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Qiang Y, Dong X, Liu X, Yang Y, Fang Y, Dou J. Conv-RGNN: An efficient Convolutional Residual Graph Neural Network for ECG classification. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 257:108406. [PMID: 39241329 DOI: 10.1016/j.cmpb.2024.108406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2024] [Revised: 06/15/2024] [Accepted: 08/31/2024] [Indexed: 09/09/2024]
Abstract
BACKGROUND AND OBJECTIVE Electrocardiogram (ECG) analysis is crucial in diagnosing cardiovascular diseases (CVDs). It is important to consider both temporal and spatial features in ECG analysis to improve automated CVDs diagnosis. Significant progress has been made in automated CVDs diagnosis based on ECG with the continuous development of deep learning. Current most researches often treat 12-lead ECG signals as synchronous sequences in Euclidean space, focusing primarily on extracting temporal features while overlooking the spatial relationships among the 12-lead. However, the spatial distribution of 12-lead ECG electrodes can be more naturally represented using non-Euclidean data structures, which makes the relationships among leads more consistent with their intrinsic characteristics. METHODS This study proposes an innovative method, Convolutional Residual Graph Neural Network (Conv-RGNN), for ECG classification. The first step is to segment the 12-lead ECG into twelve single-lead ECG, which are then mapped to nodes in a graph that captures the relationships between the different leads through spatial connections, resulting in the 12-lead ECG graph. The graph is then used as input for Conv-RGNN. A convolutional neural network with a position attention mechanism is used to extract temporal sequence information and selectively integrate contextual information to enhance semantic features at different positions. The spatial features of the 12-lead ECG graph are extracted using the residual graph neural network. RESULTS The experimental results indicate that Conv-RGNN is highly competitive in two multi-label datasets and one single-label dataset, demonstrating exceptional parameter efficiency, inference speed, model performance, and robustness. CONCLUSION The Conv-RGNN proposed in this paper offer a promising and feasible approach for intelligent diagnosis in resource-constrained environments.
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Affiliation(s)
- Yupeng Qiang
- South China University of Technology, Guangzhou, 510641, China.
| | - Xunde Dong
- South China University of Technology, Guangzhou, 510641, China.
| | - Xiuling Liu
- Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding, 071002, China.
| | - Yang Yang
- South China University of Technology, Guangzhou, 510641, China.
| | - Yihai Fang
- Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, China.
| | - Jianhong Dou
- General Hospital of the Southern Theater of the Chinese People's Liberation Army, Guangzhou, 510030, China.
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2
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Wagner P, Mehari T, Haverkamp W, Strodthoff N. Explaining deep learning for ECG analysis: Building blocks for auditing and knowledge discovery. Comput Biol Med 2024; 176:108525. [PMID: 38749322 DOI: 10.1016/j.compbiomed.2024.108525] [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: 09/21/2023] [Revised: 04/22/2024] [Accepted: 04/25/2024] [Indexed: 05/31/2024]
Abstract
Deep neural networks have become increasingly popular for analyzing ECG data because of their ability to accurately identify cardiac conditions and hidden clinical factors. However, the lack of transparency due to the black box nature of these models is a common concern. To address this issue, explainable AI (XAI) methods can be employed. In this study, we present a comprehensive analysis of post-hoc XAI methods, investigating the glocal (aggregated local attributions over multiple samples) and global (concept based XAI) perspectives. We have established a set of sanity checks to identify saliency as the most sensible attribution method. We provide a dataset-wide analysis across entire patient subgroups, which goes beyond anecdotal evidence, to establish the first quantitative evidence for the alignment of model behavior with cardiologists' decision rules. Furthermore, we demonstrate how these XAI techniques can be utilized for knowledge discovery, such as identifying subtypes of myocardial infarction. We believe that these proposed methods can serve as building blocks for a complementary assessment of the internal validity during a certification process, as well as for knowledge discovery in the field of ECG analysis.
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Affiliation(s)
| | - Temesgen Mehari
- Fraunhofer Heinrich Hertz Institute, Berlin, Germany; Physikalisch-Technische Bundesanstalt, Berlin, Germany.
| | | | - Nils Strodthoff
- Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany.
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3
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Jiang M, Bian F, Zhang J, Huang T, Xia L, Chu Y, Wang Z, Jiang J. Myocardial infarction detection method based on the continuous T-wave area feature and multi-lead-fusion deep features. Physiol Meas 2024; 45:055017. [PMID: 38697203 DOI: 10.1088/1361-6579/ad46e1] [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: 09/22/2023] [Accepted: 05/02/2024] [Indexed: 05/04/2024]
Abstract
Objective.Myocardial infarction (MI) is one of the most threatening cardiovascular diseases. This paper aims to explore a method for using an algorithm to autonomously classify MI based on the electrocardiogram (ECG).Approach.A detection method of MI that fuses continuous T-wave area (C_TWA) feature and ECG deep features is proposed. This method consists of three main parts: (1) The onset of MI is often accompanied by changes in the shape of the T-wave in the ECG, thus the area of the T-wave displayed on different heartbeats will be quite different. The adaptive sliding window method is used to detect the start and end of the T-wave, and calculate the C_TWA on the same ECG record. Additionally, the coefficient of variation of C_TWA is defined as the C_TWA feature of the ECG. (2) The multi lead fusion convolutional neural network was implemented to extract the deep features of the ECG. (3) The C_TWA feature and deep features of the ECG were fused by soft attention, and then inputted into the multi-layer perceptron to obtain the detection result.Main results.According to the inter-patient paradigm, the proposed method reached a 97.67% accuracy, 96.59% precision, and 98.96% recall on the PTB dataset, as well as reached 93.15% accuracy, 93.20% precision, and 95.14% recall on the clinical dataset.Significance.This method accurately extracts the feature of the C_TWA, and combines the deep features of the signal, thereby improving the detection accuracy and achieving favorable results on clinical datasets.
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Affiliation(s)
- Mingfeng Jiang
- School of Computer Science and Technology (School of Artificial Intelligence), Zhejiang Sci-Tech University, Hangzhou, People's Republic of China
| | - Feibiao Bian
- School of Computer Science and Technology (School of Artificial Intelligence), Zhejiang Sci-Tech University, Hangzhou, People's Republic of China
| | - Jucheng Zhang
- Department of Clinical Engineering, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, People's Republic of China
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, People's Republic of China
| | - Tianhai Huang
- Department of Clinical Engineering, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, People's Republic of China
| | - Ling Xia
- Key Laboratory for Biomedical Engineering of Ministry of Education, Institute of Biomedical Engineering, Zhejiang University, Hangzhou, People's Republic of China
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, People's Republic of China
| | - Yonghua Chu
- Department of Clinical Engineering, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, People's Republic of China
| | - Zhikang Wang
- Department of Clinical Engineering, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, People's Republic of China
| | - Jun Jiang
- Department of Cardiology, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, People's Republic of China
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4
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Xu ZH, Fan DG, Huang JQ, Wang JW, Wang Y, Li YZ. Computer-Aided Diagnosis of Laryngeal Cancer Based on Deep Learning with Laryngoscopic Images. Diagnostics (Basel) 2023; 13:3669. [PMID: 38132254 PMCID: PMC10743023 DOI: 10.3390/diagnostics13243669] [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: 11/24/2023] [Revised: 12/11/2023] [Accepted: 12/12/2023] [Indexed: 12/23/2023] Open
Abstract
Laryngeal cancer poses a significant global health burden, with late-stage diagnoses contributing to reduced survival rates. This study explores the application of deep convolutional neural networks (DCNNs), specifically the Densenet201 architecture, in the computer-aided diagnosis of laryngeal cancer using laryngoscopic images. Our dataset comprised images from two medical centers, including benign and malignant cases, and was divided into training, internal validation, and external validation groups. We compared the performance of Densenet201 with other commonly used DCNN models and clinical assessments by experienced clinicians. Densenet201 exhibited outstanding performance, with an accuracy of 98.5% in the training cohort, 92.0% in the internal validation cohort, and 86.3% in the external validation cohort. The area under the curve (AUC) values consistently exceeded 92%, signifying robust discriminatory ability. Remarkably, Densenet201 achieved high sensitivity (98.9%) and specificity (98.2%) in the training cohort, ensuring accurate detection of both positive and negative cases. In contrast, other DCNN models displayed varying degrees of performance degradation in the external validation cohort, indicating the superiority of Densenet201. Moreover, Densenet201's performance was comparable to that of an experienced clinician (Clinician A) and outperformed another clinician (Clinician B), particularly in the external validation cohort. Statistical analysis, including the DeLong test, confirmed the significance of these performance differences. Our study demonstrates that Densenet201 is a highly accurate and reliable tool for the computer-aided diagnosis of laryngeal cancer based on laryngoscopic images. The findings underscore the potential of deep learning as a complementary tool for clinicians and the importance of incorporating advanced technology in improving diagnostic accuracy and patient care in laryngeal cancer diagnosis. Future work will involve expanding the dataset and further optimizing the deep learning model.
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Affiliation(s)
- Zhi-Hui Xu
- Department of Otolaryngology, The Second Affiliated Hospital, Fujian Medical University, 950 Donghai Street, Fengze District, Quanzhou 362000, China; (Z.-H.X.)
| | - Da-Ge Fan
- Department of Pathology, The Second Affiliated Hospital, Fujian Medical University, 950 Donghai Street, Fengze District, Quanzhou 362000, China;
| | - Jian-Qiang Huang
- Department of Otolaryngology, The Second Affiliated Hospital, Fujian Medical University, 950 Donghai Street, Fengze District, Quanzhou 362000, China; (Z.-H.X.)
| | - Jia-Wei Wang
- Department of Emergency, The Second Affiliated Hospital, Fujian Medical University, 950 Donghai Street, Fengze District, Quanzhou 362000, China;
| | - Yi Wang
- CT/MRI Department, The Second Affiliated Hospital, Fujian Medical University, 950 Donghai Street, Fengze District, Quanzhou 362000, China
| | - Yuan-Zhe Li
- CT/MRI Department, The Second Affiliated Hospital, Fujian Medical University, 950 Donghai Street, Fengze District, Quanzhou 362000, China
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Ayad A, Barhoush M, Frei M, Volker B, Schmeink A. An Efficient and Private ECG Classification System Using Split and Semi-Supervised Learning. IEEE J Biomed Health Inform 2023; 27:4261-4272. [PMID: 37262112 DOI: 10.1109/jbhi.2023.3281977] [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: 06/03/2023]
Abstract
Electrocardiography (ECG) is a standard diagnostic tool for evaluating the overall heart's electrical activity and is vital for detecting many cardiovascular diseases. Classifying ECG recordings using deep neural networks has been investigated in literature and has shown very good performance. However, this performance assumes that the training data is centralized, which is often not the case in real-life scenarios, where data resides in multiple places and only a small portion of it is labeled. Therefore, in this work, we propose an ECG classification system that focuses on preserving data privacy and enhancing overall system efficiency. We analyzed the complexity of previously proposed deep learning-based models and showed that the temporal convolutional network-based models (TCN) were the most efficient. Then, we built on the TCN models a modified split-learning (SL) system that achieves the same classification performance as the basic SL but reduces the communication overhead between the server and the client by 71.7% as well as reducing the computations at the client by 46.5% compared to the original SL system based on the TCN network. Finally, we implement semi-supervised learning in our system to enhance its classification performance by 9.1%-15.7%, when the training data consists only of 10% labeled data. We have tested our proposed system on a test IoT setup and it achieved satisfactory classification accuracy while being private and energy efficient for green-AI applications.
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Altukroni A, Alsaeedi A, Gonzalez-Losada C, Lee JH, Alabudh M, Mirah M, El-Amri S, Ezz El-Deen O. Detection of the pathological exposure of pulp using an artificial intelligence tool: a multicentric study over periapical radiographs. BMC Oral Health 2023; 23:553. [PMID: 37563659 PMCID: PMC10416487 DOI: 10.1186/s12903-023-03251-0] [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: 02/26/2023] [Accepted: 07/25/2023] [Indexed: 08/12/2023] Open
Abstract
BACKGROUND Introducing artificial intelligence (AI) into the medical field proved beneficial in automating tasks and streamlining the practitioners' lives. Hence, this study was conducted to design and evaluate an AI tool called Make Sure Caries Detector and Classifier (MSc) for detecting pathological exposure of pulp on digital periapical radiographs and to compare its performance with dentists. METHODS This study was a diagnostic, multi-centric study, with 3461 digital periapical radiographs from three countries and seven centers. MSc was built using Yolov5-x model, and it was used for exposed and unexposed pulp detection. The dataset was split into a train, validate, and test dataset; the ratio was 8-1-1 to prevent overfitting. 345 images with 752 labels were randomly allocated to test MSc. The performance metrics used to test MSc performance included mean average precision (mAP), precision, F1 score, recall, and area under receiver operating characteristic curve (AUC). The metrics used to compare the performance with that of 10 certified dentists were: right diagnosis exposed (RDE), right diagnosis not exposed (RDNE), false diagnosis exposed (FDE), false diagnosis not exposed (FDNE), missed diagnosis (MD), and over diagnosis (OD). RESULTS MSc achieved a performance of more than 90% in all metrics examined: an average precision of 0.928, recall of 0.918, F1-score of 0.922, and AUC of 0.956 (P<.05). The results showed a higher mean of 1.94 for all right (correct) diagnosis parameters in MSc group, while a higher mean of 0.64 for all wrong diagnosis parameters in the dentists group (P<.05). CONCLUSIONS The designed MSc tool proved itself reliable in the detection and differentiating between exposed and unexposed pulp in the internally validated model. It also showed a better performance for the detection of exposed and unexposed pulp when compared to the 10 dentists' consensus.
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Affiliation(s)
| | - A Alsaeedi
- Department of Computer Science, College of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia
| | - C Gonzalez-Losada
- School of Dentistry, Complutense University of Madrid, Madrid, Spain
| | - J H Lee
- Department of Periodontology, College of Dentistry and Institute of Oral Bioscience, Jeonbuk National University, Jeonju, Korea
| | - M Alabudh
- Ministry of Health, Medina, Saudi Arabia
| | - M Mirah
- Department of Dental Materials, Taibah University, Medina, Saudi Arabia
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7
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Zeng W, Yuan C. Myocardial infarction detection using ITD, DWT and deterministic learning based on ECG signals. Cogn Neurodyn 2023; 17:941-964. [PMID: 37522048 PMCID: PMC10374507 DOI: 10.1007/s11571-022-09870-7] [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: 02/09/2022] [Revised: 07/16/2022] [Accepted: 08/05/2022] [Indexed: 11/03/2022] Open
Abstract
Nowadays, cardiovascular diseases (CVD) is one of the prime causes of human mortality, which has received tremendous and elaborative research interests regarding the prevention issue. Myocardial ischemia is a kind of CVD which will lead to myocardial infarction (MI). The diagnostic criterion of MI is supplemented with clinical judgement and several electrocardiographic (ECG) or vectorcardiographic (VCG) programs. However the visual inspection of ECG or VCG signals by cardiologists is tedious, laborious and subjective. To overcome such disadvantages, numerous MI detection techniques including signal processing and artificial intelligence tools have been developed. In this study, we propose a novel technique for automatic detection of MI based on disparity of cardiac system dynamics and synthesis of the standard 12-lead and Frank XYZ leads. First, 12-lead ECG signals are synthesized with Frank XYZ leads to build a hybrid 4-dimensional cardiac vector, which is decomposed into a series of proper rotation components (PRCs) by using the intrinsic time-scale decomposition (ITD) method. The novel cardiac vector may fully reflect the pathological alterations provoked by MI and may be correlated to the disparity of cardiac system dynamics between healthy and MI subjects. ITD is employed to measure the variability of cardiac vector and the first PRCs are extracted as predominant PRCs which contain most of the cardiac vector's energy. Second, four levels discrete wavelet transform with third-order Daubechies (db3) wavelet function is employed to decompose the predominant PRCs into different frequency bands, which combines with three-dimensional phase space reconstruction to derive features. The properties associated with the cardiac system dynamics are preserved. Since the frequency components above 40 Hz are lack of use in ECG analysis, in order to reduce the feature dimension, the advisable sub-band (D4) is selected for feature acquisition. Third, neural networks are then used to model, identify and classify cardiac system dynamics between normal (healthy) and MI cardiac vector signals. The difference of cardiac system dynamics between healthy control and MI cardiac vector is computed and used for the detection of MI based on a bank of estimators. Finally, experiments are carried out on the PhysioNet PTB database to assess the effectiveness of the proposed method, in which conventional 12-lead and Frank XYZ leads ECG signal fragments from 148 patients with MI and 52 healthy controls were extracted. By using the tenfold cross-validation style, the achieved average classification accuracy is reported to be 98.20%. Results verify the effectiveness of the proposed method which can serve as a potential candidate for the automatic detection of MI in the clinical application.
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Affiliation(s)
- Wei Zeng
- School of Physics and Mechanical and Electrical Engineering, Longyan University, Longyan, 364012 People’s Republic of China
| | - Chengzhi Yuan
- Department of Mechanical, Industrial and Systems Engineering, University of Rhode Island, Kingston, RI 02881 USA
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8
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Chauhan C, Tripathy RK, Agrawal M. Patient specific higher order tensor based approach for the detection and localization of myocardial infarction using 12-lead ECG. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/19/2023]
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9
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Scott RT, Sanders LM, Antonsen EL, Hastings JJA, Park SM, Mackintosh G, Reynolds RJ, Hoarfrost AL, Sawyer A, Greene CS, Glicksberg BS, Theriot CA, Berrios DC, Miller J, Babdor J, Barker R, Baranzini SE, Beheshti A, Chalk S, Delgado-Aparicio GM, Haendel M, Hamid AA, Heller P, Jamieson D, Jarvis KJ, Kalantari J, Khezeli K, Komarova SV, Komorowski M, Kothiyal P, Mahabal A, Manor U, Garcia Martin H, Mason CE, Matar M, Mias GI, Myers JG, Nelson C, Oribello J, Parsons-Wingerter P, Prabhu RK, Qutub AA, Rask J, Saravia-Butler A, Saria S, Singh NK, Snyder M, Soboczenski F, Soman K, Van Valen D, Venkateswaran K, Warren L, Worthey L, Yang JH, Zitnik M, Costes SV. Biomonitoring and precision health in deep space supported by artificial intelligence. NAT MACH INTELL 2023. [DOI: 10.1038/s42256-023-00617-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/28/2023]
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10
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Sun Q, Xu Z, Liang C, Zhang F, Li J, Liu R, Chen T, Ji B, Chen Y, Wang C. A dynamic learning-based ECG feature extraction method for myocardial infarction detection. Physiol Meas 2023; 43. [PMID: 36595315 DOI: 10.1088/1361-6579/acaa1a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 12/08/2022] [Indexed: 12/13/2022]
Abstract
Objective.Myocardial infarction (MI) is one of the leading causes of human mortality in all cardiovascular diseases globally. Currently, the 12-lead electrocardiogram (ECG) is widely used as a first-line diagnostic tool for MI. However, visual inspection of pathological ECG variations induced by MI remains a great challenge for cardiologists, since pathological changes are usually complex and slight.Approach.To have an accuracy of the MI detection, the prominent features extracted from in-depth mining of ECG signals need to be explored. In this study, a dynamic learning algorithm is applied to discover prominent features for identifying MI patients via mining the hidden inherent dynamics in ECG signals. Firstly, the distinctive dynamic features extracted from the multi-scale decomposition of dynamic modeling of the ECG signals effectively and comprehensibly represent the pathological ECG changes. Secondly, a few most important dynamic features are filtered through a hybrid feature selection algorithm based on filter and wrapper to form a representative reduced feature set. Finally, different classifiers based on the reduced feature set are trained and tested on the public PTB dataset and an independent clinical data set.Main results.Our proposed method achieves a significant improvement in detecting MI patients under the inter-patient paradigm, with an accuracy of 94.75%, sensitivity of 94.18%, and specificity of 96.33% on the PTB dataset. Furthermore, classifiers trained on PTB are verified on the test data set collected from 200 patients, yielding a maximum accuracy of 84.96%, sensitivity of 85.04%, and specificity of 84.80%.Significance.The experimental results demonstrate that our method performs distinctive dynamic feature extraction and may be used as an effective auxiliary tool to diagnose MI patients.
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Affiliation(s)
- Qinghua Sun
- School of Control Science and Engineering, Shandong University, Jinan 250061, People's Republic of China.,Center for Intelligent Medical Engineering, Shandong University, Jinan 250061, People's Republic of China
| | - Zhanfei Xu
- School of Control Science and Engineering, Shandong University, Jinan 250061, People's Republic of China
| | - Chunmiao Liang
- School of Control Science and Engineering, Shandong University, Jinan 250061, People's Republic of China
| | - Fukai Zhang
- School of Control Science and Engineering, Shandong University, Jinan 250061, People's Republic of China.,Center for Intelligent Medical Engineering, Shandong University, Jinan 250061, People's Republic of China
| | - Jiali Li
- School of Control Science and Engineering, Shandong University, Jinan 250061, People's Republic of China
| | - Rugang Liu
- Department of Emergency, Qilu Hospital of Shandong University, Jinan 250012, People's Republic of China
| | - Tianrui Chen
- School of Control Science and Engineering, Shandong University, Jinan 250061, People's Republic of China.,Center for Intelligent Medical Engineering, Shandong University, Jinan 250061, People's Republic of China
| | - Bing Ji
- School of Control Science and Engineering, Shandong University, Jinan 250061, People's Republic of China.,Center for Intelligent Medical Engineering, Shandong University, Jinan 250061, People's Republic of China
| | - Yuguo Chen
- Department of Emergency, Qilu Hospital of Shandong University, Jinan 250012, People's Republic of China
| | - Cong Wang
- School of Control Science and Engineering, Shandong University, Jinan 250061, People's Republic of China.,Center for Intelligent Medical Engineering, Shandong University, Jinan 250061, People's Republic of China
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Cabrero-Holgueras J, Pastrana S. Towards realistic privacy-preserving deep learning over encrypted medical data. Front Cardiovasc Med 2023; 10:1117360. [PMID: 37187785 PMCID: PMC10175772 DOI: 10.3389/fcvm.2023.1117360] [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] [Received: 12/06/2022] [Accepted: 03/31/2023] [Indexed: 05/17/2023] Open
Abstract
Cardiovascular disease supposes a substantial fraction of healthcare systems. The invisible nature of these pathologies demands solutions that enable remote monitoring and tracking. Deep Learning (DL) has arisen as a solution in many fields, and in healthcare, multiple successful applications exist for image enhancement and health outside hospitals. However, the computational requirements and the need for large-scale datasets limit DL. Thus, we often offload computation onto server infrastructure, and various Machine-Learning-as-a-Service (MLaaS) platforms emerged from this need. These enable the conduction of heavy computations in a cloud infrastructure, usually equipped with high-performance computing servers. Unfortunately, the technical barriers persist in healthcare ecosystems since sending sensitive data (e.g., medical records or personally identifiable information) to third-party servers involves privacy and security concerns with legal and ethical implications. In the scope of Deep Learning for Healthcare to improve cardiovascular health, Homomorphic Encryption (HE) is a promising tool to enable secure, private, and legal health outside hospitals. Homomorphic Encryption allows for privacy-preserving computations over encrypted data, thus preserving the privacy of the processed information. Efficient HE requires structural optimizations to perform the complex computation of the internal layers. One such optimization is Packed Homomorphic Encryption (PHE), which encodes multiple elements on a single ciphertext, allowing for efficient Single Instruction over Multiple Data (SIMD) operations. However, using PHE in DL circuits is not straightforward, and it demands new algorithms and data encoding, which existing literature has not adequately addressed. To fill this gap, in this work, we elaborate on novel algorithms to adapt the linear algebra operations of DL layers to PHE. Concretely, we focus on Convolutional Neural Networks. We provide detailed descriptions and insights into the different algorithms and efficient inter-layer data format conversion mechanisms. We formally analyze the complexity of the algorithms in terms of performance metrics and provide guidelines and recommendations for adapting architectures that deal with private data. Furthermore, we confirm the theoretical analysis with practical experimentation. Among other conclusions, we prove that our new algorithms speed up the processing of convolutional layers compared to the existing proposals.
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Affiliation(s)
- José Cabrero-Holgueras
- Innovation, IT Department, CERN, Geneva, Switzerland
- Computer Science Department, Universidad Carlos III de Madrid, Madrid, Spain
- Correspondence: José Cabrero-Holgueras
| | - Sergio Pastrana
- Computer Science Department, Universidad Carlos III de Madrid, Madrid, Spain
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12
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Papandrianos NI, Apostolopoulos ID, Feleki A, Moustakidis S, Kokkinos K, Papageorgiou EI. AI-based classification algorithms in SPECT myocardial perfusion imaging for cardiovascular diagnosis: a review. Nucl Med Commun 2023; 44:1-11. [PMID: 36514926 DOI: 10.1097/mnm.0000000000001634] [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: 12/15/2022]
Abstract
In the last few years, deep learning has made a breakthrough and established its position in machine learning classification problems in medical image analysis. Deep learning has recently displayed remarkable applicability in a range of different medical applications, as well as in nuclear cardiology. This paper implements a literature review protocol and reports the latest advances in artificial intelligence (AI)-based classification in SPECT myocardial perfusion imaging in heart disease diagnosis. The representative and most recent works are reported to demonstrate the use of AI and deep learning technologies in medical image analysis in nuclear cardiology for cardiovascular diagnosis. This review also analyses the primary outcomes of the presented research studies and suggests future directions focusing on the explainability of the deployed deep-learning systems in clinical practice.
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Affiliation(s)
| | | | - Anna Feleki
- Department of Energy Systems, University of Thessaly, Larisa, Greece
| | - Serafeim Moustakidis
- Department of Energy Systems, University of Thessaly, Larisa, Greece
- AIDEAS OÜ, Tallinn, Estonia
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13
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Pan W, An Y, Guan Y, Wang J. MCA-net: A multi-task channel attention network for Myocardial infarction detection and location using 12-lead ECGs. Comput Biol Med 2022; 150:106199. [PMID: 37859291 DOI: 10.1016/j.compbiomed.2022.106199] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 09/18/2022] [Accepted: 10/09/2022] [Indexed: 12/12/2022]
Abstract
PROBLEM Myocardial infarction (MI) is a classic cardiovascular disease (CVD) that requires prompt diagnosis. However, due to the complexity of its pathology, it is difficult for cardiologists to make an accurate diagnosis in a short period. AIM In the clinical, MI can be detected and located by the morphological changes on a 12-lead electrocardiogram (ECG). Therefore, we need to develop an automatic, high-performance, and easily scalable algorithm for MI detection and location using 12-lead ECGs to effectively reduce the burden on cardiologists. METHODS This paper proposes a multi-task channel attention network (MCA-net) for MI detection and location using 12-lead ECGs. It employs a channel attention network based on a residual structure to efficiently capture and integrate features from different leads. On top of this, a multi-task framework is used to additionally introduce the shared and complementary information between MI detection and location tasks to further enhance the model performance. RESULTS Our method is evaluated on two datasets (The PTB and PTBXL datasets). It achieved more than 90% accuracy for MI detection task on both datasets. For MI location tasks, we achieved 68.90% and 49.18% accuracy on the PTB dataset, respectively. And on the PTBXL dataset, we achieved more than 80% accuracy. CONCLUSION Numerous comparison experiments demonstrate that MCA-net outperforms the state-of-the-art methods and has a better generalization. Therefore, it can effectively assist cardiologists to detect and locate MI and has important implications for the early diagnosis of MI and patient prognosis.
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Affiliation(s)
- Weibai Pan
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China; Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, 410083, China.
| | - Ying An
- School of Big Data Institute, Central South University, Changsha Hunan, China.
| | - Yuxia Guan
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China; Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, 410083, China.
| | - Jianxin Wang
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China; Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, 410083, China.
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14
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Chen KW, Wang YC, Liu MH, Tsai BY, Wu MY, Hsieh PH, Wei JT, Shih ESC, Shiao YT, Hwang MJ, Wu YL, Hsu KC, Chang KC. Artificial intelligence-assisted remote detection of ST-elevation myocardial infarction using a mini-12-lead electrocardiogram device in prehospital ambulance care. Front Cardiovasc Med 2022; 9:1001982. [PMID: 36312246 PMCID: PMC9614054 DOI: 10.3389/fcvm.2022.1001982] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Accepted: 09/29/2022] [Indexed: 12/27/2022] Open
Abstract
Objective To implement an all-day online artificial intelligence (AI)-assisted detection of ST-elevation myocardial infarction (STEMI) by prehospital 12-lead electrocardiograms (ECGs) to facilitate patient triage for timely reperfusion therapy. Methods The proposed AI model combines a convolutional neural network and long short-term memory (CNN-LSTM) to predict STEMI on prehospital 12-lead ECGs obtained from mini-12-lead ECG devices equipped in ambulance vehicles in Central Taiwan. Emergency medical technicians (EMTs) from the 14 AI-implemented fire stations performed the on-site 12-lead ECG examinations using the mini portable device. The 12-lead ECG signals were transmitted to the AI center of China Medical University Hospital to classify the recordings as "STEMI" or "Not STEMI". In 11 non-AI fire stations, the ECG data were transmitted to a secure network and read by available on-line emergency physicians. The response time was defined as the time interval between the ECG transmission and ECG interpretation feedback. Results Between July 17, 2021, and March 26, 2022, the AI model classified 362 prehospital 12-lead ECGs obtained from 275 consecutive patients who had called the 119 dispatch centers of fire stations in Central Taiwan for symptoms of chest pain or shortness of breath. The AI's response time to the EMTs in ambulance vehicles was 37.2 ± 11.3 s, which was shorter than the online physicians' response time from 11 other fire stations with no AI implementation (113.2 ± 369.4 s, P < 0.001) after analyzing another set of 335 prehospital 12-lead ECGs. The evaluation metrics including accuracy, precision, specificity, recall, area under the receiver operating characteristic curve, and F1 score to assess the overall AI performance in the remote detection of STEMI were 0.992, 0.889, 0.994, 0.941, 0.997, and 0.914, respectively. During the study period, the AI model promptly identified 10 STEMI patients who underwent primary percutaneous coronary intervention (PPCI) with a median contact-to-door time of 18.5 (IQR: 16-20.8) minutes. Conclusion Implementation of an all-day real-time AI-assisted remote detection of STEMI on prehospital 12-lead ECGs in the field is feasible with a high diagnostic accuracy rate. This approach may help minimize preventable delays in contact-to-treatment times for STEMI patients who require PPCI.
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Affiliation(s)
- Ke-Wei Chen
- Division of Cardiovascular Medicine, Department of Medicine, China Medical University Hospital, Taichung, Taiwan
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan
| | - Yu-Chen Wang
- Division of Cardiovascular Medicine, Department of Medicine, China Medical University Hospital, Taichung, Taiwan
- Division of Cardiovascular Medicine, Asia University Hospital, Taichung, Taiwan
- Department of Medical Laboratory Science and Biotechnology, Asia University, Taichung, Taiwan
| | - Meng-Hsuan Liu
- AI Center for Medical Diagnosis, China Medical University Hospital, Taichung, Taiwan
| | - Being-Yuah Tsai
- AI Center for Medical Diagnosis, China Medical University Hospital, Taichung, Taiwan
| | - Mei-Yao Wu
- School of Post-Baccalaureate Chinese Medicine, China Medical University, Taichung, Taiwan
- Department of Chinese Medicine, China Medical University Hospital, Taichung, Taiwan
| | | | - Jung-Ting Wei
- Division of Cardiovascular Medicine, Department of Medicine, China Medical University Hospital, Taichung, Taiwan
- School of Medicine, China Medical University, Taichung, Taiwan
| | | | - Yi-Tzone Shiao
- Center of Institutional Research and Development, Asia University, Taichung, Taiwan
| | - Ming-Jing Hwang
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Ya-Lun Wu
- AI Center for Medical Diagnosis, China Medical University Hospital, Taichung, Taiwan
| | - Kai-Cheng Hsu
- AI Center for Medical Diagnosis, China Medical University Hospital, Taichung, Taiwan
| | - Kuan-Cheng Chang
- Division of Cardiovascular Medicine, Department of Medicine, China Medical University Hospital, Taichung, Taiwan
- School of Medicine, China Medical University, Taichung, Taiwan
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15
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Sipari D, Chaparro-Rico BDM, Cafolla D. SANE (Easy Gait Analysis System): Towards an AI-Assisted Automatic Gait-Analysis. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:10032. [PMID: 36011667 PMCID: PMC9408480 DOI: 10.3390/ijerph191610032] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 08/10/2022] [Accepted: 08/11/2022] [Indexed: 06/15/2023]
Abstract
The gait cycle of humans may be influenced by a range of variables, including neurological, orthopedic, and pathological conditions. Thus, gait analysis has a broad variety of applications, including the diagnosis of neurological disorders, the study of disease development, the assessment of the efficacy of a treatment, postural correction, and the evaluation and enhancement of sport performances. While the introduction of new technologies has resulted in substantial advancements, these systems continue to struggle to achieve a right balance between cost, analytical accuracy, speed, and convenience. The target is to provide low-cost support to those with motor impairments in order to improve their quality of life. The article provides a novel automated approach for motion characterization that makes use of artificial intelligence to perform real-time analysis, complete automation, and non-invasive, markerless analysis. This automated procedure enables rapid diagnosis and prevents human mistakes. The gait metrics obtained by the two motion tracking systems were compared to show the effectiveness of the proposed methodology.
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Affiliation(s)
- Dario Sipari
- Department of Control and Computer Engineering, Mechatronic Engineering, Politecnico di Torino, 10129 Torino, Italy
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16
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Hassannataj Joloudari J, Mojrian S, Nodehi I, Mashmool A, Kiani Zadegan Z, Khanjani Shirkharkolaie S, Alizadehsani R, Tamadon T, Khosravi S, Akbari Kohnehshari M, Hassannatajjeloudari E, Sharifrazi D, Mosavi A, Loh HW, Tan RS, Acharya UR. Application of artificial intelligence techniques for automated detection of myocardial infarction: a review. Physiol Meas 2022; 43. [PMID: 35803247 DOI: 10.1088/1361-6579/ac7fd9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 07/08/2022] [Indexed: 11/11/2022]
Abstract
Myocardial infarction (MI) results in heart muscle injury due to receiving insufficient blood flow. MI is the most common cause of mortality in middle-aged and elderly individuals worldwide. To diagnose MI, clinicians need to interpret electrocardiography (ECG) signals, which requires expertise and is subject to observer bias. Artificial intelligence-based methods can be utilized to screen for or diagnose MI automatically using ECG signals. In this work, we conducted a comprehensive assessment of artificial intelligence-based approaches for MI detection based on ECG and some other biophysical signals, including machine learning (ML) and deep learning (DL) models. The performance of traditional ML methods relies on handcrafted features and manual selection of ECG signals, whereas DL models can automate these tasks. The review observed that deep convolutional neural networks (DCNNs) yielded excellent classification performance for MI diagnosis, which explains why they have become prevalent in recent years. To our knowledge, this is the first comprehensive survey of artificial intelligence techniques employed for MI diagnosis using ECG and some other biophysical signals.
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Affiliation(s)
- Javad Hassannataj Joloudari
- Computer Engineering, University of Birjand, South Khorasan Province, Birjand, Iran, Birjand, South Khorasan, 9717434765, Iran (the Islamic Republic of)
| | - Sanaz Mojrian
- Mazandaran University of Science and Technology, Mazandaran Province, Babol, Danesh 5, No. Sheykh Tabarasi, Iran, Babol, 47166-85635, Iran (the Islamic Republic of)
| | - Issa Nodehi
- University of Qom, Qom, shahid khodakaram blvd، Iran, Qom, Qom, 1519-37195, Iran (the Islamic Republic of)
| | - Amir Mashmool
- University of Geneva, Via del Molo, 65, 16128 Genova GE, Italy, Geneva, Geneva, 16121, ITALY
| | - Zeynab Kiani Zadegan
- University of Birjand, South Khorasan Province, Birjand, Iran, Birjand, 9717434765, Iran (the Islamic Republic of)
| | - Sahar Khanjani Shirkharkolaie
- Mazandaran University of Science and Technology, Mazandaran Province, Babol, Danesh 5, No. Sheykh Tabarasi, Iran, Babol, 47166-85635, Iran (the Islamic Republic of)
| | - Roohallah Alizadehsani
- Deakin University - Geelong Waterfront Campus, IISRI, Geelong, Victoria, 3220, AUSTRALIA
| | - Tahereh Tamadon
- University of Birjand, South Khorasan Province, Birjand, Iran, Birjand, 9717434765, Iran (the Islamic Republic of)
| | - Samiyeh Khosravi
- University of Birjand, South Khorasan Province, Birjand, Iran, Birjand, 9717434765, Iran (the Islamic Republic of)
| | - Mitra Akbari Kohnehshari
- Bu Ali Sina University, QFRQ+V8H District 2, Hamedan, Iran, Hamedan, Hamedan, 6516738695, Iran (the Islamic Republic of)
| | - Edris Hassannatajjeloudari
- Maragheh University of Medical Sciences, 87VG+9J6, Maragheh, East Azerbaijan Province, Iran, Maragheh, East Azerbaijan, 55158-78151, Iran (the Islamic Republic of)
| | - Danial Sharifrazi
- Islamic Azad University Shiraz, Shiraz University, Iran, Shiraz, Fars, 74731-71987, Iran (the Islamic Republic of)
| | - Amir Mosavi
- Faculty of Informatics, Obuda University, Faculty of Informatics, Obuda University, Budapest, Hungary, Budapest, 1034, HUNGARY
| | - Hui Wen Loh
- Singapore University of Social Sciences, SG, Clementi Rd, 463, Singapore 599494, Singapore, 599491, SINGAPORE
| | - Ru-San Tan
- Department of Cardiology, National Heart Centre Singapore, 5 Hospital Dr, Singapore 169609, Singapore, 168752, SINGAPORE
| | - U Rajendra Acharya
- Electronic Computer Engineering Division, Ngee Ann Polytechnic, 535 Clementi Road, Singapore 599489, Singapore, 599489, SINGAPORE
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Fang R, Lu CC, Chuang CT, Chang WH. A visually interpretable detection method combines 3-D ECG with a multi-VGG neural network for myocardial infarction identification. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 219:106762. [PMID: 35378394 DOI: 10.1016/j.cmpb.2022.106762] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 03/04/2022] [Accepted: 03/17/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVE The automatic recognition of myocardial infarction (MI) by artificial intelligence (AI) has been an emerging topic of academic research and an existing classification method that can recognize conventional electrocardiogram (ECG) signals with high accuracy. However, they are employed to classify one-dimensional (1-D) ECG signals rather than three-dimensional (3-D) ECG images, and it is limited to provide physicians with significant recommendations to aid in diagnosis like highlighting abnormal leads. Other studies on 3-D ECG images either did not achieve high accuracy or did not employ an inter-patient classification scheme. By proposing a multi-VGG deep neural network, this study aims to develop an automatic classification method for identifying myocardial infarction with inter-patient high accuracy and proper interpretability using 3-D ECG image and a Grad-CAM++ method. METHODS We apply a multi-VGG deep convolutional neural network to top-view images of 3-D ECG, which are generated from simply denoised standard 12 leads ECG signals for classification. The multi-network method, which separately classifies QRS areas, ST areas, and whole heartbeats, was applied to improve classification performance. Furthermore, the Grad-CAM++ method was used to provide visually interpretable heatmaps for user's attention to improve network interpretability and assist physicians in MI diagnosis RESULTS: The proposed method achieved 95.65% inter-patient accuracy and exactly perfect inner-patient accuracy in the Physikalisch-Technische Bundesanstalt (PTB) diagnostic ECG database experiment. In the PTB-XL diagnostic ECG database, the proposed method achieved 97.23% inter-patient accuracy. The Grad-CAM++ experiment results also showed that the highlighted areas matched the medical diagnosis criteria for myocardial infarction. CONCLUSIONS Our method demonstrates that 3-D ECG images with AI classification can be efficiently employed for heart disease diagnosis with both high accuracy and visual interpretability.
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Affiliation(s)
- Rui Fang
- Graduate Institute of Manufacturing Technology, National Taipei University of Technology, Taipei 10608, Taiwan
| | - Chih-Cheng Lu
- Graduate Institute of Manufacturing Technology, National Taipei University of Technology, Taipei 10608, Taiwan; Department of Intelligent Automation Engineering, National Taipei University of Technology, Taipei 10608, Taiwan; Department of Mechanical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan.
| | - Cheng-Ta Chuang
- Department of Intelligent Automation Engineering, National Taipei University of Technology, Taipei 10608, Taiwan
| | - Wen-Han Chang
- Emergency Department, Mackay Memorial Hospital, Taipei 10449, Taiwan
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18
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Li W, Tang YM, Yu KM, To S. SLC-GAN: An automated myocardial infarction detection model based on generative adversarial networks and convolutional neural networks with single-lead electrocardiogram synthesis. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2021.12.083] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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19
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Xiong P, Lee SMY, Chan G. Deep Learning for Detecting and Locating Myocardial Infarction by Electrocardiogram: A Literature Review. Front Cardiovasc Med 2022; 9:860032. [PMID: 35402563 PMCID: PMC8990170 DOI: 10.3389/fcvm.2022.860032] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Accepted: 02/18/2022] [Indexed: 12/24/2022] Open
Abstract
Myocardial infarction is a common cardiovascular disorder caused by prolonged ischemia, and early diagnosis of myocardial infarction (MI) is critical for lifesaving. ECG is a simple and non-invasive approach in MI detection, localization, diagnosis, and prognosis. Population-based screening with ECG can detect MI early and help prevent it but this method is too labor-intensive and time-consuming to carry out in practice unless artificial intelligence (AI) would be able to reduce the workload. Recent advances in using deep learning (DL) for ECG screening might rekindle this hope. This review aims to take stock of 59 major DL studies applied to the ECG for MI detection and localization published in recent 5 years, covering convolutional neural network (CNN), long short-term memory (LSTM), convolutional recurrent neural network (CRNN), gated recurrent unit (GRU), residual neural network (ResNet), and autoencoder (AE). In this period, CNN obtained the best popularity in both MI detection and localization, and the highest performance has been obtained from CNN and ResNet model. The reported maximum accuracies of the six different methods are all beyond 97%. Considering the usage of different datasets and ECG leads, the network that trained on 12 leads ECG data of PTB database has obtained higher accuracy than that on smaller number leads data of other datasets. In addition, some limitations and challenges of the DL techniques are also discussed in this review.
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Affiliation(s)
- Ping Xiong
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Taipa, Macau SAR, China
| | - Simon Ming-Yuen Lee
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Taipa, Macau SAR, China
| | - Ging Chan
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Taipa, Macau SAR, China
- Department of Public Health and Medicinal Administration, Faculty of Health Sciences, University of Macau, Taipa, Macau SAR, China
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Thomas LB, Mastorides SM, Viswanadhan NA, Jakey CE, Borkowski AA. Artificial Intelligence: Review of Current and Future Applications in Medicine. Fed Pract 2022; 38:527-538. [PMID: 35136337 DOI: 10.12788/fp.0174] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Background The role of artificial intelligence (AI) in health care is expanding rapidly. Currently, there are at least 29 US Food and Drug Administration-approved AI health care devices that apply to numerous medical specialties and many more are in development. Observations With increasing expectations for all health care sectors to deliver timely, fiscally-responsible, high-quality health care, AI has potential utility in numerous areas, such as image analysis, improved workflow and efficiency, public health, and epidemiology, to aid in processing large volumes of patient and medical data. In this review, we describe basic terminology, principles, and general AI applications relating to health care. We then discuss current and future applications for a variety of medical specialties. Finally, we discuss the future potential of AI along with the potential risks and limitations of current AI technology. Conclusions AI can improve diagnostic accuracy, increase patient safety, assist with patient triage, monitor disease progression, and assist with treatment decisions.
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Affiliation(s)
- L Brannon Thomas
- James A. Haley Veterans' Hospital, Tampa, Florida.,University of South Florida, Morsani College of Medicine, Tampa
| | - Stephen M Mastorides
- James A. Haley Veterans' Hospital, Tampa, Florida.,University of South Florida, Morsani College of Medicine, Tampa
| | | | - Colleen E Jakey
- James A. Haley Veterans' Hospital, Tampa, Florida.,University of South Florida, Morsani College of Medicine, Tampa
| | - Andrew A Borkowski
- James A. Haley Veterans' Hospital, Tampa, Florida.,University of South Florida, Morsani College of Medicine, Tampa
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21
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Ghods A, Cook DJ. PIP: Pictorial Interpretable Prototype Learning for Time Series Classification. IEEE COMPUT INTELL M 2022; 17:34-45. [DOI: 10.1109/mci.2021.3129957] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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22
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Tiwari M, Piech C, Baitemirova M, Prajna NV, Srinivasan M, Lalitha P, Villegas N, Balachandar N, Chua JT, Redd T, Lietman TM, Thrun S, Lin CC. Differentiation of Active Corneal Infections from Healed Scars Using Deep Learning. Ophthalmology 2022; 129:139-146. [PMID: 34352302 PMCID: PMC8792172 DOI: 10.1016/j.ophtha.2021.07.033] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 07/16/2021] [Accepted: 07/26/2021] [Indexed: 02/03/2023] Open
Abstract
PURPOSE To develop and evaluate an automated, portable algorithm to differentiate active corneal ulcers from healed scars using only external photographs. DESIGN A convolutional neural network was trained and tested using photographs of corneal ulcers and scars. PARTICIPANTS De-identified photographs of corneal ulcers were obtained from the Steroids for Corneal Ulcers Trial (SCUT), Mycotic Ulcer Treatment Trial (MUTT), and Byers Eye Institute at Stanford University. METHODS Photographs of corneal ulcers (n = 1313) and scars (n = 1132) from the SCUT and MUTT were used to train a convolutional neural network (CNN). The CNN was tested on 2 different patient populations from eye clinics in India (n = 200) and the Byers Eye Institute at Stanford University (n = 101). Accuracy was evaluated against gold standard clinical classifications. Feature importances for the trained model were visualized using gradient-weighted class activation mapping. MAIN OUTCOME MEASURES Accuracy of the CNN was assessed via F1 score. The area under the receiver operating characteristic (ROC) curve (AUC) was used to measure the precision-recall trade-off. RESULTS The CNN correctly classified 115 of 123 active ulcers and 65 of 77 scars in patients with corneal ulcer from India (F1 score, 92.0% [95% confidence interval (CI), 88.2%-95.8%]; sensitivity, 93.5% [95% CI, 89.1%-97.9%]; specificity, 84.42% [95% CI, 79.42%-89.42%]; ROC: AUC, 0.9731). The CNN correctly classified 43 of 55 active ulcers and 42 of 46 scars in patients with corneal ulcers from Northern California (F1 score, 84.3% [95% CI, 77.2%-91.4%]; sensitivity, 78.2% [95% CI, 67.3%-89.1%]; specificity, 91.3% [95% CI, 85.8%-96.8%]; ROC: AUC, 0.9474). The CNN visualizations correlated with clinically relevant features such as corneal infiltrate, hypopyon, and conjunctival injection. CONCLUSIONS The CNN classified corneal ulcers and scars with high accuracy and generalized to patient populations outside of its training data. The CNN focused on clinically relevant features when it made a diagnosis. The CNN demonstrated potential as an inexpensive diagnostic approach that may aid triage in communities with limited access to eye care.
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Affiliation(s)
- Mo Tiwari
- Department of Computer Science, Stanford University, Stanford, California
| | - Chris Piech
- Department of Computer Science, Stanford University, Stanford, California
| | - Medina Baitemirova
- Department of Biomedical Informatics, Stanford University, Stanford, California
| | | | | | | | | | | | - Janice T Chua
- School of Medicine, University of California, Irvine, Irvine, California
| | - Travis Redd
- Department of Ophthalmology, Casey Eye Institute, Oregon Health and Science University, Portland, Oregon
| | - Thomas M Lietman
- Francis I. Proctor Foundation, University of California San Francisco, San Francisco, California
| | - Sebastian Thrun
- Department of Computer Science, Stanford University, Stanford, California
| | - Charles C Lin
- Byers Eye Institute, Stanford University, Stanford, California.
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23
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Teng M, Singla R, Yau O, Lamoureux D, Gupta A, Hu Z, Hu R, Aissiou A, Eaton S, Hamm C, Hu S, Kelly D, MacMillan KM, Malik S, Mazzoli V, Teng YW, Laricheva M, Jarus T, Field TS. Health Care Students' Perspectives on Artificial Intelligence: Countrywide Survey in Canada. JMIR MEDICAL EDUCATION 2022; 8:e33390. [PMID: 35099397 PMCID: PMC8845000 DOI: 10.2196/33390] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Revised: 11/29/2021] [Accepted: 12/17/2021] [Indexed: 05/02/2023]
Abstract
BACKGROUND Artificial intelligence (AI) is no longer a futuristic concept; it is increasingly being integrated into health care. As studies on attitudes toward AI have primarily focused on physicians, there is a need to assess the perspectives of students across health care disciplines to inform future curriculum development. OBJECTIVE This study aims to explore and identify gaps in the knowledge that Canadian health care students have regarding AI, capture how health care students in different fields differ in their knowledge and perspectives on AI, and present student-identified ways that AI literacy may be incorporated into the health care curriculum. METHODS The survey was developed from a narrative literature review of topics in attitudinal surveys on AI. The final survey comprised 15 items, including multiple-choice questions, pick-group-rank questions, 11-point Likert scale items, slider scale questions, and narrative questions. We used snowball and convenience sampling methods by distributing an email with a description and a link to the web-based survey to representatives from 18 Canadian schools. RESULTS A total of 2167 students across 10 different health professions from 18 universities across Canada responded to the survey. Overall, 78.77% (1707/2167) predicted that AI technology would affect their careers within the coming decade and 74.5% (1595/2167) reported a positive outlook toward the emerging role of AI in their respective fields. Attitudes toward AI varied by discipline. Students, even those opposed to AI, identified the need to incorporate a basic understanding of AI into their curricula. CONCLUSIONS We performed a nationwide survey of health care students across 10 different health professions in Canada. The findings would inform student-identified topics within AI and their preferred delivery formats, which would advance education across different health care professions.
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Affiliation(s)
- Minnie Teng
- Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
- School of Occupational Science and Occupational Therapy, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Rohit Singla
- Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
- School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada
| | - Olivia Yau
- Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
| | | | - Aurinjoy Gupta
- Northern Ontario School of Medicine, Thunder Bay, ON, Canada
| | - Zoe Hu
- Queen's University, Kingston, ON, Canada
| | - Ricky Hu
- Queen's University, Kingston, ON, Canada
| | | | | | - Camille Hamm
- Northern Ontario School of Medicine, Thunder Bay, ON, Canada
| | - Sophie Hu
- University of Calgary, Calgary, AB, Canada
| | - Dayton Kelly
- Northern Ontario School of Medicine, Sudbury, ON, Canada
| | | | - Shamir Malik
- Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Vienna Mazzoli
- Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Yu-Wen Teng
- Vancouver Coastal Health, Vancouver, BC, Canada
| | - Maria Laricheva
- Faculty of Arts, University of British Columbia, Vancouver, BC, Canada
| | - Tal Jarus
- Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
- School of Occupational Science and Occupational Therapy, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Thalia S Field
- Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
- Vancouver Stroke Program, Division of Neurology, University of British Columbia, Vancouver, BC, Canada
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Cao Y, Liu W, Zhang S, Xu L, Zhu B, Cui H, Geng N, Han H, Greenwald SE. Detection and Localization of Myocardial Infarction Based on Multi-Scale ResNet and Attention Mechanism. Front Physiol 2022; 13:783184. [PMID: 35153827 PMCID: PMC8832050 DOI: 10.3389/fphys.2022.783184] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Accepted: 01/05/2022] [Indexed: 12/24/2022] Open
Abstract
PURPOSE Myocardial infarction (MI) is one of the most common cardiovascular diseases, frequently resulting in death. Early and accurate diagnosis is therefore important, and the electrocardiogram (ECG) is a simple and effective method for achieving this. However, it requires assessment by a specialist; so many recent works have focused on the automatic assessment of ECG signals. METHODS For the detection and localization of MI, deep learning models have been proposed, but the diagnostic accuracy of this approaches still need to be improved. Moreover, with deep learning methods the way in which a given result was achieved lacks interpretability. In this study, ECG data was obtained from the PhysioBank open access database, and was analyzed as follows. Firstly, the 12-lead ECG signal was preprocessed to identify each beat and obtain each heart interval. Secondly, a multi-scale deep learning model combined with a residual network and attention mechanism was proposed, where the input was the 12-lead ECG recording. Through the SENet model and the Grad-CAM algorithm, the weighting of each lead was calculated and visualized. Using existing knowledge of the way in which different types of MI gave characteristic patterns in specific ECG leads, the model was used to provisionally diagnose the type of MI according to the characteristics of each of the 12 ECG leads. RESULTS Ten types of MI anterior, anterior lateral, anterior septal, inferior, inferior lateral, inferior posterior, inferior posterior lateral, lateral, posterior, and posterior lateral were diagnosed. The average accuracy, sensitivity, and specificity for MI detection of all lesion types was 99.98, 99.94, and 99.98%, respectively; and the average accuracy, sensitivity, and specificity for MI localization was 99.79, 99.88, and 99.98%, respectively. CONCLUSION When compared to existing models based on traditional machine learning methods, convolutional neural networks and recurrent neural networks, the results showed that the proposed model had better diagnostic performance, being superior in accuracy, sensitivity, and specificity.
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Affiliation(s)
- Yang Cao
- School of Intelligent Medicine, China Medical University, Shenyang, China
| | - Wenyan Liu
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Shuang Zhang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Lisheng Xu
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
- Key Laboratory of Medical Image Computing, Ministry of Education, Shenyang, China
- Neusoft Research of Intelligent Healthcare Technology, Co., Ltd., Shenyang, China
| | - Baofeng Zhu
- Neusoft Research of Intelligent Healthcare Technology, Co., Ltd., Shenyang, China
| | - Huiying Cui
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Ning Geng
- Department of Cardiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Hongguang Han
- Department of Cardiac Surgery, General Hospital of Northern Theater Command, Shenyang, China
| | - Stephen E. Greenwald
- Barts and the London School of Medicine and Dentistry, Blizard Institute, Queen Mary University of London, London, United Kingdom
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25
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Makimoto H. Artificial Intelligence in Medicine (AIM) in Cardiovascular Disorders. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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26
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Mohebbanaaz, Padma Sai Y, Rajani Kumari L. Cognitive assistant DeepNet model for detection of cardiac arrhythmia. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103221] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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Prabhakararao E, Dandapat S. Multi-Scale Convolutional Neural Network Ensemble for Multi-Class Arrhythmia Classification. IEEE J Biomed Health Inform 2021; 26:3802-3812. [PMID: 34962891 DOI: 10.1109/jbhi.2021.3138986] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The automated analysis of electrocardiogram (ECG) signals plays a crucial role in the early diagnosis and management of cardiac arrhythmias. The diverse etiology of arrhythmia and the subtle variations in the pathological ECG characteristics pose challenges in designing reliable automated methods. Existing methods mostly use single deep convolutional neural networks (DCNN) based approaches for arrhythmia classification. Such approaches may not be adequate for effectively representing diverse pathological ECG characteristics. This paper presents a novel way of using an ensemble of multiple DCNN classifiers for effective arrhythmia classification named Deep Multi-Scale Convolutional neural network Ensemble (DMSCE). Specifically, we designed multiple scale-dependent DCNN expert classifiers with different receptive fields to encode the scale-specific pathological ECG characteristics and generate the local predictions. A convolutional gating network is designed to compute the dynamic fusion weights for the experts based on their competencies. These weights are used to aggregate the local predictions and generate final diagnosis decisions. Moreover, a new error function with a correlation penalty is formulated to enable interaction and optimal diversity among experts during the training process. The model is evaluated on the PTBXL-2020 12-lead ECG and the CinC-training2017 single-lead ECG datasets and delivers state-of-the-art performance. Average F1-score of 84.5% and 88.3% are obtained for the PTBXL-2020 and the CinC-training2017 datasets, respectively. Impressive performance across various cardiac arrhythmias and the elegant generalization ability for different leads make the method suitable for reliable remote or in-hospital arrhythmia monitoring applications.
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Bodini M, Rivolta MW, Sassi R. Opening the black box: interpretability of machine learning algorithms in electrocardiography. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2021; 379:20200253. [PMID: 34689625 DOI: 10.1098/rsta.2020.0253] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 04/25/2021] [Indexed: 06/13/2023]
Abstract
Recent studies have suggested that cardiac abnormalities can be detected from the electrocardiogram (ECG) using deep machine learning (DL) models. However, most DL algorithms lack interpretability, since they do not provide any justification for their decisions. In this study, we designed two new frameworks to interpret the classification results of DL algorithms trained for 12-lead ECG classification. The frameworks allow us to highlight not only the ECG samples that contributed most to the classification, but also which between the P-wave, QRS complex and T-wave, hereafter simply called 'waves', were the most relevant for the diagnosis. The frameworks were designed to be compatible with any DL model, including the ones already trained. The frameworks were tested on a selected Deep Neural Network, trained on a publicly available dataset, to automatically classify 24 cardiac abnormalities from 12-lead ECG signals. Experimental results showed that the frameworks were able to detect the most relevant ECG waves contributing to the classification. Often the network relied on portions of the ECG which are also considered by cardiologists to detect the same cardiac abnormalities, but this was not always the case. In conclusion, the proposed frameworks may unveil whether the network relies on features which are clinically significant for the detection of cardiac abnormalities from 12-lead ECG signals, thus increasing the trust in the DL models. This article is part of the theme issue 'Advanced computation in cardiovascular physiology: new challenges and opportunities'.
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Affiliation(s)
- Matteo Bodini
- Dipartimento di Informatica 'Giovanni Degli Antoni', Università degli Studi di Milano, Via Celoria 18, 20133, Milano, Italy
| | - Massimo W Rivolta
- Dipartimento di Informatica 'Giovanni Degli Antoni', Università degli Studi di Milano, Via Celoria 18, 20133, Milano, Italy
| | - Roberto Sassi
- Dipartimento di Informatica 'Giovanni Degli Antoni', Università degli Studi di Milano, Via Celoria 18, 20133, Milano, Italy
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Tadesse GA, Javed H, Weldemariam K, Liu Y, Liu J, Chen J, Zhu T. DeepMI: Deep multi-lead ECG fusion for identifying myocardial infarction and its occurrence-time. Artif Intell Med 2021; 121:102192. [PMID: 34763807 DOI: 10.1016/j.artmed.2021.102192] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 07/07/2021] [Accepted: 10/05/2021] [Indexed: 11/16/2022]
Abstract
Myocardial Infarction (MI) has the highest mortality of all cardiovascular diseases (CVDs). Detection of MI and information regarding its occurrence-time in particular, would enable timely interventions that may improve patient outcomes, thereby reducing the global rise in CVD deaths. Electrocardiogram (ECG) recordings are currently used to screen MI patients. However, manual inspection of ECGs is time-consuming and prone to subjective bias. Machine learning methods have been adopted for automated ECG diagnosis, but most approaches require extraction of ECG beats or consider leads independently of one another. We propose an end-to-end deep learning approach, DeepMI, to classify MI from Normal cases as well as identifying the time-occurrence of MI (defined as Acute, Recent and Old), using a collection of fusion strategies on 12 ECG leads at data-, feature-, and decision-level. In order to minimise computational overhead, we employ transfer learning using existing computer vision networks. Moreover, we use recurrent neural networks to encode the longitudinal information inherent in ECGs. We validated DeepMI on a dataset collected from 17,381 patients, in which over 323,000 samples were extracted per ECG lead. We were able to classify Normal cases as well as Acute, Recent and Old onset cases of MI, with AUROCs of 96.7%, 82.9%, 68.6% and 73.8%, respectively. We have demonstrated a multi-lead fusion approach to detect the presence and occurrence-time of MI. Our end-to-end framework provides flexibility for different levels of multi-lead ECG fusion and performs feature extraction via transfer learning.
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Affiliation(s)
- Girmaw Abebe Tadesse
- Department of Engineering, University of Oxford, Oxford, United Kingdom; IBM Research, Kenya.
| | - Hamza Javed
- Department of Engineering, University of Oxford, Oxford, United Kingdom
| | | | - Yong Liu
- Department of Cardiology, Guangdong Cardiovascular Institute, Guangdong Provincial Key Laboratory of Coronary Disease, Guangdong Provincial People's Hospital, University of Technology, Guangzhou, China
| | - Jin Liu
- Department of Cardiology, Guangdong Cardiovascular Institute, Guangdong Provincial Key Laboratory of Coronary Disease, Guangdong Provincial People's Hospital, University of Technology, Guangzhou, China
| | - Jiyan Chen
- Department of Cardiology, Guangdong Cardiovascular Institute, Guangdong Provincial Key Laboratory of Coronary Disease, Guangdong Provincial People's Hospital, University of Technology, Guangzhou, China
| | - Tingting Zhu
- Department of Engineering, University of Oxford, Oxford, United Kingdom
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Rahul J, Sharma LD, Bohat VK. Short duration Vectorcardiogram based inferior myocardial infarction detection: class and subject-oriented approach. BIOMED ENG-BIOMED TE 2021; 66:489-501. [PMID: 33939896 DOI: 10.1515/bmt-2020-0329] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Accepted: 03/30/2021] [Indexed: 12/17/2022]
Abstract
Myocardial infarction (MI) happens when blood stops circulating to an explicit segment of the heart causing harm to the heart muscles. Vectorcardiography (VCG) is a technique of recording direction and magnitude of the signals that are produced by the heart in a 3-lead representation. In this work, we present a technique for detection of MI in the inferior portion of heart using short duration VCG signals. The raw signal was pre-processed using the median and Savitzky-Golay (SG) filter. The Stationary Wavelet Transform (SWT) was used for time-invariant decomposition of the signal followed by feature extraction. The selected features using minimum-redundancy-maximum-relevance (mRMR) based feature selection method were applied to the supervised classification methods. The efficacy of the proposed method was assessed under both class-oriented and a more real-life subject-oriented approach. An accuracy of 99.14 and 89.37% were achieved respectively. Results of the proposed technique are better than existing state-of-art methods and used VCG segment is shorter. Thus, a shorter segment and a high accuracy can be helpful in the automation of timely and reliable detection of MI. The satisfactory performance achieved in the subject-oriented approach shows reliability and applicability of the proposed technique.
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Affiliation(s)
- Jagdeep Rahul
- Department of Electronics & Communication Engineering, Rajiv Gandhi University, Itanagar, Arunachal Pradesh, India
| | - Lakhan Dev Sharma
- School of Electronics Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India
| | - Vijay Kumar Bohat
- Department of Computer Science & Engineering, Bennett University, Greater Noida, Uttar Pradesh, India
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31
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Liu WC, Lin CS, Tsai CS, Tsao TP, Cheng CC, Liou JT, Lin WS, Cheng SM, Lou YS, Lee CC, Lin C. A deep learning algorithm for detecting acute myocardial infarction. EUROINTERVENTION 2021; 17:765-773. [PMID: 33840640 PMCID: PMC9724911 DOI: 10.4244/eij-d-20-01155] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
BACKGROUND Delayed diagnosis or misdiagnosis of acute myocardial infarction (AMI) is not unusual in daily practice. Since a 12-lead electrocardiogram (ECG) is crucial for the detection of AMI, a systematic algorithm to strengthen ECG interpretation may have important implications for improving diagnosis. AIMS We aimed to develop a deep learning model (DLM) as a diagnostic support tool based on a 12-lead electrocardiogram. METHODS This retrospective cohort study included 1,051/697 ECGs from 737/287 coronary angiogram (CAG)-validated STEMI/NSTEMI patients and 140,336 ECGs from 76,775 non-AMI patients at the emergency department. The DLM was trained and validated in 80% and 20% of these ECGs. A human-machine competition was conducted. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were used to evaluate the performance of the DLM. RESULTS The AUC of the DLM for STEMI detection was 0.976 in the human-machine competition, which was significantly better than that of the best physicians. Furthermore, the DLM independently demonstrated sufficient diagnostic capacity for STEMI detection (AUC=0.997; sensitivity, 98.4%; specificity, 96.9%). Regarding NSTEMI detection, the AUC of the combined DLM and conventional cardiac troponin I (cTnI) increased to 0.978, which was better than that of either the DLM (0.877) or cTnI (0.950). CONCLUSIONS The DLM may serve as a timely, objective and precise diagnostic decision support tool to assist emergency medical system-based networks and frontline physicians in detecting AMI and subsequently initiating reperfusion therapy.
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Affiliation(s)
- Wen-Cheng Liu
- Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, R.O.C
| | - Chin-Sheng Lin
- Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, R.O.C
| | - Chien-Sung Tsai
- Division of Cardiovascular Surgery, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, R.O.C
| | - Tien-Ping Tsao
- Division of Cardiology, Heart Centre, Cheng Hsin Hospital, Taipei, Taiwan, R.O.C
| | - Cheng-Chung Cheng
- Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, R.O.C
| | - Jun-Ting Liou
- Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, R.O.C
| | - Wei-Shiang Lin
- Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, R.O.C
| | - Shu-Meng Cheng
- Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, R.O.C
| | - Yu-Sheng Lou
- Graduate Institute of Life Sciences, National Defense Medical Center, Taipei, Taiwan, R.O.C
| | - Chia-Cheng Lee
- Planning and Management Office, 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
- No.161 Min-Chun E. Rd, Sec. 6, Neihu, Taipei 114, Taiwan, R.O.C
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32
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Hu B, Li S, Chen Y, Kavi R, Coppola S. Applying deep neural networks and inertial measurement unit in recognizing irregular walking differences in the real world. APPLIED ERGONOMICS 2021; 96:103414. [PMID: 34087702 DOI: 10.1016/j.apergo.2021.103414] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2020] [Revised: 01/13/2021] [Accepted: 03/05/2021] [Indexed: 05/10/2023]
Abstract
Falling injuries pose serious health risks to people of all ages, and knowing the extent of exposure to irregular surfaces will increase the ability to measure fall risk. Current gait analysis methods require overly complicated instrumentation and have not been tested for external factors such as walking surfaces that are encountered in the real-world, thus the results are difficult to extrapolate to real-world situations. Artificial intelligence approaches (in particular deep learning networks of varied architectures) to analyze data collected from wearable sensors were used to identify irregular surface exposure in a real-world setting. Thirty young adults wore six Inertial Measurement Unit (IMU) sensors placed on their body (right wrist, trunks at the L5/S1 level, left and right thigh, left and right shank) while walking over eight different surfaces commonly encountered in the living community as well as occupational settings. Three variations of deep learning models were trained to solve this walking surface recognition problem: 1) convolution neural network (CNN); 2) long short term memory (LSTM) network and 3) LSTM structure with an extra global pooling layer (Global-LSTM) which learns the coordination between different data streams (e.g. different channels of the same sensor as well as different sensors). Results indicated that all three deep learning models can recognize walking surfaces with above 0.90 accuracy, with the Global-LSTM yielding the best performance at 0.92 accuracy. In terms of individual sensors, the right thigh based Global-LSTM model reported the highest accuracy (0.90 accuracy). Results from this study provide further evidence that deep learning and wearable sensors can be utilized to recognize irregular walking surfaces induced motion alteration and applied to prevent falling injuries.
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Affiliation(s)
- B Hu
- Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA; Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - S Li
- Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA
| | - Y Chen
- Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA
| | - R Kavi
- West Virginia University, Morgantown, WV, 26505, USA.
| | - S Coppola
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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Oikonomou EK, Siddique M, Antoniades C. Artificial intelligence in medical imaging: A radiomic guide to precision phenotyping of cardiovascular disease. Cardiovasc Res 2021; 116:2040-2054. [PMID: 32090243 DOI: 10.1093/cvr/cvaa021] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Revised: 11/29/2019] [Accepted: 01/23/2020] [Indexed: 12/23/2022] Open
Abstract
ABSTRACT Rapid technological advances in non-invasive imaging, coupled with the availability of large data sets and the expansion of computational models and power, have revolutionized the role of imaging in medicine. Non-invasive imaging is the pillar of modern cardiovascular diagnostics, with modalities such as cardiac computed tomography (CT) now recognized as first-line options for cardiovascular risk stratification and the assessment of stable or even unstable patients. To date, cardiovascular imaging has lagged behind other fields, such as oncology, in the clinical translational of artificial intelligence (AI)-based approaches. We hereby review the current status of AI in non-invasive cardiovascular imaging, using cardiac CT as a running example of how novel machine learning (ML)-based radiomic approaches can improve clinical care. The integration of ML, deep learning, and radiomic methods has revealed direct links between tissue imaging phenotyping and tissue biology, with important clinical implications. More specifically, we discuss the current evidence, strengths, limitations, and future directions for AI in cardiac imaging and CT, as well as lessons that can be learned from other areas. Finally, we propose a scientific framework in order to ensure the clinical and scientific validity of future studies in this novel, yet highly promising field. Still in its infancy, AI-based cardiovascular imaging has a lot to offer to both the patients and their doctors as it catalyzes the transition towards a more precise phenotyping of cardiovascular disease.
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Affiliation(s)
- Evangelos K Oikonomou
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK.,Department of Internal Medicine, Yale New Haven Hospital, Yale School of Medicine, New Haven, CT, USA
| | - Musib Siddique
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK.,Caristo Diagnostics Ltd., Oxford, UK
| | - Charalambos Antoniades
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK.,Oxford Centre of Research Excellence, British Heart Foundation, Oxford, UK.,Oxford Biomedical Research Centre, National Institute of Health Research, Oxford, UK
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Guckert M, Gumpfer N, Hannig J, Keller T, Urquhart N. A conceptual framework for establishing trust in real world intelligent systems. COGN SYST RES 2021. [DOI: 10.1016/j.cogsys.2021.04.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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35
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Gumpfer N, Grün D, Hannig J, Keller T, Guckert M. Detecting myocardial scar using electrocardiogram data and deep neural networks. Biol Chem 2021; 402:911-923. [PMID: 33006947 DOI: 10.1515/hsz-2020-0169] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Accepted: 08/30/2020] [Indexed: 01/15/2023]
Abstract
Ischaemic heart disease is among the most frequent causes of death. Early detection of myocardial pathologies can increase the benefit of therapy and reduce the number of lethal cases. Presence of myocardial scar is an indicator for developing ischaemic heart disease and can be detected with high diagnostic precision by magnetic resonance imaging. However, magnetic resonance imaging scanners are expensive and of limited availability. It is known that presence of myocardial scar has an impact on the well-established, reasonably low cost, and almost ubiquitously available electrocardiogram. However, this impact is non-specific and often hard to detect by a physician. We present an artificial intelligence based approach - namely a deep learning model - for the prediction of myocardial scar based on an electrocardiogram and additional clinical parameters. The model was trained and evaluated by applying 6-fold cross-validation to a dataset of 12-lead electrocardiogram time series together with clinical parameters. The proposed model for predicting the presence of scar tissue achieved an area under the curve score, sensitivity, specificity, and accuracy of 0.89, 70.0, 84.3, and 78.0%, respectively. This promisingly high diagnostic precision of our electrocardiogram-based deep learning models for myocardial scar detection may support a novel, comprehensible screening method.
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Affiliation(s)
- Nils Gumpfer
- Cognitive Information Systems, KITE-Kompetenzzentrum für Informationstechnologie, Technische Hochschule Mittelhessen - University of Applied Sciences, 61169 Friedberg, Germany
| | - Dimitri Grün
- Department of Internal Medicine I, Cardiology, Justus-Liebig-University Gießen, 35390 Gießen, Germany
| | - Jennifer Hannig
- Cognitive Information Systems, KITE-Kompetenzzentrum für Informationstechnologie, Technische Hochschule Mittelhessen - University of Applied Sciences, 61169 Friedberg, Germany
| | - Till Keller
- Department of Internal Medicine I, Cardiology, Justus-Liebig-University Gießen, 35390 Gießen, Germany
| | - Michael Guckert
- Cognitive Information Systems, KITE-Kompetenzzentrum für Informationstechnologie, Technische Hochschule Mittelhessen - University of Applied Sciences, 61169 Friedberg, Germany
- Department of MND - Mathematik, Naturwissenschaften und Datenverarbeitung, Technische Hochschule Mittelhessen - University of Applied Sciences, Wilhelm-Leuschner-Straße 13, 61169 Friedberg, Germany
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Bajwa J, Munir U, Nori A, Williams B. Artificial intelligence in healthcare: transforming the practice of medicine. Future Healthc J 2021; 8:e188-e194. [PMID: 34286183 DOI: 10.7861/fhj.2021-0095] [Citation(s) in RCA: 136] [Impact Index Per Article: 45.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Artificial intelligence (AI) is a powerful and disruptive area of computer science, with the potential to fundamentally transform the practice of medicine and the delivery of healthcare. In this review article, we outline recent breakthroughs in the application of AI in healthcare, describe a roadmap to building effective, reliable and safe AI systems, and discuss the possible future direction of AI augmented healthcare systems.
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Affiliation(s)
| | | | | | - Bryan Williams
- University College London, London, UK and director, NIHR UCLH Biomedical Research Centre, London, UK
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37
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Watson NF, Fernandez CR. Artificial intelligence and sleep: Advancing sleep medicine. Sleep Med Rev 2021; 59:101512. [PMID: 34166990 DOI: 10.1016/j.smrv.2021.101512] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 05/20/2021] [Accepted: 05/21/2021] [Indexed: 02/07/2023]
Abstract
Artificial intelligence (AI) allows analysis of "big data" combining clinical, environmental and laboratory based objective measures to allow a deeper understanding of sleep and sleep disorders. This development has the potential to transform sleep medicine in coming years to the betterment of patient care and our collective understanding of human sleep. This review addresses the current state of the field starting with a broad definition of the various components and analytic methods deployed in AI. We review examples of AI use in screening, endotyping, diagnosing, and treating sleep disorders and place this in the context of precision/personalized sleep medicine. We explore the opportunities for AI to both facilitate and extend providers' clinical impact and present ethical considerations regarding AI derived prognostic information. We cover early adopting specialties of AI in the clinical realm, such as radiology and pathology, to provide a road map for the challenges sleep medicine is likely to face when deploying this technology. Finally, we discuss pitfalls to ensure clinical AI implementation proceeds in the safest and most effective manner possible.
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Affiliation(s)
- Nathaniel F Watson
- Department of Neurology, University of Washington (UW) School of Medicine, USA; UW Medicine Sleep Center, USA.
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Haq IU, Haq I, Xu B. Artificial intelligence in personalized cardiovascular medicine and cardiovascular imaging. Cardiovasc Diagn Ther 2021; 11:911-923. [PMID: 34295713 DOI: 10.21037/cdt.2020.03.09] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Accepted: 03/05/2020] [Indexed: 12/27/2022]
Abstract
The collection of large, heterogeneous electronic datasets and imaging from patients with cardiovascular disease (CVD) has lent itself to the use of sophisticated analysis using artificial intelligence (AI). AI techniques such as machine learning (ML) are able to identify relationships between data points by linking input to output variables using a combination of different functions, such as neural networks. In cardiovascular medicine, this is especially pertinent for classification, diagnosis, risk prediction and treatment guidance. Common cardiovascular data sources from patients include genomic data, cardiovascular imaging, wearable sensors and electronic health records (EHR). Leveraging AI in analysing such data points: (I) for clinicians: more accurate and streamlined image interpretation and diagnosis; (II) for health systems: improved workflow and reductions in medical errors; (III) for patients: promoting health with further education and promoting primary and secondary cardiovascular health prevention. This review addresses the need for AI in cardiovascular medicine by reviewing recent literature in different cardiovascular imaging modalities: electrocardiography, echocardiography, cardiac computed tomography, cardiac nuclear imaging, and cardiac magnetic resonance (CMR) imaging as well as the role of EHR. This review aims to conceptulise these studies in relation to their clinical applications, potential limitations and future opportunities and directions.
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Affiliation(s)
- Ikram-Ul Haq
- Imperial College London Faculty of Medicine, London, UK
| | - Iqraa Haq
- Imperial College London Faculty of Medicine, London, UK
| | - Bo Xu
- Section of Cardiovascular Imaging, Robert and Suzanne Tomsich Department of Cardiovascular Medicine, Sydell and Arnold Family Heart, Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, OH, USA
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Explaining deep neural networks for knowledge discovery in electrocardiogram analysis. Sci Rep 2021; 11:10949. [PMID: 34040033 PMCID: PMC8154909 DOI: 10.1038/s41598-021-90285-5] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Accepted: 05/10/2021] [Indexed: 01/05/2023] Open
Abstract
Deep learning-based tools may annotate and interpret medical data more quickly, consistently, and accurately than medical doctors. However, as medical doctors are ultimately responsible for clinical decision-making, any deep learning-based prediction should be accompanied by an explanation that a human can understand. We present an approach called electrocardiogram gradient class activation map (ECGradCAM), which is used to generate attention maps and explain the reasoning behind deep learning-based decision-making in ECG analysis. Attention maps may be used in the clinic to aid diagnosis, discover new medical knowledge, and identify novel features and characteristics of medical tests. In this paper, we showcase how ECGradCAM attention maps can unmask how a novel deep learning model measures both amplitudes and intervals in 12-lead electrocardiograms, and we show an example of how attention maps may be used to develop novel ECG features.
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40
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Valizadeh G, Babapour Mofrad F, Shalbaf A. Parametric-based feature selection via spherical harmonic coefficients for the left ventricle myocardial infarction screening. Med Biol Eng Comput 2021; 59:1261-1283. [PMID: 33983494 DOI: 10.1007/s11517-021-02372-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Accepted: 04/27/2021] [Indexed: 11/30/2022]
Abstract
Computer-aided diagnosis (CAD) of heart diseases using machine learning techniques has recently received much attention. In this study, we present a novel parametric-based feature selection method using the three-dimensional spherical harmonic (SHs) shape descriptors of the left ventricle (LV) for intelligent myocardial infarction (MI) classification. The main hypothesis is that the SH coefficients of the parameterized endocardial shapes in MI patients are recognizable and distinguishable from healthy subjects. The SH parameterization, expansion, and registration of the LV endocardial shapes were performed, then parametric-based features were extracted. The proposed method performance was investigated by varying considered phases (i.e., the end-systole (ES) or the end-diastole (ED) frames), the spatial alignment procedures based on three modes (i.e., the center of the apical (CoA), the center of mass (CoM), and the center of the basal (CoB)), and considered orders of SH coefficients. After applying principal component analysis (PCA) on the feature vectors, support vector machine (SVM), K-nearest neighbors (K-NN), and random forest (RF) were trained and tested using the leave-one-out cross-validation (LOOCV). The proposed method validation was performed via a dataset containing healthy and MI subjects selected from the automated cardiac diagnosis challenge (ACDC) database. The promising results show the effectiveness of the proposed classification model. SVM reached the best performance with accuracy, sensitivity, specificity, and F-score of 97.50%, 95.00%, 100.00%, and 97.56%, respectively, using the introduced optimum feature set. This study demonstrates the robustness of combining the SH coefficients and machine learning techniques. We also quantify and notably highlight the contribution of different parameters in the classification and finally introduce an optimal feature set with maximum discriminant strength for the MI classification task. Moreover, the obtained results confirm that the proposed method performs more accurately than conventional point-based methods and also the current start-of-the-art, i.e., clinical measures. We showed our method's generalizability using employing it in dilated cardiomyopathy (DCM) detection and achieving promising results too. Parametric-based feature selection via spherical harmonics coefficients for the left ventricle myocardial infarction screening.
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Affiliation(s)
- Gelareh Valizadeh
- Department of Medical Radiation Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Farshid Babapour Mofrad
- Department of Medical Radiation Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
| | - Ahmad Shalbaf
- Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Strodthoff N, Wagner P, Schaeffter T, Samek W. Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL. IEEE J Biomed Health Inform 2021; 25:1519-1528. [PMID: 32903191 DOI: 10.1109/jbhi.2020.3022989] [Citation(s) in RCA: 69] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Electrocardiography (ECG) is a very common, non-invasive diagnostic procedure and its interpretation is increasingly supported by algorithms. The progress in the field of automatic ECG analysis has up to now been hampered by a lack of appropriate datasets for training as well as a lack of well-defined evaluation procedures to ensure comparability of different algorithms. To alleviate these issues, we put forward first benchmarking results for the recently published, freely accessible clinical 12-lead ECG dataset PTB-XL, covering a variety of tasks from different ECG statement prediction tasks to age and sex prediction. Among the investigated deep-learning-based timeseries classification algorithms, we find that convolutional neural networks, in particular resnet- and inception-based architectures, show the strongest performance across all tasks. We find consistent results on the ICBEB2018 challenge ECG dataset and discuss prospects of transfer learning using classifiers pretrained on PTB-XL. These benchmarking results are complemented by deeper insights into the classification algorithm in terms of hidden stratification, model uncertainty and an exploratory interpretability analysis, which provide connecting points for future research on the dataset. Our results emphasize the prospects of deep-learning-based algorithms in the field of ECG analysis, not only in terms of quantitative accuracy but also in terms of clinically equally important further quality metrics such as uncertainty quantification and interpretability. With this resource, we aim to establish the PTB-XL dataset as a resource for structured benchmarking of ECG analysis algorithms and encourage other researchers in the field to join these efforts.
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42
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Jahmunah V, Ng EYK, San TR, Acharya UR. Automated detection of coronary artery disease, myocardial infarction and congestive heart failure using GaborCNN model with ECG signals. Comput Biol Med 2021; 134:104457. [PMID: 33991857 DOI: 10.1016/j.compbiomed.2021.104457] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 04/22/2021] [Accepted: 04/26/2021] [Indexed: 01/02/2023]
Abstract
Cardiovascular diseases (CVDs) are main causes of death globally with coronary artery disease (CAD) being the most important. Timely diagnosis and treatment of CAD is crucial to reduce the incidence of CAD complications like myocardial infarction (MI) and ischemia-induced congestive heart failure (CHF). Electrocardiogram (ECG) signals are most commonly employed as the diagnostic screening tool to detect CAD. In this study, an automated system (AS) was developed for the automated categorization of electrocardiogram signals into normal, CAD, myocardial infarction (MI) and congestive heart failure (CHF) classes using convolutional neural network (CNN) and unique GaborCNN models. Weight balancing was used to balance the imbalanced dataset. High classification accuracies of more than 98.5% were obtained by the CNN and GaborCNN models respectively, for the 4-class classification of normal, coronary artery disease, myocardial infarction and congestive heart failure classes. GaborCNN is a more preferred model due to its good performance and reduced computational complexity as compared to the CNN model. To the best of our knowledge, this is the first study to propose GaborCNN model for automated categorizing of normal, coronary artery disease, myocardial infarction and congestive heart failure classes using ECG signals. Our proposed system is equipped to be validated with bigger database and has the potential to aid the clinicians to screen for CVDs using ECG signals.
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Affiliation(s)
- V Jahmunah
- Department of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore
| | - E Y K Ng
- Department of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore.
| | | | - U Rajendra Acharya
- School of Engineering, Ngee Ann Polytechnic, Singapore; Biomedical Engineering, School of Social Science and Technology, Singapore University of Social Sciences, Singapore; International Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto, Japan; Department Bioinformatics and Medical Engineering, Asia University, Taiwan; School of Management and Enterprise, University of Southern Queensland, Australia.
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43
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Xiong P, Xue Y, Zhang J, Liu M, Du H, Zhang H, Hou Z, Wang H, Liu X. Localization of myocardial infarction with multi-lead ECG based on DenseNet. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 203:106024. [PMID: 33743488 DOI: 10.1016/j.cmpb.2021.106024] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Accepted: 02/22/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND AND OBJECTIVE Myocardial infarction (MI) is a critical acute ischemic heart disease, which can be early diagnosed by electrocardiogram (ECG). However, the most research of MI localization pay more attention on the specific changes in every ECG lead independent. In our study, the research envisages the development of a novel multi-lead MI localization approach based on the densely connected convolutional network (DenseNet). METHODS Considering the correlation of the multi-lead ECG, the method using parallel 12-lead ECG, systematically exploited the correlation of the inter-lead signals. In addition, the dense connection of DenseNet enhanced the reuse of the feature information between the inter-lead and intra-lead signals. The proposed method automatically captured the effective pathological features, which improved the identification of MI. RESULTS The experimental results based on PTB diagnostic ECG database showed that the accuracy, sensitivity and specificity of the proposed method was 99.87%, 99.84% and 99.98% for 11 types of MI localization. CONCLUSIONS The proposed method has achieved superior results compared to other localization methods, which can be introduced into the clinical practice to assist the diagnosis of MI.
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Affiliation(s)
- Peng Xiong
- Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding 071000, China; College of Electronic and Information Engineering, Hebei University, Baoding 071002, China
| | - Yanping Xue
- Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding 071000, China; College of Electronic and Information Engineering, Hebei University, Baoding 071002, China
| | - Jieshuo Zhang
- Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding 071000, China; College of Physics Science and Technology, Hebei University, Baoding 071002, China
| | - Ming Liu
- Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding 071000, China; College of Electronic and Information Engineering, Hebei University, Baoding 071002, China
| | - Haiman Du
- Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding 071000, China; College of Electronic and Information Engineering, Hebei University, Baoding 071002, China
| | - Hong Zhang
- Affiliated Hospital of Hebei University, Baoding 071002, China
| | - Zengguang Hou
- Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Hongrui Wang
- Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding 071000, China; College of Electronic and Information Engineering, Hebei University, Baoding 071002, China
| | - Xiuling Liu
- Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding 071000, China; College of Electronic and Information Engineering, Hebei University, Baoding 071002, China.
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Advani D, Sharma S, Kumari S, Ambasta RK, Kumar P. Precision Oncology, Signaling and Anticancer Agents in Cancer Therapeutics. Anticancer Agents Med Chem 2021; 22:433-468. [PMID: 33687887 DOI: 10.2174/1871520621666210308101029] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Revised: 01/05/2021] [Accepted: 01/12/2021] [Indexed: 11/22/2022]
Abstract
BACKGROUND The global alliance for genomics and healthcare facilities provides innovational solutions to expedite research and clinical practices for complex and incurable health conditions. Precision oncology is an emerging field explicitly tailored to facilitate cancer diagnosis, prevention and treatment based on patients' genetic profile. Advancements in "omics" techniques, next-generation sequencing, artificial intelligence and clinical trial designs provide a platform for assessing the efficacy and safety of combination therapies and diagnostic procedures. METHOD Data were collected from Pubmed and Google scholar using keywords: "Precision medicine", "precision medicine and cancer", "anticancer agents in precision medicine" and reviewed comprehensively. RESULTS Personalized therapeutics including immunotherapy, cancer vaccines, serve as a groundbreaking solution for cancer treatment. Herein, we take a measurable view of precision therapies and novel diagnostic approaches targeting cancer treatment. The contemporary applications of precision medicine have also been described along with various hurdles identified in the successful establishment of precision therapeutics. CONCLUSION This review highlights the key breakthroughs related to immunotherapies, targeted anticancer agents, and target interventions related to cancer signaling mechanisms. The success story of this field in context to drug resistance, safety, patient survival and in improving quality of life is yet to be elucidated. We conclude that, in the near future, the field of individualized treatments may truly revolutionize the nature of cancer patient care.
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Affiliation(s)
- Dia Advani
- Molecular Neuroscience and Functional Genomics Laboratory Shahbad Daulatpur, Bawana Road, Delhi 110042. India
| | - Sudhanshu Sharma
- Molecular Neuroscience and Functional Genomics Laboratory Shahbad Daulatpur, Bawana Road, Delhi 110042. India
| | - Smita Kumari
- Molecular Neuroscience and Functional Genomics Laboratory Shahbad Daulatpur, Bawana Road, Delhi 110042. India
| | - Rashmi K Ambasta
- Molecular Neuroscience and Functional Genomics Laboratory Shahbad Daulatpur, Bawana Road, Delhi 110042. India
| | - Pravir Kumar
- Molecular Neuroscience and Functional Genomics Laboratory Shahbad Daulatpur, Bawana Road, Delhi 110042. India
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Abstract
Artificial intelligence (AI)-based systems have achieved significant success in healthcare since 2016, and AI models have accomplished medical tasks, at or above the performance levels of humans. Despite these achievements, various challenges exist in the application of AI in healthcare. One of the main challenges is safety, which is related to unsafe and incorrect actions and recommendations by AI algorithms. In response to the need to address the safety challenges, this research aimed to develop a safety controlling system (SCS) framework to reduce the risk of potential healthcare-related incidents. The framework was developed by adopting the multi-attribute value model approach (MAVT), which comprises four symmetrical parts: extracting attributes, generating weights for the attributes, developing a rating scale, and finalizing the system. The framework represents a set of attributes in different layers and can be used as a checklist in healthcare institutions with implemented AI models. Having these attributes in healthcare systems will lead to high scores in the SCS, which indicates safe application of AI models. The proposed framework provides a basis for implementing and monitoring safety legislation, identifying the risks in AI models’ activities, improving human-AI interactions, preventing incidents from occurring, and having an emergency plan for remaining risks.
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46
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Artificial Intelligence in Medicine (AIM) in Cardiovascular Disorders. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_170-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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47
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Ilan Y. Second-Generation Digital Health Platforms: Placing the Patient at the Center and Focusing on Clinical Outcomes. Front Digit Health 2020; 2:569178. [PMID: 34713042 PMCID: PMC8521820 DOI: 10.3389/fdgth.2020.569178] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Accepted: 10/02/2020] [Indexed: 12/13/2022] Open
Abstract
Artificial intelligence (AI) digital health systems have drawn much attention over the last decade. However, their implementation into medical practice occurs at a much slower pace than expected. This paper reviews some of the achievements of first-generation AI systems, and the barriers facing their implementation into medical practice. The development of second-generation AI systems is discussed with a focus on overcoming some of these obstacles. Second-generation systems are aimed at focusing on a single subject and on improving patients' clinical outcomes. A personalized closed-loop system designed to improve end-organ function and the patient's response to chronic therapies is presented. The system introduces a platform which implements a personalized therapeutic regimen and introduces quantifiable individualized-variability patterns into its algorithm. The platform is designed to achieve a clinically meaningful endpoint by ensuring that chronic therapies will have sustainable effect while overcoming compensatory mechanisms associated with disease progression and drug resistance. Second-generation systems are expected to assist patients and providers in adopting and implementing of these systems into everyday care.
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48
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van de Leur RR, Boonstra MJ, Bagheri A, Roudijk RW, Sammani A, Taha K, Doevendans PA, van der Harst P, van Dam PM, Hassink RJ, van Es R, Asselbergs FW. Big Data and Artificial Intelligence: Opportunities and Threats in Electrophysiology. Arrhythm Electrophysiol Rev 2020; 9:146-154. [PMID: 33240510 PMCID: PMC7675143 DOI: 10.15420/aer.2020.26] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Accepted: 08/03/2020] [Indexed: 12/23/2022] Open
Abstract
The combination of big data and artificial intelligence (AI) is having an increasing impact on the field of electrophysiology. Algorithms are created to improve the automated diagnosis of clinical ECGs or ambulatory rhythm devices. Furthermore, the use of AI during invasive electrophysiological studies or combining several diagnostic modalities into AI algorithms to aid diagnostics are being investigated. However, the clinical performance and applicability of created algorithms are yet unknown. In this narrative review, opportunities and threats of AI in the field of electrophysiology are described, mainly focusing on ECGs. Current opportunities are discussed with their potential clinical benefits as well as the challenges. Challenges in data acquisition, model performance, (external) validity, clinical implementation, algorithm interpretation as well as the ethical aspects of AI research are discussed. This article aims to guide clinicians in the evaluation of new AI applications for electrophysiology before their clinical implementation.
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Affiliation(s)
- Rutger R van de Leur
- Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Machteld J Boonstra
- Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Ayoub Bagheri
- Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
- Department of Methodology and Statistics, Utrecht University, Utrecht, the Netherlands
| | - Rob W Roudijk
- Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
- Netherlands Heart Institute, Utrecht, the Netherlands
| | - Arjan Sammani
- Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Karim Taha
- Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
- Netherlands Heart Institute, Utrecht, the Netherlands
| | - Pieter Afm Doevendans
- Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
- Netherlands Heart Institute, Utrecht, the Netherlands
- Central Military Hospital Utrecht, Ministerie van Defensie, Utrecht, the Netherlands
| | - Pim van der Harst
- Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Peter M van Dam
- Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Rutger J Hassink
- Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - René van Es
- Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Folkert W Asselbergs
- Department of Cardiology, Division of Heart and Lungs, University Medical Center 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
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Low JSY, Thevarajah TM, Chang SW, Goh BT, Khor SM. Biosensing based on surface-enhanced Raman spectroscopy as an emerging/next-generation point-of-care approach for acute myocardial infarction diagnosis. Crit Rev Biotechnol 2020; 40:1191-1209. [PMID: 32811205 DOI: 10.1080/07388551.2020.1808582] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Cardiovascular disease is a major global health issue. In particular, acute myocardial infarction (AMI) requires urgent attention and early diagnosis. The use of point-of-care diagnostics has resulted in the improved management of cardiovascular disease, but a major drawback is that the performance of POC devices does not rival that of central laboratory tests. Recently, many studies and advances have been made in the field of surface-enhanced Raman scattering (SERS), including the development of POC biosensors that utilize this detection method. Here, we present a review of the strengths and limitations of these emerging SERS-based biosensors for AMI diagnosis. The ability of SERS to multiplex sensing against existing POC detection methods are compared and discussed. Furthermore, SERS calibration-free methods that have recently been explored to minimize the inconvenience and eliminate the limitations caused by the limited linear range and interassay differences found in the calibration curves are outlined. In addition, the incorporation of artificial intelligence (AI) in SERS techniques to promote multivariate analysis and enhance diagnostic accuracy are discussed. The future prospects for SERS-based POC devices that include wearable POC SERS devices toward predictive, personalized medicine following the Fourth Industrial Revolution are proposed.
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Affiliation(s)
- Joyce Siew Yong Low
- Faculty of Science, Department of Chemistry, University of Malaya, Kuala Lumpur, Malaysia
| | - T Malathi Thevarajah
- Faculty of Medicine, Department of Pathology, University of Malaya, Kuala Lumpur, Malaysia
| | - Siow Wee Chang
- Faculty of Science, Institute of Biological Sciences, University of Malaya, Kuala Lumpur, Malaysia
| | - Boon Tong Goh
- Faculty of Science, Low Dimensional Materials Research Centre, Department of Physics, University of Malaya, Kuala Lumpur, Malaysia
| | - Sook Mei Khor
- Faculty of Science, Department of Chemistry, University of Malaya, Kuala Lumpur, Malaysia.,Faculty of Engineering, Centre for Innovation in Medical Engineering, University of Malaya, Kuala Lumpur, Malaysia
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50
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Al-Zaiti S, Besomi L, Bouzid Z, Faramand Z, Frisch S, Martin-Gill C, Gregg R, Saba S, Callaway C, Sejdić E. Machine learning-based prediction of acute coronary syndrome using only the pre-hospital 12-lead electrocardiogram. Nat Commun 2020; 11:3966. [PMID: 32769990 PMCID: PMC7414145 DOI: 10.1038/s41467-020-17804-2] [Citation(s) in RCA: 77] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Accepted: 07/16/2020] [Indexed: 11/30/2022] Open
Abstract
Prompt identification of acute coronary syndrome is a challenge in clinical practice. The 12-lead electrocardiogram (ECG) is readily available during initial patient evaluation, but current rule-based interpretation approaches lack sufficient accuracy. Here we report machine learning-based methods for the prediction of underlying acute myocardial ischemia in patients with chest pain. Using 554 temporal-spatial features of the 12-lead ECG, we train and test multiple classifiers on two independent prospective patient cohorts (n = 1244). While maintaining higher negative predictive value, our final fusion model achieves 52% gain in sensitivity compared to commercial interpretation software and 37% gain in sensitivity compared to experienced clinicians. Such an ultra-early, ECG-based clinical decision support tool, when combined with the judgment of trained emergency personnel, would help to improve clinical outcomes and reduce unnecessary costs in patients with chest pain.
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Affiliation(s)
- Salah Al-Zaiti
- Department of Acute & Tertiary Care Nursing, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
- Division of Cardiology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Lucas Besomi
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Zeineb Bouzid
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ziad Faramand
- Department of Acute & Tertiary Care Nursing, University of Pittsburgh, Pittsburgh, PA, USA
| | - Stephanie Frisch
- Department of Acute & Tertiary Care Nursing, University of Pittsburgh, Pittsburgh, PA, USA
| | - Christian Martin-Gill
- Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- University of Pittsburgh Medical Center (UPMC), Pittsburgh, PA, USA
| | - Richard Gregg
- Advanced Algorithms Development Research Center, Philips Healthcare, Andover, MA, USA
| | - Samir Saba
- Division of Cardiology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- University of Pittsburgh Medical Center (UPMC), Pittsburgh, PA, USA
| | - Clifton Callaway
- Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- University of Pittsburgh Medical Center (UPMC), Pittsburgh, PA, USA
| | - Ervin Sejdić
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Intelligent Systems, University of Pittsburgh, Pittsburgh, PA, USA
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