1
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Li G, Liu M, Lu J, Ma J. Edge and dense attention U-net for atrial scar segmentation in LGE-MRI. Biomed Phys Eng Express 2024; 10:055015. [PMID: 38986448 DOI: 10.1088/2057-1976/ad6161] [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: 03/18/2024] [Accepted: 07/10/2024] [Indexed: 07/12/2024]
Abstract
The segmentation of atrial scars in LGE-MRI images has huge potential value for clinical diagnosis and subsequent treatment. In clinical practice, atrial scars are usually manually calibrated by experienced experts, which is time-consuming and prone to errors. However, automatic segmentation also faces difficulties due to myocardial scars' small size and variable shape. The present study introduces a dual branch network, incorporating edge attention, and deep supervision strategy. Edge attention is introduced to fully utilize the spatial relationship between the scar and the atrium. Besides, dense attention is embedded in bottom layer to solve feature disappearance. At the same time, deep supervision accelerates the convergence of the model and improves segmentation accuracy. The experiments were conducted on the 2022 atrial and scar segmentation challenge dataset. The results demonstrate that the proposed method has achieved superior performance.
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Affiliation(s)
- Gaoyuan Li
- Department of Computer Science and Technology, Heilongjiang University, Harbin, People's Republic of China
| | - Mingxin Liu
- Department of Computer Science and Technology, Heilongjiang University, Harbin, People's Republic of China
| | - Jun Lu
- Department of Computer Science and Technology, Heilongjiang University, Harbin, People's Republic of China
| | - Jiquan Ma
- Department of Computer Science and Technology, Heilongjiang University, Harbin, People's Republic of China
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2
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Cortesi M, Giordano E. Driving cell response through deep learning, a study in simulated 3D cell cultures. Heliyon 2024; 10:e29395. [PMID: 38699000 PMCID: PMC11063986 DOI: 10.1016/j.heliyon.2024.e29395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 03/27/2024] [Accepted: 04/08/2024] [Indexed: 05/05/2024] Open
Abstract
Computational simulations are becoming increasingly relevant in biomedical research, providing strategies to reproduce experimental results, improve the resolution of in-vitro experiments, and predict the system's behavior in untested conditions. Their use to determine the features associated with an extensive response to treatment and optimize treatment schedules has, however received little attention. To bridge this gap, we propose a deep learning framework capable of reliably classifying simulated time series data and identifying class-defining features. This information will be shown to be useful for the determination of which changes in treatment schedule elicit a more extensive cellular response. This analysis pipeline will be initially tested on a synthetic dataset created ad-hoc to identify its accuracy in identifying the most relevant portion of the signals. Successively this method will be applied to simulations describing the behaviors of populations of cancer cells treated with either one or two drugs in different concentrations. The proposed method will be shown to be effective in identifying which changes in the treatment protocol lead to a more extensive response to treatment. While lacking direct experimental validation, this result holds great potential for the integration of in-silico and in-vitro analyses and the effective optimization of experimental conditions in complex experimental setups.
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Affiliation(s)
- Marilisa Cortesi
- Department of Electrical, Electronic and Information Engineering ”G.Marconi” (DEI), Alma Mater Studiorum – University of Bologna, via dell'Università 50, Cesena, 47521, FC, Italy
- Gynaecological Cancer Research Group, School of Clinical Medicine, University of New South Wales, High Street, Kensington, 2033, NSW, Australia
| | - Emanuele Giordano
- Department of Electrical, Electronic and Information Engineering ”G.Marconi” (DEI), Alma Mater Studiorum – University of Bologna, via dell'Università 50, Cesena, 47521, FC, Italy
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3
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Zhu J, Bai J, Zhou Z, Liang Y, Chen Z, Chen X, Zhang X. RAS Dataset: A 3D Cardiac LGE-MRI Dataset for Segmentation of Right Atrial Cavity. Sci Data 2024; 11:401. [PMID: 38643183 PMCID: PMC11032400 DOI: 10.1038/s41597-024-03253-9] [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: 10/30/2023] [Accepted: 04/11/2024] [Indexed: 04/22/2024] Open
Abstract
The current challenge in effectively treating atrial fibrillation (AF) stems from a limited understanding of the intricate structure of the human atria. The objective and quantitative interpretation of the right atrium (RA) in late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) scans relies heavily on its precise segmentation. Leveraging the potential of artificial intelligence (AI) for RA segmentation presents a promising solution. However, the successful implementation of AI in this context necessitates access to a substantial volume of annotated LGE-MRI images for model training. In this paper, we present a comprehensive 3D cardiac dataset comprising 50 high-resolution LGE-MRI scans, each meticulously annotated at the pixel level. The annotation process underwent rigorous standardization through crowdsourcing among a panel of medical experts, ensuring the accuracy and consistency of the annotations. Our dataset represents a significant contribution to the field, providing a valuable resource for advancing RA segmentation methods.
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Affiliation(s)
- Jinwen Zhu
- Department of Electronic Engineering, College of Information Science and Technology, Jinan University, Guangzhou, China
| | - Jieyun Bai
- Department of Electronic Engineering, College of Information Science and Technology, Jinan University, Guangzhou, China.
- Auckland Bioengineering Institute, the University of Auckland, Auckland, New Zealand.
| | - Zihao Zhou
- Department of Electronic Engineering, College of Information Science and Technology, Jinan University, Guangzhou, China
| | - Yaqi Liang
- Department of Electronic Engineering, College of Information Science and Technology, Jinan University, Guangzhou, China
| | - Zhiting Chen
- Department of Electronic Engineering, College of Information Science and Technology, Jinan University, Guangzhou, China
| | - Xiaoming Chen
- Department of Cardiology, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Xiaoshen Zhang
- Department of Cardiology, The First Affiliated Hospital of Jinan University, Guangzhou, China
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4
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Lyu Y, Bennamoun M, Sharif N, Lip GYH, Dwivedi G. Artificial Intelligence in the Image-Guided Care of Atrial Fibrillation. Life (Basel) 2023; 13:1870. [PMID: 37763273 PMCID: PMC10532509 DOI: 10.3390/life13091870] [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: 08/03/2023] [Revised: 08/19/2023] [Accepted: 08/30/2023] [Indexed: 09/29/2023] Open
Abstract
Atrial fibrillation arises mainly due to abnormalities in the cardiac conduction system and is associated with anatomical remodeling of the atria and the pulmonary veins. Cardiovascular imaging techniques, such as echocardiography, computed tomography, and magnetic resonance imaging, are crucial in the management of atrial fibrillation, as they not only provide anatomical context to evaluate structural alterations but also help in determining treatment strategies. However, interpreting these images requires significant human expertise. The potential of artificial intelligence in analyzing these images has been repeatedly suggested due to its ability to automate the process with precision comparable to human experts. This review summarizes the benefits of artificial intelligence in enhancing the clinical care of patients with atrial fibrillation through cardiovascular image analysis. It provides a detailed overview of the two most critical steps in image-guided AF management, namely, segmentation and classification. For segmentation, the state-of-the-art artificial intelligence methodologies and the factors influencing the segmentation performance are discussed. For classification, the applications of artificial intelligence in the diagnosis and prognosis of atrial fibrillation are provided. Finally, this review also scrutinizes the current challenges hindering the clinical applicability of these methods, with the aim of guiding future research toward more effective integration into clinical practice.
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Affiliation(s)
- Yiheng Lyu
- Department of Computer Science and Software Engineering, School of Physics, Mathematics and Computing, The University of Western Australia, Perth, WA 6009, Australia; (Y.L.); (M.B.)
- Harry Perkins Institute of Medical Research, The University of Western Australia, Perth, WA 6009, Australia
| | - Mohammed Bennamoun
- Department of Computer Science and Software Engineering, School of Physics, Mathematics and Computing, The University of Western Australia, Perth, WA 6009, Australia; (Y.L.); (M.B.)
| | - Naeha Sharif
- Department of Computer Science and Software Engineering, School of Physics, Mathematics and Computing, The University of Western Australia, Perth, WA 6009, Australia; (Y.L.); (M.B.)
| | - Gregory Y. H. Lip
- Liverpool Centre for Cardiovascular Science, University of Liverpool, Liverpool L69 3BX, UK
- Liverpool John Moores University, Liverpool L3 5UX, UK
- Liverpool Heart and Chest Hospital, Liverpool L14 3PE, UK
- Danish Center for Health Services Research, Department of Clinical Medicine, Aalborg University, 9220 Aalborg, Denmark
| | - Girish Dwivedi
- Harry Perkins Institute of Medical Research, The University of Western Australia, Perth, WA 6009, Australia
- Department of Cardiology, Fiona Stanley Hospital, Perth, WA 6150, Australia
- Medical School, The University of Western Australia, Perth, WA 6009, Australia
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5
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Kazi A, Betko S, Salvi A, Menon PG. Automatic Segmentation of the Left Atrium from Computed Tomography Angiography Images. Ann Biomed Eng 2023:10.1007/s10439-023-03170-9. [PMID: 36890303 DOI: 10.1007/s10439-023-03170-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 02/15/2023] [Indexed: 03/10/2023]
Abstract
The left atrial appendage (LAA) causes 91% of thrombi in atrial fibrillation patients, a potential harbinger of stroke. Leveraging computed tomography angiography (CTA) images, radiologists interpret the left atrium (LA) and LAA geometries to stratify stroke risk. Nevertheless, accurate LA segmentation remains a time-consuming task with high inter-observer variability. Binary masks of the LA and their corresponding CTA images were used to train and test a 3D U-Net to automate LA segmentation. One model was trained using the entire unified-image-volume while a second model was trained on regional patch-volumes which were run for inference and then assimilated back into the full volume. The unified-image-volume U-Net achieved median DSCs of 0.92 and 0.88 for the train and test sets, respectively; the patch-volume U-Net achieved median DSCs of 0.90 and 0.89 for the train and test sets, respectively. This indicates that the unified-image-volume and patch-volume U-Net models captured up to 88 and 89% of the LA/LAA boundary's regional complexity, respectively. Additionally, the results indicate that the LA/LAA were fully captured in most of the predicted segmentations. By automating the segmentation process, our deep learning model can expedite LA/LAA shape, informing stratification of stroke risk.
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Affiliation(s)
- Amaan Kazi
- University of Pittsburgh, 302 Benedum Hall, 3700 O'Hara Street, Pittsburgh, PA, 15213, USA.,ImageRx, Pittsburgh, PA, USA
| | - Sage Betko
- Statistics & Machine Learning, Carnegie Mellon University, Pittsburgh, PA, USA.,ImageRx, Pittsburgh, PA, USA
| | - Anish Salvi
- University of Pittsburgh, 302 Benedum Hall, 3700 O'Hara Street, Pittsburgh, PA, 15213, USA.,ImageRx, Pittsburgh, PA, USA
| | - Prahlad G Menon
- University of Pittsburgh, 302 Benedum Hall, 3700 O'Hara Street, Pittsburgh, PA, 15213, USA. .,ImageRx, Pittsburgh, PA, USA.
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6
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Bhan A, Mangipudi PS, Goyal A. Left atrium MRI image segmentation using efficient Xception stochastic depth based generative adversarial network. INTERNATIONAL JOURNAL OF HEALTHCARE MANAGEMENT 2023. [DOI: 10.1080/20479700.2023.2166206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Affiliation(s)
- Anupama Bhan
- Department of Electronics and Communication Engineering, Amity School of Engineering and Technology, Amity University, Noida, India
| | - Partha Sarathi Mangipudi
- Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University, Noida, India
| | - Ayush Goyal
- Department of Electrical Engineering and Computer Science, Frank H. Dotterweich College of Engineering, Texas A&M University – Kingsville, Kingsville, TX, USA
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Grigoriadis GI, Zaridis D, Pezoulas VC, Nikopoulos S, Sakellarios AI, Tachos NS, Naka KK, Michalis LK, Fotiadis DI. Segmentation of left atrium using CT images and a deep learning model. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:3839-3842. [PMID: 36086640 DOI: 10.1109/embc48229.2022.9871623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The left atrium (LA) is one of the cardiac cavities with the most complex anatomical structures. Its role in the clinical diagnosis and patient's management is critical, as it is responsible for the atrial fibrillation, a condition that promotes the thrombogenesis inside the left atrial appendage. The development of an automated approach for LA segmentation is a demanding task mainly due to its anatomical complexity and the variation of its shape among patients. In this study, we focus to develop an unbiased pipeline capable to segment the atrial cavity from CT images. For evaluation purposes state-of-the-art metrics were used to assess the segmentation results. Particularly, the results indicated the mean values of the dice score 80%, the hausdorff distance 11.78mm, the average surface distance 2.24mm and the rand error index 0.2.
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Li L, Zimmer VA, Schnabel JA, Zhuang X. Medical image analysis on left atrial LGE MRI for atrial fibrillation studies: A review. Med Image Anal 2022; 77:102360. [PMID: 35124370 PMCID: PMC7614005 DOI: 10.1016/j.media.2022.102360] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 11/04/2021] [Accepted: 01/10/2022] [Indexed: 02/08/2023]
Abstract
Late gadolinium enhancement magnetic resonance imaging (LGE MRI) is commonly used to visualize and quantify left atrial (LA) scars. The position and extent of LA scars provide important information on the pathophysiology and progression of atrial fibrillation (AF). Hence, LA LGE MRI computing and analysis are essential for computer-assisted diagnosis and treatment stratification of AF patients. Since manual delineations can be time-consuming and subject to intra- and inter-expert variability, automating this computing is highly desired, which nevertheless is still challenging and under-researched. This paper aims to provide a systematic review on computing methods for LA cavity, wall, scar, and ablation gap segmentation and quantification from LGE MRI, and the related literature for AF studies. Specifically, we first summarize AF-related imaging techniques, particularly LGE MRI. Then, we review the methodologies of the four computing tasks in detail and summarize the validation strategies applied in each task as well as state-of-the-art results on public datasets. Finally, the possible future developments are outlined, with a brief survey on the potential clinical applications of the aforementioned methods. The review indicates that the research into this topic is still in the early stages. Although several methods have been proposed, especially for the LA cavity segmentation, there is still a large scope for further algorithmic developments due to performance issues related to the high variability of enhancement appearance and differences in image acquisition.
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Affiliation(s)
- Lei Li
- School of Data Science, Fudan University, Shanghai, China; School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China; School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Veronika A Zimmer
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK; Department of Informatics, Technical University of Munich, Germany
| | - Julia A Schnabel
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK; Department of Informatics, Technical University of Munich, Germany; Helmholtz Center Munich, Germany
| | - Xiahai Zhuang
- School of Data Science, Fudan University, Shanghai, China.
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9
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Liu X, Han C, Lin Z, Sun Z, Zhang Y, Wang X, Zhang X, Wang X. Semi-automatic quantitative analysis of the pelvic bony structures on apparent diffusion coefficient maps based on deep learning: establishment of reference ranges. Quant Imaging Med Surg 2022; 12:576-591. [PMID: 34993103 DOI: 10.21037/qims-21-123] [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: 01/29/2021] [Accepted: 07/30/2021] [Indexed: 11/06/2022]
Abstract
BACKGROUND Apparent diffusion coefficient (ADC) maps provide quantitative information on both normal and abnormal tissues. However, it is difficult to distinguish between these tissues unless consistent and precise ADC values can be obtained from normal tissues. For this study we developed a deep learning-based convolutional neural network (CNN) for pelvic bony structure segmentation and established the reference ranges of ADC parameters for normal pelvic bony structures. METHODS We retrospectively enrolled 767 prostate cancer (PCa) patients for quantitative ADC analyses of normal pelvic bony structures. A subset of 288 patients who did not receive treatment for PCa (S1) were used to develop a CNN model for the segmentation of 8 pelvic bony structures (lumbar vertebra, sacrococcyx, ilium, acetabulum, femoral head, femoral neck, ischium, and pubis). The proposed CNN was used for the automated segmentation of these pelvic bony structures from a subset of 405 patients who did not receive treatment (S2) and 74 patients who received treatment [radiotherapy (S3) or endocrine therapy (S4)]. The 95% confidence interval (CI) was used to establish reference ranges for the ADC values from the normal pelvic bony structures of S1 and S2. RESULTS The Dice scores (Sørensen-Dice coefficient) for the CNN segmentation of the 8 pelvic bones on the ADC maps ranged from 0.90±0.02 (ilium) to 0.95±0.03 (femoral head) in the S1 testing set. In the S2 data set, the Dice scores showed no significant difference among the different scanners (P>0.05), and no significant differences were found among the S2, S3, and S4 data sets. The correlation analysis revealed that the b value and field strength were significantly correlated with ADC values (all P<0.001), while age and treatment were not significant variables (all P>0.05). The ADC reference ranges (95% CI) were as follows: lumbar vertebra, 1.11 (0.90-1.54); sacrococcyx, 0.82 (0.61-1.15); ilium, 0.57 (0.45-0.62); acetabulum, 0.59 (0.40-0.69); femoral head, 0.46 (0.25-0.58); femoral neck, 0.43 (0.25-0.48); ischium, 0.45 (0.26-0.55); and pubis, 0.57 (0.45-0.65). CONCLUSIONS This study preliminarily established reference ranges for the ADC values of normal pelvic bony structures. The image acquisition parameters had an influence on the ADC values.
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Affiliation(s)
- Xiang Liu
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Chao Han
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Ziying Lin
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Zhaonan Sun
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Yaofeng Zhang
- Beijing Smart Tree Medical Technology Co. Ltd., Beijing, China
| | - Xiangpeng Wang
- Beijing Smart Tree Medical Technology Co. Ltd., Beijing, China
| | - Xiaodong Zhang
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Xiaoying Wang
- Department of Radiology, Peking University First Hospital, Beijing, China
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10
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Wu Y, Tang Z, Li B, Firmin D, Yang G. Recent Advances in Fibrosis and Scar Segmentation From Cardiac MRI: A State-of-the-Art Review and Future Perspectives. Front Physiol 2021; 12:709230. [PMID: 34413789 PMCID: PMC8369509 DOI: 10.3389/fphys.2021.709230] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Accepted: 06/28/2021] [Indexed: 12/03/2022] Open
Abstract
Segmentation of cardiac fibrosis and scars is essential for clinical diagnosis and can provide invaluable guidance for the treatment of cardiac diseases. Late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) has been successful in guiding the clinical diagnosis and treatment reliably. For LGE CMR, many methods have demonstrated success in accurately segmenting scarring regions. Co-registration with other non-contrast-agent (non-CA) modalities [e.g., balanced steady-state free precession (bSSFP) cine magnetic resonance imaging (MRI)] can further enhance the efficacy of automated segmentation of cardiac anatomies. Many conventional methods have been proposed to provide automated or semi-automated segmentation of scars. With the development of deep learning in recent years, we can also see more advanced methods that are more efficient in providing more accurate segmentations. This paper conducts a state-of-the-art review of conventional and current state-of-the-art approaches utilizing different modalities for accurate cardiac fibrosis and scar segmentation.
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Affiliation(s)
- Yinzhe Wu
- National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom.,Department of Bioengineering, Faculty of Engineering, Imperial College London, London, United Kingdom
| | - Zeyu Tang
- National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom.,Department of Bioengineering, Faculty of Engineering, Imperial College London, London, United Kingdom
| | - Binghuan Li
- Department of Bioengineering, Faculty of Engineering, Imperial College London, London, United Kingdom
| | - David Firmin
- National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom.,Cardiovascular Biomedical Research Unit, Royal Brompton Hospital, London, United Kingdom
| | - Guang Yang
- National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom.,Cardiovascular Biomedical Research Unit, Royal Brompton Hospital, London, United Kingdom
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Jeon B, Jung S, Shim H, Chang HJ. Bayesian Estimation of Geometric Morphometric Landmarks for Simultaneous Localization of Multiple Anatomies in Cardiac CT Images. ENTROPY (BASEL, SWITZERLAND) 2021; 23:E64. [PMID: 33401695 PMCID: PMC7824462 DOI: 10.3390/e23010064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Revised: 12/18/2020] [Accepted: 12/27/2020] [Indexed: 11/16/2022]
Abstract
We propose a robust method to simultaneously localize multiple objects in cardiac computed tomography angiography (CTA) images. The relative prior distributions of the multiple objects in the three-dimensional (3D) space can be obtained through integrating the geometric morphological relationship of each target object to some reference objects. In cardiac CTA images, the cross-sections of ascending and descending aorta can play the role of the reference objects. We employed the maximum a posteriori (MAP) estimator that utilizes anatomic prior knowledge to address this problem of localizing multiple objects. We propose a new feature for each pixel using the relative distances, which can define any objects that have unclear boundaries. Our experimental results targeting four pulmonary veins (PVs) and the left atrial appendage (LAA) in cardiac CTA images demonstrate the robustness of the proposed method. The method could also be extended to localize other multiple objects in different applications.
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Affiliation(s)
- Byunghwan Jeon
- School of Computer Science, Kyungil University, Gyeongsan 38428, Korea;
| | - Sunghee Jung
- CONNECT-AI R&D Center, Yonsei University College of Medicine, Seoul 03722,Korea; (S.J.); (H.S.)
| | - Hackjoon Shim
- CONNECT-AI R&D Center, Yonsei University College of Medicine, Seoul 03722,Korea; (S.J.); (H.S.)
| | - Hyuk-Jae Chang
- CONNECT-AI R&D Center, Yonsei University College of Medicine, Seoul 03722,Korea; (S.J.); (H.S.)
- Division of Cardiology Department of Internal Medicine, Yonsei University College of Medicine, Seoul 03722, Korea
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