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Zheng YL, Cai PY, Li J, Huang DH, Wang WD, Li MM, Du JR, Wang YG, Cai YL, Zhang RC, Wu CC, Lin S, Lin HL. A novel radiomics-based technique for identifying vulnerable coronary plaques: a follow-up study. Coron Artery Dis 2024:00019501-990000000-00232. [PMID: 38767051 DOI: 10.1097/mca.0000000000001389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
BACKGROUND Previous reports have suggested that coronary computed tomography angiography (CCTA)-based radiomics analysis is a potentially helpful tool for assessing vulnerable plaques. We aimed to investigate whether coronary radiomic analysis of CCTA images could identify vulnerable plaques in patients with stable angina pectoris. METHODS This retrospective study included patients initially diagnosed with stable angina pectoris. Patients were randomly divided into either the training or test dataset at an 8 : 2 ratio. Radiomics features were extracted from CCTA images. Radiomics models for predicting vulnerable plaques were developed using the support vector machine (SVM) algorithm. The model performance was assessed using the area under the curve (AUC); the accuracy, sensitivity, and specificity were calculated to compare the diagnostic performance using the two cohorts. RESULTS A total of 158 patients were included in the analysis. The SVM radiomics model performed well in predicting vulnerable plaques, with AUC values of 0.977 and 0.875 for the training and test cohorts, respectively. With optimal cutoff values, the radiomics model showed accuracies of 0.91 and 0.882 in the training and test cohorts, respectively. CONCLUSION Although further larger population studies are necessary, this novel CCTA radiomics model may identify vulnerable plaques in patients with stable angina pectoris.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | - Shu Lin
- Centre of Neurological and Metabolic Research, the Second Affiliated Hospital of Fujian Medical University, Quanzhou, China and
- Diabetes and Metabolism Division, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
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2
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Klüner LV, Chan K, Antoniades C. Using artificial intelligence to study atherosclerosis from computed tomography imaging: A state-of-the-art review of the current literature. Atherosclerosis 2024:117580. [PMID: 38852022 DOI: 10.1016/j.atherosclerosis.2024.117580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 05/03/2024] [Accepted: 05/14/2024] [Indexed: 06/10/2024]
Abstract
With the enormous progress in the field of cardiovascular imaging in recent years, computed tomography (CT) has become readily available to phenotype atherosclerotic coronary artery disease. New analytical methods using artificial intelligence (AI) enable the analysis of complex phenotypic information of atherosclerotic plaques. In particular, deep learning-based approaches using convolutional neural networks (CNNs) facilitate tasks such as lesion detection, segmentation, and classification. New radiotranscriptomic techniques even capture underlying bio-histochemical processes through higher-order structural analysis of voxels on CT images. In the near future, the international large-scale Oxford Risk Factors And Non-invasive Imaging (ORFAN) study will provide a powerful platform for testing and validating prognostic AI-based models. The goal is the transition of these new approaches from research settings into a clinical workflow. In this review, we present an overview of existing AI-based techniques with focus on imaging biomarkers to determine the degree of coronary inflammation, coronary plaques, and the associated risk. Further, current limitations using AI-based approaches as well as the priorities to address these challenges will be discussed. This will pave the way for an AI-enabled risk assessment tool to detect vulnerable atherosclerotic plaques and to guide treatment strategies for patients.
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Affiliation(s)
- Laura Valentina Klüner
- Acute Multidisciplinary Imaging and Interventional Centre, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, Oxford NIHR Biomedical Research Centre, University of Oxford, United Kingdom
| | - Kenneth Chan
- Acute Multidisciplinary Imaging and Interventional Centre, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, Oxford NIHR Biomedical Research Centre, University of Oxford, United Kingdom
| | - Charalambos Antoniades
- Acute Multidisciplinary Imaging and Interventional Centre, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, Oxford NIHR Biomedical Research Centre, University of Oxford, United Kingdom.
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3
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Fandaros M, Kwok C, Wolf Z, Labropoulos N, Yin W. Patient-Specific Numerical Simulations of Coronary Artery Hemodynamics and Biomechanics: A Pathway to Clinical Use. Cardiovasc Eng Technol 2024:10.1007/s13239-024-00731-4. [PMID: 38710896 DOI: 10.1007/s13239-024-00731-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 04/29/2024] [Indexed: 05/08/2024]
Abstract
PURPOSE Numerical models that simulate the behaviors of the coronary arteries have been greatly improved by the addition of fluid-structure interaction (FSI) methods. Although computationally demanding, FSI models account for the movement of the arterial wall and more adequately describe the biomechanical conditions at and within the arterial wall. This offers greater physiological relevance over Computational Fluid Dynamics (CFD) models, which assume the walls do not move or deform. Numerical simulations of patient-specific cases have been greatly bolstered by the use of imaging modalities such as Computed Tomography Angiography (CTA), Magnetic Resonance Imaging (MRI), Optical Coherence Tomography (OCT), and Intravascular Ultrasound (IVUS) to reconstruct accurate 2D and 3D representations of artery geometries. The goal of this study was to conduct a comprehensive review on CFD and FSI models on coronary arteries, and evaluate their translational potential. METHODS This paper reviewed recent work on patient-specific numerical simulations of coronary arteries that describe the biomechanical conditions associated with atherosclerosis using CFD and FSI models. Imaging modality for geometry collection and clinical applications were also discussed. RESULTS Numerical models using CFD and FSI approaches are commonly used to study biomechanics within the vasculature. At high temporal and spatial resolution (compared to most cardiac imaging modalities), these numerical models can generate large amount of biomechanics data. CONCLUSIONS Physiologically relevant FSI models can more accurately describe atherosclerosis pathogenesis, and help to translate biomechanical assessment to clinical evaluation.
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Affiliation(s)
- Marina Fandaros
- Department of Biomedical Engineering, Stony Brook University, Bioengineering Building, Room 109, 11794, Stony Brook, NY, USA
| | - Chloe Kwok
- Department of Biomedical Engineering, Stony Brook University, Bioengineering Building, Room 109, 11794, Stony Brook, NY, USA
| | - Zachary Wolf
- Department of Biomedical Engineering, Stony Brook University, Bioengineering Building, Room 109, 11794, Stony Brook, NY, USA
| | - Nicos Labropoulos
- Department of Surgery, Stony Brook Medicine, 11794, Stony Brook, NY, USA
| | - Wei Yin
- Department of Biomedical Engineering, Stony Brook University, Bioengineering Building, Room 109, 11794, Stony Brook, NY, USA.
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Cui YM, Wang HL, Cao R, Bai H, Sun D, Feng JX, Lu XF. The Segmentation of Multiple Types of Uterine Lesions in Magnetic Resonance Images Using a Sequential Deep Learning Method with Image-Level Annotations. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:374-385. [PMID: 38343259 DOI: 10.1007/s10278-023-00931-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 10/25/2023] [Accepted: 10/27/2023] [Indexed: 03/02/2024]
Abstract
Fully supervised medical image segmentation methods use pixel-level labels to achieve good results, but obtaining such large-scale, high-quality labels is cumbersome and time consuming. This study aimed to develop a weakly supervised model that only used image-level labels to achieve automatic segmentation of four types of uterine lesions and three types of normal tissues on magnetic resonance images. The MRI data of the patients were retrospectively collected from the database of our institution, and the T2-weighted sequence images were selected and only image-level annotations were made. The proposed two-stage model can be divided into four sequential parts: the pixel correlation module, the class re-activation map module, the inter-pixel relation network module, and the Deeplab v3 + module. The dice similarity coefficient (DSC), the Hausdorff distance (HD), and the average symmetric surface distance (ASSD) were employed to evaluate the performance of the model. The original dataset consisted of 85,730 images from 316 patients with four different types of lesions (i.e., endometrial cancer, uterine leiomyoma, endometrial polyps, and atypical hyperplasia of endometrium). A total number of 196, 57, and 63 patients were randomly selected for model training, validation, and testing. After being trained from scratch, the proposed model showed a good segmentation performance with an average DSC of 83.5%, HD of 29.3 mm, and ASSD of 8.83 mm, respectively. As far as the weakly supervised methods using only image-level labels are concerned, the performance of the proposed model is equivalent to the state-of-the-art weakly supervised methods.
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Affiliation(s)
- Yu-Meng Cui
- Department of Gynecology, Dalian Women and Children's Medical Group, Dalian, 116033, China
| | - Hua-Li Wang
- Department of Gynecology, Dalian Women and Children's Medical Group, Dalian, 116033, China
| | - Rui Cao
- Department of Gynecology, Dalian Women and Children's Medical Group, Dalian, 116033, China
| | - Hong Bai
- Department of Gynecology, Dalian Women and Children's Medical Group, Dalian, 116033, China
| | - Dan Sun
- Department of Gynecology, Dalian Women and Children's Medical Group, Dalian, 116033, China
| | - Jiu-Xiang Feng
- Department of Gynecology, Dalian Women and Children's Medical Group, Dalian, 116033, China
| | - Xue-Feng Lu
- School of Food Science and Engineering, Dalian Ocean University, Dalian, 116023, China.
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5
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Sun Z, Silberstein J, Vaccarezza M. Cardiovascular Computed Tomography in the Diagnosis of Cardiovascular Disease: Beyond Lumen Assessment. J Cardiovasc Dev Dis 2024; 11:22. [PMID: 38248892 PMCID: PMC10816599 DOI: 10.3390/jcdd11010022] [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: 11/22/2023] [Revised: 01/10/2024] [Accepted: 01/11/2024] [Indexed: 01/23/2024] Open
Abstract
Cardiovascular CT is being widely used in the diagnosis of cardiovascular disease due to the rapid technological advancements in CT scanning techniques. These advancements include the development of multi-slice CT, from early generation to the latest models, which has the capability of acquiring images with high spatial and temporal resolution. The recent emergence of photon-counting CT has further enhanced CT performance in clinical applications, providing improved spatial and contrast resolution. CT-derived fractional flow reserve is superior to standard CT-based anatomical assessment for the detection of lesion-specific myocardial ischemia. CT-derived 3D-printed patient-specific models are also superior to standard CT, offering advantages in terms of educational value, surgical planning, and the simulation of cardiovascular disease treatment, as well as enhancing doctor-patient communication. Three-dimensional visualization tools including virtual reality, augmented reality, and mixed reality are further advancing the clinical value of cardiovascular CT in cardiovascular disease. With the widespread use of artificial intelligence, machine learning, and deep learning in cardiovascular disease, the diagnostic performance of cardiovascular CT has significantly improved, with promising results being presented in terms of both disease diagnosis and prediction. This review article provides an overview of the applications of cardiovascular CT, covering its performance from the perspective of its diagnostic value based on traditional lumen assessment to the identification of vulnerable lesions for the prediction of disease outcomes with the use of these advanced technologies. The limitations and future prospects of these technologies are also discussed.
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Affiliation(s)
- Zhonghua Sun
- Curtin Medical School, Curtin University, Perth, WA 6102, Australia; (J.S.); (M.V.)
- Curtin Health Innovation Research Institute (CHIRI), Curtin University, Perth, WA 6102, Australia
| | - Jenna Silberstein
- Curtin Medical School, Curtin University, Perth, WA 6102, Australia; (J.S.); (M.V.)
| | - Mauro Vaccarezza
- Curtin Medical School, Curtin University, Perth, WA 6102, Australia; (J.S.); (M.V.)
- Curtin Health Innovation Research Institute (CHIRI), Curtin University, Perth, WA 6102, Australia
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Weichsel L, Giesen A, André F, Renker M, Baumann S, Breitbart P, Beer M, Maurovitch-Horvat P, Szilveszter B, Vattay B, Buss SJ, Marwan M, Giannopoulos AA, Kelle S, Frey N, Korosoglou G. Comparison of Two Contemporary Quantitative Atherosclerotic Plaque Assessment Tools for Coronary Computed Tomography Angiography: Single-Center Analysis and Multi-Center Patient Cohort Validation. Diagnostics (Basel) 2024; 14:154. [PMID: 38248031 PMCID: PMC10814854 DOI: 10.3390/diagnostics14020154] [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: 12/10/2023] [Revised: 01/01/2024] [Accepted: 01/02/2024] [Indexed: 01/23/2024] Open
Abstract
BACKGROUND Coronary computed tomography angiography (CCTA) provides non-invasive quantitative assessments of plaque burden and composition. The quantitative assessment of plaque components requires the use of analysis software that provides reproducible semi-automated plaque detection and analysis. However, commercially available plaque analysis software can vary widely in the degree of automation, resulting in differences in terms of reproducibility and time spent. AIM To compare the reproducibility and time spent of two CCTA analysis software tools using different algorithms for the quantitative assessment of coronary plaque volumes and composition in two independent patient cohorts. METHODS The study population included 100 patients from two different cohorts: 50 patients from a single-center (Siemens Healthineers, SOMATOM Force (DSCT)) and another 50 patients from a multi-center study (5 different > 64 slice CT scanner types). Quantitative measurements of total calcified and non-calcified plaque volume of the right coronary artery (RCA), left anterior descending (LAD), and left circumflex coronary artery (LCX) were performed on a total of 300 coronaries by two independent readers, using two different CCTA analysis software tools (Tool #1: Siemens Healthineers, syngo.via Frontier CT Coronary Plaque Analysis and Tool #2: Siemens Healthineers, successor CT Coronary Plaque Analysis prototype). In addition, the total time spent for the analysis was recorded with both programs. RESULTS The patients in cohorts 1 and 2 were 62.8 ± 10.2 and 70.9 ± 11.7 years old, respectively, 10 (20.0%) and 35 (70.0%) were female and 34 (68.0%) and 20 (40.0%), respectively, had hyperlipidemia. In Cohort #1, the inter- and intra-observer variabilities for the assessment of plaque volumes per patient for Tool #1 versus Tool #2 were 22.8%, 22.0%, and 26.0% versus 2.3%, 3.9%, and 2.5% and 19.7%, 21.4%, and 22.1% versus 0.2%, 0.1%, and 0.3%, respectively, for total, noncalcified, and calcified lesions (p < 0.001 for all between Tools #1 and 2 both for inter- and intra-observer). The inter- and intra-observer variabilities using Tool #2 remained low at 2.9%, 2.7%, and 3.0% and 3.8%, 3.7%, and 4.0%, respectively, for total, non-calcified, and calcified lesions in Cohort #2. For each dataset, the median processing time was higher for Tool #1 versus Tool #2 (459.5 s IQR = 348.0-627.0 versus 208.5 s; IQR = 198.0-216.0) (p < 0.001). CONCLUSION The plaque analysis Tool #2 (CT-guided PCI) encompassing a higher degree of automated support required less manual editing, was more time-efficient, and showed a higher intra- and inter-observer reproducibility for the quantitative assessment of plaque volumes both in a representative single-center and in a multi-center validation cohort.
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Affiliation(s)
- Loris Weichsel
- GRN Hospital Weinheim, Cardiology, Vascular Medicine & Pneumology, 69469 Weinheim, Germany; (L.W.); (A.G.)
- Cardiac Imaging Center Weinheim, Hector Foundations, 69469 Weinheim, Germany
| | - Alexander Giesen
- GRN Hospital Weinheim, Cardiology, Vascular Medicine & Pneumology, 69469 Weinheim, Germany; (L.W.); (A.G.)
- Cardiac Imaging Center Weinheim, Hector Foundations, 69469 Weinheim, Germany
| | - Florian André
- Department of Cardiology, Angiology and Pneumology, University of Heidelberg, 69120 Heidelberg, Germany; (F.A.); (N.F.)
- DZHK (German Centre for Cardiovascular Research), Partner Site Heidelberg/Mannheim, 69120 Heidelberg, Germany
| | - Matthias Renker
- Department of Cardiology, Campus Kerckhoff, Justus Liebig University Giessen, 61231 Bad Nauheim, Germany;
- DZHK (German Centre for Cardiovascular Research), Partner Site Rhein Main, 61231 Bad Nauheim, Germany
| | - Stefan Baumann
- Department of Cardiology, District Hospital Bergstraße, 64646 Heppenheim, Germany;
- First Department of Medicine-Cardiology, University Medical Center Mannheim, 68167 Mannheim, Germany
| | - Philipp Breitbart
- Department of Cardiology and Angiology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79189 Bad Krozingen, Germany;
| | - Meinrad Beer
- Department for Diagnostic and Interventional Radiology, University Hospital Ulm, 89081 Ulm, Germany;
| | - Pal Maurovitch-Horvat
- Heart and Vascular Center, Semmelweis University, 1122 Budapest, Hungary; (P.M.-H.); (B.S.); (B.V.)
| | - Bálint Szilveszter
- Heart and Vascular Center, Semmelweis University, 1122 Budapest, Hungary; (P.M.-H.); (B.S.); (B.V.)
| | - Borbála Vattay
- Heart and Vascular Center, Semmelweis University, 1122 Budapest, Hungary; (P.M.-H.); (B.S.); (B.V.)
| | | | - Mohamed Marwan
- Department of Cardiology, University of Erlangen, 91054 Erlangen, Germany;
| | - Andreas A. Giannopoulos
- Department of Nuclear Medicine, Cardiac Imaging, University Hospital Zurich, 8091 Zurich, Switzerland;
| | - Sebastian Kelle
- Deutsches Herzzentrum der Charité, Department of Cardiology, Angiology and Intensive Care Medicine, Charité-University Medicine Berlin, 10117 Berlin, Germany;
| | - Norbert Frey
- Department of Cardiology, Angiology and Pneumology, University of Heidelberg, 69120 Heidelberg, Germany; (F.A.); (N.F.)
- DZHK (German Centre for Cardiovascular Research), Partner Site Heidelberg/Mannheim, 69120 Heidelberg, Germany
| | - Grigorios Korosoglou
- GRN Hospital Weinheim, Cardiology, Vascular Medicine & Pneumology, 69469 Weinheim, Germany; (L.W.); (A.G.)
- Cardiac Imaging Center Weinheim, Hector Foundations, 69469 Weinheim, Germany
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7
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Kumari V, Kumar N, Kumar K S, Kumar A, Skandha SS, Saxena S, Khanna NN, Laird JR, Singh N, Fouda MM, Saba L, Singh R, Suri JS. Deep Learning Paradigm and Its Bias for Coronary Artery Wall Segmentation in Intravascular Ultrasound Scans: A Closer Look. J Cardiovasc Dev Dis 2023; 10:485. [PMID: 38132653 PMCID: PMC10743870 DOI: 10.3390/jcdd10120485] [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: 07/27/2023] [Revised: 10/15/2023] [Accepted: 11/07/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND AND MOTIVATION Coronary artery disease (CAD) has the highest mortality rate; therefore, its diagnosis is vital. Intravascular ultrasound (IVUS) is a high-resolution imaging solution that can image coronary arteries, but the diagnosis software via wall segmentation and quantification has been evolving. In this study, a deep learning (DL) paradigm was explored along with its bias. METHODS Using a PRISMA model, 145 best UNet-based and non-UNet-based methods for wall segmentation were selected and analyzed for their characteristics and scientific and clinical validation. This study computed the coronary wall thickness by estimating the inner and outer borders of the coronary artery IVUS cross-sectional scans. Further, the review explored the bias in the DL system for the first time when it comes to wall segmentation in IVUS scans. Three bias methods, namely (i) ranking, (ii) radial, and (iii) regional area, were applied and compared using a Venn diagram. Finally, the study presented explainable AI (XAI) paradigms in the DL framework. FINDINGS AND CONCLUSIONS UNet provides a powerful paradigm for the segmentation of coronary walls in IVUS scans due to its ability to extract automated features at different scales in encoders, reconstruct the segmented image using decoders, and embed the variants in skip connections. Most of the research was hampered by a lack of motivation for XAI and pruned AI (PAI) models. None of the UNet models met the criteria for bias-free design. For clinical assessment and settings, it is necessary to move from a paper-to-practice approach.
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Affiliation(s)
- Vandana Kumari
- School of Computer Science and Engineering, Galgotias University, Greater Noida 201310, India; (V.K.); (S.K.K.)
| | - Naresh Kumar
- Department of Applied Computational Science and Engineering, G L Bajaj Institute of Technology and Management, Greater Noida 201310, India
| | - Sampath Kumar K
- School of Computer Science and Engineering, Galgotias University, Greater Noida 201310, India; (V.K.); (S.K.K.)
| | - Ashish Kumar
- School of CSET, Bennett University, Greater Noida 201310, India;
| | - Sanagala S. Skandha
- Department of CSE, CMR College of Engineering and Technology, Hyderabad 501401, India;
| | - Sanjay Saxena
- Department of Computer Science and Engineering, IIT Bhubaneswar, Bhubaneswar 751003, India;
| | - Narendra N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110076, India;
| | - John R. Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA 94574, USA;
| | - Narpinder Singh
- Department of Food Science and Technology, Graphic Era, Deemed to be University, Dehradun 248002, India;
| | - Mostafa M. Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USA;
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09100 Cagliari, Italy;
| | - Rajesh Singh
- Department of Research and Innovation, Uttaranchal Institute of Technology, Uttaranchal University, Dehradun 248007, India;
| | - Jasjit S. Suri
- Stroke Diagnostics and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA
- Department of Computer Science & Engineering, Graphic Era, Deemed to be University, Dehradun 248002, India
- Monitoring and Diagnosis Division, AtheroPoint™, Roseville, CA 95661, USA
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Javaid A, Zghyer F, Kim C, Spaulding EM, Isakadze N, Ding J, Kargillis D, Gao Y, Rahman F, Brown DE, Saria S, Martin SS, Kramer CM, Blumenthal RS, Marvel FA. Medicine 2032: The future of cardiovascular disease prevention with machine learning and digital health technology. Am J Prev Cardiol 2022; 12:100379. [PMID: 36090536 PMCID: PMC9460561 DOI: 10.1016/j.ajpc.2022.100379] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 08/21/2022] [Accepted: 08/28/2022] [Indexed: 11/30/2022] Open
Abstract
Machine learning (ML) refers to computational algorithms that iteratively improve their ability to recognize patterns in data. The digitization of our healthcare infrastructure is generating an abundance of data from electronic health records, imaging, wearables, and sensors that can be analyzed by ML algorithms to generate personalized risk assessments and promote guideline-directed medical management. ML's strength in generating insights from complex medical data to guide clinical decisions must be balanced with the potential to adversely affect patient privacy, safety, health equity, and clinical interpretability. This review provides a primer on key advances in ML for cardiovascular disease prevention and how they may impact clinical practice.
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