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Hosseini MS, Bejnordi BE, Trinh VQH, Chan L, Hasan D, Li X, Yang S, Kim T, Zhang H, Wu T, Chinniah K, Maghsoudlou S, Zhang R, Zhu J, Khaki S, Buin A, Chaji F, Salehi A, Nguyen BN, Samaras D, Plataniotis KN. Computational pathology: A survey review and the way forward. J Pathol Inform 2024; 15:100357. [PMID: 38420608 PMCID: PMC10900832 DOI: 10.1016/j.jpi.2023.100357] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 12/21/2023] [Accepted: 12/23/2023] [Indexed: 03/02/2024] Open
Abstract
Computational Pathology (CPath) is an interdisciplinary science that augments developments of computational approaches to analyze and model medical histopathology images. The main objective for CPath is to develop infrastructure and workflows of digital diagnostics as an assistive CAD system for clinical pathology, facilitating transformational changes in the diagnosis and treatment of cancer that are mainly address by CPath tools. With evergrowing developments in deep learning and computer vision algorithms, and the ease of the data flow from digital pathology, currently CPath is witnessing a paradigm shift. Despite the sheer volume of engineering and scientific works being introduced for cancer image analysis, there is still a considerable gap of adopting and integrating these algorithms in clinical practice. This raises a significant question regarding the direction and trends that are undertaken in CPath. In this article we provide a comprehensive review of more than 800 papers to address the challenges faced in problem design all-the-way to the application and implementation viewpoints. We have catalogued each paper into a model-card by examining the key works and challenges faced to layout the current landscape in CPath. We hope this helps the community to locate relevant works and facilitate understanding of the field's future directions. In a nutshell, we oversee the CPath developments in cycle of stages which are required to be cohesively linked together to address the challenges associated with such multidisciplinary science. We overview this cycle from different perspectives of data-centric, model-centric, and application-centric problems. We finally sketch remaining challenges and provide directions for future technical developments and clinical integration of CPath. For updated information on this survey review paper and accessing to the original model cards repository, please refer to GitHub. Updated version of this draft can also be found from arXiv.
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Affiliation(s)
- Mahdi S Hosseini
- Department of Computer Science and Software Engineering (CSSE), Concordia Univeristy, Montreal, QC H3H 2R9, Canada
| | | | - Vincent Quoc-Huy Trinh
- Institute for Research in Immunology and Cancer of the University of Montreal, Montreal, QC H3T 1J4, Canada
| | - Lyndon Chan
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Danial Hasan
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Xingwen Li
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Stephen Yang
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Taehyo Kim
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Haochen Zhang
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Theodore Wu
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Kajanan Chinniah
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Sina Maghsoudlou
- Department of Computer Science and Software Engineering (CSSE), Concordia Univeristy, Montreal, QC H3H 2R9, Canada
| | - Ryan Zhang
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Jiadai Zhu
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Samir Khaki
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Andrei Buin
- Huron Digitial Pathology, St. Jacobs, ON N0B 2N0, Canada
| | - Fatemeh Chaji
- Department of Computer Science and Software Engineering (CSSE), Concordia Univeristy, Montreal, QC H3H 2R9, Canada
| | - Ala Salehi
- Department of Electrical and Computer Engineering, University of New Brunswick, Fredericton, NB E3B 5A3, Canada
| | - Bich Ngoc Nguyen
- University of Montreal Hospital Center, Montreal, QC H2X 0C2, Canada
| | - Dimitris Samaras
- Department of Computer Science, Stony Brook University, Stony Brook, NY 11794, United States
| | - Konstantinos N Plataniotis
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
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Halinkovic M, Fabian O, Felsoova A, Kveton M, Benesova W. Intrinsically explainable deep learning architecture for semantic segmentation of histological structures in heart tissue. Comput Biol Med 2024; 177:108624. [PMID: 38795420 DOI: 10.1016/j.compbiomed.2024.108624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 05/16/2024] [Accepted: 05/16/2024] [Indexed: 05/28/2024]
Abstract
BACKGROUND Analysis of structures contained in tissue samples and the relevant contextual information is of utmost importance to histopathologists during diagnosis. Cardiac biopsies require in-depth analysis of the relationships between biological structures. Statistical measures are insufficient for determining a model's viability and applicability in the diagnostic process. A deeper understanding of predictions is necessary in order to support histopathologists. METHODS We propose a method for providing supporting information in the form of segmentation of histological structures to histopathologists based on these principles. The proposed method utilizes nuclei type and density information in addition to standard image input provided at two different zoom levels for the semantic segmentation of blood vessels, inflammation, and endocardium in heart tissue. RESULTS The proposed method was able to reach state-of-the-art segmentation results. The overall quality and viability of the predictions was qualitatively evaluated by two pathologists and a histotechnologist. CONCLUSIONS The decision process of the proposed deep learning model utilizes the provided information sources correctly and simulates the decision process of histopathologists via the usage of a custom-designed attention gate that provides a combination of spatial and encoder attention mechanisms. The implementation is available at https://github.com/mathali/IEDL-segmentation-of-heart-tissue.
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Affiliation(s)
- Matej Halinkovic
- Faculty of Informatics and Information Technologies Slovak University of Technology, Bratislava, 842 16, Slovakia.
| | - Ondrej Fabian
- Institute for Clinical and Experimental Medicine, Prague, 140 21, Czechia; Third Faculty of Medicine, Charles University, Prague, 100 00, Czechia
| | - Andrea Felsoova
- Institute for Clinical and Experimental Medicine, Prague, 140 21, Czechia; Second Faculty of Medicine, Charles University, Prague, 100 00, Czechia
| | - Martin Kveton
- Institute for Clinical and Experimental Medicine, Prague, 140 21, Czechia; Third Faculty of Medicine, Charles University, Prague, 100 00, Czechia
| | - Wanda Benesova
- Faculty of Informatics and Information Technologies Slovak University of Technology, Bratislava, 842 16, Slovakia
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Stamate E, Piraianu AI, Ciobotaru OR, Crassas R, Duca O, Fulga A, Grigore I, Vintila V, Fulga I, Ciobotaru OC. Revolutionizing Cardiology through Artificial Intelligence-Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment-A Comprehensive Review of the Past 5 Years. Diagnostics (Basel) 2024; 14:1103. [PMID: 38893630 PMCID: PMC11172021 DOI: 10.3390/diagnostics14111103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Revised: 05/12/2024] [Accepted: 05/23/2024] [Indexed: 06/21/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI) can radically change almost every aspect of the human experience. In the medical field, there are numerous applications of AI and subsequently, in a relatively short time, significant progress has been made. Cardiology is not immune to this trend, this fact being supported by the exponential increase in the number of publications in which the algorithms play an important role in data analysis, pattern discovery, identification of anomalies, and therapeutic decision making. Furthermore, with technological development, there have appeared new models of machine learning (ML) and deep learning (DP) that are capable of exploring various applications of AI in cardiology, including areas such as prevention, cardiovascular imaging, electrophysiology, interventional cardiology, and many others. In this sense, the present article aims to provide a general vision of the current state of AI use in cardiology. RESULTS We identified and included a subset of 200 papers directly relevant to the current research covering a wide range of applications. Thus, this paper presents AI applications in cardiovascular imaging, arithmology, clinical or emergency cardiology, cardiovascular prevention, and interventional procedures in a summarized manner. Recent studies from the highly scientific literature demonstrate the feasibility and advantages of using AI in different branches of cardiology. CONCLUSIONS The integration of AI in cardiology offers promising perspectives for increasing accuracy by decreasing the error rate and increasing efficiency in cardiovascular practice. From predicting the risk of sudden death or the ability to respond to cardiac resynchronization therapy to the diagnosis of pulmonary embolism or the early detection of valvular diseases, AI algorithms have shown their potential to mitigate human error and provide feasible solutions. At the same time, limits imposed by the small samples studied are highlighted alongside the challenges presented by ethical implementation; these relate to legal implications regarding responsibility and decision making processes, ensuring patient confidentiality and data security. All these constitute future research directions that will allow the integration of AI in the progress of cardiology.
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Affiliation(s)
- Elena Stamate
- Department of Cardiology, Emergency University Hospital of Bucharest, 050098 Bucharest, Romania; (E.S.); (V.V.)
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
| | - Alin-Ionut Piraianu
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
| | - Oana Roxana Ciobotaru
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Railway Hospital Galati, 800223 Galati, Romania
| | - Rodica Crassas
- Emergency County Hospital Braila, 810325 Braila, Romania;
| | - Oana Duca
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Emergency County Hospital Braila, 810325 Braila, Romania;
| | - Ana Fulga
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei Street, 800578 Galati, Romania
| | - Ionica Grigore
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Emergency County Hospital Braila, 810325 Braila, Romania;
| | - Vlad Vintila
- Department of Cardiology, Emergency University Hospital of Bucharest, 050098 Bucharest, Romania; (E.S.); (V.V.)
- Clinical Department of Cardio-Thoracic Pathology, University of Medicine and Pharmacy “Carol Davila” Bucharest, 37 Dionisie Lupu Street, 4192910 Bucharest, Romania
| | - Iuliu Fulga
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei Street, 800578 Galati, Romania
| | - Octavian Catalin Ciobotaru
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Railway Hospital Galati, 800223 Galati, Romania
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Peyster E, Yuan C, Arabyarmohammadi S, Lal P, Feldman M, Fu P, Margulies K, Madabhushi A. Computational Pathology Assessments of Cardiac Stromal Remodeling: Clinical Correlates and Prognostic Implications in Heart Transplantation. RESEARCH SQUARE 2024:rs.3.rs-4364681. [PMID: 38798599 PMCID: PMC11118694 DOI: 10.21203/rs.3.rs-4364681/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Both overt and indolent inflammatory insults in heart transplantation can accelerate pathologic cardiac remodeling, but there are few tools for monitoring the speed and severity of remodeling over time. To address this need, we developed an automated computational pathology system to measure pathologic remodeling in transplant biopsy samples in a large, retrospective cohort of n=2167 digitized heart transplant biopsy slides. Biopsy images were analyzed to identify the pathologic stromal changes associated with future allograft loss or advanced allograft vasculopathy. Biopsy images were then analyzed to assess which historical allo-inflammatory events drive progression of these pathologic stromal changes over time in serial biopsy samples. The top-5 features of pathologic stromal remodeling most strongly associated with adverse outcomes were also strongly associated with histories of both overt and indolent inflammatory events. Our findings identify previously unappreciated subgroups of higher- and lower-risk transplant patients, and highlight the translational potential of digital pathology analysis.
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Peyster E, Smith D, Bittermann T, Bravo P, Margulies K. Beyond the Granuloma: New Insights into Cardiac Sarcoidosis Using Spatial Proteomics. RESEARCH SQUARE 2024:rs.3.rs-4289663. [PMID: 38766184 PMCID: PMC11100892 DOI: 10.21203/rs.3.rs-4289663/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Cardiac sarcoidosis is poorly understood, challenging to diagnose, and portends a poor prognosis. A lack of animal models necessitates the use of residual human samples to study sarcoidosis, which in turn necessitates the use of analytical tools compatible with archival, fixed tissue. We employed high-plex spatial protein analysis within a large cohort of archival human cardiac sarcoidosis and control tissue samples, studying the immunologic, fibrotic, and metabolic landscape of sarcoidosis at different stages of disease, in different cardiac tissue compartments, and in tissue regions with and without overt inflammation. Utilizing a small set of differentially expressed protein biomarkers, we also report the development of a predictive model capable of accurately discriminating between control cardiac tissue and sarcoidosis tissue, even when no histologic evidence of sarcoidosis is present. This finding has major translational implications, with the potential to markedly improve the diagnostic yield of clinical biopsies obtained from suspected sarcoidosis patients.
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Makimoto H, Kohro T. Adopting artificial intelligence in cardiovascular medicine: a scoping review. Hypertens Res 2024; 47:685-699. [PMID: 37907600 DOI: 10.1038/s41440-023-01469-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Revised: 09/03/2023] [Accepted: 09/26/2023] [Indexed: 11/02/2023]
Abstract
Recent years have witnessed significant transformations in cardiovascular medicine, driven by the rapid evolution of artificial intelligence (AI). This scoping review was conducted to capture the breadth of AI applications within cardiovascular science. Employing a structured approach, we sourced relevant articles from PubMed, with an emphasis on journals encompassing general cardiology and digital medicine. We applied filters to highlight cardiovascular articles published in journals focusing on general internal medicine, cardiology and digital medicine, thereby identifying the prevailing trends in the field. Following a comprehensive full-text screening, a total of 140 studies were identified. Over the preceding 5 years, cardiovascular medicine's interplay with AI has seen an over tenfold augmentation. This expansive growth encompasses multiple cardiovascular subspecialties, including but not limited to, general cardiology, ischemic heart disease, heart failure, and arrhythmia. Deep learning emerged as the predominant methodology. The majority of AI endeavors in this domain have been channeled toward enhancing diagnostic and prognostic capabilities, utilizing resources such as hospital datasets, electrocardiograms, and echocardiography. A significant uptrend was observed in AI's application for omics data analysis. However, a clear gap persists in AI's full-scale integration into the clinical decision-making framework. AI, particularly deep learning, has demonstrated robust applications across cardiovascular subspecialties, indicating its transformative potential in this field. As we continue on this trajectory, ensuring the alignment of technological progress with medical ethics becomes crucial. The abundant digital health data today further accentuates the need for meticulous systematic reviews, tailoring them to each cardiovascular subspecialty.
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Affiliation(s)
- Hisaki Makimoto
- Data Science Center/Cardiovascular Center, Jichi Medical University, Shimotsuke, Japan.
| | - Takahide Kohro
- Data Science Center/Cardiovascular Center, Jichi Medical University, Shimotsuke, Japan
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Arabayarmohammadi S, Yuan C, Viswanathan VS, Lal P, Feldman MD, Fu P, Margulies KB, Madabhushi A, Peyster EG. Failing to Make the Grade: Conventional Cardiac Allograft Rejection Grading Criteria Are Inadequate for Predicting Rejection Severity. Circ Heart Fail 2024; 17:e010950. [PMID: 38348670 PMCID: PMC10940208 DOI: 10.1161/circheartfailure.123.010950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 12/07/2023] [Indexed: 02/22/2024]
Abstract
BACKGROUND Cardiac allograft rejection is the leading cause of early graft failure and is a major focus of postheart transplant patient care. While histological grading of endomyocardial biopsy samples remains the diagnostic standard for acute rejection, this standard has limited diagnostic accuracy. Discordance between biopsy rejection grade and patient clinical trajectory frequently leads to both overtreatment of indolent processes and delayed treatment of aggressive ones, spurring the need to investigate the adequacy of the current histological criteria for assessing clinically important rejection outcomes. METHODS N=2900 endomyocardial biopsy images were assigned a rejection grade label (high versus low grade) and a clinical trajectory label (evident versus silent rejection). Using an image analysis approach, n=370 quantitative morphology features describing the lymphocytes and stroma were extracted from each slide. Two models were constructed to compare the subset of features associated with rejection grades versus those associated with clinical trajectories. A proof-of-principle machine learning pipeline-the cardiac allograft rejection evaluator-was then developed to test the feasibility of identifying the clinical severity of a rejection event. RESULTS The histopathologic findings associated with conventional rejection grades differ substantially from those associated with clinically evident allograft injury. Quantitative assessment of a small set of well-defined morphological features can be leveraged to more accurately reflect the severity of rejection compared with that achieved by the International Society of Heart and Lung Transplantation grades. CONCLUSIONS Conventional endomyocardial samples contain morphological information that enables accurate identification of clinically evident rejection events, and this information is incompletely captured by the current, guideline-endorsed, rejection grading criteria.
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Affiliation(s)
- Sara Arabayarmohammadi
- Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Cai Yuan
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, 30322, USA
| | - Vidya Sankar Viswanathan
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, 30322, USA
| | - Priti Lal
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Michael D. Feldman
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Pingfu Fu
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Kenneth B. Margulies
- Cardiovascular Research Institute, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Anant Madabhushi
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, 30322, USA
- Atlanta Veterans Affairs Medical Center
| | - Eliot G. Peyster
- Cardiovascular Research Institute, University of Pennsylvania, Philadelphia, PA, 19104, USA
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Kveton M, Hudec L, Vykopal I, Halinkovic M, Laco M, Felsoova A, Benesova W, Fabian O. Digital pathology in cardiac transplant diagnostics: from biopsies to algorithms. Cardiovasc Pathol 2024; 68:107587. [PMID: 37926351 DOI: 10.1016/j.carpath.2023.107587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 10/03/2023] [Accepted: 10/30/2023] [Indexed: 11/07/2023] Open
Abstract
In the field of heart transplantation, the ability to accurately and promptly diagnose cardiac allograft rejection is crucial. This comprehensive review explores the transformative role of digital pathology and computational pathology, especially through machine learning, in this critical domain. These methodologies harness large datasets to extract subtle patterns and valuable information that extend beyond human perceptual capabilities, potentially enhancing diagnostic outcomes. Current research indicates that these computer-based systems could offer accuracy and performance matching, or even exceeding, that of expert pathologists, thereby introducing more objectivity and reducing observer variability. Despite promising results, several challenges such as limited sample sizes, diverse data sources, and the absence of standardized protocols pose significant barriers to the widespread adoption of these techniques. The future of digital pathology in heart transplantation diagnostics depends on utilizing larger, more diverse patient cohorts, standardizing data collection, processing, and evaluation protocols, and fostering collaborative research efforts. The integration of various data types, including clinical, demographic, and imaging information, could further refine diagnostic precision. As researchers address these challenges and promote collaborative efforts, digital pathology has the potential to become an integral part of clinical practice, ultimately improving patient care in heart transplantation.
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Affiliation(s)
- Martin Kveton
- Third Faculty of Medicine, Charles University, Prague, Czech Republic; Clinical and Transplant Pathology Centre, Institute for Clinical and Experimental Medicine, Prague, Czech Republic.
| | - Lukas Hudec
- Faculty of Informatics and Information Technologies, Slovak University of Technology, Bratislava, Slovakia
| | - Ivan Vykopal
- Faculty of Informatics and Information Technologies, Slovak University of Technology, Bratislava, Slovakia
| | - Matej Halinkovic
- Faculty of Informatics and Information Technologies, Slovak University of Technology, Bratislava, Slovakia
| | - Miroslav Laco
- Faculty of Informatics and Information Technologies, Slovak University of Technology, Bratislava, Slovakia
| | - Andrea Felsoova
- Clinical and Transplant Pathology Centre, Institute for Clinical and Experimental Medicine, Prague, Czech Republic; Department of Histology and Embryology, Second Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Wanda Benesova
- Faculty of Informatics and Information Technologies, Slovak University of Technology, Bratislava, Slovakia
| | - Ondrej Fabian
- Clinical and Transplant Pathology Centre, Institute for Clinical and Experimental Medicine, Prague, Czech Republic; Department of Pathology and Molecular Medicine, Third Faculty of Medicine, Charles University and Thomayer Hospital, Prague, Czech Republic
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Vardas PE, Vardas EP, Tzeis S. Medicine at the dawn of the metaclinical era. Eur Heart J 2023; 44:4729-4730. [PMID: 37794638 DOI: 10.1093/eurheartj/ehad599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/06/2023] Open
Affiliation(s)
- Panos E Vardas
- Biomedical Research Foundation Academy of Athens, Heart Sector, Hygeia Hospitals Group, HHG, Erithrou Stavrou 5, Attica, Athens 15123, Greece
| | - Emmanouil P Vardas
- Department of Cardiology, Athens General Hospital G. Gennimatas, Leoforos Mesogeion 154, Attica, Athens 11527, Greece
| | - Stylianos Tzeis
- Department of Cardiology, Mitera Hospital, Hygeia Group, Erythrou Stavrou 6, Attica, Athens 15123, Greece
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Mehlman Y, Valledor AF, Moeller C, Rubinstein G, Lotan D, Rahman S, Oh KT, Bae D, DeFilippis EM, Lin EF, Lee SH, Raikhelkar JK, Fried J, Theodoropoulos K, Colombo PC, Yuzefpolskaya M, Latif F, Clerkin KJ, Sayer GT, Uriel N. The utilization of molecular microscope in management of heart transplant recipients in the era of noninvasive monitoring. Clin Transplant 2023; 37:e15131. [PMID: 37897211 DOI: 10.1111/ctr.15131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 09/03/2023] [Accepted: 09/06/2023] [Indexed: 10/29/2023]
Abstract
INTRODUCTION Monitoring for graft rejection is a fundamental tenet of post-transplant follow-up. In heart transplantation (HT) in particular, rejection has been traditionally assessed with endomyocardial biopsy (EMB). EMB has potential complications and noted limitations, including interobserver variability in interpretation. Additional tests, such as basic cardiac biomarkers, cardiac imaging, gene expression profiling (GEP) scores, donor-derived cell-free DNA (dd-cfDNA) and the novel molecular microscope diagnostic system (MMDx) have become critical tools in rejection surveillance beyond standard EMB. METHODS This paper describes an illustrative case followed by a review of MMDx within the context of other noninvasive screening modalities for rejection. CONCLUSIONS We suggest MMDx be used to assist with early detection of rejection in cases of discordance between EMB and other noninvasive studies.
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Affiliation(s)
- Yonatan Mehlman
- Division of Cardiology, Center for Advanced Cardiac Care, Columbia University Irving Medical Center, New York, New York, USA
| | - Andrea Fernendez Valledor
- Division of Cardiology, Center for Advanced Cardiac Care, Columbia University Irving Medical Center, New York, New York, USA
| | - Cathrine Moeller
- Division of Cardiology, Center for Advanced Cardiac Care, Columbia University Irving Medical Center, New York, New York, USA
| | - Gal Rubinstein
- Division of Cardiology, Center for Advanced Cardiac Care, Columbia University Irving Medical Center, New York, New York, USA
| | - Dor Lotan
- Division of Cardiology, Center for Advanced Cardiac Care, Columbia University Irving Medical Center, New York, New York, USA
| | - Salwa Rahman
- Division of Cardiology, Center for Advanced Cardiac Care, Columbia University Irving Medical Center, New York, New York, USA
| | - Kyung T Oh
- Division of Cardiology, Center for Advanced Cardiac Care, Columbia University Irving Medical Center, New York, New York, USA
| | - David Bae
- Division of Cardiology, Center for Advanced Cardiac Care, Columbia University Irving Medical Center, New York, New York, USA
| | - Ersilia M DeFilippis
- Division of Cardiology, Center for Advanced Cardiac Care, Columbia University Irving Medical Center, New York, New York, USA
| | - Edward F Lin
- Division of Cardiology, Center for Advanced Cardiac Care, Columbia University Irving Medical Center, New York, New York, USA
| | - Sun Hi Lee
- Division of Cardiology, Center for Advanced Cardiac Care, Columbia University Irving Medical Center, New York, New York, USA
| | - Jayant K Raikhelkar
- Division of Cardiology, Center for Advanced Cardiac Care, Columbia University Irving Medical Center, New York, New York, USA
| | - Justin Fried
- Division of Cardiology, Center for Advanced Cardiac Care, Columbia University Irving Medical Center, New York, New York, USA
| | - Kleanthis Theodoropoulos
- Division of Cardiology, Center for Advanced Cardiac Care, Columbia University Irving Medical Center, New York, New York, USA
| | - Paolo C Colombo
- Division of Cardiology, Center for Advanced Cardiac Care, Columbia University Irving Medical Center, New York, New York, USA
| | - Melana Yuzefpolskaya
- Division of Cardiology, Center for Advanced Cardiac Care, Columbia University Irving Medical Center, New York, New York, USA
| | - Farhana Latif
- Division of Cardiology, Center for Advanced Cardiac Care, Columbia University Irving Medical Center, New York, New York, USA
| | - Kevin J Clerkin
- Division of Cardiology, Center for Advanced Cardiac Care, Columbia University Irving Medical Center, New York, New York, USA
| | - Gabriel T Sayer
- Division of Cardiology, Center for Advanced Cardiac Care, Columbia University Irving Medical Center, New York, New York, USA
| | - Nir Uriel
- Division of Cardiology, Center for Advanced Cardiac Care, Columbia University Irving Medical Center, New York, New York, USA
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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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12
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Kobashigawa J, Hall S, Shah P, Fine B, Halloran P, Jackson AM, Khush KK, Margulies KB, Sani MM, Patel JK, Patel N, Peyster E. The evolving use of biomarkers in heart transplantation: Consensus of an expert panel. Am J Transplant 2023; 23:727-735. [PMID: 36870390 PMCID: PMC10387364 DOI: 10.1016/j.ajt.2023.02.025] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Revised: 02/24/2023] [Accepted: 02/27/2023] [Indexed: 03/06/2023]
Abstract
In heart transplantation, the use of biomarkers to detect the risk of rejection has been evolving. In this setting, it is becoming less clear as to what is the most reliable test or combination of tests to detect rejection and assess the state of the alloimmune response. Therefore, a virtual expert panel was organized in heart and kidney transplantation to evaluate emerging diagnostics and how they may be best utilized to monitor and manage transplant patients. This manuscript covers the heart content of the conference and is a work product of the American Society of Transplantation's Thoracic and Critical Care Community of Practice. This paper reviews currently available and emerging diagnostic assays and defines the unmet needs for biomarkers in heart transplantation. Highlights of the in-depth discussions among conference participants that led to development of consensus statements are included. This conference should serve as a platform to further build consensus within the heart transplant community regarding the optimal framework to implement biomarkers into management protocols and to improve biomarker development, validation and clinical utility. Ultimately, these biomarkers and novel diagnostics should improve outcomes and optimize quality of life for our transplant patients.
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Affiliation(s)
- Jon Kobashigawa
- Department of Cardiology, Cedars-Sinai Smidt Heart Institute, Los Angeles, California, USA.
| | - Shelley Hall
- Department of Cardiology, Baylor University Medical Center, Dallas, Texas, USA
| | - Palak Shah
- Department of Cardiology, Inova Heart and Vascular Institute, Falls Church, Virginia, USA
| | - Barry Fine
- Department of Cardiology, Columbia University Irving Medical Center, New York, USA
| | - Phil Halloran
- Department of Medicine Division of Nephrology, University of Alberta, Edmonton, Canada
| | - Annette M Jackson
- Department of Surgery, Duke University, Durham, North Carolina, USA; Department of Immunology, Duke University, Durham, North Carolina, USA
| | - Kiran K Khush
- Department of Medicine, Stanford University, Stanford, California, USA
| | - Kenneth B Margulies
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Maryam Mojarrad Sani
- Department of Cardiology, Cedars-Sinai Smidt Heart Institute, Los Angeles, California, USA
| | - Jignesh K Patel
- Department of Cardiology, Cedars-Sinai Smidt Heart Institute, Los Angeles, California, USA
| | - Nikhil Patel
- Department of Cardiology, Cedars-Sinai Smidt Heart Institute, Los Angeles, California, USA
| | - Eliot Peyster
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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13
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Palmieri V, Montisci A, Vietri MT, Colombo PC, Sala S, Maiello C, Coscioni E, Donatelli F, Napoli C. Artificial intelligence, big data and heart transplantation: Actualities. Int J Med Inform 2023; 176:105110. [PMID: 37285695 DOI: 10.1016/j.ijmedinf.2023.105110] [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: 03/05/2023] [Revised: 05/21/2023] [Accepted: 05/22/2023] [Indexed: 06/09/2023]
Abstract
BACKGROUND As diagnostic and prognostic models developed by traditional statistics perform poorly in real-world, artificial intelligence (AI) and Big Data (BD) may improve the supply chain of heart transplantation (HTx), allocation opportunities, correct treatments, and finally optimize HTx outcome. We explored available studies, and discussed opportunities and limits of medical application of AI to the field of HTx. METHOD A systematic overview of studies published up to December 31st, 2022, in English on peer-revied journals, have been identified through PUBMED-MEDLINE-WEB of Science, referring to HTx, AI, BD. Studies were grouped in 4 domains based on main studies' objectives and results: etiology, diagnosis, prognosis, treatment. A systematic attempt was made to evaluate studies by the Prediction model Risk Of Bias ASsessment Tool (PROBAST) and the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD). RESULTS Among the 27 publications selected, none used AI applied to BD. Of the selected studies, 4 fell in the domain of etiology, 6 in the domain of diagnosis, 3 in the domain of treatment, and 17 in that of prognosis, as AI was most frequently used for algorithmic prediction and discrimination of survival, but in retrospective cohorts and registries. AI-based algorithms appeared superior to probabilistic functions to predict patterns, but external validation was rarely employed. Indeed, based on PROBAST, selected studies showed, to some extent, significant risk of bias (especially in the domain of predictors and analysis). In addition, as example of applicability in the real-world, a free-use prediction algorithm developed through AI failed to predict 1-year mortality post-HTx in cases from our center. CONCLUSIONS While AI-based prognostic and diagnostic functions performed better than those developed by traditional statistics, risk of bias, lack of external validation, and relatively poor applicability, may affect AI-based tools. More unbiased research with high quality BD meant for AI, transparency and external validations, are needed to have medical AI as a systematic aid to clinical decision making in HTx.
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Affiliation(s)
- Vittorio Palmieri
- Azienda Ospedaliera dei Colli Monaldi-Cotugno-CTO, Department of Cardiac Surgery and Transplantation, Naples, Italy.
| | - Andrea Montisci
- Division of Cardiothoracic Intensive Care, Cardiothoracic Department, ASST Spedali Civili, Brescia, Italy
| | - Maria Teresa Vietri
- Department of Precision Medicine, "Luigi Vanvitelli" University of Campania School of Medicine, Naples, Italy
| | - Paolo C Colombo
- Milstein Division of Cardiology, Department of Medicine, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
| | - Silvia Sala
- Chair of Anesthesia and Intensive Care, University of Brescia, Brescia, Italy
| | - Ciro Maiello
- Azienda Ospedaliera dei Colli Monaldi-Cotugno-CTO, Department of Cardiac Surgery and Transplantation, Naples, Italy
| | - Enrico Coscioni
- Department of Cardiac Surgery, AOU San Giovanni di Dio e Ruggi d'Aragona, Salerno, Italy
| | - Francesco Donatelli
- Department of Cardiac Surgery, Istituto Clinico Sant'Ambrogio, Milan, Italy; Chair of Cardiac Surgery, University of Milan, Milan, Italy
| | - Claudio Napoli
- Department of Advanced Medical and Surgical Sciences (DAMSS), "Luigi Vanvitelli" University of Campania School of Medicine, Naples, Italy
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14
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Seraphin TP, Luedde M, Roderburg C, van Treeck M, Scheider P, Buelow RD, Boor P, Loosen SH, Provaznik Z, Mendelsohn D, Berisha F, Magnussen C, Westermann D, Luedde T, Brochhausen C, Sossalla S, Kather JN. Prediction of heart transplant rejection from routine pathology slides with self-supervised deep learning. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2023; 4:265-274. [PMID: 37265858 PMCID: PMC10232288 DOI: 10.1093/ehjdh/ztad016] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 02/07/2023] [Indexed: 06/03/2023]
Abstract
Aims One of the most important complications of heart transplantation is organ rejection, which is diagnosed on endomyocardial biopsies by pathologists. Computer-based systems could assist in the diagnostic process and potentially improve reproducibility. Here, we evaluated the feasibility of using deep learning in predicting the degree of cellular rejection from pathology slides as defined by the International Society for Heart and Lung Transplantation (ISHLT) grading system. Methods and results We collected 1079 histopathology slides from 325 patients from three transplant centres in Germany. We trained an attention-based deep neural network to predict rejection in the primary cohort and evaluated its performance using cross-validation and by deploying it to three cohorts. For binary prediction (rejection yes/no), the mean area under the receiver operating curve (AUROC) was 0.849 in the cross-validated experiment and 0.734, 0.729, and 0.716 in external validation cohorts. For a prediction of the ISHLT grade (0R, 1R, 2/3R), AUROCs were 0.835, 0.633, and 0.905 in the cross-validated experiment and 0.764, 0.597, and 0.913; 0.631, 0.633, and 0.682; and 0.722, 0.601, and 0.805 in the validation cohorts, respectively. The predictions of the artificial intelligence model were interpretable by human experts and highlighted plausible morphological patterns. Conclusion We conclude that artificial intelligence can detect patterns of cellular transplant rejection in routine pathology, even when trained on small cohorts.
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Affiliation(s)
| | | | | | - Marko van Treeck
- Department of Medicine III, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074 Aachen, Germany
| | - Pascal Scheider
- Institute of Pathology, RWTH Aachen University Hospital, Pauwelsstraße 30, 52074 Aachen, Germany
| | - Roman D Buelow
- Institute of Pathology, RWTH Aachen University Hospital, Pauwelsstraße 30, 52074 Aachen, Germany
| | - Peter Boor
- Institute of Pathology, RWTH Aachen University Hospital, Pauwelsstraße 30, 52074 Aachen, Germany
| | - Sven H Loosen
- Department of Gastroenterology, Hepatology and Infectious Diseases, University Hospital Duesseldorf, Medical Faculty at Heinrich-Heine-University, Moorenstr. 5, 40225 Dusseldorf, Germany
| | - Zdenek Provaznik
- Department of Cardiothoracic Surgery, University Medical Center Regensburg, Franz-Josef-Strauß-Allee 11, 93053 Regensburg, Germany
| | - Daniel Mendelsohn
- Institute of Pathology, University of Regensburg, Franz-Josef-Strauß-Allee 11, 93053 Regensburg, Germany
| | - Filip Berisha
- Department of Cardiology, University Heart and Vascular Center Hamburg, University Medical Center Hamburg-Hospital Eppendorf, Martinistraße 52, 20251 Hamburg, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Hamburg/Kiel/Lübeck, Potsdamer Str. 58, 10785 Berlin, Germany
| | - Christina Magnussen
- Department of Cardiology, University Heart and Vascular Center Hamburg, University Medical Center Hamburg-Hospital Eppendorf, Martinistraße 52, 20251 Hamburg, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Hamburg/Kiel/Lübeck, Potsdamer Str. 58, 10785 Berlin, Germany
| | - Dirk Westermann
- Department of Cardiology, University Heart and Vascular Center Hamburg, University Medical Center Hamburg-Hospital Eppendorf, Martinistraße 52, 20251 Hamburg, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Hamburg/Kiel/Lübeck, Potsdamer Str. 58, 10785 Berlin, Germany
| | - Tom Luedde
- Department of Gastroenterology, Hepatology and Infectious Diseases, University Hospital Duesseldorf, Medical Faculty at Heinrich-Heine-University, Moorenstr. 5, 40225 Dusseldorf, Germany
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15
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Holzhauser L, DeFilippis EM, Nikolova A, Byku M, Contreras JP, De Marco T, Hall S, Khush KK, Vest AR. The End of Endomyocardial Biopsy?: A Practical Guide for Noninvasive Heart Transplant Rejection Surveillance. JACC. HEART FAILURE 2023; 11:263-276. [PMID: 36682960 DOI: 10.1016/j.jchf.2022.11.002] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 10/31/2022] [Accepted: 11/03/2022] [Indexed: 01/13/2023]
Abstract
Noninvasive heart transplant rejection surveillance using gene expression profiling (GEP) to monitor immune activation is widely used among heart transplant programs. With the new development of donor-derived cell-free DNA (dd-cfDNA) assays, more programs are transitioning to a predominantly noninvasive rejection surveillance protocol with a reduced frequency of endomyocardial biopsies. As a result, many practical questions arise that potentially delay implementation of these valuable new tools. The purpose of this review is to provide practical guidance for clinicians transitioning toward a less invasive acute rejection monitoring protocol after heart transplantation, and to answer 10 common questions about the GEP and dd-cfDNA assays. Evidence supporting GEP and dd-cfDNA testing is reviewed, as well as guidance on test interpretation and future directions.
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Affiliation(s)
- Luise Holzhauser
- Division of Cardiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ersilia M DeFilippis
- Division of Cardiology, Columbia University Irving Medical Center, New York, New York, USA
| | - Andriana Nikolova
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Mirnela Byku
- Department of Cardiology, University of North Carolina in Chapel Hill, North Carolina, USA
| | | | - Teresa De Marco
- Division of Cardiology, University of California, San Francisco, California, USA
| | - Shelley Hall
- Baylor University Medical Center, Dallas, Texas, USA
| | - Kiran K Khush
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, California, USA
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16
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Adedinsewo D, Hardway HD, Morales-Lara AC, Wieczorek MA, Johnson PW, Douglass EJ, Dangott BJ, Nakhleh RE, Narula T, Patel PC, Goswami RM, Lyle MA, Heckman AJ, Leoni-Moreno JC, Steidley DE, Arsanjani R, Hardaway B, Abbas M, Behfar A, Attia ZI, Lopez-Jimenez F, Noseworthy PA, Friedman P, Carter RE, Yamani M. Non-invasive detection of cardiac allograft rejection among heart transplant recipients using an electrocardiogram based deep learning model. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2023; 4:71-80. [PMID: 36974261 PMCID: PMC10039431 DOI: 10.1093/ehjdh/ztad001] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 12/08/2022] [Indexed: 01/15/2023]
Abstract
Aims Current non-invasive screening methods for cardiac allograft rejection have shown limited discrimination and are yet to be broadly integrated into heart transplant care. Given electrocardiogram (ECG) changes have been reported with severe cardiac allograft rejection, this study aimed to develop a deep-learning model, a form of artificial intelligence, to detect allograft rejection using the 12-lead ECG (AI-ECG). Methods and results Heart transplant recipients were identified across three Mayo Clinic sites between 1998 and 2021. Twelve-lead digital ECG data and endomyocardial biopsy results were extracted from medical records. Allograft rejection was defined as moderate or severe acute cellular rejection (ACR) based on International Society for Heart and Lung Transplantation guidelines. The extracted data (7590 unique ECG-biopsy pairs, belonging to 1427 patients) was partitioned into training (80%), validation (10%), and test sets (10%) such that each patient was included in only one partition. Model performance metrics were based on the test set (n = 140 patients; 758 ECG-biopsy pairs). The AI-ECG detected ACR with an area under the receiver operating curve (AUC) of 0.84 [95% confidence interval (CI): 0.78-0.90] and 95% (19/20; 95% CI: 75-100%) sensitivity. A prospective proof-of-concept screening study (n = 56; 97 ECG-biopsy pairs) showed the AI-ECG detected ACR with AUC = 0.78 (95% CI: 0.61-0.96) and 100% (2/2; 95% CI: 16-100%) sensitivity. Conclusion An AI-ECG model is effective for detection of moderate-to-severe ACR in heart transplant recipients. Our findings could improve transplant care by providing a rapid, non-invasive, and potentially remote screening option for cardiac allograft function.
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Affiliation(s)
- Demilade Adedinsewo
- Department of Cardiovascular Medicine, Division of Cardiovascular Diseases, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL 32224, USA
| | - Heather D Hardway
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL, USA
| | - Andrea Carolina Morales-Lara
- Department of Cardiovascular Medicine, Division of Cardiovascular Diseases, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL 32224, USA
| | - Mikolaj A Wieczorek
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL, USA
| | - Patrick W Johnson
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL, USA
| | - Erika J Douglass
- Department of Cardiovascular Medicine, Division of Cardiovascular Diseases, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL 32224, USA
| | - Bryan J Dangott
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Jacksonville, FL, USA
| | - Raouf E Nakhleh
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Jacksonville, FL, USA
| | - Tathagat Narula
- Department of Transplantation, Mayo Clinic, Jacksonville, FL, USA
| | - Parag C Patel
- Department of Transplantation, Mayo Clinic, Jacksonville, FL, USA
| | - Rohan M Goswami
- Department of Transplantation, Mayo Clinic, Jacksonville, FL, USA
| | - Melissa A Lyle
- Department of Transplantation, Mayo Clinic, Jacksonville, FL, USA
| | - Alexander J Heckman
- Department of Cardiovascular Medicine, Division of Cardiovascular Diseases, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL 32224, USA
| | | | - D Eric Steidley
- Department of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ, USA
| | - Reza Arsanjani
- Department of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ, USA
| | - Brian Hardaway
- Department of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ, USA
| | - Mohsin Abbas
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Atta Behfar
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Zachi I Attia
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | | | | | - Paul Friedman
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Rickey E Carter
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL, USA
| | - Mohamad Yamani
- Department of Cardiovascular Medicine, Division of Cardiovascular Diseases, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL 32224, USA
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17
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Al-Ani MA, Bai C, Hashky A, Parker AM, Vilaro JR, Aranda JM, Shickel B, Rashidi P, Bihorac A, Ahmed MM, Mardini MT. Artificial intelligence guidance of advanced heart failure therapies: A systematic scoping review. Front Cardiovasc Med 2023; 10:1127716. [PMID: 36910520 PMCID: PMC9999024 DOI: 10.3389/fcvm.2023.1127716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 02/07/2023] [Indexed: 03/14/2023] Open
Abstract
Introduction Artificial intelligence can recognize complex patterns in large datasets. It is a promising technology to advance heart failure practice, as many decisions rely on expert opinions in the absence of high-quality data-driven evidence. Methods We searched Embase, Web of Science, and PubMed databases for articles containing "artificial intelligence," "machine learning," or "deep learning" and any of the phrases "heart transplantation," "ventricular assist device," or "cardiogenic shock" from inception until August 2022. We only included original research addressing post heart transplantation (HTx) or mechanical circulatory support (MCS) clinical care. Review and data extraction were performed in accordance with PRISMA-Scr guidelines. Results Of 584 unique publications detected, 31 met the inclusion criteria. The majority focused on outcome prediction post HTx (n = 13) and post durable MCS (n = 7), as well as post HTx and MCS management (n = 7, n = 3, respectively). One study addressed temporary mechanical circulatory support. Most studies advocated for rapid integration of AI into clinical practice, acknowledging potential improvements in management guidance and reliability of outcomes prediction. There was a notable paucity of external data validation and integration of multiple data modalities. Conclusion Our review showed mounting innovation in AI application in management of MCS and HTx, with the largest evidence showing improved mortality outcome prediction.
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Affiliation(s)
- Mohammad A Al-Ani
- Division of Cardiovascular Medicine, University of Florida, Gainesville, FL, United States
| | - Chen Bai
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, United States
| | - Amal Hashky
- Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL, United States
| | - Alex M Parker
- Division of Cardiovascular Medicine, University of Florida, Gainesville, FL, United States
| | - Juan R Vilaro
- Division of Cardiovascular Medicine, University of Florida, Gainesville, FL, United States
| | - Juan M Aranda
- Division of Cardiovascular Medicine, University of Florida, Gainesville, FL, United States
| | - Benjamin Shickel
- Department of Medicine, University of Florida, Gainesville, FL, United States.,Intelligent Critical Care Center (IC3), University of Florida, Gainesville, FL, United States
| | - Parisa Rashidi
- Intelligent Critical Care Center (IC3), University of Florida, Gainesville, FL, United States.,Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
| | - Azra Bihorac
- Department of Medicine, University of Florida, Gainesville, FL, United States.,Intelligent Critical Care Center (IC3), University of Florida, Gainesville, FL, United States
| | - Mustafa M Ahmed
- Division of Cardiovascular Medicine, University of Florida, Gainesville, FL, United States
| | - Mamoun T Mardini
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, United States
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18
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Viswanathan VS, Toro P, Corredor G, Mukhopadhyay S, Madabhushi A. The state of the art for artificial intelligence in lung digital pathology. J Pathol 2022; 257:413-429. [PMID: 35579955 PMCID: PMC9254900 DOI: 10.1002/path.5966] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 04/26/2022] [Accepted: 05/15/2022] [Indexed: 12/03/2022]
Abstract
Lung diseases carry a significant burden of morbidity and mortality worldwide. The advent of digital pathology (DP) and an increase in computational power have led to the development of artificial intelligence (AI)-based tools that can assist pathologists and pulmonologists in improving clinical workflow and patient management. While previous works have explored the advances in computational approaches for breast, prostate, and head and neck cancers, there has been a growing interest in applying these technologies to lung diseases as well. The application of AI tools on radiology images for better characterization of indeterminate lung nodules, fibrotic lung disease, and lung cancer risk stratification has been well documented. In this article, we discuss methodologies used to build AI tools in lung DP, describing the various hand-crafted and deep learning-based unsupervised feature approaches. Next, we review AI tools across a wide spectrum of lung diseases including cancer, tuberculosis, idiopathic pulmonary fibrosis, and COVID-19. We discuss the utility of novel imaging biomarkers for different types of clinical problems including quantification of biomarkers like PD-L1, lung disease diagnosis, risk stratification, and prediction of response to treatments such as immune checkpoint inhibitors. We also look briefly at some emerging applications of AI tools in lung DP such as multimodal data analysis, 3D pathology, and transplant rejection. Lastly, we discuss the future of DP-based AI tools, describing the challenges with regulatory approval, developing reimbursement models, planning clinical deployment, and addressing AI biases. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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Affiliation(s)
| | - Paula Toro
- Department of PathologyCleveland ClinicClevelandOHUSA
| | - Germán Corredor
- Department of Biomedical EngineeringCase Western Reserve UniversityClevelandOHUSA
- Louis Stokes Cleveland VA Medical CenterClevelandOHUSA
| | | | - Anant Madabhushi
- Department of Biomedical EngineeringCase Western Reserve UniversityClevelandOHUSA
- Louis Stokes Cleveland VA Medical CenterClevelandOHUSA
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19
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Wang X, Barrera C, Bera K, Viswanathan VS, Azarianpour-Esfahani S, Koyuncu C, Velu P, Feldman MD, Yang M, Fu P, Schalper KA, Mahdi H, Lu C, Velcheti V, Madabhushi A. Spatial interplay patterns of cancer nuclei and tumor-infiltrating lymphocytes (TILs) predict clinical benefit for immune checkpoint inhibitors. SCIENCE ADVANCES 2022; 8:eabn3966. [PMID: 35648850 PMCID: PMC9159577 DOI: 10.1126/sciadv.abn3966] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Immune checkpoint inhibitors (ICIs) show prominent clinical activity across multiple advanced tumors. However, less than half of patients respond even after molecule-based selection. Thus, improved biomarkers are required. In this study, we use an image analysis to capture morphologic attributes relating to the spatial interaction and architecture of tumor cells and tumor-infiltrating lymphocytes (TILs) from digitized H&E images. We evaluate the association of image features with progression-free (PFS) and overall survival in non-small cell lung cancer (NSCLC) (N = 187) and gynecological cancer (N = 39) patients treated with ICIs. We demonstrated that the classifier trained with NSCLC alone was associated with PFS in independent NSCLC cohorts and also in gynecological cancer. The classifier was also associated with clinical outcome independent of clinical factors. Moreover, the classifier was associated with PFS even with low PD-L1 expression. These findings suggest that image analysis can be used to predict clinical end points in patients receiving ICI.
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Affiliation(s)
- Xiangxue Wang
- Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, USA
- Corresponding author. (X.W.); (A.M.)
| | - Cristian Barrera
- Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, USA
| | - Kaustav Bera
- Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, USA
| | - Vidya Sankar Viswanathan
- Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, USA
| | - Sepideh Azarianpour-Esfahani
- Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, USA
| | - Can Koyuncu
- Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, USA
| | - Priya Velu
- Department of Pathology and Laboratory Medicine, Weill Cornell Medical College, New York, NY, USA
| | - Michael D. Feldman
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael Yang
- Department of Pathology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Pingfu Fu
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Kurt A. Schalper
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA
| | - Haider Mahdi
- Magee-Womens Hospital and Magee-Womens Research Institute, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Cheng Lu
- Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, USA
| | - Vamsidhar Velcheti
- Department of Hematology and Oncology, NYU Langone Health, New York, NY, USA
| | - Anant Madabhushi
- Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, USA
- Louis Stokes Cleveland Veterans Affairs Medical Center, Cleveland, OH, USA
- Corresponding author. (X.W.); (A.M.)
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20
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Arabyarmohammadi S, Leo P, Viswanathan VS, Janowczyk A, Corredor G, Fu P, Meyerson H, Metheny L, Madabhushi A. Machine Learning to Predict Risk of Relapse Using Cytologic Image Markers in Patients With Acute Myeloid Leukemia Posthematopoietic Cell Transplantation. JCO Clin Cancer Inform 2022; 6:e2100156. [PMID: 35522898 PMCID: PMC9126529 DOI: 10.1200/cci.21.00156] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 01/28/2022] [Accepted: 03/08/2022] [Indexed: 01/03/2023] Open
Abstract
PURPOSE Allogenic hematopoietic stem-cell transplant (HCT) is a curative therapy for acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS). Relapse post-HCT is the most common cause of treatment failure and is associated with a poor prognosis. Pathologist-based visual assessment of aspirate images and the manual myeloblast counting have shown to be predictive of relapse post-HCT. However, this approach is time-intensive and subjective. The premise of this study was to explore whether computer-extracted morphology and texture features from myeloblasts' chromatin patterns could help predict relapse and prognosticate relapse-free survival (RFS) after HCT. MATERIALS AND METHODS In this study, Wright-Giemsa-stained post-HCT aspirate images were collected from 92 patients with AML/MDS who were randomly assigned into a training set (St = 52) and a validation set (Sv = 40). First, a deep learning-based model was developed to segment myeloblasts. A total of 214 texture and shape descriptors were then extracted from the segmented myeloblasts on aspirate slide images. A risk score on the basis of texture features of myeloblast chromatin patterns was generated by using the least absolute shrinkage and selection operator with a Cox regression model. RESULTS The risk score was associated with RFS in St (hazard ratio = 2.38; 95% CI, 1.4 to 3.95; P = .0008) and Sv (hazard ratio = 1.57; 95% CI, 1.01 to 2.45; P = .044). We also demonstrate that this resulting signature was predictive of AML relapse with an area under the receiver operating characteristic curve of 0.71 within Sv. All the relevant code is available at GitHub. CONCLUSION The texture features extracted from chromatin patterns of myeloblasts can predict post-HCT relapse and prognosticate RFS of patients with AML/MDS.
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Affiliation(s)
- Sara Arabyarmohammadi
- Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH
| | - Patrick Leo
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH
| | | | - Andrew Janowczyk
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH
- Lausanne University Hospital, Precision Oncology Center, Vaud, Switzerland
| | - German Corredor
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH
| | - Pingfu Fu
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH
| | - Howard Meyerson
- Department of Pathology, University Hospitals Cleveland Medical Center, Cleveland, OH
| | - Leland Metheny
- Department of Hematology and Oncology, University Hospitals Cleveland Medical Center, Cleveland, OH
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH
- Louis Stokes Veterans Administration Medical Center, Cleveland, OH
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21
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Peyster EG, Janowczyk A, Swamidoss A, Kethireddy S, Feldman MD, Margulies KB. Computational Analysis of Routine Biopsies Improves Diagnosis and Prediction of Cardiac Allograft Vasculopathy. Circulation 2022; 145:1563-1577. [PMID: 35405081 DOI: 10.1161/circulationaha.121.058459] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background: Cardiac allograft vasculopathy (CAV) is a leading cause of morbidity and mortality for heart transplant recipients. While clinical risk factors for CAV have been established, no personalized prognostic test exists to confidently identify patients at high vs. low risk of developing aggressive CAV. The aim of this investigation was to leverage computational methods for analyzing digital pathology images from routine endomyocardial biopsies (EMB) to develop a precision medicine tool for predicting CAV years before overt clinical presentation. Methods: Clinical data from 1-year post-transplant was collected on 302 transplant recipients from the University of Pennsylvania, including 53 'early CAV' patients and 249 'no-CAV' controls. This data was used to generate a 'clinical model' (ClinCAV-Pr) for predicting future CAV development. From this cohort, n=183 archived EMBs were collected for CD31 and modified trichrome staining and then digitally scanned. These included 1-year post-transplant EMBs from 50 'early CAV' patients and 82 no-CAV patients, as well as 51 EMBs from 'disease control' patients obtained at the time of definitive coronary angiography confirming CAV. Using biologically-inspired, hand-crafted features extracted from digitized EMBs, quantitative histologic models for differentiating no-CAV from disease controls (HistoCAV-Dx), and for predicting future CAV from 1-year post-transplant EMBs were developed (HistoCAV-Pr). The performance of histologic and clinical models for predicting future CAV (i.e. HistoCAV-Pr and ClinCAV-Pr, respectively) were compared in a held-out validation set, before being combined to assess the added predictive value of an integrated predictive model (iCAV-Pr). Results: ClinCAV-Pr achieved modest performance on the independent test set, with area under the receiver operating curve (AUROC) of 0.70. The HistoCAV-Dx model for diagnosing CAV achieved excellent discrimination, with an AUROC of 0.91, while HistoCAV-Pr model for predicting CAV achieved good performance with an AUROC of 0.80. The integrated iCAV-Pr model achieved excellent predictive performance, with an AUROC of 0.93 on the held-out test set. Conclusions: Prediction of future CAV development is greatly improved by incorporation of computationally extracted histologic features. These results suggest morphologic details contained within regularly obtained biopsy tissue have the potential to enhance precision and personalization of treatment plans for post-heart transplant patients.
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Affiliation(s)
- Eliot G Peyster
- Cardiovascular Research Institute (E.G.P., K.B.M.), University of Pennsylvania, Philadelphia
| | - Andrew Janowczyk
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH (A.J., A.S., S.K.)
- Department of Oncology, Lausanne University Hospital and Lausanne University, Switzerland (A.J.)
| | - Abigail Swamidoss
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH (A.J., A.S., S.K.)
| | - Samhith Kethireddy
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH (A.J., A.S., S.K.)
| | - Michael D Feldman
- Department of Pathology and Laboratory Medicine (M.D.F.), University of Pennsylvania, Philadelphia
| | - Kenneth B Margulies
- Cardiovascular Research Institute (E.G.P., K.B.M.), University of Pennsylvania, Philadelphia
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22
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Deep learning-enabled assessment of cardiac allograft rejection from endomyocardial biopsies. Nat Med 2022; 28:575-582. [PMID: 35314822 DOI: 10.1038/s41591-022-01709-2] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 01/19/2022] [Indexed: 02/07/2023]
Abstract
Endomyocardial biopsy (EMB) screening represents the standard of care for detecting allograft rejections after heart transplant. Manual interpretation of EMBs is affected by substantial interobserver and intraobserver variability, which often leads to inappropriate treatment with immunosuppressive drugs, unnecessary follow-up biopsies and poor transplant outcomes. Here we present a deep learning-based artificial intelligence (AI) system for automated assessment of gigapixel whole-slide images obtained from EMBs, which simultaneously addresses detection, subtyping and grading of allograft rejection. To assess model performance, we curated a large dataset from the United States, as well as independent test cohorts from Turkey and Switzerland, which includes large-scale variability across populations, sample preparations and slide scanning instrumentation. The model detects allograft rejection with an area under the receiver operating characteristic curve (AUC) of 0.962; assesses the cellular and antibody-mediated rejection type with AUCs of 0.958 and 0.874, respectively; detects Quilty B lesions, benign mimics of rejection, with an AUC of 0.939; and differentiates between low-grade and high-grade rejections with an AUC of 0.833. In a human reader study, the AI system showed non-inferior performance to conventional assessment and reduced interobserver variability and assessment time. This robust evaluation of cardiac allograft rejection paves the way for clinical trials to establish the efficacy of AI-assisted EMB assessment and its potential for improving heart transplant outcomes.
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23
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Zhou L, Wolfson A, Vaidya AS. Noninvasive methods to reduce cardiac complications postheart transplant. Curr Opin Organ Transplant 2022; 27:45-51. [PMID: 34907978 DOI: 10.1097/mot.0000000000000953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE OF REVIEW Long-term success of heart transplantation is limited by allograft rejection and cardiac allograft vasculopathy (CAV). Classic management has relied on frequent invasive testing to screen for early features of rejection and CAV to allow for early treatment. In this review, we discuss new developments in the screening and prevention of allograft rejection and CAV. RECENT FINDINGS Newer noninvasive screening techniques show excellent sensitivity and specificity for the detection of clinically significant rejection. New biomarkers and treatment targets continue to be identified and await further studies regarding their utility in preventing allograft vasculopathy. SUMMARY Noninvasive imaging and biomarker testing continue to show promise as alternatives to invasive testing for allograft rejection. Continued validation of their effectiveness may lead to new surveillance protocols with reduced frequency of invasive testing. Furthermore, these noninvasive methods will allow for more personalized strategies to reduce the complications of long-term immunosuppression whereas continuing the decline in the overall rate of allograft rejection.
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Affiliation(s)
- Leon Zhou
- Department of Cardiology, Keck School of Medicine, Los Angeles, California, USA
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24
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Piening BD, Dowdell AK, Zhang M, Loza BL, Walls D, Gao H, Mohebnasab M, Li YR, Elftmann E, Wei E, Gandla D, Lad H, Chaib H, Sweitzer NK, Deng M, Pereira AC, Cadeiras M, Shaked A, Snyder MP, Keating BJ. Whole Transcriptome Profiling of Prospective Endomyocardial Biopsies Reveals Prognostic and Diagnostic Signatures of Cardiac Allograft Rejection. J Heart Lung Transplant 2022; 41:840-848. [DOI: 10.1016/j.healun.2022.01.1377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2021] [Revised: 01/22/2022] [Accepted: 01/25/2022] [Indexed: 11/26/2022] Open
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25
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Jiao Y, Yuan J, Sodimu OM, Qiang Y, Ding Y. Deep Neural Network-Aided Histopathological Analysis of Myocardial Injury. Front Cardiovasc Med 2022; 8:724183. [PMID: 35083295 PMCID: PMC8784602 DOI: 10.3389/fcvm.2021.724183] [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] [Received: 06/12/2021] [Accepted: 12/17/2021] [Indexed: 11/13/2022] Open
Abstract
Deep neural networks have become the mainstream approach for analyzing and interpreting histology images. In this study, we established and validated an interpretable DNN model to assess endomyocardial biopsy (EMB) data of patients with myocardial injury. Deep learning models were used to extract features and classify EMB histopathological images of heart failure cases diagnosed with either ischemic cardiomyopathy or idiopathic dilated cardiomyopathy and non-failing cases (organ donors without a history of heart failure). We utilized the gradient-weighted class activation mapping (Grad-CAM) technique to emphasize injured regions, providing an entry point to assess the dominant morphology in the process of a comprehensive evaluation. To visualize clustered regions of interest (ROI), we utilized uniform manifold approximation and projection (UMAP) embedding for dimension reduction. We further implemented a multi-model ensemble mechanism to improve the quantitative metric (area under the receiver operating characteristic curve, AUC) to 0.985 and 0.992 on ROI-level and case-level, respectively, outperforming the achievement of 0.971 ± 0.017 and 0.981 ± 0.020 based on the sub-models. Collectively, this new methodology provides a robust and interpretive framework to explore local histopathological patterns, facilitating the automatic and high-throughput quantification of cardiac EMB analysis.
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26
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Vardas PE, Asselbergs FW, van Smeden M, Friedman P. The year in cardiovascular medicine 2021: digital health and innovation. Eur Heart J 2022; 43:271-279. [PMID: 34974610 DOI: 10.1093/eurheartj/ehab874] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 11/15/2021] [Accepted: 11/23/2021] [Indexed: 12/15/2022] Open
Abstract
This article presents some of the most important developments in the field of digital medicine that have appeared over the last 12 months and are related to cardiovascular medicine. The article consists of three main sections, as follows: (i) artificial intelligence-enabled cardiovascular diagnostic tools, techniques, and methodologies, (ii) big data and prognostic models for cardiovascular risk protection, and (iii) wearable devices in cardiovascular risk assessment, cardiovascular disease prevention, diagnosis, and management. To conclude the article, the authors present a brief further prospective on this new domain, highlighting existing gaps that are specifically related to artificial intelligence technologies, such as explainability, cost-effectiveness, and, of course, the importance of proper regulatory oversight for each clinical implementation.
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Affiliation(s)
- Panos E Vardas
- Heart Sector, Hygeia Hospitals Group, HHG, 5, Erithrou Stavrou, Marousi, Athens 15123, Greece.,European Heart Agency, ESC, Brussels, Belgium
| | - Folkert W Asselbergs
- Department of Cardiology, Division of Heart & Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.,Health Data Research UK and Institute of Health Informatics, University College London, London, UK
| | - Maarten van Smeden
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Paul Friedman
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
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27
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Bauersachs J, de Boer RA, Lindenfeld J, Bozkurt B. The year in cardiovascular medicine 2021: heart failure and cardiomyopathies. Eur Heart J 2022; 43:367-376. [PMID: 34974611 PMCID: PMC9383181 DOI: 10.1093/eurheartj/ehab887] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 10/27/2021] [Accepted: 11/16/2021] [Indexed: 12/22/2022] Open
Abstract
In the year 2021, the universal definition and classification of heart failure (HF) was published that defines HF as a clinical syndrome with symptoms and/or signs caused by a cardiac abnormality and corroborated by elevated natriuretic peptide levels or objective evidence of cardiogenic congestion. This definition and the classification of HF with reduced ejection fraction (HFrEF), mildly reduced, and HF with preserved ejection fraction (HFpEF) is consistent with the 2021 ESC Guidelines on HF. Among several other new recommendations, these guidelines give a Class I indication for the use of the sodium–glucose co-transporter 2 (SGLT2) inhibitors dapagliflozin and empagliflozin in HFrEF patients. As the first evidence-based treatment for HFpEF, in the EMPEROR-Preserved trial, empagliflozin reduced the composite endpoint of cardiovascular death and HF hospitalizations. Several reports in 2021 have provided novel and detailed analyses of device and medical therapy in HF, especially regarding sacubitril/valsartan, SGLT2 inhibitors, mineralocorticoid receptor antagonists, ferric carboxymaltose, soluble guanylate cyclase activators, and cardiac myosin activators. In patients hospitalized with COVID-19, acute HF and myocardial injury is quite frequent, whereas myocarditis and long-term damage to the heart are rather uncommon.
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Affiliation(s)
- Johann Bauersachs
- Department of Cardiology and Angiology, Hannover Medical School, Carl-Neuberg-Straße 1, 30625 Hannover, Germany
| | - Rudolf A de Boer
- Department of Cardiology, University Medical Center Groningen, Groningen, The Netherlands
| | - JoAnn Lindenfeld
- Vanderbilt Heart and Vascular Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Biykem Bozkurt
- Winters Center for Heart Failure, Cardiology, Baylor College of Medicine and Michael E. DeBakey VA Medical Center, Houston TX, USA
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28
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Alfonso F, Rivero F, Segovia-Cubero J. Early diagnosis of cardiac allograft vasculopathy: biopsy, liquid biopsy, non-invasive imaging, coronary imaging, or coronary physiology? Eur Heart J 2021; 42:4930-4933. [PMID: 34665226 DOI: 10.1093/eurheartj/ehab722] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Affiliation(s)
- Fernando Alfonso
- Department of Cardiology, Hospital Universitario de La Princesa, Universidad Autónoma de Madrid, IIS-IP, CIBERCV, Madrid, Spain
| | - Fernando Rivero
- Department of Cardiology, Hospital Universitario de La Princesa, Universidad Autónoma de Madrid, IIS-IP, CIBERCV, Madrid, Spain
| | - Javier Segovia-Cubero
- Department of Cardiology, Hospital Universitario Puerta de Hierro, Majadahonda, Universidad Autónoma de Madrid, CIVERCV, Madrid, Spain
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29
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Glass C, Lafata KJ, Jeck W, Horstmeyer R, Cooke C, Everitt J, Glass M, Dov D, Seidman MA. The Role of Machine Learning in Cardiovascular Pathology. Can J Cardiol 2021; 38:234-245. [PMID: 34813876 DOI: 10.1016/j.cjca.2021.11.008] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 11/15/2021] [Accepted: 11/17/2021] [Indexed: 02/07/2023] Open
Abstract
Machine learning has seen slow but steady uptake in diagnostic pathology over the past decade to assess digital whole-slide images. Machine learning tools have incredible potential to standardise, and likely even improve, histopathologic diagnoses, but they are not yet widely used in clinical practice. We describe the principles of these tools and technologies and some successful preclinical and pretranslational efforts in cardiovascular pathology, as well as a roadmap for moving forward. In nonhuman animal models, one proof-of-principle application is in rodent progressive cardiomyopathy, which is of particular significance to drug toxicity studies. Basic science successes include screening the quality of differentiated stem cells and characterising cardiomyocyte developmental stages, with potential applications for research and toxicology/drug safety screening using derived or native human pluripotent stem cells differentiated into cardiomyocytes. Translational studies of particular note include those with success in diagnosing the various forms of heart allograft rejection. For fully realising the value of these tools in clinical cardiovascular pathology, we identify 3 essential challenges. First is image quality standardisation to ensure that algorithms can be developed and implemented on robust, consistent data. The second is consensus diagnosis; experts don't always agree, and thus "truth" may be difficult to establish, but the algorithms themselves may provide a solution. The third is the need for large-enough data sets to facilitate robust algorithm development, necessitating large cross-institutional shared image databases. The power of histopathology-based machine learning technologies is tremendous, and we outline the next steps needed to capitalise on this power.
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Affiliation(s)
- Carolyn Glass
- Division of Artificial Intelligence and Computational Pathology, Duke AI Health, Duke University Medical Center, Durham, North Carolina, USA; Department of Pathology, Duke University Medical Center, Durham, North Carolina, USA.
| | - Kyle J Lafata
- Department of Radiology, Duke University School of Medicine, Durham, North Carolina, USA; Department of Radiation Oncology, Duke University School of Medicine, Durham, North Carolina, USA; Department of Electrical and Computer Engineering, Duke Pratt School of Engineering, Duke University, Durham, North Carolina, USA
| | - William Jeck
- Division of Artificial Intelligence and Computational Pathology, Duke AI Health, Duke University Medical Center, Durham, North Carolina, USA; Department of Pathology, Duke University Medical Center, Durham, North Carolina, USA
| | - Roarke Horstmeyer
- Division of Artificial Intelligence and Computational Pathology, Duke AI Health, Duke University Medical Center, Durham, North Carolina, USA; Department of Biomedical Engineering, Duke Pratt School of Engineering, Durham, North Carolina, USA
| | - Colin Cooke
- Department of Electrical and Computer Engineering, Duke Pratt School of Engineering, Duke University, Durham, North Carolina, USA
| | - Jeffrey Everitt
- Division of Artificial Intelligence and Computational Pathology, Duke AI Health, Duke University Medical Center, Durham, North Carolina, USA; Department of Pathology, Duke University Medical Center, Durham, North Carolina, USA
| | - Matthew Glass
- Division of Artificial Intelligence and Computational Pathology, Duke AI Health, Duke University Medical Center, Durham, North Carolina, USA; Department of Anesthesiology, Duke University Medical Center, Durham, North Carolina, USA
| | - David Dov
- Division of Artificial Intelligence and Computational Pathology, Duke AI Health, Duke University Medical Center, Durham, North Carolina, USA; Tel-Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Michael A Seidman
- Laboratory Medicine Program, University Health Network, Toronto, Ontario, Canada; Department of Laboratory Medicine and Pathobiology, University of Toronto, Ontario, Canada
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30
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Leening MJG, Mahmoud KD. Non-efficacy benefits and non-inferiority margins: a scoping review of contemporary high-impact non-inferiority trials in clinical cardiology. Eur J Epidemiol 2021; 36:1103-1109. [PMID: 34792692 PMCID: PMC8629871 DOI: 10.1007/s10654-021-00820-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Accepted: 11/02/2021] [Indexed: 12/19/2022]
Affiliation(s)
- Maarten J G Leening
- Department of Cardiology, Erasmus MC - University Medical Center Rotterdam, PO Box 2040, 3000 CA, Rotterdam, The Netherlands.
- Department of Epidemiology, Erasmus MC - University Medical Center Rotterdam, PO Box 2040, 3000 CA, Rotterdam, The Netherlands.
| | - Karim D Mahmoud
- Department of Cardiology, Erasmus MC - University Medical Center Rotterdam, PO Box 2040, 3000 CA, Rotterdam, The Netherlands
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31
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Abstract
Despite the overall success of heart transplantation as a definitive treatment for endstage heart failure, cardiac allograft rejection remains an important cause of morbidity and mortality. Endomyocardial biopsy has been the standard of care for rejection monitoring, but is associated with several diagnostic limitations and serious procedural complications. The use of molecular diagnostics has emerged over the past decade as a tool to potentially circumvent some of these limitations. We present an update on novel molecular approaches to detecting transplant rejection, focusing on 4 categories: microarray technology, gene expression profiling, cell-free DNA and microRNA.
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Affiliation(s)
- Lillian Benck
- Department of Cardiology, Cedars-Sinai Smidt Heart Institute
| | - Takuma Sato
- Department of Cardiovascular Medicine, Hokkaido University Graduate School of Medicine
| | - Jon Kobashigawa
- Department of Cardiology, Cedars-Sinai Smidt Heart Institute
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32
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Howlett JG, Crespo-Leiro MG. The International Endomyocardial Biopsy Position Paper: A Basis for Integration Into Modern Clinical Practice. J Card Fail 2021; 28:e5-e7. [PMID: 34242780 DOI: 10.1016/j.cardfail.2021.06.014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2021] [Accepted: 06/27/2021] [Indexed: 10/20/2022]
Affiliation(s)
- Jonathan G Howlett
- Cumming School of Medicine, University of Calgary, Libin Cardiovascular Institute, Calgary, Canada.
| | - Maria G Crespo-Leiro
- Complexo Hospitalario Universitario A Coruña, Instituto Investigación Biomédica A Coruña, La Coruña, Spain; Universidade da Coruña, Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares, La Coruña, Spain
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33
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Crea F. The Universal Definition of Heart Failure, risk prediction in cardiogenic shock, artificial intelligence in cardiac allograft rejection, and the genetics of dilated cardiomyopathy. Eur Heart J 2021; 42:2317-2320. [PMID: 34153987 DOI: 10.1093/eurheartj/ehab370] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Affiliation(s)
- Filippo Crea
- Department of Cardiovascular Medicine, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.,Department of Cardiovascular and Pulmonary Sciences, Catholic University of the Sacred Heart, Rome, Italy
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34
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Veta M, van Diest PJ, Vink A. Can automatic image analysis replace the pathologist in cardiac allograft rejection diagnosis? Eur Heart J 2021; 42:2370-2372. [PMID: 34000014 DOI: 10.1093/eurheartj/ehab226] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Affiliation(s)
- Mitko Veta
- Medical Image Analysis Group (IMAG/e), Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Paul J van Diest
- Department of Pathology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Aryan Vink
- Department of Pathology, University Medical Center Utrecht, Utrecht, The Netherlands
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