review retraces the history of AI in nephropathology and provides insights into potential future developments. METHODS Electronic searches in PubMed-MEDLINE and Embase were made to extract pertinent articles from the literature. Works about automated image analysis or the application of an AI algorithm on non-neoplastic kidney histological samples were included and analyzed to extract information such as publication year, AI task, and learning type. Prepublication servers and reviews were not included. RESULTS Seventy-six (76) original research articles were selected. Most of the studies were conducted in the United States in the last 7 years. To date, research has been mainly conducted on relatively easy tasks, like single-stain glomerular segmentation. However, there is a trend towards developing more complex tasks such as glomerular multi-stain classification. CONCLUSION Deep learning has been used to identify patterns in complex histopathology data and looks promising for the comprehensive assessment of renal biopsy, through the use of multiple stains and virtual staining techniques. Hybrid and collaborative learning approaches have also been explored to utilize large amounts of unlabeled data. A diverse team of experts, including nephropathologists, computer scientists, and clinicians, is crucial for the development of AI systems for nephropathology. Collaborative efforts among multidisciplinary experts result in clinically relevant and effective AI tools.
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Affiliation(s)
- Giorgio Cazzaniga
- Department of Medicine and Surgery, Pathology, Fondazione IRCCS San Gerardo dei Tintori, Università di Milano-Bicocca, Monza, Italy.
| | - Mattia Rossi
- Division of Nephrology, Department of Medicine, University of Verona, Piazzale Aristide Stefani, 1, 37126, Verona, Italy
| | - Albino Eccher
- Department of Pathology and Diagnostics, University and Hospital Trust of Verona, P.le Stefani n. 1, 37126, Verona, Italy
- Department of Medical and Surgical Sciences for Children and Adults, University of Modena and Reggio Emilia, University Hospital of Modena, Modena, Italy
| | - Ilaria Girolami
- Department of Pathology and Diagnostics, University and Hospital Trust of Verona, P.le Stefani n. 1, 37126, Verona, Italy
| | - Vincenzo L'Imperio
- Department of Medicine and Surgery, Pathology, Fondazione IRCCS San Gerardo dei Tintori, Università di Milano-Bicocca, Monza, Italy
| | - Hien Van Nguyen
- Department of Electrical and Computer Engineering, University of Houston, Houston, TX, 77004, USA
| | - Jan Ulrich Becker
- Institute of Pathology, University Hospital of Cologne, Cologne, Germany
| | - María Gloria Bueno García
- VISILAB Research Group, E.T.S. Ingenieros Industriales, University of Castilla-La Mancha, Ciudad Real, Spain
| | - Marta Sbaraglia
- Department of Pathology, Azienda Ospedale-Università Padova, Padua, Italy
- Department of Medicine, University of Padua School of Medicine, Padua, Italy
| | - Angelo Paolo Dei Tos
- Department of Pathology, Azienda Ospedale-Università Padova, Padua, Italy
- Department of Medicine, University of Padua School of Medicine, Padua, Italy
| | - Giovanni Gambaro
- Division of Nephrology, Department of Medicine, University of Verona, Piazzale Aristide Stefani, 1, 37126, Verona, Italy
| | - Fabio Pagni
- Department of Medicine and Surgery, Pathology, Fondazione IRCCS San Gerardo dei Tintori, Università di Milano-Bicocca, Monza, Italy
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Rahman MA, Yilmaz I, Albadri ST, Salem FE, Dangott BJ, Taner CB, Nassar A, Akkus Z. Artificial Intelligence Advances in Transplant Pathology. Bioengineering (Basel) 2023; 10:1041. [PMID: 37760142 PMCID: PMC10525684 DOI: 10.3390/bioengineering10091041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 08/15/2023] [Accepted: 08/31/2023] [Indexed: 09/29/2023] Open
Abstract
Transplant pathology plays a critical role in ensuring that transplanted organs function properly and the immune systems of the recipients do not reject them. To improve outcomes for transplant recipients, accurate diagnosis and timely treatment are essential. Recent advances in artificial intelligence (AI)-empowered digital pathology could help monitor allograft rejection and weaning of immunosuppressive drugs. To explore the role of AI in transplant pathology, we conducted a systematic search of electronic databases from January 2010 to April 2023. The PRISMA checklist was used as a guide for screening article titles, abstracts, and full texts, and we selected articles that met our inclusion criteria. Through this search, we identified 68 articles from multiple databases. After careful screening, only 14 articles were included based on title and abstract. Our review focuses on the AI approaches applied to four transplant organs: heart, lungs, liver, and kidneys. Specifically, we found that several deep learning-based AI models have been developed to analyze digital pathology slides of biopsy specimens from transplant organs. The use of AI models could improve clinicians' decision-making capabilities and reduce diagnostic variability. In conclusion, our review highlights the advancements and limitations of AI in transplant pathology. We believe that these AI technologies have the potential to significantly improve transplant outcomes and pave the way for future advancements in this field.
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Affiliation(s)
- Md Arafatur Rahman
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Jacksonville, FL 32224, USA
- Department of Mathematics, Florida State University, Tallahassee, FL 32306, USA
| | - Ibrahim Yilmaz
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Jacksonville, FL 32224, USA
- Computational Pathology and Artificial Intelligence, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Sam T. Albadri
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Fadi E. Salem
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Bryan J. Dangott
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Jacksonville, FL 32224, USA
- Computational Pathology and Artificial Intelligence, Mayo Clinic, Jacksonville, FL 32224, USA
| | - C. Burcin Taner
- Department of Transplantation Surgery, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Aziza Nassar
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Zeynettin Akkus
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Jacksonville, FL 32224, USA
- Computational Pathology and Artificial Intelligence, Mayo Clinic, Jacksonville, FL 32224, USA
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Smith B, Grande J, Ryan M, Smith M, Denic A, Hermsen M, Park W, Kremers W, Stegall M. Automated scoring of total inflammation in renal allograft biopsies. Clin Transplant 2023; 37:e14837. [PMID: 36259615 DOI: 10.1111/ctr.14837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 09/26/2022] [Accepted: 10/13/2022] [Indexed: 01/18/2023]
Abstract
BACKGROUND Computer-assisted scoring is gaining prominence in the evaluation of renal histology; however, much of the focus has been on identifying larger objects such as glomeruli. Total inflammation impacts graft outcome, and its quantification requires tools to identify objects at the cellular level or smaller. The goal of the current study was to use CD45 stained slides coupled with image analysis tools to quantify the amount of non-glomerular inflammation within the cortex. METHODS Sixty renal transplant whole slide images were used for digital image analysis. Multiple thresholding methods using pixel intensity and object size were used to identify inflammation in the cortex. Additionally, convolutional neural networks were used to separate glomeruli from other objects in the cortex. This combined measure of inflammation was then correlated with rescored Banff total inflammation classification and outcomes. RESULTS Identification of glomeruli on biopsies had high fidelity (mean pixelwise dice coefficient of .858). Continuous total inflammation scores correlated well with Banff rescoring (maximum Pearson correlation .824). A separate set of thresholds resulted in a significant correlation with alloimmune graft loss. CONCLUSIONS Automated scoring of inflammation showed a high correlation with Banff scoring. Digital image analysis provides a powerful tool for analysis of renal pathology, not only because it is reproducible and can be automated, but also because it provides much more granular data for studies.
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Affiliation(s)
- Byron Smith
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Joseph Grande
- Department of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota, USA
| | - Maggie Ryan
- Department of Anatomic Pathology, Mayo Clinic, Scottsdale, Arizona, USA
| | - Maxwell Smith
- Department of Anatomic Pathology, Mayo Clinic, Scottsdale, Arizona, USA
| | - Aleksandar Denic
- Department of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota, USA
| | - Meyke Hermsen
- Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Walter Park
- William J. von Liebig Center for Transplantation and Clinical Regeneration, Mayo Clinic, Rochester, Minnesota, USA.,Department of Surgery, Mayo Clinic, Rochester, Minnesota, USA
| | - Walter Kremers
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA.,William J. von Liebig Center for Transplantation and Clinical Regeneration, Mayo Clinic, Rochester, Minnesota, USA
| | - Mark Stegall
- William J. von Liebig Center for Transplantation and Clinical Regeneration, Mayo Clinic, Rochester, Minnesota, USA.,Department of Surgery, Mayo Clinic, Rochester, Minnesota, USA
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Cordova C, Muñoz R, Olivares R, Minonzio JG, Lozano C, Gonzalez P, Marchant I, González-Arriagada W, Olivero P. HER2 classification in breast cancer cells: A new explainable machine learning application for immunohistochemistry. Oncol Lett 2022; 25:44. [PMID: 36644146 PMCID: PMC9811637 DOI: 10.3892/ol.2022.13630] [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: 09/15/2022] [Accepted: 10/31/2022] [Indexed: 12/15/2022] Open
Abstract
The immunohistochemical (IHC) evaluation of epidermal growth factor 2 (HER2) for the diagnosis of breast cancer is still qualitative with a high degree of inter-observer variability, and thus requires the incorporation of complementary techniques such as fluorescent in situ hybridization (FISH) to resolve the diagnosis. Implementing automatic algorithms to classify IHC biomarkers is crucial for typifying the tumor and deciding on therapy for each patient with better performance. The present study aims to demonstrate that, using an explainable Machine Learning (ML) model for the classification of HER2 photomicrographs, it is possible to determine criteria to improve the value of IHC analysis. We trained a logistic regression-based supervised ML model with 393 IHC microscopy images from 131 patients, to discriminate between upregulated and normal expression of the HER2 protein. Pathologists' diagnoses (IHC only) vs. the final diagnosis complemented with FISH (IHC + FISH) were used as training outputs. Basic performance metrics and receiver operating characteristic curve analysis were used together with an explainability algorithm based on Shapley Additive exPlanations (SHAP) values to understand training differences. The model could discriminate amplified IHC from normal expression with better performance when the training output was the IHC + FISH final diagnosis (IHC vs. IHC + FISH: area under the curve, 0.94 vs. 0.81). This may be explained by the increased analytical impact of the membrane distribution criteria over the global intensity of the signal, according to SHAP value interpretation. The classification model improved its performance when the training input was the final diagnosis, downplaying the weighting of the intensity of the IHC signal, suggesting that to improve pathological diagnosis before FISH consultation, it is necessary to emphasize subcellular patterns of staining.
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Affiliation(s)
- Claudio Cordova
- Cell Function and Structure Laboratory (EFC Lab.), Faculty of Medicine, Universidad de Valparaíso, Valparaíso 2341386, Chile,PhD Program in Health Sciences and Engineering, Faculty of Engineering, Universidad de Valparaíso, Valparaíso 2362735, Chile
| | - Roberto Muñoz
- PhD Program in Health Sciences and Engineering, Faculty of Engineering, Universidad de Valparaíso, Valparaíso 2362735, Chile,School of Informatics Engineering, Faculty of Engineering, Universidad de Valparaíso, Valparaíso 2362735, Chile
| | - Rodrigo Olivares
- School of Informatics Engineering, Faculty of Engineering, Universidad de Valparaíso, Valparaíso 2362735, Chile,Center for Research and Development in Health Engineering, Faculty of Engineering, Universidad de Valparaíso, Valparaíso 2362735, Chile
| | - Jean-Gabriel Minonzio
- PhD Program in Health Sciences and Engineering, Faculty of Engineering, Universidad de Valparaíso, Valparaíso 2362735, Chile,School of Informatics Engineering, Faculty of Engineering, Universidad de Valparaíso, Valparaíso 2362735, Chile,Center for Research and Development in Health Engineering, Faculty of Engineering, Universidad de Valparaíso, Valparaíso 2362735, Chile,Millennium Institute for Intelligent Healthcare: iHEALTH, Faculty of Engineering, Universidad de Valparaíso, Valparaíso 2362735, Chile
| | - Carlo Lozano
- Pathological Anatomy Service, Carlos Van Buren Hospital, Valparaíso 2340105, Chile
| | - Paulina Gonzalez
- Pathological Anatomy Service, Carlos Van Buren Hospital, Valparaíso 2340105, Chile,School of Medical Technology, Andrés Bello National University (UNAB), Viña del Mar, 2520000, Chile
| | - Ivanny Marchant
- Medical Modeling Laboratory, Faculty of Medicine, Universidad de Valparaíso, Valparaíso 2362735, Chile
| | - Wilfredo González-Arriagada
- Faculty of Dentistry, Universidad de los Andes, Santiago 7620086, Chile,Biomedical Research and Innovation Center (CIIB), Universidad de los Andes, Santiago 7620086, Chile
| | - Pablo Olivero
- Cell Function and Structure Laboratory (EFC Lab.), Faculty of Medicine, Universidad de Valparaíso, Valparaíso 2341386, Chile,PhD Program in Health Sciences and Engineering, Faculty of Engineering, Universidad de Valparaíso, Valparaíso 2362735, Chile,Correspondence to: Dr Pablo Olivero, Cell Function and Structure Laboratory (EFC Lab.), Faculty of Engineering, Universidad de Valparaíso, 2664 Hontaneda, Valparaíso 2341386, Chile, E-mail:
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Artificial Intelligence-Assisted Renal Pathology: Advances and Prospects. J Clin Med 2022; 11:jcm11164918. [PMID: 36013157 PMCID: PMC9410196 DOI: 10.3390/jcm11164918] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 07/30/2022] [Accepted: 08/11/2022] [Indexed: 11/17/2022] Open
Abstract
Digital imaging and advanced microscopy play a pivotal role in the diagnosis of kidney diseases. In recent years, great achievements have been made in digital imaging, providing novel approaches for precise quantitative assessments of nephropathology and relieving burdens of renal pathologists. Developing novel methods of artificial intelligence (AI)-assisted technology through multidisciplinary interaction among computer engineers, renal specialists, and nephropathologists could prove beneficial for renal pathology diagnoses. An increasing number of publications has demonstrated the rapid growth of AI-based technology in nephrology. In this review, we offer an overview of AI-assisted renal pathology, including AI concepts and the workflow of processing digital image data, focusing on the impressive advances of AI application in disease-specific backgrounds. In particular, this review describes the applied computer vision algorithms for the segmentation of kidney structures, diagnosis of specific pathological changes, and prognosis prediction based on images. Lastly, we discuss challenges and prospects to provide an objective view of this topic.
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Glomerulus Semantic Segmentation Using Ensemble of Deep Learning Models. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2022. [DOI: 10.1007/s13369-022-06608-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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