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Nechita LC, Nechita A, Voipan AE, Voipan D, Debita M, Fulga A, Fulga I, Musat CL. AI-Enhanced ECG Applications in Cardiology: Comprehensive Insights from the Current Literature with a Focus on COVID-19 and Multiple Cardiovascular Conditions. Diagnostics (Basel) 2024; 14:1839. [PMID: 39272624 DOI: 10.3390/diagnostics14171839] [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: 07/15/2024] [Revised: 08/17/2024] [Accepted: 08/22/2024] [Indexed: 09/15/2024] Open
Abstract
The application of artificial intelligence (AI) in electrocardiography is revolutionizing cardiology and providing essential insights into the consequences of the COVID-19 pandemic. This comprehensive review explores AI-enhanced ECG (AI-ECG) applications in risk prediction and diagnosis of heart diseases, with a dedicated chapter on COVID-19-related complications. Introductory concepts on AI and machine learning (ML) are explained to provide a foundational understanding for those seeking knowledge, supported by examples from the literature and current practices. We analyze AI and ML methods for arrhythmias, heart failure, pulmonary hypertension, mortality prediction, cardiomyopathy, mitral regurgitation, hypertension, pulmonary embolism, and myocardial infarction, comparing their effectiveness from both medical and AI perspectives. Special emphasis is placed on AI applications in COVID-19 and cardiology, including detailed comparisons of different methods, identifying the most suitable AI approaches for specific medical applications and analyzing their strengths, weaknesses, accuracy, clinical relevance, and key findings. Additionally, we explore AI's role in the emerging field of cardio-oncology, particularly in managing chemotherapy-induced cardiotoxicity and detecting cardiac masses. This comprehensive review serves as both an insightful guide and a call to action for further research and collaboration in the integration of AI in cardiology, aiming to enhance precision medicine and optimize clinical decision-making.
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Affiliation(s)
- Luiza Camelia Nechita
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 800008 Galati, Romania
| | - Aurel Nechita
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 800008 Galati, Romania
| | - Andreea Elena Voipan
- Faculty of Automation, Computers, Electrical Engineering and Electronics, Dunarea de Jos University of Galati, 800008 Galati, Romania
| | - Daniel Voipan
- Faculty of Automation, Computers, Electrical Engineering and Electronics, Dunarea de Jos University of Galati, 800008 Galati, Romania
| | - Mihaela Debita
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 800008 Galati, Romania
| | - Ana Fulga
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 800008 Galati, Romania
| | - Iuliu Fulga
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 800008 Galati, Romania
| | - Carmina Liana Musat
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 800008 Galati, Romania
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Salih AM, Galazzo IB, Gkontra P, Rauseo E, Lee AM, Lekadir K, Radeva P, Petersen SE, Menegaz G. A review of evaluation approaches for explainable AI with applications in cardiology. Artif Intell Rev 2024; 57:240. [PMID: 39132011 PMCID: PMC11315784 DOI: 10.1007/s10462-024-10852-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/03/2024] [Indexed: 08/13/2024]
Abstract
Explainable artificial intelligence (XAI) elucidates the decision-making process of complex AI models and is important in building trust in model predictions. XAI explanations themselves require evaluation as to accuracy and reasonableness and in the context of use of the underlying AI model. This review details the evaluation of XAI in cardiac AI applications and has found that, of the studies examined, 37% evaluated XAI quality using literature results, 11% used clinicians as domain-experts, 11% used proxies or statistical analysis, with the remaining 43% not assessing the XAI used at all. We aim to inspire additional studies within healthcare, urging researchers not only to apply XAI methods but to systematically assess the resulting explanations, as a step towards developing trustworthy and safe models. Supplementary Information The online version contains supplementary material available at 10.1007/s10462-024-10852-w.
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Affiliation(s)
- Ahmed M. Salih
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ UK
- Department of Population Health Sciences, University of Leicester, University Rd, Leicester, LE1 7RH UK
- Department of Computer Science, University of Zakho, Duhok road, Zakho, Kurdistan Iraq
| | - Ilaria Boscolo Galazzo
- Department of Engineering for Innovative Medicine, University of Verona, S. Francesco, 22, 37129 Verona, Italy
| | - Polyxeni Gkontra
- Artificial Intelligence in Medicine Lab (BCN-AIM), Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Gran Via de les Corts Catalanes, 585, 08007 Barcelona, Spain
| | - Elisa Rauseo
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ UK
| | - Aaron Mark Lee
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ UK
| | - Karim Lekadir
- Artificial Intelligence in Medicine Lab (BCN-AIM), Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Gran Via de les Corts Catalanes, 585, 08007 Barcelona, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, Barcelona, Spain
| | - Petia Radeva
- Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Gran Via de les Corts Catalanes, 585, 08007 Barcelona, Spain
| | - Steffen E. Petersen
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ UK
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, West Smithfield, London, UK
- Health Data Research, London, UK
- Alan Turing Institute, London, UK
| | - Gloria Menegaz
- Department of Engineering for Innovative Medicine, University of Verona, S. Francesco, 22, 37129 Verona, Italy
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3
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Huang T, Huang Z, Peng X, Pang L, Sun J, Wu J, He J, Fu K, Wu J, Sun X. Construction and validation of risk prediction models for pulmonary embolism in hospitalized patients based on different machine learning methods. Front Cardiovasc Med 2024; 11:1308017. [PMID: 38984357 PMCID: PMC11232034 DOI: 10.3389/fcvm.2024.1308017] [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: 10/06/2023] [Accepted: 06/11/2024] [Indexed: 07/11/2024] Open
Abstract
Objective This study aims to apply different machine learning (ML) methods to construct risk prediction models for pulmonary embolism (PE) in hospitalized patients, and to evaluate and compare the predictive efficacy and clinical benefit of each model. Methods We conducted a retrospective study involving 332 participants (172 PE positive cases and 160 PE negative cases) recruited from Guangdong Medical University. Participants were randomly divided into a training group (70%) and a validation group (30%). Baseline data were analyzed using univariate analysis, and potential independent risk factors associated with PE were further identified through univariate and multivariate logistic regression analysis. Six ML models, namely Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), Support Vector Machine (SVM), and AdaBoost were developed. The predictive efficacy of each model was compared using the receiver operating characteristic (ROC) curve analysis and the area under the curve (AUC). Clinical benefit was assessed using decision curve analysis (DCA). Results Logistic regression analysis identified lower extremity deep venous thrombosis, elevated D-dimer, shortened activated partial prothrombin time, and increased red blood cell distribution width as potential independent risk factors for PE. Among the six ML models, the RF model achieved the highest AUC of 0.778. Additionally, DCA consistently indicated that the RF model offered the greatest clinical benefit. Conclusion This study developed six ML models, with the RF model exhibiting the highest predictive efficacy and clinical benefit in the identification and prediction of PE occurrence in hospitalized patients.
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Affiliation(s)
- Tao Huang
- Emergency Medicine Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China
| | - Zhihai Huang
- Emergency Medicine Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China
| | - Xiaodong Peng
- Emergency Medicine Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China
| | - Lingpin Pang
- Emergency Medicine Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China
| | - Jie Sun
- Emergency Medicine Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China
| | - Jinbo Wu
- Emergency Medicine Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China
| | - Jinman He
- Emergency Medicine Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China
| | - Kaili Fu
- Respiratory and Critical Care Medicine, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China
| | - Jun Wu
- Respiratory and Critical Care Medicine, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China
| | - Xishi Sun
- Emergency Medicine Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China
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Goettling M, Hammer A, Malberg H, Schmidt M. xECGArch: a trustworthy deep learning architecture for interpretable ECG analysis considering short-term and long-term features. Sci Rep 2024; 14:13122. [PMID: 38849417 PMCID: PMC11161651 DOI: 10.1038/s41598-024-63656-x] [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: 11/23/2023] [Accepted: 05/30/2024] [Indexed: 06/09/2024] Open
Abstract
Deep learning-based methods have demonstrated high classification performance in the detection of cardiovascular diseases from electrocardiograms (ECGs). However, their blackbox character and the associated lack of interpretability limit their clinical applicability. To overcome existing limitations, we present a novel deep learning architecture for interpretable ECG analysis (xECGArch). For the first time, short- and long-term features are analyzed by two independent convolutional neural networks (CNNs) and combined into an ensemble, which is extended by methods of explainable artificial intelligence (xAI) to whiten the blackbox. To demonstrate the trustworthiness of xECGArch, perturbation analysis was used to compare 13 different xAI methods. We parameterized xECGArch for atrial fibrillation (AF) detection using four public ECG databases ( n = 9854 ECGs) and achieved an F1 score of 95.43% in AF versus non-AF classification on an unseen ECG test dataset. A systematic comparison of xAI methods showed that deep Taylor decomposition provided the most trustworthy explanations ( + 24 % compared to the second-best approach). xECGArch can account for short- and long-term features corresponding to clinical features of morphology and rhythm, respectively. Further research will focus on the relationship between xECGArch features and clinical features, which may help in medical applications for diagnosis and therapy.
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Affiliation(s)
- Marc Goettling
- Institute of Biomedical Engineering, TU Dresden, Fetscherstr. 29, 01307, Dresden, Germany
| | - Alexander Hammer
- Institute of Biomedical Engineering, TU Dresden, Fetscherstr. 29, 01307, Dresden, Germany
| | - Hagen Malberg
- Institute of Biomedical Engineering, TU Dresden, Fetscherstr. 29, 01307, Dresden, Germany
| | - Martin Schmidt
- Institute of Biomedical Engineering, TU Dresden, Fetscherstr. 29, 01307, Dresden, Germany.
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5
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Moreno-Sánchez PA, García-Isla G, Corino VDA, Vehkaoja A, Brukamp K, van Gils M, Mainardi L. ECG-based data-driven solutions for diagnosis and prognosis of cardiovascular diseases: A systematic review. Comput Biol Med 2024; 172:108235. [PMID: 38460311 DOI: 10.1016/j.compbiomed.2024.108235] [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: 08/11/2023] [Revised: 02/07/2024] [Accepted: 02/25/2024] [Indexed: 03/11/2024]
Abstract
Cardiovascular diseases (CVD) are a leading cause of death globally, and result in significant morbidity and reduced quality of life. The electrocardiogram (ECG) plays a crucial role in CVD diagnosis, prognosis, and prevention; however, different challenges still remain, such as an increasing unmet demand for skilled cardiologists capable of accurately interpreting ECG. This leads to higher workload and potential diagnostic inaccuracies. Data-driven approaches, such as machine learning (ML) and deep learning (DL) have emerged to improve existing computer-assisted solutions and enhance physicians' ECG interpretation of the complex mechanisms underlying CVD. However, many ML and DL models used to detect ECG-based CVD suffer from a lack of explainability, bias, as well as ethical, legal, and societal implications (ELSI). Despite the critical importance of these Trustworthy Artificial Intelligence (AI) aspects, there is a lack of comprehensive literature reviews that examine the current trends in ECG-based solutions for CVD diagnosis or prognosis that use ML and DL models and address the Trustworthy AI requirements. This review aims to bridge this knowledge gap by providing a systematic review to undertake a holistic analysis across multiple dimensions of these data-driven models such as type of CVD addressed, dataset characteristics, data input modalities, ML and DL algorithms (with a focus on DL), and aspects of Trustworthy AI like explainability, bias and ethical considerations. Additionally, within the analyzed dimensions, various challenges are identified. To these, we provide concrete recommendations, equipping other researchers with valuable insights to understand the current state of the field comprehensively.
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Affiliation(s)
| | - Guadalupe García-Isla
- Department of Electronics Information and Bioengineering, Politecnico di Milano, Italy
| | - Valentina D A Corino
- Department of Electronics Information and Bioengineering, Politecnico di Milano, Italy
| | - Antti Vehkaoja
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | | | - Mark van Gils
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Luca Mainardi
- Department of Electronics Information and Bioengineering, Politecnico di Milano, Italy
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Sagar D, Dwivedi T, Gupta A, Aggarwal P, Bhatnagar S, Mohan A, Kaur P, Gupta R. Clinical Features Predicting COVID-19 Severity Risk at the Time of Hospitalization. Cureus 2024; 16:e57336. [PMID: 38690475 PMCID: PMC11059179 DOI: 10.7759/cureus.57336] [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] [Accepted: 03/31/2024] [Indexed: 05/02/2024] Open
Abstract
The global spread of COVID-19 has led to significant mortality and morbidity worldwide. Early identification of COVID-19 patients who are at high risk of developing severe disease can help in improved patient management, care, and treatment, as well as in the effective allocation of hospital resources. The severity prediction at the time of hospitalization can be extremely helpful in deciding the treatment of COVID-19 patients. To this end, this study presents an interpretable artificial intelligence (AI) model, named COVID-19 severity predictor (CoSP) that predicts COVID-19 severity using the clinical features at the time of hospital admission. We utilized a dataset comprising 64 demographic and laboratory features of 7,416 confirmed COVID-19 patients that were collected at the time of hospital admission. The proposed hierarchical CoSP model performs four-class COVID severity risk prediction into asymptomatic, mild, moderate, and severe categories. CoSP yielded better performance with good interpretability, as observed via Shapley analysis on COVID severity prediction compared to the other popular ML methods, with an area under the received operating characteristic curve (AUC-ROC) of 0.95, an area under the precision-recall curve (AUPRC) of 0.91, and a weighted F1-score of 0.83. Out of 64 initial features, 19 features were inferred as predictive of the severity of COVID-19 disease by the CoSP model. Therefore, an AI model predicting COVID-19 severity may be helpful for early intervention, optimizing resource allocation, and guiding personalized treatments, potentially enabling healthcare professionals to save lives and allocate resources effectively in the fight against the pandemic.
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Affiliation(s)
- Dikshant Sagar
- Computer Science, Indraprastha Institute of Information Technology - Delhi, Delhi, IND
- Computer Science, Calfornia State University, Los Angeles, Los Angeles, USA
| | - Tanima Dwivedi
- Oncology, Dr. B.R.A Institute-Rotary Cancer Hospital, All India Institute of Medical Sciences, New Delhi, IND
| | - Anubha Gupta
- Centre of Excellence in Healthcare, Indraprastha Institute of Information Technology - Delhi, Delhi, IND
| | - Priya Aggarwal
- Electronics and Communication Engineering, Indraprastha Institute of Information Technology - Delhi, Delhi, IND
| | - Sushma Bhatnagar
- Onco-Anaesthesia and Palliative Medicine, Dr. B.R.A Institute-Rotary Cancer Hospital, All India Institute of Medical Sciences, New Delhi, IND
| | - Anant Mohan
- Pulmonary, Critical Care and Sleep Medicine, All India Institute of Medical Sciences, New Delhi, IND
| | - Punit Kaur
- Biophysics, All India Institute of Medical Sciences, New Delhi, IND
| | - Ritu Gupta
- Oncology, Dr. B.R.A Institute-Rotary Cancer Hospital, All India Institute of Medical Sciences, New Delhi, IND
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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] [Received: 04/05/2023] [Revised: 08/31/2023] [Accepted: 09/19/2023] [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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Choi G, Ziyang G, Wu J, Esposito C, Choi C. Multi modal biometrics based implicit driver identification system using multi-TF images of ECG and EMG. Comput Biol Med 2023; 159:106851. [PMID: 37099975 DOI: 10.1016/j.compbiomed.2023.106851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 03/02/2023] [Accepted: 03/30/2023] [Indexed: 04/05/2023]
Abstract
As security is emphasized inside and outside the vehicle, research on driver identification technology using bio-signals is being actively studied. The bio-signals acquired by the behavioral characteristics of the driver include artifacts generated according to the driving environment, which could potentially degrade the accuracy of the identification system. Existing driver identification systems either remove the normalization process of bio-signals in the preprocessing stage or use artifacts included in a single bio-signals, resulting in low identification accuracy. To solve these problems in a real situation, we propose a driver identification system that converts ECG and EMG signals obtained from different driving conditions into 2D spectrograms through multi-TF image and uses multi-stream CNN. The proposed system consists of a preprocessing phase of ECG and EMG signals, a multi-TF image conversion process, and a driver identification stage using a multi-stream-based CNN. Under all driving conditions, the driver identification system reached an average accuracy of 96.8% and an F1 score of 0.973, which overperformed the existing driver identification systems by more than 1%.
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9
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Dwivedi K, Rajpal A, Rajpal S, Agarwal M, Kumar V, Kumar N. An explainable AI-driven biomarker discovery framework for Non-Small Cell Lung Cancer classification. Comput Biol Med 2023; 153:106544. [PMID: 36652866 DOI: 10.1016/j.compbiomed.2023.106544] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 12/17/2022] [Accepted: 01/10/2023] [Indexed: 01/13/2023]
Abstract
Non-Small Cell Lung Cancer (NSCLC) exhibits intrinsic heterogeneity at the molecular level that aids in distinguishing between its two prominent subtypes - Lung Adenocarcinoma (LUAD) and Lung Squamous Cell Carcinoma (LUSC). This paper proposes a novel explainable AI (XAI)-based deep learning framework to discover a small set of NSCLC biomarkers. The proposed framework comprises three modules - an autoencoder to shrink the input feature space, a feed-forward neural network to classify NSCLC instances into LUAD and LUSC, and a biomarker discovery module that leverages the combined network comprising the autoencoder and the feed-forward neural network. In the biomarker discovery module, XAI methods uncovered a set of 52 relevant biomarkers for NSCLC subtype classification. To evaluate the classification performance of the discovered biomarkers, multiple machine-learning models are constructed using these biomarkers. Using 10-Fold cross-validation, Multilayer Perceptron achieved an accuracy of 95.74% (±1.27) at 95% confidence interval. Further, using Drug-Gene Interaction Database, we observe that 14 of the discovered biomarkers are druggable. In addition, 28 biomarkers aid the prediction of the survivability of the patients. Out of 52 discovered biomarkers, we find that 45 biomarkers have been reported in previous studies on distinguishing between the two NSCLC subtypes. To the best of our knowledge, the remaining seven biomarkers have not yet been reported for NSCLC subtyping and could be further explored for their contribution to targeted therapy of lung cancer.
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Affiliation(s)
- Kountay Dwivedi
- Department of Computer Science, University of Delhi, Delhi, India.
| | - Ankit Rajpal
- Department of Computer Science, University of Delhi, Delhi, India.
| | | | | | - Virendra Kumar
- Department of Nuclear Magnetic Resonance Imaging, All India Institute of Medical Sciences, New Delhi, India.
| | - Naveen Kumar
- Department of Computer Science, University of Delhi, Delhi, India.
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10
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Sakr AS, Pławiak P, Tadeusiewicz R, Pławiak J, Sakr M, Hammad M. ECG-COVID: An End-to-End Deep Model Based on Electrocardiogram for COVID-19 Detection. Inf Sci (N Y) 2022; 619:324-339. [PMCID: PMC9673093 DOI: 10.1016/j.ins.2022.11.069] [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: 02/23/2022] [Revised: 10/05/2022] [Accepted: 11/14/2022] [Indexed: 11/19/2022]
Abstract
The early and accurate detection of COVID-19 is vital nowadays to avoid the vast and rapid spread of this virus and ease lockdown restrictions. As a result, researchers developed methods to diagnose COVID-19. However, these methods have several limitations. Therefore, presenting new methods is essential to improve the diagnosis of COVID-19. Recently, investigation of the electrocardiogram (ECG) signals becoming an easy way to detect COVID-19 since the ECG process is non-invasive and easy to use. Therefore, we proposed in this paper a novel end-to-end deep learning model (ECG-COVID) based on ECG for COVID-19 detection. We employed several deep Convolutional Neural Networks (CNNs) on a dataset of 1109 ECG images, which is built for screening the perception of COVID-19 and cardiac patients. After that, we selected the most efficient model as our model for evaluation. The proposed model is end-to-end where the input ECG images are fed directly to the model for the final decision without using any additional stages. The proposed method achieved an average accuracy of 98.81%, Precision of 98.8%, Sensitivity of 98.8% and, F1-score of 98.81% for COVID-19 detection. As cases of corona continue to rise and hospitalizations continue again, hospitals may find our study helpful when dealing with these patients who did not get significantly worse.
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Affiliation(s)
- Ahmed S. Sakr
- Department of Information System, Faculty of Computers and Information, Menoufia University, Egypt
| | - 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,Corresponding authors
| | - Ryszard Tadeusiewicz
- AGH University of Science and Technology, Department of Biocybernetics and Biomedical Engineering, Krakow, Poland
| | - Joanna Pławiak
- Faculty of Electrical and Computer Engineering, Cracow University of Technology, Warsaw 24, 31-155 Krakow, Poland
| | - Mohamed Sakr
- Computer Science Department, Faculty of Computers and Information, Menoufia University, Egypt
| | - Mohamed Hammad
- Department of Information Technology, Faculty of Computers and Information, Menoufia University, Egypt,Corresponding authors
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