1
|
Choi S, Choi K, Yun HK, Kim SH, Choi HH, Park YS, Joo S. Diagnosis of atrial fibrillation based on AI-detected anomalies of ECG segments. Heliyon 2024; 10:e23597. [PMID: 38187293 PMCID: PMC10770559 DOI: 10.1016/j.heliyon.2023.e23597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 12/06/2023] [Accepted: 12/07/2023] [Indexed: 01/09/2024] Open
Abstract
Early detection of atrial fibrillation (AF) is crucial for its effective management and prevention. Various methods for detecting AF using deep learning (DL) based on supervised learning with a large labeled dataset have a remarkable performance. However, supervised learning has several problems, as it is time-consuming for labeling and has a data dependency problem. Moreover, most of the DL methods do not provide any clinical evidence to physicians regarding the analysis of electrocardiography (ECG) for classification or detection of AF. To address these limitations, in this study, we proposed a novel AF diagnosis system using unsupervised learning for anomaly detection with three segments, PreQ, QRS, and PostS, based on the normal ECG. Two independent datasets, PTB-XL and China, were used in three experiments. We used a long short-term memory (LSTM)-based autoencoder to train the segments of the normal ECG. Based on the threshold of anomaly scores using mean squared error (MSE), it distinguished between normal and AF segments. In Experiment A, the best score was that of PreQ, which detected AF with an AUROC score of 0.96. In Experiment B and C for cross validation of each dataset, the best scores were also of PreQ, with AUROC scores of 0.9 and 0.95, respectively. To verify the significance of the anomaly score in distinguishing between AF and normal segments, we utilized an XG-Boosted model after generating anomaly scores in the three segments. The XG-Boosted model achieved an AUROC score of 0.98 and an F1 score of 0.94. AF detection using DL has been controversial among many physicians. However, our study differentiates itself from previous studies in that we can demonstrate evidence that distinguishes AF from normal segments based on the anomaly score.
Collapse
Affiliation(s)
- Sanghoon Choi
- Department of Biomedical Engineering, University of Ulsan College of Medicine, Seoul, Republic of Korea
- Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, Seoul, Republic of Korea
| | - Kyungmin Choi
- Department of Biomedical Engineering, University of Ulsan College of Medicine, Seoul, Republic of Korea
- Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, Seoul, Republic of Korea
| | - Hong Kyun Yun
- Department of Biomedical Engineering, University of Ulsan College of Medicine, Seoul, Republic of Korea
- Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, Seoul, Republic of Korea
| | - Su Hyeon Kim
- Department of Biomedical Engineering, University of Ulsan College of Medicine, Seoul, Republic of Korea
- Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, Seoul, Republic of Korea
| | - Hyeon-Hwa Choi
- Department of Biomedical Engineering, University of Ulsan College of Medicine, Seoul, Republic of Korea
- Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, Seoul, Republic of Korea
| | - Yi-Seul Park
- Department of Biomedical Engineering, University of Ulsan College of Medicine, Seoul, Republic of Korea
- Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, Seoul, Republic of Korea
| | - Segyeong Joo
- Department of Biomedical Engineering, University of Ulsan College of Medicine, Seoul, Republic of Korea
- Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, Seoul, Republic of Korea
| |
Collapse
|
2
|
Kolk MZH, Ruipérez-Campillo S, Alvarez-Florez L, Deb B, Bekkers EJ, Allaart CP, Van Der Lingen ALCJ, Clopton P, Išgum I, Wilde AAM, Knops RE, Narayan SM, Tjong FVY. Dynamic prediction of malignant ventricular arrhythmias using neural networks in patients with an implantable cardioverter-defibrillator. EBioMedicine 2024; 99:104937. [PMID: 38118401 PMCID: PMC10772563 DOI: 10.1016/j.ebiom.2023.104937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 10/20/2023] [Accepted: 12/12/2023] [Indexed: 12/22/2023] Open
Abstract
BACKGROUND Risk stratification for ventricular arrhythmias currently relies on static measurements that fail to adequately capture dynamic interactions between arrhythmic substrate and triggers over time. We trained and internally validated a dynamic machine learning (ML) model and neural network that extracted features from longitudinally collected electrocardiograms (ECG), and used these to predict the risk of malignant ventricular arrhythmias. METHODS A multicentre study in patients implanted with an implantable cardioverter-defibrillator (ICD) between 2007 and 2021 in two academic hospitals was performed. Variational autoencoders (VAEs), which combine neural networks with variational inference principles, and can learn patterns and structure in data without explicit labelling, were trained to encode the mean ECG waveforms from the limb leads into 16 variables. Supervised dynamic ML models using these latent ECG representations and clinical baseline information were trained to predict malignant ventricular arrhythmias treated by the ICD. Model performance was evaluated on a hold-out set, using time-dependent receiver operating characteristic (ROC) and calibration curves. FINDINGS 2942 patients (61.7 ± 13.9 years, 25.5% female) were included, with a total of 32,129 ECG recordings during a mean follow-up of 43.9 ± 35.9 months. The mean time-varying area under the ROC curve for the dynamic model was 0.738 ± 0.07, compared to 0.639 ± 0.03 for a static (i.e. baseline-only model). Feature analyses indicated dynamic changes in latent ECG representations, particularly those affecting the T-wave morphology, were of highest importance for model predictions. INTERPRETATION Dynamic ML models and neural networks effectively leverage routinely collected longitudinal ECG recordings for personalised and updated predictions of malignant ventricular arrhythmias, outperforming static models. FUNDING This publication is part of the project DEEP RISK ICD (with project number 452019308) of the research programme Rubicon which is (partly) financed by the Dutch Research Council (NWO). This research is partly funded by the Amsterdam Cardiovascular Sciences (personal grant F.V.Y.T).
Collapse
Affiliation(s)
- Maarten Z H Kolk
- Department of Clinical and Experimental Cardiology, Amsterdam UMC Location University of Amsterdam, Heart Center, Meibergdreef 9, Amsterdam, the Netherlands; Amsterdam Cardiovascular Sciences, Heart Failure & Arrhythmias, Amsterdam, the Netherlands
| | - Samuel Ruipérez-Campillo
- Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA; Department of Information Technology and Electrical Engineering, Swiss Federal Institute of Technology Zurich (ETHz), Gloriastrasse 35, Zurich, Switzerland; ITACA Institute, Universtitat Politècnica de València, Camino de Vera S/n, Valencia, Spain
| | - Laura Alvarez-Florez
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center Location University of Amsterdam, Meibergdreef 9, Amsterdam, the Netherlands
| | - Brototo Deb
- Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA
| | - Erik J Bekkers
- Faculty of Science, University of Amsterdam, Science Park 904, Amsterdam, the Netherlands
| | - Cornelis P Allaart
- Department of Cardiology, Amsterdam UMC, Location VU Medical Center, De Boelelaan 1118, Amsterdam, the Netherlands
| | | | - Paul Clopton
- Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA
| | - Ivana Išgum
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center Location University of Amsterdam, Meibergdreef 9, Amsterdam, the Netherlands; Faculty of Science, University of Amsterdam, Science Park 904, Amsterdam, the Netherlands; Department of Radiology and Nuclear Medicine, Amsterdam UMC Location University of Amsterdam, Meibergdreef 9, Amsterdam, the Netherlands
| | - Arthur A M Wilde
- Department of Clinical and Experimental Cardiology, Amsterdam UMC Location University of Amsterdam, Heart Center, Meibergdreef 9, Amsterdam, the Netherlands; Amsterdam Cardiovascular Sciences, Heart Failure & Arrhythmias, Amsterdam, the Netherlands
| | - Reinoud E Knops
- Department of Clinical and Experimental Cardiology, Amsterdam UMC Location University of Amsterdam, Heart Center, Meibergdreef 9, Amsterdam, the Netherlands; Amsterdam Cardiovascular Sciences, Heart Failure & Arrhythmias, Amsterdam, the Netherlands
| | - Sanjiv M Narayan
- Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA
| | - Fleur V Y Tjong
- Department of Clinical and Experimental Cardiology, Amsterdam UMC Location University of Amsterdam, Heart Center, Meibergdreef 9, Amsterdam, the Netherlands; Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA; Amsterdam Cardiovascular Sciences, Heart Failure & Arrhythmias, Amsterdam, the Netherlands.
| |
Collapse
|
3
|
Zelger P, Brunner A, Zelger B, Willenbacher E, Unterberger SH, Stalder R, Huck CW, Willenbacher W, Pallua JD. Deep learning analysis of mid-infrared microscopic imaging data for the diagnosis and classification of human lymphomas. JOURNAL OF BIOPHOTONICS 2023; 16:e202300015. [PMID: 37578837 DOI: 10.1002/jbio.202300015] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 07/19/2023] [Accepted: 08/09/2023] [Indexed: 08/15/2023]
Abstract
The present study presents an alternative analytical workflow that combines mid-infrared (MIR) microscopic imaging and deep learning to diagnose human lymphoma and differentiate between small and large cell lymphoma. We could show that using a deep learning approach to analyze MIR hyperspectral data obtained from benign and malignant lymph node pathology results in high accuracy for correct classification, learning the distinct region of 3900 to 850 cm-1 . The accuracy is above 95% for every pair of malignant lymphoid tissue and still above 90% for the distinction between benign and malignant lymphoid tissue for binary classification. These results demonstrate that a preliminary diagnosis and subtyping of human lymphoma could be streamlined by applying a deep learning approach to analyze MIR spectroscopic data.
Collapse
Affiliation(s)
- P Zelger
- University Hospital of Hearing, Voice and Speech Disorders, Medical University of Innsbruck, Innsbruck, Austria
| | - A Brunner
- Institute of Pathology, Neuropathology and Molecular Pathology, Medical University of Innsbruck, Innsbruck, Austria
| | - B Zelger
- Institute of Pathology, Neuropathology and Molecular Pathology, Medical University of Innsbruck, Innsbruck, Austria
| | - E Willenbacher
- University Hospital of Internal Medicine V, Hematology & Oncology, Medical University of Innsbruck, Innsbruck, Austria
| | - S H Unterberger
- Institute of Material-Technology, Leopold-Franzens University Innsbruck, Innsbruck, Austria
| | - R Stalder
- Institute of Mineralogy and Petrography, Leopold-Franzens University Innsbruck, Innsbruck, Austria
| | - C W Huck
- Institute of Analytical Chemistry and Radiochemistry, Innsbruck, Austria
| | - W Willenbacher
- University Hospital of Internal Medicine V, Hematology & Oncology, Medical University of Innsbruck, Innsbruck, Austria
- Oncotyrol, Centre for Personalized Cancer Medicine, Innsbruck, Austria
| | - J D Pallua
- University Hospital for Orthopedics and Traumatology, Medical University of Innsbruck, Innsbruck, Austria
| |
Collapse
|
4
|
Xu Y, Zheng X, Li Y, Ye X, Cheng H, Wang H, Lyu J. Exploring patient medication adherence and data mining methods in clinical big data: A contemporary review. J Evid Based Med 2023; 16:342-375. [PMID: 37718729 DOI: 10.1111/jebm.12548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 08/30/2023] [Indexed: 09/19/2023]
Abstract
BACKGROUND Increasingly, patient medication adherence data are being consolidated from claims databases and electronic health records (EHRs). Such databases offer an indirect avenue to gauge medication adherence in our data-rich healthcare milieu. The surge in data accessibility, coupled with the pressing need for its conversion to actionable insights, has spotlighted data mining, with machine learning (ML) emerging as a pivotal technique. Nonadherence poses heightened health risks and escalates medical costs. This paper elucidates the synergistic interaction between medical database mining for medication adherence and the role of ML in fostering knowledge discovery. METHODS We conducted a comprehensive review of EHR applications in the realm of medication adherence, leveraging ML techniques. We expounded on the evolution and structure of medical databases pertinent to medication adherence and harnessed both supervised and unsupervised ML paradigms to delve into adherence and its ramifications. RESULTS Our study underscores the applications of medical databases and ML, encompassing both supervised and unsupervised learning, for medication adherence in clinical big data. Databases like SEER and NHANES, often underutilized due to their intricacies, have gained prominence. Employing ML to excavate patient medication logs from these databases facilitates adherence analysis. Such findings are pivotal for clinical decision-making, risk stratification, and scholarly pursuits, aiming to elevate healthcare quality. CONCLUSION Advanced data mining in the era of big data has revolutionized medication adherence research, thereby enhancing patient care. Emphasizing bespoke interventions and research could herald transformative shifts in therapeutic modalities.
Collapse
Affiliation(s)
- Yixian Xu
- Department of Anesthesiology, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Xinkai Zheng
- Department of Dermatology, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Yuanjie Li
- Planning & Discipline Construction Office, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Xinmiao Ye
- Department of Anesthesiology, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Hongtao Cheng
- School of Nursing, Jinan University, Guangzhou, China
| | - Hao Wang
- Department of Anesthesiology, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Jun Lyu
- Department of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization, Guangzhou, China
| |
Collapse
|
5
|
Ali M, Haque AKMN, Sadik N, Ahmed T, Baten MZ. Predicting strongly localized resonant modes of light in disordered arrays of dielectric scatterers: a machine learning approach. OPTICS EXPRESS 2023; 31:826-842. [PMID: 36785131 DOI: 10.1364/oe.475495] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 11/27/2022] [Indexed: 06/18/2023]
Abstract
In this work, we predict the most strongly confined resonant mode of light in strongly disordered systems of dielectric scatterers employing the data-driven approach of machine learning. For training, validation, and test purposes of the proposed regression architecture-based deep neural network (DNN), a dataset containing resonant characteristics of light in 8,400 random arrays of dielectric scatterers is generated employing finite difference time domain (FDTD) analysis technique. To enhance the convergence and accuracy of the overall model, an auto-encoder is utilized as the weight initializer of the regression model, which contains three convolutional layers and three fully connected layers. Given the refractive index profile of the disordered system, the trained model can instantaneously predict the Anderson localized resonant wavelength of light with a minimum error of 0.0037%. A correlation coefficient of 0.95 or higher is obtained between the FDTD simulation results and DNN predictions. Such a high level of accuracy is maintained in inhomogeneous disordered media containing Gaussian distribution of diameter of the scattering particles. Moreover, the prediction scheme is found to be robust against any combination of diameters and fill factors of the disordered medium. The proposed model thereby leverages the benefits of machine learning for predicting the complex behavior of light in strongly disordered systems.
Collapse
|
6
|
van de Leur RR, Bos MN, Taha K, Sammani A, Yeung MW, van Duijvenboden S, Lambiase PD, Hassink RJ, van der Harst P, Doevendans PA, Gupta DK, van Es R. Improving explainability of deep neural network-based electrocardiogram interpretation using variational auto-encoders . EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2022; 3:390-404. [PMID: 36712164 PMCID: PMC9707974 DOI: 10.1093/ehjdh/ztac038] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 06/16/2022] [Indexed: 02/01/2023]
Abstract
Aims Deep neural networks (DNNs) perform excellently in interpreting electrocardiograms (ECGs), both for conventional ECG interpretation and for novel applications such as detection of reduced ejection fraction (EF). Despite these promising developments, implementation is hampered by the lack of trustworthy techniques to explain the algorithms to clinicians. Especially, currently employed heatmap-based methods have shown to be inaccurate. Methods and results We present a novel pipeline consisting of a variational auto-encoder (VAE) to learn the underlying factors of variation of the median beat ECG morphology (the FactorECG), which are subsequently used in common and interpretable prediction models. As the ECG factors can be made explainable by generating and visualizing ECGs on both the model and individual level, the pipeline provides improved explainability over heatmap-based methods. By training on a database with 1.1 million ECGs, the VAE can compress the ECG into 21 generative ECG factors, most of which are associated with physiologically valid underlying processes. Performance of the explainable pipeline was similar to 'black box' DNNs in conventional ECG interpretation [area under the receiver operating curve (AUROC) 0.94 vs. 0.96], detection of reduced EF (AUROC 0.90 vs. 0.91), and prediction of 1-year mortality (AUROC 0.76 vs. 0.75). Contrary to the 'black box' DNNs, our pipeline provided explainability on which morphological ECG changes were important for prediction. Results were confirmed in a population-based external validation dataset. Conclusions Future studies on DNNs for ECGs should employ pipelines that are explainable to facilitate clinical implementation by gaining confidence in artificial intelligence and making it possible to identify biased models.
Collapse
Affiliation(s)
| | | | - Karim Taha
- Department of Cardiology, University Medical Center Utrecht, Internal ref E03.511, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands,Netherlands Heart Institute, Moreelsepark 1, 3511 EP Utrecht, The Netherlands
| | - Arjan Sammani
- Department of Cardiology, University Medical Center Utrecht, Internal ref E03.511, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Ming Wai Yeung
- Department of Cardiology, University Medical Center Groningen, Hanzeplein 1. 9713 GZ Groningen, The Netherlands
| | - Stefan van Duijvenboden
- Institute of Cardiovascular Science, University College London, 62 Huntley St, London Wc1E 6Dd, UK
| | - Pier D Lambiase
- Institute of Cardiovascular Science, University College London, 62 Huntley St, London Wc1E 6Dd, UK
| | - Rutger J Hassink
- Department of Cardiology, University Medical Center Utrecht, Internal ref E03.511, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Pim van der Harst
- Department of Cardiology, University Medical Center Utrecht, Internal ref E03.511, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Pieter A Doevendans
- Department of Cardiology, University Medical Center Utrecht, Internal ref E03.511, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands,Netherlands Heart Institute, Moreelsepark 1, 3511 EP Utrecht, The Netherlands,Central Military Hospital, Lundlaan 1, 3584 Utrecht, The Netherlands
| | - Deepak K Gupta
- Informatics Institute, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands
| | - René van Es
- Corresponding author. Tel: +31 88 757 3453, Fax: +31 88 757 3453,
| |
Collapse
|
7
|
Bio-Signals in Medical Applications and Challenges Using Artificial Intelligence. JOURNAL OF SENSOR AND ACTUATOR NETWORKS 2022. [DOI: 10.3390/jsan11010017] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Artificial Intelligence (AI) has broadly connected the medical field at various levels of diagnosis based on the congruous data generated. Different types of bio-signal can be used to monitor a patient’s condition and in decision making. Medical equipment uses signals to communicate information to care staff. AI algorithms and approaches will help to predict health problems and check the health status of organs, while AI prediction, classification, and regression algorithms are helping the medical industry to protect from health hazards. The early prediction and detection of health conditions will guide people to stay healthy. This paper represents the scope of bio-signals using AI in the medical area. It will illustrate possible case studies relevant to bio-signals generated through IoT sensors. The bio-signals that retrospectively occur are discussed, and the new challenges of medical diagnosis using bio-signals are identified.
Collapse
|