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Ashayeri H, Sobhi N, Pławiak P, Pedrammehr S, Alizadehsani R, Jafarizadeh A. Transfer Learning in Cancer Genetics, Mutation Detection, Gene Expression Analysis, and Syndrome Recognition. Cancers (Basel) 2024; 16:2138. [PMID: 38893257 DOI: 10.3390/cancers16112138] [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: 05/05/2024] [Revised: 05/30/2024] [Accepted: 06/01/2024] [Indexed: 06/21/2024] Open
Abstract
Artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL), has revolutionized medical research, facilitating advancements in drug discovery and cancer diagnosis. ML identifies patterns in data, while DL employs neural networks for intricate processing. Predictive modeling challenges, such as data labeling, are addressed by transfer learning (TL), leveraging pre-existing models for faster training. TL shows potential in genetic research, improving tasks like gene expression analysis, mutation detection, genetic syndrome recognition, and genotype-phenotype association. This review explores the role of TL in overcoming challenges in mutation detection, genetic syndrome detection, gene expression, or phenotype-genotype association. TL has shown effectiveness in various aspects of genetic research. TL enhances the accuracy and efficiency of mutation detection, aiding in the identification of genetic abnormalities. TL can improve the diagnostic accuracy of syndrome-related genetic patterns. Moreover, TL plays a crucial role in gene expression analysis in order to accurately predict gene expression levels and their interactions. Additionally, TL enhances phenotype-genotype association studies by leveraging pre-trained models. In conclusion, TL enhances AI efficiency by improving mutation prediction, gene expression analysis, and genetic syndrome detection. Future studies should focus on increasing domain similarities, expanding databases, and incorporating clinical data for better predictions.
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Affiliation(s)
- Hamidreza Ashayeri
- Student Research Committee, Tabriz University of Medical Sciences, Tabriz 5165665811, Iran
| | - Navid Sobhi
- Nikookari Eye Center, Tabriz University of Medical Sciences, Tabriz 5165665811, Iran
| | - Paweł Pławiak
- Department of Computer Science, Faculty of Computer Science and Telecommunications, Cracow University of Technology, Warszawska 24, 31-155 Krakow, Poland
- Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Bałtycka 5, 44-100 Gliwice, Poland
| | - Siamak Pedrammehr
- Faculty of Design, Tabriz Islamic Art University, Tabriz 5164736931, Iran
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Burwood, VIC 3216, Australia
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Burwood, VIC 3216, Australia
| | - Ali Jafarizadeh
- Nikookari Eye Center, Tabriz University of Medical Sciences, Tabriz 5165665811, Iran
- Immunology Research Center, Tabriz University of Medical Sciences, Tabriz 5165665811, Iran
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Sigfstead S, Jiang R, Avram R, Davies B, Krahn AD, Cheung CC. Applying Artificial Intelligence for Phenotyping of Inherited Arrhythmia Syndromes. Can J Cardiol 2024:S0828-282X(24)00335-0. [PMID: 38670456 DOI: 10.1016/j.cjca.2024.04.014] [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: 02/21/2024] [Revised: 04/08/2024] [Accepted: 04/21/2024] [Indexed: 04/28/2024] Open
Abstract
Inherited arrhythmia disorders account for a significant proportion of sudden cardiac death, particularly among young individuals. Recent advances in our understanding of these syndromes have improved patient diagnosis and care, yet certain clinical gaps remain, particularly within case ascertainment, access to genetic testing and risk stratification. Artificial intelligence (AI), specifically machine learning and its subset deep learning, present promising solutions to these challenges. The capacity of AI to process vast amounts of patient data and identify disease patterns differentiates them from traditional methods, which are time and resource intensive. To date, AI models have demonstrated immense potential in condition detection (including asymptomatic/concealed disease) and genotype and phenotype identification, exceeding expert cardiologists in these tasks. Additionally, they have exhibited applicability for general population screening, improving case ascertainment in a set of conditions that are often asymptomatic such as left ventricular dysfunction. Third, models have displayed ability to improve testing protocols, as through model identification of disease and genotype, specific clinical testing (e.g. drug challenges or further diagnostic imaging) can be avoided, reducing health care expenses, speeding diagnosis, and possibly allowing for more incremental or targeted genetic testing approaches. These significant benefits warrant continued investigation of the field, particularly regarding the development and implementation of clinically applicable screening tools. This review summarizes key developments in the field, including studies in Long QT Syndrome, Brugada Syndrome, Hypertrophic Cardiomyopathy, and Arrhythmogenic Cardiomyopathies, and provides direction for effective future AI implementation in clinical practice.
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Affiliation(s)
- Sophie Sigfstead
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB
| | - River Jiang
- Division of Cardiology, University of British Columbia, Vancouver, BC
| | - Robert Avram
- Heartwise (heartwise.ai), Montreal Heart Institute, Montreal, QC; Department of Medicine, Montreal Heart Institute, Université de Montréal, Montreal, QC
| | - Brianna Davies
- Center for Cardiovascular Innovation, Division of Cardiology, Department of Medicine, University of British Columbia, Vancouver, BC
| | - Andrew D Krahn
- Center for Cardiovascular Innovation, Division of Cardiology, Department of Medicine, University of British Columbia, Vancouver, BC.
| | - Christopher C Cheung
- Division of Cardiology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON
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Kolk MZH, Ruipérez-Campillo S, Alvarez-Florez L, Deb B, Bekkers EJ, Allaart CP, Van Der Lingen ALCJ, Clopton P, Išgum I, Wilde AAM, Knops RE, Narayan SM, Tjong FVY. Dynamic prediction of malignant ventricular arrhythmias using neural networks in patients with an implantable cardioverter-defibrillator. EBioMedicine 2024; 99:104937. [PMID: 38118401 PMCID: PMC10772563 DOI: 10.1016/j.ebiom.2023.104937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 10/20/2023] [Accepted: 12/12/2023] [Indexed: 12/22/2023] Open
Abstract
BACKGROUND Risk stratification for ventricular arrhythmias currently relies on static measurements that fail to adequately capture dynamic interactions between arrhythmic substrate and triggers over time. We trained and internally validated a dynamic machine learning (ML) model and neural network that extracted features from longitudinally collected electrocardiograms (ECG), and used these to predict the risk of malignant ventricular arrhythmias. METHODS A multicentre study in patients implanted with an implantable cardioverter-defibrillator (ICD) between 2007 and 2021 in two academic hospitals was performed. Variational autoencoders (VAEs), which combine neural networks with variational inference principles, and can learn patterns and structure in data without explicit labelling, were trained to encode the mean ECG waveforms from the limb leads into 16 variables. Supervised dynamic ML models using these latent ECG representations and clinical baseline information were trained to predict malignant ventricular arrhythmias treated by the ICD. Model performance was evaluated on a hold-out set, using time-dependent receiver operating characteristic (ROC) and calibration curves. FINDINGS 2942 patients (61.7 ± 13.9 years, 25.5% female) were included, with a total of 32,129 ECG recordings during a mean follow-up of 43.9 ± 35.9 months. The mean time-varying area under the ROC curve for the dynamic model was 0.738 ± 0.07, compared to 0.639 ± 0.03 for a static (i.e. baseline-only model). Feature analyses indicated dynamic changes in latent ECG representations, particularly those affecting the T-wave morphology, were of highest importance for model predictions. INTERPRETATION Dynamic ML models and neural networks effectively leverage routinely collected longitudinal ECG recordings for personalised and updated predictions of malignant ventricular arrhythmias, outperforming static models. FUNDING This publication is part of the project DEEP RISK ICD (with project number 452019308) of the research programme Rubicon which is (partly) financed by the Dutch Research Council (NWO). This research is partly funded by the Amsterdam Cardiovascular Sciences (personal grant F.V.Y.T).
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Affiliation(s)
- Maarten Z H Kolk
- Department of Clinical and Experimental Cardiology, Amsterdam UMC Location University of Amsterdam, Heart Center, Meibergdreef 9, Amsterdam, the Netherlands; Amsterdam Cardiovascular Sciences, Heart Failure & Arrhythmias, Amsterdam, the Netherlands
| | - Samuel Ruipérez-Campillo
- Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA; Department of Information Technology and Electrical Engineering, Swiss Federal Institute of Technology Zurich (ETHz), Gloriastrasse 35, Zurich, Switzerland; ITACA Institute, Universtitat Politècnica de València, Camino de Vera S/n, Valencia, Spain
| | - Laura Alvarez-Florez
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center Location University of Amsterdam, Meibergdreef 9, Amsterdam, the Netherlands
| | - Brototo Deb
- Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA
| | - Erik J Bekkers
- Faculty of Science, University of Amsterdam, Science Park 904, Amsterdam, the Netherlands
| | - Cornelis P Allaart
- Department of Cardiology, Amsterdam UMC, Location VU Medical Center, De Boelelaan 1118, Amsterdam, the Netherlands
| | | | - Paul Clopton
- Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA
| | - Ivana Išgum
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center Location University of Amsterdam, Meibergdreef 9, Amsterdam, the Netherlands; Faculty of Science, University of Amsterdam, Science Park 904, Amsterdam, the Netherlands; Department of Radiology and Nuclear Medicine, Amsterdam UMC Location University of Amsterdam, Meibergdreef 9, Amsterdam, the Netherlands
| | - Arthur A M Wilde
- Department of Clinical and Experimental Cardiology, Amsterdam UMC Location University of Amsterdam, Heart Center, Meibergdreef 9, Amsterdam, the Netherlands; Amsterdam Cardiovascular Sciences, Heart Failure & Arrhythmias, Amsterdam, the Netherlands
| | - Reinoud E Knops
- Department of Clinical and Experimental Cardiology, Amsterdam UMC Location University of Amsterdam, Heart Center, Meibergdreef 9, Amsterdam, the Netherlands; Amsterdam Cardiovascular Sciences, Heart Failure & Arrhythmias, Amsterdam, the Netherlands
| | - Sanjiv M Narayan
- Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA
| | - Fleur V Y Tjong
- Department of Clinical and Experimental Cardiology, Amsterdam UMC Location University of Amsterdam, Heart Center, Meibergdreef 9, Amsterdam, the Netherlands; Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA; Amsterdam Cardiovascular Sciences, Heart Failure & Arrhythmias, Amsterdam, the Netherlands.
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Raileanu G, de Jong JSSG. Electrocardiogram Interpretation Using Artificial Intelligence: Diagnosis of Cardiac and Extracardiac Pathologic Conditions. How Far Has Machine Learning Reached? Curr Probl Cardiol 2024; 49:102097. [PMID: 37739276 DOI: 10.1016/j.cpcardiol.2023.102097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 09/18/2023] [Indexed: 09/24/2023]
Abstract
Artificial intelligence (AI) is already widely used in different fields of medicine, making possible the integration of the paraclinical exams with the clinical findings in patients, for a more accurate and rapid diagnosis and treatment decision. The electrocardiogram remains one of the most important, fastest, cheapest, and noninvasive methods of diagnosis in cardiology, despite the rapid development and progression of the technology. Even if studied a long time ago, it still has a lot of less understood features that, with a better understanding, can give more clues to a correct and prompt diagnosis in a short time. The use of AI in the interpretation of the ECG improved the accuracy and the time to diagnosis in different cardiovascular diseases, and more than this, explaining the decision to make AI diagnosis improved the human understanding of the different features of the ECG that might be considered for a more accurate diagnosis. The purpose of this article is to provide an overview of the most recently published articles about the use of AI in ECG interpretation.
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Affiliation(s)
- Gabriela Raileanu
- Department of Cardiology, Onze Lieve Vrouwe Gasthuis, Amsterdam, The Netherlands.
| | - Jonas S S G de Jong
- Department of Cardiology, Onze Lieve Vrouwe Gasthuis, Amsterdam, The Netherlands
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Valbuena Rubio S, García-Ordás MT, García-Olalla Olivera O, Alaiz-Moretón H, González-Alonso MI, Benítez-Andrades JA. Survival and grade of the glioma prediction using transfer learning. PeerJ Comput Sci 2023; 9:e1723. [PMID: 38192446 PMCID: PMC10773899 DOI: 10.7717/peerj-cs.1723] [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: 08/03/2023] [Accepted: 11/06/2023] [Indexed: 01/10/2024]
Abstract
Glioblastoma is a highly malignant brain tumor with a life expectancy of only 3-6 months without treatment. Detecting and predicting its survival and grade accurately are crucial. This study introduces a novel approach using transfer learning techniques. Various pre-trained networks, including EfficientNet, ResNet, VGG16, and Inception, were tested through exhaustive optimization to identify the most suitable architecture. Transfer learning was applied to fine-tune these models on a glioblastoma image dataset, aiming to achieve two objectives: survival and tumor grade prediction.The experimental results show 65% accuracy in survival prediction, classifying patients into short, medium, or long survival categories. Additionally, the prediction of tumor grade achieved an accuracy of 97%, accurately differentiating low-grade gliomas (LGG) and high-grade gliomas (HGG). The success of the approach is attributed to the effectiveness of transfer learning, surpassing the current state-of-the-art methods. In conclusion, this study presents a promising method for predicting the survival and grade of glioblastoma. Transfer learning demonstrates its potential in enhancing prediction models, particularly in scenarios with limited large datasets. These findings hold promise for improving diagnostic and treatment approaches for glioblastoma patients.
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Affiliation(s)
| | - María Teresa García-Ordás
- SECOMUCI Research Group, Escuela de Ingenierías Industrial e Informática, Universidad de León, León, Spain
| | | | - Héctor Alaiz-Moretón
- SECOMUCI Research Group, Escuela de Ingenierías Industrial e Informática, Universidad de León, León, Spain
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Avula V, Wu KC, Carrick RT. Clinical Applications, Methodology, and Scientific Reporting of Electrocardiogram Deep-Learning Models: A Systematic Review. JACC. ADVANCES 2023; 2:100686. [PMID: 38288263 PMCID: PMC10824530 DOI: 10.1016/j.jacadv.2023.100686] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/31/2024]
Abstract
BACKGROUND The electrocardiogram (ECG) is one of the most common diagnostic tools available to assess cardio-vascular health. The advent of advanced computational techniques such as deep learning has dramatically expanded the breadth of clinical problems that can be addressed using ECG data, leading to increasing popularity of ECG deep-learning models aimed at predicting clinical endpoints. OBJECTIVES The purpose of this study was to define the current landscape of clinically relevant ECG deep-learning models and examine practices in the scientific reporting of these studies. METHODS We performed a systematic review of PubMed and EMBASE databases to identify clinically relevant ECG deep-learning models published through July 1, 2022. RESULTS We identified 44 manuscripts including 53 unique, clinically relevant ECG deep-learning models. The rate of publication of ECG deep-learning models is increasing rapidly. The most common clinical applications of ECG deep learning were identification of cardiomyopathy (14/53 [26%]), followed by arrhythmia detection (9/53 [17%]). Methodologic reporting varied; while 33/44 (75%) publications included model architecture diagrams, complete information required to reproduce these models was provided in only 10/44 (23%). Saliency analysis was performed in 20/44 (46%) of publications. Only 18/53 (34%) models were tested within external validation cohorts. Model code or resources allowing for model implementation by external groups were available for only 5/44 (11%) publications. CONCLUSIONS While ECG deep-learning models are increasingly clinically relevant, their reporting is highly variable, and few publications provide sufficient detail for methodologic reproduction or model validation by external groups. The field of ECG deep learning would benefit from adherence to a set of standardized scientific reporting guidelines.
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Affiliation(s)
- Vennela Avula
- Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Katherine C. Wu
- Division of Cardiology, Johns Hopkins University Department of Medicine, Baltimore, Maryland, USA
| | - Richard T. Carrick
- Division of Cardiology, Johns Hopkins University Department of Medicine, Baltimore, Maryland, USA
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Vandenberk B, Chew DS, Prasana D, Gupta S, Exner DV. Successes and challenges of artificial intelligence in cardiology. Front Digit Health 2023; 5:1201392. [PMID: 37448836 PMCID: PMC10336354 DOI: 10.3389/fdgth.2023.1201392] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 06/19/2023] [Indexed: 07/15/2023] Open
Abstract
In the past decades there has been a substantial evolution in data management and data processing techniques. New data architectures made analysis of big data feasible, healthcare is orienting towards personalized medicine with digital health initiatives, and artificial intelligence (AI) is becoming of increasing importance. Despite being a trendy research topic, only very few applications reach the stage where they are implemented in clinical practice. This review provides an overview of current methodologies and identifies clinical and organizational challenges for AI in healthcare.
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Affiliation(s)
- Bert Vandenberk
- Department of Cardiac Sciences, Libin Cardiovascular Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium
| | - Derek S. Chew
- Department of Cardiac Sciences, Libin Cardiovascular Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Dinesh Prasana
- Intelense Inc., Markham, ON, Canada
- IOT/AI- Caliber Interconnect Pvt Ltd., Coimbatore, India
| | | | - Derek V. Exner
- Department of Cardiac Sciences, Libin Cardiovascular Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
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Li N, Liu L, Yang Z, Qin S. A self-adjusting ant colony clustering algorithm for ECG arrhythmia classification based on a correction mechanism. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 235:107519. [PMID: 37040683 DOI: 10.1016/j.cmpb.2023.107519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 03/17/2023] [Accepted: 03/28/2023] [Indexed: 05/08/2023]
Abstract
BACKGROUND AND OBJECTIVE As a representative type of cardiovascular disease, persistent arrhythmias can often become life-threatening. In recent years, machine learning-based ECG arrhythmia classification aided methods have been effective in assisting physicians with their diagnosis, but these methods have problems such as complex model structures, poor feature perception ability, and low classification accuracy. METHODS In this paper, a self-adjusting ant colony clustering algorithm for ECG arrhythmia classification based on a correction mechanism is proposed. This method does not distinguish between subjects when establishing the dataset in order to reduce the effect of differences in ECG signal features between individuals, thus improving the robustness of the model. When the classification is achieved, a correction mechanism is introduced to correct outliers caused by the accumulation of errors in the classification process in order to improve the classification accuracy of the model. According to the principle that the flow rate of gas can be increased under the convergence channel, a dynamically updated pheromone volatilization coefficient ρ, namely the increased flow rate ρ, is introduced to help the model converge more stably and faster. As the ants move, the next transfer target is selected by a truly self-adjusting transfer method, and the transfer probability is dynamically adjusted according to the pheromone concentration and the path distance. RESULTS Based on the MIT-BIH arrhythmia dataset, the new algorithm achieved classification of five heart rhythm types, with an overall accuracy of 99.00%. Compared to other experimental models, the classification accuracy of the proposed method represents a 0.2% to 16.6% improvement, and compared to other current studies, the classification accuracy of the proposed method is 0.65% to 7.5% better. CONCLUSIONS This paper addresses the shortcomings of ECG arrhythmia classification methods based on feature engineering, traditional machine learning and deep learning, and presents a self-adjusting ant colony clustering algorithm for ECG arrhythmia classification based on a correction mechanism. Experiments demonstrate the superiority of the proposed method compared to basic models as well as those with improved partial structures. Furthermore, the proposed method achieves very high classification accuracy with a simple structure and fewer iterations than other current methods.
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Affiliation(s)
- Ning Li
- Xi'an University of Technology, Xi'an, China.
| | - Linyue Liu
- Xi'an Technological University, Xi'an, China
| | | | - Shuguang Qin
- The Second Affiliate Hospital of Xi'an Jiaotong University, Xi'an, China
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Lou YS, Lin CS, Fang WH, Lee CC, Lin C. Extensive deep learning model to enhance electrocardiogram application via latent cardiovascular feature extraction from identity identification. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 231:107359. [PMID: 36738606 DOI: 10.1016/j.cmpb.2023.107359] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 12/22/2022] [Accepted: 01/17/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVE Deep learning models (DLMs) have been successfully applied in biomedicine primarily using supervised learning with large, annotated databases. However, scarce training resources limit the potential of DLMs for electrocardiogram (ECG) analysis. METHODS We have developed a novel pre-training strategy for unsupervised identity identification with an area under the receiver operating characteristic curve (AUC) >0.98. Accordingly, a DLM pre-trained with identity identification can be applied to 70 patient characteristic predictions using transfer learning (TL). These ECG-based patient characteristics were then used for cardiovascular disease (CVD) risk prediction. The DLMs were trained using 507,729 ECGs from 222,473 patients and validated using two independent validation sets (n = 27,824/31,925). RESULTS The DLMs using our method exhibited better performance than directly trained DLMs. Additionally, our DLM performed better than those of previous studies in terms of gender (AUC [internal/external] = 0.982/0.968), age (correlation = 0.886/0.892), low ejection fraction (AUC = 0.942/0.951), and critical markers not addressed previously, including high B-type natriuretic peptide (AUC = 0.921/0.899). Additionally, approximately 50% of the ECG-based characteristics provided significantly more prediction information for cardiovascular risk than real characteristics. CONCLUSIONS This is the first study to use identity identification as a pre-training task for TL in ECG analysis. An extensive exploration of the relationship between ECG and 70 patient characteristics was conducted. Our DLM-enhanced ECG interpretation system extensively advanced ECG-related patient characteristic prediction and mortality risk management for cardiovascular diseases.
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Affiliation(s)
- Yu-Sheng Lou
- Graduate Institutes of Life Sciences, National Defense Medical Center, Taipei, Taiwan; School of Public Health, National Defense Medical Center, Taipei, Taiwan
| | - Chin-Sheng Lin
- Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, R.O.C.; Medical Technology Education Center, School of Medicine, National Defense Medical Center, Taipei, Taiwan, R.O.C
| | - Wen-Hui Fang
- Department of Family and Community Medicine, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, R.O.C
| | - Chia-Cheng Lee
- Department of Medical Informatics, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, R.O.C.; Division of Colorectal Surgery, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, R.O.C
| | - Chin Lin
- Graduate Institutes of Life Sciences, National Defense Medical Center, Taipei, Taiwan; School of Public Health, National Defense Medical Center, Taipei, Taiwan; Medical Technology Education Center, School of Medicine, National Defense Medical Center, Taipei, Taiwan, R.O.C.; School of Medicine, National Defense Medical Center, Taipei, Taiwan, R.O.C..
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Vafiadaki E, Glijnis PC, Doevendans PA, Kranias EG, Sanoudou D. Phospholamban R14del disease: The past, the present and the future. Front Cardiovasc Med 2023; 10:1162205. [PMID: 37144056 PMCID: PMC10151546 DOI: 10.3389/fcvm.2023.1162205] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 04/03/2023] [Indexed: 05/06/2023] Open
Abstract
Arrhythmogenic cardiomyopathy affects significant number of patients worldwide and is characterized by life-threatening ventricular arrhythmias and sudden cardiac death. Mutations in multiple genes with diverse functions have been reported to date including phospholamban (PLN), a key regulator of sarcoplasmic reticulum (SR) Ca2+ homeostasis and cardiac contractility. The PLN-R14del variant in specific is recognized as the cause in an increasing number of patients worldwide, and extensive investigations have enabled rapid advances towards the delineation of PLN-R14del disease pathogenesis and discovery of an effective treatment. We provide a critical overview of current knowledge on PLN-R14del disease pathophysiology, including clinical, animal model, cellular and biochemical studies, as well as diverse therapeutic approaches that are being pursued. The milestones achieved in <20 years, since the discovery of the PLN R14del mutation (2006), serve as a paradigm of international scientific collaboration and patient involvement towards finding a cure.
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Affiliation(s)
- Elizabeth Vafiadaki
- Center of Basic Research, Biomedical Research Foundation of the Academy of Athens, Athens, Greece
- Correspondence: Elizabeth Vafiadaki Despina Sanoudou
| | - Pieter C. Glijnis
- Stichting Genetische Hartspierziekte PLN, Phospholamban Foundation, Wieringerwerf, Netherlands
| | - Pieter A. Doevendans
- Netherlands Heart Institute, Utrecht, Netherlands
- Department of Cardiology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Evangelia G. Kranias
- Center of Basic Research, Biomedical Research Foundation of the Academy of Athens, Athens, Greece
- Department of Pharmacology and Systems Physiology, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Despina Sanoudou
- Center of Basic Research, Biomedical Research Foundation of the Academy of Athens, Athens, Greece
- Clinical Genomics and Pharmacogenomics Unit, 4th Department of Internal Medicine, Attikon Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece
- Center for New Biotechnologies and Precision Medicine, Medical School, National and Kapodistrian University of Athens, Athens, Greece
- Correspondence: Elizabeth Vafiadaki Despina Sanoudou
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Interpretable Machine Learning Techniques in ECG-Based Heart Disease Classification: A Systematic Review. Diagnostics (Basel) 2022; 13:diagnostics13010111. [PMID: 36611403 PMCID: PMC9818170 DOI: 10.3390/diagnostics13010111] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 12/22/2022] [Accepted: 12/23/2022] [Indexed: 12/31/2022] Open
Abstract
Heart disease is one of the leading causes of mortality throughout the world. Among the different heart diagnosis techniques, an electrocardiogram (ECG) is the least expensive non-invasive procedure. However, the following are challenges: the scarcity of medical experts, the complexity of ECG interpretations, the manifestation similarities of heart disease in ECG signals, and heart disease comorbidity. Machine learning algorithms are viable alternatives to the traditional diagnoses of heart disease from ECG signals. However, the black box nature of complex machine learning algorithms and the difficulty in explaining a model's outcomes are obstacles for medical practitioners in having confidence in machine learning models. This observation paves the way for interpretable machine learning (IML) models as diagnostic tools that can build a physician's trust and provide evidence-based diagnoses. Therefore, in this systematic literature review, we studied and analyzed the research landscape in interpretable machine learning techniques by focusing on heart disease diagnosis from an ECG signal. In this regard, the contribution of our work is manifold; first, we present an elaborate discussion on interpretable machine learning techniques. In addition, we identify and characterize ECG signal recording datasets that are readily available for machine learning-based tasks. Furthermore, we identify the progress that has been achieved in ECG signal interpretation using IML techniques. Finally, we discuss the limitations and challenges of IML techniques in interpreting ECG signals.
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Transfer Learning Improving Predictive Mortality Models for Patients in End-Stage Renal Disease. ELECTRONICS 2022. [DOI: 10.3390/electronics11091447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Deep learning is becoming a fundamental piece in the paradigm shift from evidence-based to data-based medicine. However, its learning capacity is rarely exploited when working with small data sets. Through transfer learning (TL), information from a source domain is transferred to a target one to enhance a learning task in such domain. The proposed TL mechanisms are based on sample and feature space augmentation. Thus, deep autoencoders extract complex representations for the data in the TL approach. Their latent representations, the so-called codes, are handled to transfer information among domains. The transfer of samples is carried out by computing a latent space mapping matrix that links codes from both domains for later reconstruction. The feature space augmentation is based on the computation of the average of the most similar codes from one domain. Such an average augments the features in a target domain. The proposed framework is evaluated in the prediction of mortality in patients in end-stage renal disease, transferring information related to the mortality of patients with acute kidney injury from the massive database MIMIC-III. Compared to other TL mechanisms, the proposed approach improves 6–11% in previous mortality predictive models. The integration of TL approaches into learning tasks in pathologies with data volume issues could encourage the use of data-based medicine in a clinical setting.
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Petmezas G, Stefanopoulos L, Kilintzis V, Tzavelis A, Rogers JA, Katsaggelos AK, Maglaveras N. State-of-the-art Deep Learning Methods on Electrocardiogram Data: A Systematic Review (Preprint). JMIR Med Inform 2022; 10:e38454. [PMID: 35969441 PMCID: PMC9425174 DOI: 10.2196/38454] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 06/03/2022] [Accepted: 07/03/2022] [Indexed: 11/13/2022] Open
Abstract
Background Electrocardiogram (ECG) is one of the most common noninvasive diagnostic tools that can provide useful information regarding a patient’s health status. Deep learning (DL) is an area of intense exploration that leads the way in most attempts to create powerful diagnostic models based on physiological signals. Objective This study aimed to provide a systematic review of DL methods applied to ECG data for various clinical applications. Methods The PubMed search engine was systematically searched by combining “deep learning” and keywords such as “ecg,” “ekg,” “electrocardiogram,” “electrocardiography,” and “electrocardiology.” Irrelevant articles were excluded from the study after screening titles and abstracts, and the remaining articles were further reviewed. The reasons for article exclusion were manuscripts written in any language other than English, absence of ECG data or DL methods involved in the study, and absence of a quantitative evaluation of the proposed approaches. Results We identified 230 relevant articles published between January 2020 and December 2021 and grouped them into 6 distinct medical applications, namely, blood pressure estimation, cardiovascular disease diagnosis, ECG analysis, biometric recognition, sleep analysis, and other clinical analyses. We provide a complete account of the state-of-the-art DL strategies per the field of application, as well as major ECG data sources. We also present open research problems, such as the lack of attempts to address the issue of blood pressure variability in training data sets, and point out potential gaps in the design and implementation of DL models. Conclusions We expect that this review will provide insights into state-of-the-art DL methods applied to ECG data and point to future directions for research on DL to create robust models that can assist medical experts in clinical decision-making.
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Affiliation(s)
- Georgios Petmezas
- Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies, The Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Leandros Stefanopoulos
- Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies, The Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Vassilis Kilintzis
- Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies, The Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Andreas Tzavelis
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, United States
| | - John A Rogers
- Department of Material Science, Northwestern University, Evanston, IL, United States
| | - Aggelos K Katsaggelos
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, United States
| | - Nicos Maglaveras
- Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies, The Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
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14
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van de Leur RR, Bleijendaal H, Taha K, Mast T, Gho JMIH, Linschoten M, van Rees B, Henkens MTHM, Heymans S, Sturkenboom N, Tio RA, Offerhaus JA, Bor WL, Maarse M, Haerkens-Arends HE, Kolk MZH, van der Lingen ACJ, Selder JJ, Wierda EE, van Bergen PFMM, Winter MM, Zwinderman AH, Doevendans PA, van der Harst P, Pinto YM, Asselbergs FW, van Es R, Tjong FVY. Electrocardiogram-based mortality prediction in patients with COVID-19 using machine learning. Neth Heart J 2022; 30:312-318. [PMID: 35301688 PMCID: PMC8929464 DOI: 10.1007/s12471-022-01670-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/27/2022] [Indexed: 11/09/2022] Open
Abstract
Background and purpose The electrocardiogram (ECG) is frequently obtained in the work-up of COVID-19 patients. So far, no study has evaluated whether ECG-based machine learning models have added value to predict in-hospital mortality specifically in COVID-19 patients. Methods Using data from the CAPACITY-COVID registry, we studied 882 patients admitted with COVID-19 across seven hospitals in the Netherlands. Raw format 12-lead ECGs recorded within 72 h of admission were studied. With data from five hospitals (n = 634), three models were developed: (a) a logistic regression baseline model using age and sex, (b) a least absolute shrinkage and selection operator (LASSO) model using age, sex and human annotated ECG features, and (c) a pre-trained deep neural network (DNN) using age, sex and the raw ECG waveforms. Data from two hospitals (n = 248) was used for external validation. Results Performances for models a, b and c were comparable with an area under the receiver operating curve of 0.73 (95% confidence interval [CI] 0.65–0.79), 0.76 (95% CI 0.68–0.82) and 0.77 (95% CI 0.70–0.83) respectively. Predictors of mortality in the LASSO model were age, low QRS voltage, ST depression, premature atrial complexes, sex, increased ventricular rate, and right bundle branch block. Conclusion This study shows that the ECG-based prediction models could be helpful for the initial risk stratification of patients diagnosed with COVID-19, and that several ECG abnormalities are associated with in-hospital all-cause mortality of COVID-19 patients. Moreover, this proof-of-principle study shows that the use of pre-trained DNNs for ECG analysis does not underperform compared with time-consuming manual annotation of ECG features. Supplementary Information The online version of this article (10.1007/s12471-022-01670-2) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- R R van de Leur
- Department of Cardiology, Division of Heart and Lungs, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands.,Netherlands Heart Institute, Utrecht, The Netherlands
| | - H Bleijendaal
- Department of Clinical and Experimental Cardiology, Amsterdam University Medical Centres, Heart Center, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam, The Netherlands.,Department of Clinical Epidemiology, Biostatistics & Bioinformatics, Amsterdam University Medical Centres, University of Amsterdam, Amsterdam, The Netherlands
| | - K Taha
- Department of Cardiology, Division of Heart and Lungs, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands.,Netherlands Heart Institute, Utrecht, The Netherlands
| | - T Mast
- Department of Cardiology, Catharina Hospital Eindhoven, Eindhoven, The Netherlands
| | - J M I H Gho
- Department of Cardiology, Division of Heart and Lungs, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands.,Department of Cardiology, Jeroen Bosch Hospital, 's-Hertogenbosch, The Netherlands
| | - M Linschoten
- Department of Cardiology, Division of Heart and Lungs, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands
| | - B van Rees
- Department of Cardiology, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
| | - M T H M Henkens
- Department of Cardiology, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
| | - S Heymans
- Department of Cardiology, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands.,Centre for Molecular and Vascular Biology, Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium
| | - N Sturkenboom
- Department of Cardiology, Catharina Hospital Eindhoven, Eindhoven, The Netherlands
| | - R A Tio
- Department of Cardiology, Catharina Hospital Eindhoven, Eindhoven, The Netherlands
| | - J A Offerhaus
- Department of Clinical and Experimental Cardiology, Amsterdam University Medical Centres, Heart Center, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam, The Netherlands
| | - W L Bor
- Department of Cardiology, St. Antonius Hospital, Nieuwegein, The Netherlands
| | - M Maarse
- Department of Clinical and Experimental Cardiology, Amsterdam University Medical Centres, Heart Center, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam, The Netherlands.,Department of Cardiology, St. Antonius Hospital, Nieuwegein, The Netherlands
| | - H E Haerkens-Arends
- Department of Cardiology, Jeroen Bosch Hospital, 's-Hertogenbosch, The Netherlands
| | - M Z H Kolk
- Department of Clinical and Experimental Cardiology, Amsterdam University Medical Centres, Heart Center, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam, The Netherlands
| | - A C J van der Lingen
- Department of Cardiology, Amsterdam University Medical Centres, Amsterdam Cardiovascular Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - J J Selder
- Department of Cardiology, Amsterdam University Medical Centres, Amsterdam Cardiovascular Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - E E Wierda
- Department of Cardiology, Dijklander Hospital, Hoorn, The Netherlands
| | | | - M M Winter
- Department of Clinical and Experimental Cardiology, Amsterdam University Medical Centres, Heart Center, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam, The Netherlands
| | - A H Zwinderman
- Department of Clinical Epidemiology, Biostatistics & Bioinformatics, Amsterdam University Medical Centres, University of Amsterdam, Amsterdam, The Netherlands
| | - P A Doevendans
- Department of Cardiology, Division of Heart and Lungs, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands.,Netherlands Heart Institute, Utrecht, The Netherlands.,Central Military Hospital, Utrecht, The Netherlands
| | - P van der Harst
- Department of Cardiology, Division of Heart and Lungs, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Y M Pinto
- Department of Clinical and Experimental Cardiology, Amsterdam University Medical Centres, Heart Center, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam, The Netherlands
| | - F W Asselbergs
- Department of Cardiology, Division of Heart and Lungs, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands.,Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK.,Health Data Research UK and Institute of Health Informatics, University College London, London, UK
| | - R van Es
- Department of Cardiology, Division of Heart and Lungs, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands
| | - F V Y Tjong
- Department of Clinical and Experimental Cardiology, Amsterdam University Medical Centres, Heart Center, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam, The Netherlands.
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15
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Ebbehoj A, Thunbo MØ, Andersen OE, Glindtvad MV, Hulman A. Transfer learning for non-image data in clinical research: A scoping review. PLOS DIGITAL HEALTH 2022; 1:e0000014. [PMID: 36812540 PMCID: PMC9931256 DOI: 10.1371/journal.pdig.0000014] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 12/15/2021] [Indexed: 01/14/2023]
Abstract
BACKGROUND Transfer learning is a form of machine learning where a pre-trained model trained on a specific task is reused as a starting point and tailored to another task in a different dataset. While transfer learning has garnered considerable attention in medical image analysis, its use for clinical non-image data is not well studied. Therefore, the objective of this scoping review was to explore the use of transfer learning for non-image data in the clinical literature. METHODS AND FINDINGS We systematically searched medical databases (PubMed, EMBASE, CINAHL) for peer-reviewed clinical studies that used transfer learning on human non-image data. We included 83 studies in the review. More than half of the studies (63%) were published within 12 months of the search. Transfer learning was most often applied to time series data (61%), followed by tabular data (18%), audio (12%) and text (8%). Thirty-three (40%) studies applied an image-based model to non-image data after transforming data into images (e.g. spectrograms). Twenty-nine (35%) studies did not have any authors with a health-related affiliation. Many studies used publicly available datasets (66%) and models (49%), but fewer shared their code (27%). CONCLUSIONS In this scoping review, we have described current trends in the use of transfer learning for non-image data in the clinical literature. We found that the use of transfer learning has grown rapidly within the last few years. We have identified studies and demonstrated the potential of transfer learning in clinical research in a wide range of medical specialties. More interdisciplinary collaborations and the wider adaption of reproducible research principles are needed to increase the impact of transfer learning in clinical research.
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Affiliation(s)
- Andreas Ebbehoj
- Department of Endocrinology and Internal Medicine, Aarhus University Hospital, Denmark
- Department of Clinical Medicine, Aarhus University, Denmark
| | | | | | | | - Adam Hulman
- Steno Diabetes Center Aarhus, Aarhus University Hospital, Denmark
- * E-mail:
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16
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Connie T, Tan YF, Goh MKO, Hon HW, Kadim Z, Wong LP. Explainable health prediction from facial features with transfer learning. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-211737] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
In the recent years, Artificial Intelligence (AI) has been widely deployed in the healthcare industry. The new AI technology enables efficient and personalized healthcare systems for the public. In this paper, transfer learning with pre-trained VGGFace model is applied to identify sick symptoms based on the facial features of a person. As the deep learning model’s operation is unknown for making a decision, this paper investigates the use of Explainable AI (XAI) techniques for soliciting explanations for the predictions made by the model. Various XAI techniques including Integrated Gradient, Explainable region-based AI (XRAI) and Local Interpretable Model-Agnostic Explanations (LIME) are studied. XAI is crucial to increase the model’s transparency and reliability for practical deployment. Experimental results demonstrate that the attribution method can give proper explanations for the decisions made by highlighting important attributes in the images. The facial features that account for positive and negative classes predictions are highlighted appropriately for effective visualization. XAI can help to increase accountability and trustworthiness of the healthcare system as it provides insights for understanding how a conclusion is derived from the AI model.
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Affiliation(s)
- Tee Connie
- Faculty of Information Science and Technology, Multimedia University, Jalan Ayer Keroh Lama, Melaka, Malaysia
| | - Yee Fan Tan
- Faculty of Information Science and Technology, Multimedia University, Jalan Ayer Keroh Lama, Melaka, Malaysia
| | - Michael Kah Ong Goh
- Faculty of Information Science and Technology, Multimedia University, Jalan Ayer Keroh Lama, Melaka, Malaysia
| | - Hock Woon Hon
- Advanced Informatics Lab, Mimos Berhad, Taman Teknologi Malaysia, Kuala Lumpur, Malaysia
| | - Zulaikha Kadim
- Advanced Informatics Lab, Mimos Berhad, Taman Teknologi Malaysia, Kuala Lumpur, Malaysia
| | - Li Pei Wong
- School of Computer Sciences, Universiti Sains Malaysia, Malaysia
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17
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Bleijendaal H, Croon PM, Pool MDO, Malekzadeh A, Aufiero S, Amin AS, Zwinderman AH, Pinto YM, Wilde AA, Winter MM. Clinical applicability of artificial intelligence for patients with an inherited heart disease: a scoping review. Trends Cardiovasc Med 2022:S1050-1738(22)00013-5. [DOI: 10.1016/j.tcm.2022.01.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 01/06/2022] [Accepted: 01/23/2022] [Indexed: 01/22/2023]
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18
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Concatenation of Pre-Trained Convolutional Neural Networks for Enhanced COVID-19 Screening Using Transfer Learning Technique. ELECTRONICS 2021. [DOI: 10.3390/electronics11010103] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Coronavirus (COVID-19) is the most prevalent coronavirus infection with respiratory symptoms such as fever, cough, dyspnea, pneumonia, and weariness being typical in the early stages. On the other hand, COVID-19 has a direct impact on the circulatory and respiratory systems as it causes a failure to some human organs or severe respiratory distress in extreme circumstances. Early diagnosis of COVID-19 is extremely important for the medical community to limit its spread. For a large number of suspected cases, manual diagnostic methods based on the analysis of chest images are insufficient. Faced with this situation, artificial intelligence (AI) techniques have shown great potential in automatic diagnostic tasks. This paper aims at proposing a fast and precise medical diagnosis support system (MDSS) that can distinguish COVID-19 precisely in chest-X-ray images. This MDSS uses a concatenation technique that aims to combine pre-trained convolutional neural networks (CNN) depend on the transfer learning (TL) technique to build a highly accurate model. The models enable storage and application of knowledge learned from a pre-trained CNN to a new task, viz., COVID-19 case detection. For this purpose, we employed the concatenation method to aggregate the performances of numerous pre-trained models to confirm the reliability of the proposed method for identifying the patients with COVID-19 disease from X-ray images. The proposed system was trialed on a dataset that included four classes: normal, viral-pneumonia, tuberculosis, and COVID-19 cases. Various general evaluation methods were used to evaluate the effectiveness of the proposed model. The first proposed model achieved an accuracy rate of 99.80% while the second model reached an accuracy of 99.71%.
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19
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Bleijendaal H, Wilde AAM. From a Polish 3-Year-Old Boy Who Visited Maastricht to Automatic Detection Using Deep Learning: Brugada Syndrome Is Being Revolutionised. Can J Cardiol 2021; 38:149-151. [PMID: 34571168 DOI: 10.1016/j.cjca.2021.09.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 09/15/2021] [Accepted: 09/16/2021] [Indexed: 11/28/2022] Open
Affiliation(s)
- Hidde Bleijendaal
- Heart Center, Department of Clinical Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands.
| | - Arthur A M Wilde
- Heart Center, Department of Clinical Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands.
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