251
|
Mistry P. The New Frontiers of AI in Medicine. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_56-1] [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]
|
252
|
Seetharam K, Brito D, Farjo PD, Sengupta PP. The Role of Artificial Intelligence in Cardiovascular Imaging: State of the Art Review. Front Cardiovasc Med 2020; 7:618849. [PMID: 33426010 PMCID: PMC7786371 DOI: 10.3389/fcvm.2020.618849] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2020] [Accepted: 12/08/2020] [Indexed: 12/15/2022] Open
Abstract
In this current digital landscape, artificial intelligence (AI) has established itself as a powerful tool in the commercial industry and is an evolving technology in healthcare. Cutting-edge imaging modalities outputting multi-dimensional data are becoming increasingly complex. In this era of data explosion, the field of cardiovascular imaging is undergoing a paradigm shift toward machine learning (ML) driven platforms. These diverse algorithms can seamlessly analyze information and automate a range of tasks. In this review article, we explore the role of ML in the field of cardiovascular imaging.
Collapse
Affiliation(s)
- Karthik Seetharam
- Department of Cardiology, West Virginia University Medicine Heart & Vascular Institute, Morgantown, WV, United States
| | - Daniel Brito
- Department of Cardiology, West Virginia University Medicine Heart & Vascular Institute, Morgantown, WV, United States
| | - Peter D Farjo
- Department of Cardiology, West Virginia University Medicine Heart & Vascular Institute, Morgantown, WV, United States
| | - Partho P Sengupta
- Department of Cardiology, West Virginia University Medicine Heart & Vascular Institute, Morgantown, WV, United States
| |
Collapse
|
253
|
Modarai B. Collective Consciousness on Complex Aortic Repair: Time to Focus on Data Capture. Eur J Vasc Endovasc Surg 2020; 61:238. [PMID: 33358101 DOI: 10.1016/j.ejvs.2020.11.045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 11/20/2020] [Accepted: 11/25/2020] [Indexed: 11/18/2022]
Affiliation(s)
- Bijan Modarai
- Academic Department of Vascular Surgery, Cardiovascular Division, King's College London, London, UK; BHF Centre of Research Excellence & NIHR Biomedical Research Centre at King's Health Partners, St Thomas' Hospital, London, UK.
| |
Collapse
|
254
|
Corral-Acero J, Margara F, Marciniak M, Rodero C, Loncaric F, Feng Y, Gilbert A, Fernandes JF, Bukhari HA, Wajdan A, Martinez MV, Santos MS, Shamohammdi M, Luo H, Westphal P, Leeson P, DiAchille P, Gurev V, Mayr M, Geris L, Pathmanathan P, Morrison T, Cornelussen R, Prinzen F, Delhaas T, Doltra A, Sitges M, Vigmond EJ, Zacur E, Grau V, Rodriguez B, Remme EW, Niederer S, Mortier P, McLeod K, Potse M, Pueyo E, Bueno-Orovio A, Lamata P. The 'Digital Twin' to enable the vision of precision cardiology. Eur Heart J 2020; 41:4556-4564. [PMID: 32128588 PMCID: PMC7774470 DOI: 10.1093/eurheartj/ehaa159] [Citation(s) in RCA: 193] [Impact Index Per Article: 48.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Revised: 11/29/2019] [Accepted: 02/24/2020] [Indexed: 12/26/2022] Open
Abstract
Providing therapies tailored to each patient is the vision of precision medicine, enabled by the increasing ability to capture extensive data about individual patients. In this position paper, we argue that the second enabling pillar towards this vision is the increasing power of computers and algorithms to learn, reason, and build the 'digital twin' of a patient. Computational models are boosting the capacity to draw diagnosis and prognosis, and future treatments will be tailored not only to current health status and data, but also to an accurate projection of the pathways to restore health by model predictions. The early steps of the digital twin in the area of cardiovascular medicine are reviewed in this article, together with a discussion of the challenges and opportunities ahead. We emphasize the synergies between mechanistic and statistical models in accelerating cardiovascular research and enabling the vision of precision medicine.
Collapse
Affiliation(s)
| | - Francesca Margara
- Department of Computer Science, British Heart Foundation Centre of Research Excellence, University of Oxford, Oxford, UK
| | - Maciej Marciniak
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, UK
| | - Cristobal Rodero
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, UK
| | - Filip Loncaric
- Institut Clínic Cardiovascular, Hospital Clínic, Universitat de Barcelona, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Yingjing Feng
- IHU Liryc, Electrophysiology and Heart Modeling Institute, fondation Bordeaux Université, Pessac-Bordeaux F-33600, France
- IMB, UMR 5251, University of Bordeaux, Talence F-33400, France
| | | | - Joao F Fernandes
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, UK
| | - Hassaan A Bukhari
- IMB, UMR 5251, University of Bordeaux, Talence F-33400, France
- Aragón Institute of Engineering Research, Universidad de Zaragoza, IIS Aragón, Zaragoza, Spain
| | - Ali Wajdan
- The Intervention Centre, Oslo University Hospital, Rikshospitalet, Oslo, Norway
| | | | | | - Mehrdad Shamohammdi
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
| | - Hongxing Luo
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
| | - Philip Westphal
- Medtronic PLC, Bakken Research Center, Maastricht, the Netherlands
| | - Paul Leeson
- Radcliffe Department of Medicine, Division of Cardiovascular Medicine, Oxford Cardiovascular Clinical Research Facility, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Paolo DiAchille
- Healthcare and Life Sciences Research, IBM T.J. Watson Research Center, Yorktown Heights, NY, USA
| | - Viatcheslav Gurev
- Healthcare and Life Sciences Research, IBM T.J. Watson Research Center, Yorktown Heights, NY, USA
| | - Manuel Mayr
- King’s British Heart Foundation Centre, King’s College London, London, UK
| | - Liesbet Geris
- Virtual Physiological Human Institute, Leuven, Belgium
| | - Pras Pathmanathan
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, USA
| | - Tina Morrison
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, USA
| | | | - Frits Prinzen
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
| | - Tammo Delhaas
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
| | - Ada Doltra
- Institut Clínic Cardiovascular, Hospital Clínic, Universitat de Barcelona, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Marta Sitges
- Institut Clínic Cardiovascular, Hospital Clínic, Universitat de Barcelona, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- CIBERCV, Instituto de Salud Carlos III, (CB16/11/00354), CERCA Programme/Generalitat de, Catalunya, Spain
| | - Edward J Vigmond
- IHU Liryc, Electrophysiology and Heart Modeling Institute, fondation Bordeaux Université, Pessac-Bordeaux F-33600, France
- IMB, UMR 5251, University of Bordeaux, Talence F-33400, France
| | - Ernesto Zacur
- Department of Engineering Science, University of Oxford, Oxford, UK
| | - Vicente Grau
- Department of Engineering Science, University of Oxford, Oxford, UK
| | - Blanca Rodriguez
- Department of Computer Science, British Heart Foundation Centre of Research Excellence, University of Oxford, Oxford, UK
| | - Espen W Remme
- The Intervention Centre, Oslo University Hospital, Rikshospitalet, Oslo, Norway
| | - Steven Niederer
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, UK
| | | | | | - Mark Potse
- IHU Liryc, Electrophysiology and Heart Modeling Institute, fondation Bordeaux Université, Pessac-Bordeaux F-33600, France
- IMB, UMR 5251, University of Bordeaux, Talence F-33400, France
- Inria Bordeaux Sud-Ouest, CARMEN team, Talence F-33400, France
| | - Esther Pueyo
- Aragón Institute of Engineering Research, Universidad de Zaragoza, IIS Aragón, Zaragoza, Spain
- CIBER in Bioengineering, Biomaterials and Nanomedicine (CIBER‐BBN), Madrid, Spain
| | - Alfonso Bueno-Orovio
- Department of Computer Science, British Heart Foundation Centre of Research Excellence, University of Oxford, Oxford, UK
| | - Pablo Lamata
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, UK
| |
Collapse
|
255
|
Caradu C, Spampinato B, Vrancianu AM, Bérard X, Ducasse E. Fully automatic volume segmentation of infrarenal abdominal aortic aneurysm computed tomography images with deep learning approaches versus physician controlled manual segmentation. J Vasc Surg 2020; 74:246-256.e6. [PMID: 33309556 DOI: 10.1016/j.jvs.2020.11.036] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Accepted: 11/12/2020] [Indexed: 02/06/2023]
Abstract
OBJECTIVE Imaging software has become critical tools in the diagnosis and decision making for the treatment of abdominal aortic aneurysms (AAA). However, the interobserver reproducibility of the maximum cross-section diameter is poor. This study aimed to present and assess the quality of a new fully automated software (PRAEVAorta) that enables fast and robust detection of the aortic lumen and the infrarenal AAA characteristics including the presence of thrombus. METHODS To evaluate the segmentation obtained with this new software, we performed a quantitative comparison with the results obtained from a semiautomatic segmentation manually corrected by a senior and a junior surgeon on a dataset of 100 preoperative computed tomography angiographies from patients with infrarenal AAAs (13,465 slices). The Dice similarity coefficient (DSC), Jaccard index, sensitivity, specificity, volumetric similarity (VS), Hausdorff distance, maximum aortic transverse diameter, and the duration of segmentation were calculated between the two methods and, for the semiautomatic software, also between the two observers. RESULTS The analyses demonstrated an excellent correlation of the volumes, surfaces, and diameters measured with the fully automatic and manually corrected segmentation methods, with a Pearson's coefficient correlation of greater than 0.90 (P < .0001). Overall, a comparison between the fully automatic and manually corrected segmentation method by the senior surgeon revealed a mean Dice similarity coefficient of 0.95 ± 0.01, a Jaccard index of 0.91 ± 0.02, sensitivity of 0.94 ± 0.02, specificity of 0.97 ± 0.01, VS of 0.98 ± 0.01, and mean Hausdorff distance per slice of 4.61 ± 7.26 mm. The mean VS reached 0.95 ± 0.04 for the lumen and 0.91 ± 0.07 for the thrombus. For the fully automatic method, the segmentation time varied from 27 seconds to 4 minutes per patient vs 5 minutes to 80 minutes for the manually corrected methods (P < .0001). CONCLUSIONS By enabling a fast and fully automated detailed analysis of the anatomic characteristics of infrarenal AAAs, this software could have strong applications in daily clinical practice and clinical research.
Collapse
Affiliation(s)
- Caroline Caradu
- Vascular Surgery Department, Bordeaux University Hospital, Bordeaux, France
| | | | | | - Xavier Bérard
- Vascular Surgery Department, Bordeaux University Hospital, Bordeaux, France
| | - Eric Ducasse
- Vascular Surgery Department, Bordeaux University Hospital, Bordeaux, France.
| |
Collapse
|
256
|
Martini N, Aimo A, Barison A, Della Latta D, Vergaro G, Aquaro GD, Ripoli A, Emdin M, Chiappino D. Deep learning to diagnose cardiac amyloidosis from cardiovascular magnetic resonance. J Cardiovasc Magn Reson 2020; 22:84. [PMID: 33287829 PMCID: PMC7720569 DOI: 10.1186/s12968-020-00690-4] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Accepted: 11/17/2020] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Cardiovascular magnetic resonance (CMR) is part of the diagnostic work-up for cardiac amyloidosis (CA). Deep learning (DL) is an application of artificial intelligence that may allow to automatically analyze CMR findings and establish the likelihood of CA. METHODS 1.5 T CMR was performed in 206 subjects with suspected CA (n = 100, 49% with unexplained left ventricular (LV) hypertrophy; n = 106, 51% with blood dyscrasia and suspected light-chain amyloidosis). Patients were randomly assigned to the training (n = 134, 65%), validation (n = 30, 15%), and testing subgroups (n = 42, 20%). Short axis, 2-chamber, 4-chamber late gadolinium enhancement (LGE) images were evaluated by 3 networks (DL algorithms). The tags "amyloidosis present" or "absent" were attributed when the average probability of CA from the 3 networks was ≥ 50% or < 50%, respectively. The DL strategy was compared to a machine learning (ML) algorithm considering all manually extracted features (LV volumes, mass and function, LGE pattern, early blood-pool darkening, pericardial and pleural effusion, etc.), to reproduce exam reading by an experienced operator. RESULTS The DL strategy displayed good diagnostic accuracy (88%), with an area under the curve (AUC) of 0.982. The precision (positive predictive value), recall score (sensitivity), and F1 score (a measure of test accuracy) were 83%, 95%, and 89% respectively. A ML algorithm considering all CMR features had a similar diagnostic yield to DL strategy (AUC 0.952 vs. 0.982; p = 0.39). CONCLUSIONS A DL approach evaluating LGE acquisitions displayed a similar diagnostic performance for CA to a ML-based approach, which simulates CMR reading by experienced operators.
Collapse
MESH Headings
- Aged
- Aged, 80 and over
- Amyloid Neuropathies, Familial/diagnostic imaging
- Amyloid Neuropathies, Familial/pathology
- Amyloid Neuropathies, Familial/physiopathology
- Cardiomyopathy, Hypertrophic/diagnostic imaging
- Cardiomyopathy, Hypertrophic/pathology
- Cardiomyopathy, Hypertrophic/physiopathology
- Deep Learning
- Female
- Humans
- Hypertrophy, Left Ventricular/diagnostic imaging
- Hypertrophy, Left Ventricular/pathology
- Hypertrophy, Left Ventricular/physiopathology
- Image Processing, Computer-Assisted
- Immunoglobulin Light-chain Amyloidosis/diagnostic imaging
- Immunoglobulin Light-chain Amyloidosis/pathology
- Immunoglobulin Light-chain Amyloidosis/physiopathology
- Magnetic Resonance Imaging, Cine
- Male
- Myocardium/pathology
- Predictive Value of Tests
- Reproducibility of Results
- Ventricular Function, Left
- Ventricular Remodeling
Collapse
Affiliation(s)
- Nicola Martini
- Deep Health Unit, Fondazione Toscana Gabriele Monasterio, Pisa-Massa, Italy.
| | - Alberto Aimo
- Institute of Life Sciences, Scuola Superiore Sant'Anna, Pisa, Italy
- Cardiology Division, Fondazione Toscana Gabriele Monasterio, Pisa, Italy
| | - Andrea Barison
- Institute of Life Sciences, Scuola Superiore Sant'Anna, Pisa, Italy
- Cardiology Division, Fondazione Toscana Gabriele Monasterio, Pisa, Italy
| | | | - Giuseppe Vergaro
- Institute of Life Sciences, Scuola Superiore Sant'Anna, Pisa, Italy
- Cardiology Division, Fondazione Toscana Gabriele Monasterio, Pisa, Italy
| | | | - Andrea Ripoli
- Deep Health Unit, Fondazione Toscana Gabriele Monasterio, Pisa-Massa, Italy
| | - Michele Emdin
- Institute of Life Sciences, Scuola Superiore Sant'Anna, Pisa, Italy
- Cardiology Division, Fondazione Toscana Gabriele Monasterio, Pisa, Italy
| | - Dante Chiappino
- Deep Health Unit, Fondazione Toscana Gabriele Monasterio, Pisa-Massa, Italy
| |
Collapse
|
257
|
Wilk B, Wisenberg G, Dharmakumar R, Thiessen JD, Goldhawk DE, Prato FS. Hybrid PET/MR imaging in myocardial inflammation post-myocardial infarction. J Nucl Cardiol 2020; 27:2083-2099. [PMID: 31797321 PMCID: PMC7391987 DOI: 10.1007/s12350-019-01973-9] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2019] [Revised: 11/13/2019] [Accepted: 11/14/2019] [Indexed: 01/24/2023]
Abstract
Hybrid PET/MR imaging is an emerging imaging modality combining positron emission tomography (PET) and magnetic resonance imaging (MRI) in the same system. Since the introduction of clinical PET/MRI in 2011, it has had some impact (e.g., imaging the components of inflammation in myocardial infarction), but its role could be much greater. Many opportunities remain unexplored and will be highlighted in this review. The inflammatory process post-myocardial infarction has many facets at a cellular level which may affect the outcome of the patient, specifically the effects on adverse left ventricular remodeling, and ultimately prognosis. The goal of inflammation imaging is to track the process non-invasively and quantitatively to determine the best therapeutic options for intervention and to monitor those therapies. While PET and MRI, acquired separately, can image aspects of inflammation, hybrid PET/MRI has the potential to advance imaging of myocardial inflammation. This review contains a description of hybrid PET/MRI, its application to inflammation imaging in myocardial infarction and the challenges, constraints, and opportunities in designing data collection protocols. Finally, this review explores opportunities in PET/MRI: improved registration, partial volume correction, machine learning, new approaches in the development of PET and MRI pulse sequences, and the use of novel injection strategies.
Collapse
Affiliation(s)
- B Wilk
- Department of Medical Imaging, Western University, London, Canada.
- Lawson Health Research Institute, London, Canada.
- Collaborative Graduate Program in Molecular Imaging, Western University, London, Canada.
| | - G Wisenberg
- Department of Medical Imaging, Western University, London, Canada
- MyHealth Centre, Arva, Canada
| | - R Dharmakumar
- Biomedical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - J D Thiessen
- Department of Medical Imaging, Western University, London, Canada
- Lawson Health Research Institute, London, Canada
- Collaborative Graduate Program in Molecular Imaging, Western University, London, Canada
| | - D E Goldhawk
- Department of Medical Imaging, Western University, London, Canada
- Lawson Health Research Institute, London, Canada
- Collaborative Graduate Program in Molecular Imaging, Western University, London, Canada
| | - F S Prato
- Department of Medical Imaging, Western University, London, Canada
- Lawson Health Research Institute, London, Canada
- Collaborative Graduate Program in Molecular Imaging, Western University, London, Canada
| |
Collapse
|
258
|
Kawasaki T, Kidoh M, Kido T, Sueta D, Fujimoto S, Kumamaru KK, Uetani T, Tanabe Y, Ueda T, Sakabe D, Oda S, Yamashiro T, Tsujita K, Kato S, Yuki H, Utsunomiya D. Evaluation of Significant Coronary Artery Disease Based on CT Fractional Flow Reserve and Plaque Characteristics Using Random Forest Analysis in Machine Learning. Acad Radiol 2020; 27:1700-1708. [PMID: 32057618 DOI: 10.1016/j.acra.2019.12.013] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Revised: 12/08/2019] [Accepted: 12/17/2019] [Indexed: 12/15/2022]
Abstract
RATIONALE AND OBJECTIVES Fractional flow reserve (FFR) is an established technique for detecting lesion-specific ischemia but is invasive. Our objective was to investigate the effects of combined assessment of coronary CT angiography (CCTA) imaging features and CT-FFR on detecting lesion-specific ischemia by comparing with invasive FFR. MATERIALS AND METHODS Forty-seven patients who had 60 coronary vessels with 30%-90% stenosis were included. Six anatomic CCTA descriptors (Agatston score, stenosis severity, mean plaque CT attenuation value, noncalcified and calcified plaque volumes, remodeling index) and a functional descriptor (CT-FFR) were measured. Random forest was used to identify which descriptors were useful to identify ischemia-related lesion. Receiver-operating characteristic (ROC) curves were calculated for 2 models: i.e. Model-1 for anatomical CT descriptors and Model-2 for anatomical CT descriptors plus CT-FFR. RESULTS Stenosis severity (40.8 ± 15.7% vs 57.6 ± 14.1%), noncalcified plaque volume (190 ± 100 vs 254.8 ± 133.3), and remodeling index (1.04 ± 0.12 vs 1.11 ± 0.13) were significantly higher in ischemia-related lesions than nonischemia-related lesions. CT-FFR was 0.84 ± 0.14 and 0.71 ± 0.14, respectively, for ischemia-related and nonischemia-related lesions, and the difference was significant. The area under the ROC curve was 0.738 and 0.835 in Model-1 and Model-2, respectively. Reclassification of ischemic lesion risk was significantly improved after adding CT-FFR: net reclassification improvement was 0.297 and integrated discrimination improvement was 0.254. CONCLUSION Combined assessment of anatomical CCTA features and functional CT-FFR was helpful for detecting lesion-specific ischemia.
Collapse
|
259
|
Wang QC, Wang ZY. Big Data and Atrial Fibrillation: Current Understanding and New Opportunities. J Cardiovasc Transl Res 2020; 13:944-952. [PMID: 32378163 DOI: 10.1007/s12265-020-10008-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Accepted: 04/16/2020] [Indexed: 10/24/2022]
Abstract
Atrial fibrillation (AF) is the most common arrhythmia with diverse etiology that remarkably relates to high morbidity and mortality. With the advancements in intensive clinical and basic research, the understanding of electrophysiological and pathophysiological mechanism, as well as treatment of AF have made huge progress. However, many unresolved issues remain, including the core mechanisms and key intervention targets. Big data approach has produced new insights into the improvement of the situation. A large amount of data have been accumulated in the field of AF research, thus using the big data to achieve prevention and precise treatment of AF may be the direction of future development. In this review, we will discuss the current understanding of big data and explore the potential applications of big data in AF research, including predictive models of disease processes, disease heterogeneity, drug safety and development, precision medicine, and the potential source for big data acquisition. Grapical abstract.
Collapse
Affiliation(s)
- Qian-Chen Wang
- Department of Cardiovascular Medicine, Xiangya Hospital, Central South University, No.87 Xiangya Road, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, No.87 Xiangya Road, Changsha, China
| | - Zhen-Yu Wang
- Department of Cardiovascular Medicine, the Second Xiangya Hospital, Central South University, No.139 Renmin Road, Changsha, Hunan, China.
| |
Collapse
|
260
|
Daubert MA, Tailor T, James O, Shaw LJ, Douglas PS, Koweek L. Multimodality cardiac imaging in the 21st century: evolution, advances and future opportunities for innovation. Br J Radiol 2020; 94:20200780. [PMID: 33237824 DOI: 10.1259/bjr.20200780] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Cardiovascular imaging has significantly evolved since the turn of the century. Progress in the last two decades has been marked by advances in every modality used to image the heart, including echocardiography, cardiac magnetic resonance, cardiac CT and nuclear cardiology. There has also been a dramatic increase in hybrid and fusion modalities that leverage the unique capabilities of two imaging techniques simultaneously, as well as the incorporation of artificial intelligence and machine learning into the clinical workflow. These advances in non-invasive cardiac imaging have guided patient management and improved clinical outcomes. The technological developments of the past 20 years have also given rise to new imaging subspecialities and increased the demand for dedicated cardiac imagers who are cross-trained in multiple modalities. This state-of-the-art review summarizes the evolution of multimodality cardiac imaging in the 21st century and highlights opportunities for future innovation.
Collapse
Affiliation(s)
- Melissa A Daubert
- Division of Cardiology, Department of Medicine, Duke University Medical Center, Durham, North Carolina, USA
| | - Tina Tailor
- Division of Cardiothoracic Imaging, Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA
| | - Olga James
- Division of Cardiothoracic Imaging, Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA
| | - Leslee J Shaw
- Department of Radiology, Cornell Medical Center, New York, New York, USA
| | - Pamela S Douglas
- Division of Cardiology, Department of Medicine, Duke University Medical Center, Durham, North Carolina, USA
| | - Lynne Koweek
- Division of Cardiothoracic Imaging, Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA
| |
Collapse
|
261
|
Itchhaporia D. Artificial intelligence in cardiology. Trends Cardiovasc Med 2020; 32:34-41. [PMID: 33242635 DOI: 10.1016/j.tcm.2020.11.007] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2019] [Revised: 10/19/2020] [Accepted: 11/16/2020] [Indexed: 12/22/2022]
Abstract
This review examines the current state and application of artificial intelligence (AI) and machine learning (ML) in cardiovascular medicine. AI is changing the clinical practice of medicine in other specialties. With progress continuing in this emerging technology, the impact for cardiovascular medicine is highlighted to provide insight for the practicing clinician and to identify potential patient benefits.
Collapse
Affiliation(s)
- Dipti Itchhaporia
- Hoag Hospital Newport Beach and University of California, 520 Superior Avenue, Suite 325, Newport Beach, Irvine, CA 92663, United States.
| |
Collapse
|
262
|
Wernly B, Mamandipoor B, Baldia P, Jung C, Osmani V. Machine learning predicts mortality in septic patients using only routinely available ABG variables: a multi-centre evaluation. Int J Med Inform 2020; 145:104312. [PMID: 33126059 DOI: 10.1016/j.ijmedinf.2020.104312] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Revised: 09/26/2020] [Accepted: 10/20/2020] [Indexed: 12/13/2022]
Abstract
PURPOSE To evaluate the application of machine learning methods, specifically Deep Neural Networks (DNN) models for intensive care (ICU) mortality prediction. The aim was to predict mortality within 96 hours after admission to mirror the clinical situation of patient evaluation after an ICU trial, which consists of 24-48 hours of ICU treatment and then "re-triage". The input variables were deliberately restricted to ABG values to maximise real-world practicability. METHODS We retrospectively evaluated septic patients in the multi-centre eICU dataset as well as single centre MIMIC-III dataset. Included were all patients alive after 48 hours with available data on ABG (n = 3979 and n = 9655 ICU stays for the multi-centre and single centre respectively). The primary endpoint was 96 -h-mortality. RESULTS The model was developed using long short-term memory (LSTM), a type of DNN designed to learn temporal dependencies between variables. Input variables were all ABG values within the first 48 hours. The SOFA score (AUC of 0.72) was moderately predictive. Logistic regression showed good performance (AUC of 0.82). The best performance was achieved by the LSTM-based model with AUC of 0.88 in the multi-centre study and AUC of 0.85 in the single centre study. CONCLUSIONS An LSTM-based model could help physicians with the "re-triage" and the decision to restrict treatment in patients with a poor prognosis.
Collapse
Affiliation(s)
- Bernhard Wernly
- Department of Cardiology, Paracelsus Medical University of Salzburg, Austria; Division of Cardiology, Department of Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden.
| | | | - Philipp Baldia
- University Hospital Düsseldorf, Heinrich-Heine-University Düsseldorf, Medical Faculty, Division of Cardiology, Pulmonology and Vascular Medicine, Germany
| | - Christian Jung
- University Hospital Düsseldorf, Heinrich-Heine-University Düsseldorf, Medical Faculty, Division of Cardiology, Pulmonology and Vascular Medicine, Germany
| | - Venet Osmani
- Fondazione Bruno Kessler Research Institute, Trento, Italy
| |
Collapse
|
263
|
Steps to use artificial intelligence in echocardiography. J Echocardiogr 2020; 19:21-27. [PMID: 33044715 PMCID: PMC7549428 DOI: 10.1007/s12574-020-00496-4] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 09/29/2020] [Accepted: 10/01/2020] [Indexed: 11/27/2022]
Abstract
Artificial intelligence (AI) has influenced every field of cardiovascular imaging in all phases from acquisition to reporting. Compared with computed tomography and magnetic resonance imaging, there is an issue of high observer variation in the interpretation of echocardiograms. Therefore, AI can help minimize the observer variation and provide accurate diagnosis in the field of echocardiography. In this review, we summarize the necessity for automated diagnosis in the echocardiographic field, and discuss the results of AI application to echocardiography and future perspectives. Currently, there are two roles for AI in cardiovascular imaging. One is the automation of tasks performed by humans, such as image segmentation, measurement of cardiac structural and functional parameters. The other is the discovery of clinically important insights. Most reported applications were focused on the automation of tasks. Moreover, algorithms that can obtain cardiac measurements are also being reported. In the next stage, AI can be expected to expand and enrich existing knowledge. With the continual evolution of technology, cardiologists should become well versed in this new knowledge of AI and be able to harness it as a tool. AI can be incorporated into everyday clinical practice and become a valuable aid for many healthcare professionals dealing with cardiovascular diseases.
Collapse
|
264
|
Artificial Intelligence in Cardiac CT: Automated Calcium Scoring and Plaque Analysis. CURRENT CARDIOVASCULAR IMAGING REPORTS 2020. [DOI: 10.1007/s12410-020-09549-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
|
265
|
Zolotarev AM, Hansen BJ, Ivanova EA, Helfrich KM, Li N, Janssen PML, Mohler PJ, Mokadam NA, Whitson BA, Fedorov MV, Hummel JD, Dylov DV, Fedorov VV. Optical Mapping-Validated Machine Learning Improves Atrial Fibrillation Driver Detection by Multi-Electrode Mapping. Circ Arrhythm Electrophysiol 2020; 13:e008249. [PMID: 32921129 DOI: 10.1161/circep.119.008249] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
BACKGROUND Atrial fibrillation (AF) can be maintained by localized intramural reentrant drivers. However, AF driver detection by clinical surface-only multielectrode mapping (MEM) has relied on subjective interpretation of activation maps. We hypothesized that application of machine learning to electrogram frequency spectra may accurately automate driver detection by MEM and add some objectivity to the interpretation of MEM findings. METHODS Temporally and spatially stable single AF drivers were mapped simultaneously in explanted human atria (n=11) by subsurface near-infrared optical mapping (NIOM; 0.3 mm2 resolution) and 64-electrode MEM (higher density or lower density with 3 and 9 mm2 resolution, respectively). Unipolar MEM and NIOM recordings were processed by Fourier transform analysis into 28 407 total Fourier spectra. Thirty-five features for machine learning were extracted from each Fourier spectrum. RESULTS Targeted driver ablation and NIOM activation maps efficiently defined the center and periphery of AF driver preferential tracks and provided validated annotations for driver versus nondriver electrodes in MEM arrays. Compared with analysis of single electrogram frequency features, averaging the features from each of the 8 neighboring electrodes, significantly improved classification of AF driver electrograms. The classification metrics increased when less strict annotation, including driver periphery electrodes, were added to driver center annotation. Notably, f1-score for the binary classification of higher-density catheter data set was significantly higher than that of lower-density catheter (0.81±0.02 versus 0.66±0.04, P<0.05). The trained algorithm correctly highlighted 86% of driver regions with higher density but only 80% with lower-density MEM arrays (81% for lower-density+higher-density arrays together). CONCLUSIONS The machine learning model pretrained on Fourier spectrum features allows efficient classification of electrograms recordings as AF driver or nondriver compared with the NIOM gold-standard. Future application of NIOM-validated machine learning approach may improve the accuracy of AF driver detection for targeted ablation treatment in patients.
Collapse
Affiliation(s)
- Alexander M Zolotarev
- Department of Physiology and Cell Biology and Bob and Corrine Frick Center for Heart Failure and Arrhythmia (A.M.Z., B.J.H., K.M.H., N.L., P.M.L.J., P.J.M., V.V.F.), The Ohio State University Wexner Medical Center, Columbus, OH.,Center of Computational and Data-Intensive Science and Engineering, Skolkovo Institute of Science and Technology, Moscow, Russia (A.M.Z., E.A.I., M.V.F., D.V.D.)
| | - Brian J Hansen
- Department of Physiology and Cell Biology and Bob and Corrine Frick Center for Heart Failure and Arrhythmia (A.M.Z., B.J.H., K.M.H., N.L., P.M.L.J., P.J.M., V.V.F.), The Ohio State University Wexner Medical Center, Columbus, OH
| | - Ekaterina A Ivanova
- Center of Computational and Data-Intensive Science and Engineering, Skolkovo Institute of Science and Technology, Moscow, Russia (A.M.Z., E.A.I., M.V.F., D.V.D.)
| | - Katelynn M Helfrich
- Department of Physiology and Cell Biology and Bob and Corrine Frick Center for Heart Failure and Arrhythmia (A.M.Z., B.J.H., K.M.H., N.L., P.M.L.J., P.J.M., V.V.F.), The Ohio State University Wexner Medical Center, Columbus, OH
| | - Ning Li
- Department of Physiology and Cell Biology and Bob and Corrine Frick Center for Heart Failure and Arrhythmia (A.M.Z., B.J.H., K.M.H., N.L., P.M.L.J., P.J.M., V.V.F.), The Ohio State University Wexner Medical Center, Columbus, OH.,Davis Heart and Lung Research Institute (N.L., P.M.L.J., P.J.M., N.A.M., B.A.W., J.D.H., V.V.F.), The Ohio State University Wexner Medical Center, Columbus, OH
| | - Paul M L Janssen
- Department of Physiology and Cell Biology and Bob and Corrine Frick Center for Heart Failure and Arrhythmia (A.M.Z., B.J.H., K.M.H., N.L., P.M.L.J., P.J.M., V.V.F.), The Ohio State University Wexner Medical Center, Columbus, OH.,Davis Heart and Lung Research Institute (N.L., P.M.L.J., P.J.M., N.A.M., B.A.W., J.D.H., V.V.F.), The Ohio State University Wexner Medical Center, Columbus, OH
| | - Peter J Mohler
- Department of Physiology and Cell Biology and Bob and Corrine Frick Center for Heart Failure and Arrhythmia (A.M.Z., B.J.H., K.M.H., N.L., P.M.L.J., P.J.M., V.V.F.), The Ohio State University Wexner Medical Center, Columbus, OH.,Davis Heart and Lung Research Institute (N.L., P.M.L.J., P.J.M., N.A.M., B.A.W., J.D.H., V.V.F.), The Ohio State University Wexner Medical Center, Columbus, OH
| | - Nahush A Mokadam
- Davis Heart and Lung Research Institute (N.L., P.M.L.J., P.J.M., N.A.M., B.A.W., J.D.H., V.V.F.), The Ohio State University Wexner Medical Center, Columbus, OH.,Division of Cardiac Surgery (N.A.M., B.A.W., J.D.H.), The Ohio State University Wexner Medical Center, Columbus, OH
| | - Bryan A Whitson
- Davis Heart and Lung Research Institute (N.L., P.M.L.J., P.J.M., N.A.M., B.A.W., J.D.H., V.V.F.), The Ohio State University Wexner Medical Center, Columbus, OH.,Division of Cardiac Surgery (N.A.M., B.A.W., J.D.H.), The Ohio State University Wexner Medical Center, Columbus, OH
| | - Maxim V Fedorov
- Center of Computational and Data-Intensive Science and Engineering, Skolkovo Institute of Science and Technology, Moscow, Russia (A.M.Z., E.A.I., M.V.F., D.V.D.)
| | - John D Hummel
- Davis Heart and Lung Research Institute (N.L., P.M.L.J., P.J.M., N.A.M., B.A.W., J.D.H., V.V.F.), The Ohio State University Wexner Medical Center, Columbus, OH.,Division of Cardiac Surgery (N.A.M., B.A.W., J.D.H.), The Ohio State University Wexner Medical Center, Columbus, OH.,Department of Internal Medicine (J.D.H), The Ohio State University Wexner Medical Center, Columbus, OH
| | - Dmitry V Dylov
- Center of Computational and Data-Intensive Science and Engineering, Skolkovo Institute of Science and Technology, Moscow, Russia (A.M.Z., E.A.I., M.V.F., D.V.D.)
| | - Vadim V Fedorov
- Department of Physiology and Cell Biology and Bob and Corrine Frick Center for Heart Failure and Arrhythmia (A.M.Z., B.J.H., K.M.H., N.L., P.M.L.J., P.J.M., V.V.F.), The Ohio State University Wexner Medical Center, Columbus, OH.,Davis Heart and Lung Research Institute (N.L., P.M.L.J., P.J.M., N.A.M., B.A.W., J.D.H., V.V.F.), The Ohio State University Wexner Medical Center, Columbus, OH
| |
Collapse
|
266
|
Sengupta PP, Shrestha S, Berthon B, Messas E, Donal E, Tison GH, Min JK, D'hooge J, Voigt JU, Dudley J, Verjans JW, Shameer K, Johnson K, Lovstakken L, Tabassian M, Piccirilli M, Pernot M, Yanamala N, Duchateau N, Kagiyama N, Bernard O, Slomka P, Deo R, Arnaout R. Proposed Requirements for Cardiovascular Imaging-Related Machine Learning Evaluation (PRIME): A Checklist: Reviewed by the American College of Cardiology Healthcare Innovation Council. JACC Cardiovasc Imaging 2020; 13:2017-2035. [PMID: 32912474 PMCID: PMC7953597 DOI: 10.1016/j.jcmg.2020.07.015] [Citation(s) in RCA: 111] [Impact Index Per Article: 27.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Revised: 07/15/2020] [Accepted: 07/16/2020] [Indexed: 12/20/2022]
Abstract
Machine learning (ML) has been increasingly used within cardiology, particularly in the domain of cardiovascular imaging. Due to the inherent complexity and flexibility of ML algorithms, inconsistencies in the model performance and interpretation may occur. Several review articles have been recently published that introduce the fundamental principles and clinical application of ML for cardiologists. This paper builds on these introductory principles and outlines a more comprehensive list of crucial responsibilities that need to be completed when developing ML models. This paper aims to serve as a scientific foundation to aid investigators, data scientists, authors, editors, and reviewers involved in machine learning research with the intent of uniform reporting of ML investigations. An independent multidisciplinary panel of ML experts, clinicians, and statisticians worked together to review the theoretical rationale underlying 7 sets of requirements that may reduce algorithmic errors and biases. Finally, the paper summarizes a list of reporting items as an itemized checklist that highlights steps for ensuring correct application of ML models and the consistent reporting of model specifications and results. It is expected that the rapid pace of research and development and the increased availability of real-world evidence may require periodic updates to the checklist.
Collapse
Affiliation(s)
- Partho P Sengupta
- West Virginia University Heart and Vascular Institute, Division of Cardiology, Morgantown, West Virginia.
| | - Sirish Shrestha
- West Virginia University Heart and Vascular Institute, Division of Cardiology, Morgantown, West Virginia
| | - Béatrice Berthon
- Physique pour la Médecine Paris, Inserm U1273, CNRS FRE 2031, ESPCI Paris, PSL Research University, Paris, France
| | - Emmanuel Messas
- Université Paris Descartes, Sorbonne Paris Cité, Paris, France
| | - Erwan Donal
- Département de Cardiologie et Maladies Vasculaires, Service de Cardiologie et maladies vasculaires, CHU Rennes, Rennes, France
| | - Geoffrey H Tison
- Division of Cardiology, Department of Medicine, University of California San Francisco, San Francisco, California
| | | | - Jan D'hooge
- Laboratory on Cardiovascular Imaging and Dynamics, Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium
| | - Jens-Uwe Voigt
- Department of Cardiovascular Science, KU Leuven, Leuven, Belgium; Department of Cardiovascular Diseases, University Hospitals Leuven, Belgium
| | - Joel Dudley
- Department of Genetics and Genomic Sciences and Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York; Institute for Next Generation Healthcare, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Johan W Verjans
- Australian Institute for Machine Learning, University of Adelaide, North Terrace, Adelaide, South Australia, Australia; Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Khader Shameer
- Department of Genetics and Genomic Sciences and Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York; Institute for Next Generation Healthcare, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Kipp Johnson
- Department of Genetics and Genomic Sciences and Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York; Institute for Next Generation Healthcare, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Lasse Lovstakken
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Mahdi Tabassian
- Laboratory on Cardiovascular Imaging and Dynamics, Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium
| | - Marco Piccirilli
- West Virginia University Heart and Vascular Institute, Division of Cardiology, Morgantown, West Virginia
| | - Mathieu Pernot
- Physique pour la Médecine Paris, Inserm U1273, CNRS FRE 2031, ESPCI Paris, PSL Research University, Paris, France
| | - Naveena Yanamala
- West Virginia University Heart and Vascular Institute, Division of Cardiology, Morgantown, West Virginia
| | - Nicolas Duchateau
- CREATIS, CNRS UMR 5220, INSERM U1206, Université Lyon 1, INSA-LYON, France
| | - Nobuyuki Kagiyama
- West Virginia University Heart and Vascular Institute, Division of Cardiology, Morgantown, West Virginia
| | - Olivier Bernard
- CREATIS, CNRS UMR 5220, INSERM U1206, Université Lyon 1, INSA-LYON, France
| | - Piotr Slomka
- Department of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, California
| | - Rahul Deo
- Division of Cardiology, Department of Medicine, University of California San Francisco, San Francisco, California
| | - Rima Arnaout
- Division of Cardiology, Department of Medicine, University of California San Francisco, San Francisco, California
| |
Collapse
|
267
|
Jiang B, Guo N, Ge Y, Zhang L, Oudkerk M, Xie X. Development and application of artificial intelligence in cardiac imaging. Br J Radiol 2020; 93:20190812. [PMID: 32017605 DOI: 10.1259/bjr.20190812] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
In this review, we describe the technical aspects of artificial intelligence (AI) in cardiac imaging, starting with radiomics, basic algorithms of deep learning and application tasks of algorithms, until recently the availability of the public database. Subsequently, we conducted a systematic literature search for recently published clinically relevant studies on AI in cardiac imaging. As a result, 24 and 14 studies using CT and MRI, respectively, were included and summarized. From these studies, it can be concluded that AI is widely applied in cardiac applications in the clinic, including coronary calcium scoring, coronary CT angiography, fractional flow reserve CT, plaque analysis, left ventricular myocardium analysis, diagnosis of myocardial infarction, prognosis of coronary artery disease, assessment of cardiac function, and diagnosis and prognosis of cardiomyopathy. These advancements show that AI has a promising prospect in cardiac imaging.
Collapse
Affiliation(s)
- Beibei Jiang
- Radiology Department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai 200080, China
| | - Ning Guo
- Shukun (Beijing) Technology Co, Ltd., Jinhui Bd, Qiyang Rd, Beijing 100102, China
| | - Yinghui Ge
- Radiology Department, Central China Fuwai Hospital, Fuwai Avenue 1, Zhengzhou 450046, China
| | - Lu Zhang
- Radiology Department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai 200080, China
| | - Matthijs Oudkerk
- Institute for DiagNostic Accuracy, Prof. Wiersma Straat 5, 9713GH Groningen, The Netherlands.,University of Groningen, Faculty of Medical Sciences, 9700AB Groningen, The Netherlands
| | - Xueqian Xie
- Radiology Department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai 200080, China
| |
Collapse
|
268
|
Cardiac magnetic resonance imaging and computed tomography for the pediatric cardiologist. PROGRESS IN PEDIATRIC CARDIOLOGY 2020. [DOI: 10.1016/j.ppedcard.2020.101273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
|
269
|
Lekadir K, Leiner T, Young AA, Petersen SE. Editorial: Current and Future Role of Artificial Intelligence in Cardiac Imaging. Front Cardiovasc Med 2020; 7:137. [PMID: 32850987 PMCID: PMC7426695 DOI: 10.3389/fcvm.2020.00137] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Accepted: 06/30/2020] [Indexed: 11/13/2022] Open
Affiliation(s)
- Karim Lekadir
- Universitat de Barcelona, Artificial Intelligence in Medicine Lab (BCN-AIM), Departament de Matemàtiques and Informàtica, Barcelona, Spain
| | - Tim Leiner
- Department of Radiology, Utrecht University Medical Centre, Utrecht, Netherlands
| | - Alistair A Young
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Steffen E Petersen
- Barts Heart Centre, Barts Health NHS Trust, London, United Kingdom.,NIHR Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
| |
Collapse
|
270
|
Loncaric F, Camara O, Piella G, Bijnens B. Integration of artificial intelligence into clinical patient management: focus on cardiac imaging. ACTA ACUST UNITED AC 2020; 74:72-80. [PMID: 32819849 DOI: 10.1016/j.rec.2020.07.003] [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: 06/16/2020] [Accepted: 07/01/2020] [Indexed: 10/23/2022]
Abstract
Cardiac imaging is a crucial component in the management of patients with heart disease, and as such it influences multiple, inter-related parts of the clinical workflow: physician-patient contact, image acquisition, image pre- and postprocessing, study reporting, diagnostics and outcome predictions, medical interventions, and, finally, knowledge-building through clinical research. With the gradual and ubiquitous infiltration of artificial intelligence into cardiology, it has become clear that, when used appropriately, it will influence and potentially improve-through automation, standardization and data integration-all components of the clinical workflow. This review aims to present a comprehensive view of full integration of artificial intelligence into the standard clinical patient management-with a focus on cardiac imaging, but applicable to all information handling-and to discuss current barriers that remain to be overcome before its widespread implementation and integration.
Collapse
Affiliation(s)
- Filip Loncaric
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.
| | - Oscar Camara
- BCN MedTech, Departament de Tecnologies de la Informació i les Comunicacions, Universitat Pompeu Fabra, Barcelona, Spain
| | - Gemma Piella
- BCN MedTech, Departament de Tecnologies de la Informació i les Comunicacions, Universitat Pompeu Fabra, Barcelona, Spain
| | - Bart Bijnens
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; ICREA, Barcelona, Spain
| |
Collapse
|
271
|
Artificial Intelligence Applications to Improve Risk Prediction Tools in Electrophysiology. CURRENT CARDIOVASCULAR RISK REPORTS 2020. [DOI: 10.1007/s12170-020-00649-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
|
272
|
Feeny AK, Chung MK, Madabhushi A, Attia ZI, Cikes M, Firouznia M, Friedman PA, Kalscheur MM, Kapa S, Narayan SM, Noseworthy PA, Passman RS, Perez MV, Peters NS, Piccini JP, Tarakji KG, Thomas SA, Trayanova NA, Turakhia MP, Wang PJ. Artificial Intelligence and Machine Learning in Arrhythmias and Cardiac Electrophysiology. Circ Arrhythm Electrophysiol 2020; 13:e007952. [PMID: 32628863 PMCID: PMC7808396 DOI: 10.1161/circep.119.007952] [Citation(s) in RCA: 74] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Artificial intelligence (AI) and machine learning (ML) in medicine are currently areas of intense exploration, showing potential to automate human tasks and even perform tasks beyond human capabilities. Literacy and understanding of AI/ML methods are becoming increasingly important to researchers and clinicians. The first objective of this review is to provide the novice reader with literacy of AI/ML methods and provide a foundation for how one might conduct an ML study. We provide a technical overview of some of the most commonly used terms, techniques, and challenges in AI/ML studies, with reference to recent studies in cardiac electrophysiology to illustrate key points. The second objective of this review is to use examples from recent literature to discuss how AI and ML are changing clinical practice and research in cardiac electrophysiology, with emphasis on disease detection and diagnosis, prediction of patient outcomes, and novel characterization of disease. The final objective is to highlight important considerations and challenges for appropriate validation, adoption, and deployment of AI technologies into clinical practice.
Collapse
Affiliation(s)
- Albert K Feeny
- Cleveland Clinic Lerner College of Medicine (A.K.F., M.K.C.), Case Western Reserve University, OH
| | - Mina K Chung
- Cleveland Clinic Lerner College of Medicine (A.K.F., M.K.C.), Case Western Reserve University, OH
- Department of Cardiovascular Medicine, Cleveland Clinic, OH (M.K.C., K.G.T., S.A.T.)
| | - Anant Madabhushi
- Department of Biomedical Engineering (A.M., M.F.), Case Western Reserve University, OH
- Louis Stokes Cleveland Veterans Affairs Medical Center, Cleveland, OH (A.M.)
| | - Zachi I Attia
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, MN (Z.I.A., P.A.F., S.K., P.A.N., )
| | - Maja Cikes
- Department of Cardiovascular Diseases, University of Zagreb School of Medicine & University Hospital Center Zagreb, Croatia (M.C.)
| | - Marjan Firouznia
- Department of Biomedical Engineering (A.M., M.F.), Case Western Reserve University, OH
| | - Paul A Friedman
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, MN (Z.I.A., P.A.F., S.K., P.A.N., )
| | - Matthew M Kalscheur
- Division of Cardiovascular Medicine, Department of Medicine, School of Medicine & Public Health, University of Wisconsin (M.M.K.)
- William S. Middleton Veterans Hospital, Madison, WI (M.M.K.)
| | - Suraj Kapa
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, MN (Z.I.A., P.A.F., S.K., P.A.N., )
| | - Sanjiv M Narayan
- Division of Cardiovascular Medicine, Stanford University, CA (S.M.N., M.V.P., M.P.T., P.J.W.)
- Veterans Affairs Palo Alto Health Care System, CA (S.M.N., M.V.P., M.P.T., P.J.W.)
| | - Peter A Noseworthy
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, MN (Z.I.A., P.A.F., S.K., P.A.N., )
| | - Rod S Passman
- Division of Cardiology, Northwestern University, Feinberg School of Medicine, Chicago, IL (R.S.P.)
| | - Marco V Perez
- Division of Cardiovascular Medicine, Stanford University, CA (S.M.N., M.V.P., M.P.T., P.J.W.)
- Veterans Affairs Palo Alto Health Care System, CA (S.M.N., M.V.P., M.P.T., P.J.W.)
| | - Nicholas S Peters
- National Heart Lung Institute & Centre for Cardiac Engineering, Imperial College London, United Kingdom (N.S.P.)
| | - Jonathan P Piccini
- Duke Clinical Research Institute, Duke University Medical Center, Durham, NC (J.P.P.)
| | - Khaldoun G Tarakji
- Department of Cardiovascular Medicine, Cleveland Clinic, OH (M.K.C., K.G.T., S.A.T.)
| | - Suma A Thomas
- Department of Cardiovascular Medicine, Cleveland Clinic, OH (M.K.C., K.G.T., S.A.T.)
| | - Natalia A Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD (N.A.T.)
| | - Mintu P Turakhia
- Division of Cardiovascular Medicine, Stanford University, CA (S.M.N., M.V.P., M.P.T., P.J.W.)
- Veterans Affairs Palo Alto Health Care System, CA (S.M.N., M.V.P., M.P.T., P.J.W.)
- Center for Digital Health, Stanford University School of Medicine, CA (M.P.T.)
| | - Paul J Wang
- Division of Cardiovascular Medicine, Stanford University, CA (S.M.N., M.V.P., M.P.T., P.J.W.)
- Veterans Affairs Palo Alto Health Care System, CA (S.M.N., M.V.P., M.P.T., P.J.W.)
| |
Collapse
|
273
|
Abstract
PURPOSE OF REVIEW Echocardiography is an indispensable tool in diagnostic cardiology and is fundamental to clinical care. Significant advances in cardiovascular imaging technology paralleled by rapid growth in electronic medical records, miniaturized devices, real-time monitoring, and wearable devices using body sensor network technology have led to the development of complex data. RECENT FINDINGS The intricate nature of these data can be overwhelming and exceed the capabilities of current statistical software. Machine learning (ML), a branch of artificial intelligence (AI), can help health care providers navigate through this complex labyrinth of information and unravel hidden discoveries. Furthermore, ML algorithms can help automate several tasks in echocardiography and clinical care. ML can serve as a valuable diagnostic tool for physicians in the field of echocardiography. In addition, it can help expand the capabilities of research and discover alternative pathways in medical management. In this review article, we describe the role of AI and ML in echocardiography.
Collapse
Affiliation(s)
- Karthik Seetharam
- West Virginia University Heart & Vascular Institute, 1 Medical Center Drive, Morgantown, WV, 26506, USA
| | - Sameer Raina
- West Virginia University Heart & Vascular Institute, 1 Medical Center Drive, Morgantown, WV, 26506, USA
| | - Partho P Sengupta
- West Virginia University Heart & Vascular Institute, 1 Medical Center Drive, Morgantown, WV, 26506, USA.
| |
Collapse
|
274
|
Improved long-term prognostic value of coronary CT angiography-derived plaque measures and clinical parameters on adverse cardiac outcome using machine learning. Eur Radiol 2020; 31:486-493. [PMID: 32725337 DOI: 10.1007/s00330-020-07083-2] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 06/21/2020] [Accepted: 07/17/2020] [Indexed: 01/27/2023]
Abstract
OBJECTIVES To evaluate the long-term prognostic value of coronary CT angiography (cCTA)-derived plaque measures and clinical parameters on major adverse cardiac events (MACE) using machine learning (ML). METHODS Datasets of 361 patients (61.9 ± 10.3 years, 65% male) with suspected coronary artery disease (CAD) who underwent cCTA were retrospectively analyzed. MACE was recorded. cCTA-derived adverse plaque features and conventional CT risk scores together with cardiovascular risk factors were provided to a ML model to predict MACE. A boosted ensemble algorithm (RUSBoost) utilizing decision trees as weak learners with repeated nested cross-validation to train and validate the model was used. Performance of the ML model was calculated using the area under the curve (AUC). RESULTS MACE was observed in 31 patients (8.6%) after a median follow-up of 5.4 years. Discriminatory power was significantly higher for the ML model (AUC 0.96 [95%CI 0.93-0.98]) compared with conventional CT risk scores including Agatston calcium score (AUC 0.84 [95%CI 0.80-0.87]), segment involvement score (AUC 0.88 [95%CI 0.84-0.91]), and segment stenosis score (AUC 0.89 [95%CI 0.86-0.92], all p < 0.05). Similar results were shown for adverse plaque measures (AUCs 0.72-0.82, all p < 0.05) and clinical parameters including the Framingham risk score (AUCs 0.71-0.76, all p < 0.05). The ML model yielded significantly higher diagnostic performance compared with logistic regression analysis (AUC 0.96 vs. 0.92, p = 0.024). CONCLUSION Integration of a ML model improves the long-term prediction of MACE when compared with conventional CT risk scores, adverse plaque measures, and clinical information. ML algorithms may improve the integration of patient's information to enhance risk stratification. KEY POINTS • A machine learning (ML) model portends high discriminatory power to predict major adverse cardiac events (MACE). • ML-based risk stratification shows superior diagnostic performance for MACE prediction over coronary CT angiography (cCTA)-derived risk scores or clinical parameters alone. • A ML model outperforms conventional logistic regression analysis for the prediction of MACE.
Collapse
|
275
|
Pieszko K, Hiczkiewicz J, Budzianowski J, Musielak B, Hiczkiewicz D, Faron W, Rzeźniczak J, Burchardt P. Clinical applications of artificial intelligence in cardiology on the verge of the decade. Cardiol J 2020; 28:460-472. [PMID: 32648252 DOI: 10.5603/cj.a2020.0093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Revised: 04/29/2020] [Accepted: 05/25/2020] [Indexed: 11/25/2022] Open
Abstract
Artificial intelligence (AI) has been hailed as the fourth industrial revolution and its influence on people's lives is increasing. The research on AI applications in medicine is progressing rapidly. This revolution shows promise for more precise diagnoses, streamlined workflows, increased accessibility to healthcare services and new insights into ever-growing population-wide datasets. While some applications have already found their way into contemporary patient care, we are still in the early days of the AI-era in medicine. Despite the popularity of these new technologies, many practitioners lack an understanding of AI methods, their benefits, and pitfalls. This review aims to provide information about the general concepts of machine learning (ML) with special focus on the applications of such techniques in cardiovascular medicine. It also sets out the current trends in research related to medical applications of AI. Along with new possibilities, new threats arise - acknowledging and understanding them is as important as understanding the ML methodology itself. Therefore, attention is also paid to the current opinions and guidelines regarding the validation and safety of AI-powered tools.
Collapse
Affiliation(s)
- Konrad Pieszko
- University of Zielona Góra, Department of Medicine and Medical Sciences, ul. Licealna 9, 65-417 Zielona Góra, Poland. .,Nowa Sól Multidisciplinary Hospital, Clinical Department of Cardiology,, ul. Chałubińskiego 7, 67-100 Nowa Sól, Poland.
| | - Jarosław Hiczkiewicz
- University of Zielona Góra, Department of Medicine and Medical Sciences, ul. Licealna 9, 65-417 Zielona Góra, Poland.,Nowa Sól Multidisciplinary Hospital, Clinical Department of Cardiology,, ul. Chałubińskiego 7, 67-100 Nowa Sól, Poland
| | - Jan Budzianowski
- University of Zielona Góra, Department of Medicine and Medical Sciences, ul. Licealna 9, 65-417 Zielona Góra, Poland.,Nowa Sól Multidisciplinary Hospital, Clinical Department of Cardiology,, ul. Chałubińskiego 7, 67-100 Nowa Sól, Poland
| | - Bogdan Musielak
- University of Zielona Góra, Department of Medicine and Medical Sciences, ul. Licealna 9, 65-417 Zielona Góra, Poland.,Nowa Sól Multidisciplinary Hospital, Clinical Department of Cardiology,, ul. Chałubińskiego 7, 67-100 Nowa Sól, Poland
| | - Dariusz Hiczkiewicz
- University of Zielona Góra, Department of Medicine and Medical Sciences, ul. Licealna 9, 65-417 Zielona Góra, Poland.,Nowa Sól Multidisciplinary Hospital, Clinical Department of Cardiology,, ul. Chałubińskiego 7, 67-100 Nowa Sól, Poland
| | - Wojciech Faron
- Nowa Sól Multidisciplinary Hospital, Clinical Department of Cardiology,, ul. Chałubińskiego 7, 67-100 Nowa Sól, Poland
| | - Janusz Rzeźniczak
- Józefa Strusia Hospital, Cardiology Clinic, Szwajcarska 3,, 61-285 Poznań, Poland
| | - Paweł Burchardt
- Józefa Strusia Hospital, Cardiology Clinic, Szwajcarska 3,, 61-285 Poznań, Poland.,Department of Biology and Environmental Protection, Poznań University of Medical Sciences, ul. Rokietnicka 8, 60-806 Poznań, Poland
| |
Collapse
|
276
|
Lee H, Martin S, Burt JR, Bagherzadeh PS, Rapaka S, Gray HN, Leonard TJ, Schwemmer C, Schoepf UJ. Machine Learning and Coronary Artery Calcium Scoring. Curr Cardiol Rep 2020; 22:90. [PMID: 32647932 DOI: 10.1007/s11886-020-01337-7] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
PURPOSE OF REVIEW To summarize current artificial intelligence (AI)-based applications for coronary artery calcium scoring (CACS) and their potential clinical impact. RECENT FINDINGS Recent evolution of AI-based technologies in medical imaging has accelerated progress in CACS performed in diverse types of CT examinations, providing promising results for future clinical application in this field. CACS plays a key role in risk stratification of coronary artery disease (CAD) and patient management. Recent emergence of AI algorithms, particularly deep learning (DL)-based applications, have provided considerable progress in CACS. Many investigations have focused on the clinical role of DL models in CACS and showed excellent agreement between those algorithms and manual scoring, not only in dedicated coronary calcium CT but also in coronary CT angiography (CCTA), low-dose chest CT, and standard chest CT. Therefore, the potential of AI-based CACS may become more influential in the future.
Collapse
Affiliation(s)
- Heon Lee
- Department of Radiology, Soonchunhyang University Hospital Bucheon, 170 Jomaru-ro, Bucheon, 14584, Republic of Korea
| | - Simon Martin
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, 25 Courtenay Drive, Charleston, SC, 29425, USA
| | - Jeremy R Burt
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, 25 Courtenay Drive, Charleston, SC, 29425, USA
| | | | - Saikiran Rapaka
- Siemens Healthcare GmbH, Siemensstr. 3, 91301, Forchheim, Germany
| | - Hunter N Gray
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, 25 Courtenay Drive, Charleston, SC, 29425, USA
| | - Tyler J Leonard
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, 25 Courtenay Drive, Charleston, SC, 29425, USA
| | - Chris Schwemmer
- Siemens Healthcare GmbH, Siemensstr. 3, 91301, Forchheim, Germany
| | - U Joseph Schoepf
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, 25 Courtenay Drive, Charleston, SC, 29425, USA.
| |
Collapse
|
277
|
Ischemia and outcome prediction by cardiac CT based machine learning. Int J Cardiovasc Imaging 2020; 36:2429-2439. [DOI: 10.1007/s10554-020-01929-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Accepted: 06/26/2020] [Indexed: 12/30/2022]
|
278
|
Abstract
The combination of pediatric cardiology being both a perceptual and a cognitive subspecialty demands a complex decision-making model which makes artificial intelligence a particularly attractive technology with great potential. The prototypical artificial intelligence system would autonomously impute patient data into a collaborative database that stores, syncs, interprets and ultimately classifies the patient's profile to specific disease phenotypes to compare against a large aggregate of shared peer health data and outcomes, the current medical body of literature and ongoing trials to offer morbidity and mortality prediction, drug therapy options targeted to each patient's genetic profile, tailored surgical plans and recommendations for timing of sequential imaging. The focus of this review paper is to offer a primer on artificial intelligence and paediatric cardiology by briefly discussing the history of artificial intelligence in medicine, modern and future applications in adult and paediatric cardiology across selected concentrations, and current barriers to implementation of these technologies.
Collapse
|
279
|
De Cannière H, Corradi F, Smeets CJP, Schoutteten M, Varon C, Van Hoof C, Van Huffel S, Groenendaal W, Vandervoort P. Wearable Monitoring and Interpretable Machine Learning Can Objectively Track Progression in Patients during Cardiac Rehabilitation. SENSORS (BASEL, SWITZERLAND) 2020; 20:E3601. [PMID: 32604829 PMCID: PMC7349532 DOI: 10.3390/s20123601] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 06/12/2020] [Accepted: 06/22/2020] [Indexed: 12/17/2022]
Abstract
Cardiovascular diseases (CVD) are often characterized by their multifactorial complexity. This makes remote monitoring and ambulatory cardiac rehabilitation (CR) therapy challenging. Current wearable multimodal devices enable remote monitoring. Machine learning (ML) and artificial intelligence (AI) can help in tackling multifaceted datasets. However, for clinical acceptance, easy interpretability of the AI models is crucial. The goal of the present study was to investigate whether a multi-parameter sensor could be used during a standardized activity test to interpret functional capacity in the longitudinal follow-up of CR patients. A total of 129 patients were followed for 3 months during CR using 6-min walking tests (6MWT) equipped with a wearable ECG and accelerometer device. Functional capacity was assessed based on 6MWT distance (6MWD). Linear and nonlinear interpretable models were explored to predict 6MWD. The t-distributed stochastic neighboring embedding (t-SNE) technique was exploited to embed and visualize high dimensional data. The performance of support vector machine (SVM) models, combining different features and using different kernel types, to predict functional capacity was evaluated. The SVM model, using chronotropic response and effort as input features, showed a mean absolute error of 42.8 m (±36.8 m). The 3D-maps derived using the t-SNE technique visualized the relationship between sensor-derived biomarkers and functional capacity, which enables tracking of the evolution of patients throughout the CR program. The current study showed that wearable monitoring combined with interpretable ML can objectively track clinical progression in a CR population. These results pave the road towards ambulatory CR.
Collapse
Affiliation(s)
- Hélène De Cannière
- Mobile Health Unit, Faculty of Medicine and Life Sciences, Hasselt University, 3500 Hasselt, Belgium; (C.J.P.S.); (M.S.); (P.V.)
- Future Health Department, Ziekenhuis Oost-Limburg, 3600 Genk, Belgium
| | - Federico Corradi
- imec the Netherlands/Holst Centre, 5656AE Eindhoven, The Netherlands; (F.C.); (W.G.)
| | - Christophe J. P. Smeets
- Mobile Health Unit, Faculty of Medicine and Life Sciences, Hasselt University, 3500 Hasselt, Belgium; (C.J.P.S.); (M.S.); (P.V.)
- Future Health Department, Ziekenhuis Oost-Limburg, 3600 Genk, Belgium
- imec the Netherlands/Holst Centre, 5656AE Eindhoven, The Netherlands; (F.C.); (W.G.)
| | - Melanie Schoutteten
- Mobile Health Unit, Faculty of Medicine and Life Sciences, Hasselt University, 3500 Hasselt, Belgium; (C.J.P.S.); (M.S.); (P.V.)
- Future Health Department, Ziekenhuis Oost-Limburg, 3600 Genk, Belgium
| | - Carolina Varon
- KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, 3001 Leuven, Belgium; (C.V.); (C.V.H.); (S.V.H.)
- TU Delft, Department of Microelectronics, Circuits and Systems (CAS), 2600AA Delft, The Netherlands
| | - Chris Van Hoof
- KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, 3001 Leuven, Belgium; (C.V.); (C.V.H.); (S.V.H.)
- imec vzw Belgium, 3001 Leuven, Belgium
| | - Sabine Van Huffel
- KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, 3001 Leuven, Belgium; (C.V.); (C.V.H.); (S.V.H.)
| | - Willemijn Groenendaal
- imec the Netherlands/Holst Centre, 5656AE Eindhoven, The Netherlands; (F.C.); (W.G.)
| | - Pieter Vandervoort
- Mobile Health Unit, Faculty of Medicine and Life Sciences, Hasselt University, 3500 Hasselt, Belgium; (C.J.P.S.); (M.S.); (P.V.)
- Future Health Department, Ziekenhuis Oost-Limburg, 3600 Genk, Belgium
- Department of Cardiology, Ziekenhuis Oost-Limburg, 3600 Genk, Belgium
| |
Collapse
|
280
|
Ting DSJ, Foo VH, Yang LWY, Sia JT, Ang M, Lin H, Chodosh J, Mehta JS, Ting DSW. Artificial intelligence for anterior segment diseases: Emerging applications in ophthalmology. Br J Ophthalmol 2020; 105:158-168. [PMID: 32532762 DOI: 10.1136/bjophthalmol-2019-315651] [Citation(s) in RCA: 86] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Revised: 02/21/2020] [Accepted: 03/24/2020] [Indexed: 12/12/2022]
Abstract
With the advancement of computational power, refinement of learning algorithms and architectures, and availability of big data, artificial intelligence (AI) technology, particularly with machine learning and deep learning, is paving the way for 'intelligent' healthcare systems. AI-related research in ophthalmology previously focused on the screening and diagnosis of posterior segment diseases, particularly diabetic retinopathy, age-related macular degeneration and glaucoma. There is now emerging evidence demonstrating the application of AI to the diagnosis and management of a variety of anterior segment conditions. In this review, we provide an overview of AI applications to the anterior segment addressing keratoconus, infectious keratitis, refractive surgery, corneal transplant, adult and paediatric cataracts, angle-closure glaucoma and iris tumour, and highlight important clinical considerations for adoption of AI technologies, potential integration with telemedicine and future directions.
Collapse
Affiliation(s)
- Darren Shu Jeng Ting
- Academic Ophthalmology, University of Nottingham, Nottingham, UK.,Department of Ophthalmology, Queen's Medical Centre, Nottingham, UK.,Singapore Eye Research Institute, Singapore
| | | | | | - Josh Tjunrong Sia
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Marcus Ang
- Singapore Eye Research Institute, Singapore.,Cornea And Ext Disease, Singapore National Eye Centre, Singapore
| | - Haotian Lin
- Sun Yat-Sen University Zhongshan Ophthalmic Center, Guangzhou, China
| | - James Chodosh
- Ophthalmology, Massachusetts Eye and Ear Infirmary Howe Laboratory Harvard Medical School, Boston, Massachusetts, USA
| | - Jodhbir S Mehta
- Singapore Eye Research Institute, Singapore.,Cornea And Ext Disease, Singapore National Eye Centre, Singapore
| | - Daniel Shu Wei Ting
- Singapore Eye Research Institute, Singapore .,Vitreo-retinal Department, Singapore National Eye Center, Singapore
| |
Collapse
|
281
|
Loncaric F, Cikes M, Sitges M, Bijnens B. Comprehensive data integration-Toward a more personalized assessment of diastolic function. Echocardiography 2020; 37:1926-1935. [PMID: 32520404 DOI: 10.1111/echo.14749] [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: 03/13/2020] [Revised: 05/04/2020] [Accepted: 05/12/2020] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND AND AIM The main challenge of assessing diastolic function is the balance between clinical utility, in the sense of usability and time-efficiency, and overall applicability, in the sense of precision for the patient under investigation. In this review, we aim to explore the challenges of integrating data in the assessment of diastolic function and discuss the perspectives of a more comprehensive data integration approach. METHODS Review of traditional and novel approaches regarding data integration in the assessment of diastolic function. RESULTS Comprehensive data integration can lead to improved understanding of disease phenotypes and better relation of these phenotypes to underlying pathophysiological processes-which may help affirm diagnostic reasoning, guide treatment options, and reduce limitations related to previously unaddressed confounders. The optimal assessment of diastolic function should ideally integrate all relevant clinical information with all available structural and functional whole cardiac cycle echocardiographic data-envisioning a personalized approach to patient care, a high-reaching future goal in medicine. CONCLUSION Complete data integration seems to be a long-lasting goal, the way forward in diastology, and machine learning seems to be one of the tools suited for the challenge. With perpetual evidence that traditional approaches to complex problems may not the optimal solution, there is room for a steady and cautious, and inherently very exciting paradigm shift toward novel diagnostic tools and workflows to reach a more personalized, comprehensive, and integrated assessment of cardiac function.
Collapse
Affiliation(s)
- Filip Loncaric
- Institute of Biomedical Research August Pi Sunyer (IDIBAPS), Barcelona, Spain
| | - Maja Cikes
- Department of Cardiovascular Diseases, University of Zagreb School of Medicine, University Hospital Centre Zagreb, Zagreb, Croatia
| | - Marta Sitges
- Institute of Biomedical Research August Pi Sunyer (IDIBAPS), Barcelona, Spain.,CERCA Programme/Generalitat de Catalunya.,Institut Clínic Cardiovascular, Hospital Clínic, Universitat de Barcelona, Barcelona, Spain.,CIBERCV, Instituto de Salud Carlos III (CB16/11/00354)
| | - Bart Bijnens
- Institute of Biomedical Research August Pi Sunyer (IDIBAPS), Barcelona, Spain.,ICREA, Barcelona, Spain.,KULeuven, Leuven, Belgium
| |
Collapse
|
282
|
Davis A, Billick K, Horton K, Jankowski M, Knoll P, Marshall JE, Paloma A, Palma R, Adams DB. Artificial Intelligence and Echocardiography: A Primer for Cardiac Sonographers. J Am Soc Echocardiogr 2020; 33:1061-1066. [PMID: 32536431 PMCID: PMC7289098 DOI: 10.1016/j.echo.2020.04.025] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Revised: 04/21/2020] [Accepted: 04/21/2020] [Indexed: 12/21/2022]
Abstract
Artificial intelligence (AI) is emerging as a key component in diagnostic medical imaging, including echocardiography. AI with deep learning has already been used with automated view labeling, measurements, and interpretation. As the development and use of AI in echocardiography increase, potential concerns may be raised by cardiac sonographers and the profession. This report, from a sonographer's perspective, focuses on defining AI, the basics of the technology, identifying some current applications of AI, and how the use of AI may improve patient care in the future. AI will have a strong role in echocardiography. AI will guide image acquisition and optimization. AI for image analysis may aid in interpretation. AI is a tool that will not replace sonographers but will help them be more efficient.
Collapse
Affiliation(s)
| | | | | | | | - Peg Knoll
- University of California, Irvine, Irvine, California
| | | | | | | | | |
Collapse
|
283
|
Lin A, Kolossváry M, Išgum I, Maurovich-Horvat P, Slomka PJ, Dey D. Artificial intelligence: improving the efficiency of cardiovascular imaging. Expert Rev Med Devices 2020; 17:565-577. [PMID: 32510252 PMCID: PMC7382901 DOI: 10.1080/17434440.2020.1777855] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Accepted: 06/01/2020] [Indexed: 12/14/2022]
Abstract
INTRODUCTION Artificial intelligence (AI) describes the use of computational techniques to mimic human intelligence. In healthcare, this typically involves large medical datasets being used to predict a diagnosis, identify new disease genotypes or phenotypes, or guide treatment strategies. Noninvasive imaging remains a cornerstone for the diagnosis, risk stratification, and management of patients with cardiovascular disease. AI can facilitate every stage of the imaging process, from acquisition and reconstruction, to segmentation, measurement, interpretation, and subsequent clinical pathways. AREAS COVERED In this paper, we review state-of-the-art AI techniques and their current applications in cardiac imaging, and discuss the future role of AI as a precision medicine tool. EXPERT OPINION Cardiovascular medicine is primed for scalable AI applications which can interpret vast amounts of clinical and imaging data in greater depth than ever before. AI-augmented medical systems have the potential to improve workflow and provide reproducible and objective quantitative results which can inform clinical decisions. In the foreseeable future, AI may work in the background of cardiac image analysis software and routine clinical reporting, automatically collecting data and enabling real-time diagnosis and risk stratification.
Collapse
Affiliation(s)
- Andrew Lin
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, United States
| | - Márton Kolossváry
- MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, Budapest, Hungary
| | - Ivana Išgum
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, Amsterdam, Netherlands
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, Amsterdam, Netherlands
- Amsterdam Cardiovascular Sciences, Amsterdam University Medical Center, Amsterdam, Netherlands
| | - Pál Maurovich-Horvat
- MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, Budapest, Hungary
- Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| | - Piotr J Slomka
- Artificial Intelligence in Medicine, Cedars-Sinai Medical Center, Los Angeles, California, United States
| | - Damini Dey
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, United States
| |
Collapse
|
284
|
Kwak S, Lee Y, Ko T, Yang S, Hwang IC, Park JB, Yoon YE, Kim HL, Kim HK, Kim YJ, Cho GY, Sohn DW, Won S, Lee SP. Unsupervised Cluster Analysis of Patients With Aortic Stenosis Reveals Distinct Population With Different Phenotypes and Outcomes. Circ Cardiovasc Imaging 2020; 13:e009707. [PMID: 32418453 DOI: 10.1161/circimaging.119.009707] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
BACKGROUND There is a lack of studies investigating the heterogeneity of patients with aortic stenosis (AS). We explored whether cluster analysis identifies distinct subgroups with different prognostic significances in AS. METHODS Newly diagnosed patients with moderate or severe AS were prospectively enrolled between 2013 and 2016 (n=398, mean 71 years, 55% male). Among demographics, laboratory, and echocardiography parameters (n=32), 11 variables were selected through dimension reduction and used for unsupervised clustering. Phenotypes and causes of mortality were compared between the clusters. RESULTS Three clusters with markedly different features were identified. Cluster 1 (n=60) was predominantly associated with cardiac dysfunction, cluster 2 (n=86) consisted of elderly with comorbidities, especially end-stage renal disease, whereas cluster 3 (n=252) demonstrated neither cardiac dysfunction nor comorbidities. Although AS severity did not differ, there was a significant difference in adverse outcomes between the clusters during a median 2.4 years follow-up (mortality rate, 13.3% versus 19.8% versus 6.0% for cluster 1, 2, and 3, P<0.001). Particularly, compared with cluster 3, cluster 1 was associated with only cardiac mortality (adjusted hazard ratio, 7.37 [95% CI, 2.00-27.13]; P=0.003), whereas cluster 2 was associated with higher noncardiac mortality (adjusted hazard ratio, 3.35 [95% CI, 1.26-8.90]; P=0.015). Phenotypes and association of clusters with specific outcomes were reproduced in an independent validation cohort (n=262). CONCLUSIONS Unsupervised cluster analysis of patients with AS revealed 3 distinct groups with different causes of death. This provides a new perspective in the categorization of patients with AS that takes into account comorbidities and extravalvular cardiac dysfunction.
Collapse
Affiliation(s)
- Soongu Kwak
- Department of Internal Medicine (S.K., S.Y., J.-B.P., H.-K.K., Y.-J.K., D.-W.S., S.-P.L.), Seoul National University Hospital
| | - Yunhwan Lee
- Department of Public Health Sciences, Seoul National University (Y.L., S.W.)
| | - Taehoon Ko
- Office of Hospital Information (T.K.), Seoul National University Hospital
| | - Seokhun Yang
- Department of Internal Medicine (S.K., S.Y., J.-B.P., H.-K.K., Y.-J.K., D.-W.S., S.-P.L.), Seoul National University Hospital
| | - In-Chang Hwang
- Department of Internal Medicine, Seoul National University Bundang Hospital, Gyeonggi-do (I.-C.H., Y.E.Y., G.-Y.C.)
| | - Jun-Bean Park
- Department of Internal Medicine (S.K., S.Y., J.-B.P., H.-K.K., Y.-J.K., D.-W.S., S.-P.L.), Seoul National University Hospital
| | - Yeonyee E Yoon
- Department of Internal Medicine, Seoul National University Bundang Hospital, Gyeonggi-do (I.-C.H., Y.E.Y., G.-Y.C.)
| | - Hack-Lyoung Kim
- Department of Internal Medicine, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, South Korea (H.-L.K.)
| | - Hyung-Kwan Kim
- Department of Internal Medicine (S.K., S.Y., J.-B.P., H.-K.K., Y.-J.K., D.-W.S., S.-P.L.), Seoul National University Hospital
| | - Yong-Jin Kim
- Department of Internal Medicine (S.K., S.Y., J.-B.P., H.-K.K., Y.-J.K., D.-W.S., S.-P.L.), Seoul National University Hospital
| | - Goo-Yeong Cho
- Department of Internal Medicine, Seoul National University Bundang Hospital, Gyeonggi-do (I.-C.H., Y.E.Y., G.-Y.C.)
| | - Dae-Won Sohn
- Department of Internal Medicine (S.K., S.Y., J.-B.P., H.-K.K., Y.-J.K., D.-W.S., S.-P.L.), Seoul National University Hospital
| | - Sungho Won
- Department of Public Health Sciences, Seoul National University (Y.L., S.W.)
| | - Seung-Pyo Lee
- Department of Internal Medicine (S.K., S.Y., J.-B.P., H.-K.K., Y.-J.K., D.-W.S., S.-P.L.), Seoul National University Hospital
| |
Collapse
|
285
|
Shi Z, Hu B, Schoepf UJ, Savage RH, Dargis DM, Pan CW, Li XL, Ni QQ, Lu GM, Zhang LJ. Artificial Intelligence in the Management of Intracranial Aneurysms: Current Status and Future Perspectives. AJNR Am J Neuroradiol 2020; 41:373-379. [PMID: 32165361 DOI: 10.3174/ajnr.a6468] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Accepted: 12/16/2019] [Indexed: 12/13/2022]
Abstract
Intracranial aneurysms with subarachnoid hemorrhage lead to high morbidity and mortality. It is of critical importance to detect aneurysms, identify risk factors of rupture, and predict treatment response of aneurysms to guide clinical interventions. Artificial intelligence has received worldwide attention for its impressive performance in image-based tasks. Artificial intelligence serves as an adjunct to physicians in a series of clinical settings, which substantially improves diagnostic accuracy while reducing physicians' workload. Computer-assisted diagnosis systems of aneurysms based on MRA and CTA using deep learning have been evaluated, and excellent performances have been reported. Artificial intelligence has also been used in automated morphologic calculation, rupture risk stratification, and outcomes prediction with the implementation of machine learning methods, which have exhibited incremental value. This review summarizes current advances of artificial intelligence in the management of aneurysms, including detection and prediction. The challenges and future directions of clinical implementations of artificial intelligence are briefly discussed.
Collapse
Affiliation(s)
- Z Shi
- From the Department of Medical Imaging (Z.S., B.H., Q.Q.N., G.M.L., L.J.Z.), Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, China
| | - B Hu
- From the Department of Medical Imaging (Z.S., B.H., Q.Q.N., G.M.L., L.J.Z.), Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, China
| | - U J Schoepf
- Division of Cardiovascular Imaging (U.J.S., R.H.S., D.M.D.), Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina
| | - R H Savage
- Division of Cardiovascular Imaging (U.J.S., R.H.S., D.M.D.), Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina
| | - D M Dargis
- Division of Cardiovascular Imaging (U.J.S., R.H.S., D.M.D.), Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina
| | - C W Pan
- DeepWise AI Lab (C.W.P., X.L.L.), Beijing, China
| | - X L Li
- DeepWise AI Lab (C.W.P., X.L.L.), Beijing, China.,Peng Cheng Laboratory (X.L.L.), Vanke Cloud City Phase I, Nanshan District, Shenzhen, Guangdong, China
| | - Q Q Ni
- From the Department of Medical Imaging (Z.S., B.H., Q.Q.N., G.M.L., L.J.Z.), Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, China
| | - G M Lu
- From the Department of Medical Imaging (Z.S., B.H., Q.Q.N., G.M.L., L.J.Z.), Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, China
| | - L J Zhang
- From the Department of Medical Imaging (Z.S., B.H., Q.Q.N., G.M.L., L.J.Z.), Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, China
| |
Collapse
|
286
|
|
287
|
Raffort J, Adam C, Carrier M, Ballaith A, Coscas R, Jean-Baptiste E, Hassen-Khodja R, Chakfé N, Lareyre F. Artificial intelligence in abdominal aortic aneurysm. J Vasc Surg 2020; 72:321-333.e1. [PMID: 32093909 DOI: 10.1016/j.jvs.2019.12.026] [Citation(s) in RCA: 79] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2019] [Accepted: 12/07/2019] [Indexed: 12/11/2022]
Abstract
OBJECTIVE Abdominal aortic aneurysm (AAA) is a life-threatening disease, and the only curative treatment relies on open or endovascular repair. The decision to treat relies on the evaluation of the risk of AAA growth and rupture, which can be difficult to assess in practice. Artificial intelligence (AI) has revealed new insights into the management of cardiovascular diseases, but its application in AAA has so far been poorly described. The aim of this review was to summarize the current knowledge on the potential applications of AI in patients with AAA. METHODS A comprehensive literature review was performed. The MEDLINE database was searched according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The search strategy used a combination of keywords and included studies using AI in patients with AAA published between May 2019 and January 2000. Two authors independently screened titles and abstracts and performed data extraction. The search of published literature identified 34 studies with distinct methodologies, aims, and study designs. RESULTS AI was used in patients with AAA to improve image segmentation and for quantitative analysis and characterization of AAA morphology, geometry, and fluid dynamics. AI allowed computation of large data sets to identify patterns that may be predictive of AAA growth and rupture. Several predictive and prognostic programs were also developed to assess patients' postoperative outcomes, including mortality and complications after endovascular aneurysm repair. CONCLUSIONS AI represents a useful tool in the interpretation and analysis of AAA imaging by enabling automatic quantitative measurements and morphologic characterization. It could be used to help surgeons in preoperative planning. AI-driven data management may lead to the development of computational programs for the prediction of AAA evolution and risk of rupture as well as postoperative outcomes. AI could also be used to better evaluate the indications and types of surgical treatment and to plan the postoperative follow-up. AI represents an attractive tool for decision-making and may facilitate development of personalized therapeutic approaches for patients with AAA.
Collapse
Affiliation(s)
- Juliette Raffort
- Clinical Chemistry Laboratory, University Hospital of Nice, Nice, France; Université Côte d'Azur, CHU, Inserm U1065, C3M, Nice, France
| | - Cédric Adam
- Laboratory of Applied Mathematics and Computer Science (MICS), CentraleSupélec, Université Paris-Saclay, Paris, France
| | - Marion Carrier
- Laboratory of Applied Mathematics and Computer Science (MICS), CentraleSupélec, Université Paris-Saclay, Paris, France
| | - Ali Ballaith
- Department of Vascular Surgery, University Hospital of Nice, Nice, France
| | - Raphael Coscas
- Department of Vascular Surgery, Ambroise Paré University Hospital, Assistance Publique-Hôpitaux de Paris (AP-HP), Boulogne, France; Inserm U1018 Team 5, Versailles-Saint-Quentin et Paris-Saclay Universities, Versailles, France
| | - Elixène Jean-Baptiste
- Université Côte d'Azur, CHU, Inserm U1065, C3M, Nice, France; Department of Vascular Surgery, University Hospital of Nice, Nice, France
| | - Réda Hassen-Khodja
- Université Côte d'Azur, CHU, Inserm U1065, C3M, Nice, France; Department of Vascular Surgery, University Hospital of Nice, Nice, France
| | - Nabil Chakfé
- Department of Vascular Surgery and Kidney Transplantation, University Hospital of Strasbourg, and GEPROVAS, Strasbourg, France
| | - Fabien Lareyre
- Université Côte d'Azur, CHU, Inserm U1065, C3M, Nice, France; Department of Vascular Surgery, University Hospital of Nice, Nice, France.
| |
Collapse
|
288
|
Machine Learning and Deep Neural Networks Applications in Coronary Flow Assessment. J Thorac Imaging 2020; 35 Suppl 1:S66-S71. [DOI: 10.1097/rti.0000000000000483] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
|
289
|
Clinical Inference From Cardiovascular Imaging: Paradigm Shift Towards Machine-Based Intelligent Platform. CURRENT TREATMENT OPTIONS IN CARDIOVASCULAR MEDICINE 2020. [DOI: 10.1007/s11936-020-0805-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
|
290
|
Eisenberg E, McElhinney PA, Commandeur F, Chen X, Cadet S, Goeller M, Razipour A, Gransar H, Cantu S, Miller RJH, Slomka PJ, Wong ND, Rozanski A, Achenbach S, Tamarappoo BK, Berman DS, Dey D. Deep Learning-Based Quantification of Epicardial Adipose Tissue Volume and Attenuation Predicts Major Adverse Cardiovascular Events in Asymptomatic Subjects. Circ Cardiovasc Imaging 2020; 13:e009829. [PMID: 32063057 DOI: 10.1161/circimaging.119.009829] [Citation(s) in RCA: 75] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
BACKGROUND Epicardial adipose tissue (EAT) volume (cm3) and attenuation (Hounsfield units) may predict major adverse cardiovascular events (MACE). We aimed to evaluate the prognostic value of fully automated deep learning-based EAT volume and attenuation measurements quantified from noncontrast cardiac computed tomography. METHODS Our study included 2068 asymptomatic subjects (56±9 years, 59% male) from the EISNER trial (Early Identification of Subclinical Atherosclerosis by Noninvasive Imaging Research) with long-term follow-up after coronary artery calcium measurement. EAT volume and mean attenuation were quantified using automated deep learning software from noncontrast cardiac computed tomography. MACE was defined as myocardial infarction, late (>180 days) revascularization, and cardiac death. EAT measures were compared to coronary artery calcium score and atherosclerotic cardiovascular disease risk score for MACE prediction. RESULTS At 14±3 years, 223 subjects suffered MACE. Increased EAT volume and decreased EAT attenuation were both independently associated with MACE. Atherosclerotic cardiovascular disease risk score, coronary artery calcium, and EAT volume were associated with increased risk of MACE (hazard ratio [95%CI]: 1.03 [1.01-1.04]; 1.25 [1.19-1.30]; and 1.35 [1.07-1.68], P<0.01 for all) and EAT attenuation was inversely associated with MACE (hazard ratio, 0.83 [95% CI, 0.72-0.96]; P=0.01), with corresponding Harrell C statistic of 0.76. MACE risk progressively increased with EAT volume ≥113 cm3 and coronary artery calcium ≥100 AU and was highest in subjects with both (P<0.02 for all). In 1317 subjects, EAT volume was correlated with inflammatory biomarkers C-reactive protein, myeloperoxidase, and adiponectin reduction; EAT attenuation was inversely related to these biomarkers. CONCLUSIONS Fully automated EAT volume and attenuation quantification by deep learning from noncontrast cardiac computed tomography can provide prognostic value for the asymptomatic patient, without additional imaging or physician interaction.
Collapse
Affiliation(s)
- Evann Eisenberg
- Department of Imaging and Medicine and the Smidt Heart Institute (E.E., S.C., H.G., S.C., R.J.H.M., P.J.S., B.K.T., D.S.B.), Cedars-Sinai Medical Center, Los Angeles, CA
| | - Priscilla A McElhinney
- Biomedical Imaging Research Institute (P.A.M., F.C., X.C., M.G., A.R., D.D.), Cedars-Sinai Medical Center, Los Angeles, CA
| | - Frederic Commandeur
- Biomedical Imaging Research Institute (P.A.M., F.C., X.C., M.G., A.R., D.D.), Cedars-Sinai Medical Center, Los Angeles, CA
| | - Xi Chen
- Biomedical Imaging Research Institute (P.A.M., F.C., X.C., M.G., A.R., D.D.), Cedars-Sinai Medical Center, Los Angeles, CA
| | - Sebastien Cadet
- Department of Imaging and Medicine and the Smidt Heart Institute (E.E., S.C., H.G., S.C., R.J.H.M., P.J.S., B.K.T., D.S.B.), Cedars-Sinai Medical Center, Los Angeles, CA
| | - Markus Goeller
- Biomedical Imaging Research Institute (P.A.M., F.C., X.C., M.G., A.R., D.D.), Cedars-Sinai Medical Center, Los Angeles, CA.,Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Faculty of Medicine, Department of Cardiology, Erlangen, Germany (M.G., S.A.)
| | - Aryabod Razipour
- Biomedical Imaging Research Institute (P.A.M., F.C., X.C., M.G., A.R., D.D.), Cedars-Sinai Medical Center, Los Angeles, CA
| | - Heidi Gransar
- Department of Imaging and Medicine and the Smidt Heart Institute (E.E., S.C., H.G., S.C., R.J.H.M., P.J.S., B.K.T., D.S.B.), Cedars-Sinai Medical Center, Los Angeles, CA
| | - Stephanie Cantu
- Department of Imaging and Medicine and the Smidt Heart Institute (E.E., S.C., H.G., S.C., R.J.H.M., P.J.S., B.K.T., D.S.B.), Cedars-Sinai Medical Center, Los Angeles, CA
| | - Robert J H Miller
- Department of Imaging and Medicine and the Smidt Heart Institute (E.E., S.C., H.G., S.C., R.J.H.M., P.J.S., B.K.T., D.S.B.), Cedars-Sinai Medical Center, Los Angeles, CA
| | - Piotr J Slomka
- Department of Imaging and Medicine and the Smidt Heart Institute (E.E., S.C., H.G., S.C., R.J.H.M., P.J.S., B.K.T., D.S.B.), Cedars-Sinai Medical Center, Los Angeles, CA
| | - Nathan D Wong
- Department of Medicine, University of California at Irvine, CA (N.D.W.)
| | - Alan Rozanski
- Division of Cardiology, Mount Sinai St Lukes Hospital, New York, NY (A.R.)
| | - Stephan Achenbach
- Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Faculty of Medicine, Department of Cardiology, Erlangen, Germany (M.G., S.A.)
| | - Balaji K Tamarappoo
- Department of Imaging and Medicine and the Smidt Heart Institute (E.E., S.C., H.G., S.C., R.J.H.M., P.J.S., B.K.T., D.S.B.), Cedars-Sinai Medical Center, Los Angeles, CA
| | - Daniel S Berman
- Department of Imaging and Medicine and the Smidt Heart Institute (E.E., S.C., H.G., S.C., R.J.H.M., P.J.S., B.K.T., D.S.B.), Cedars-Sinai Medical Center, Los Angeles, CA
| | - Damini Dey
- Biomedical Imaging Research Institute (P.A.M., F.C., X.C., M.G., A.R., D.D.), Cedars-Sinai Medical Center, Los Angeles, CA
| |
Collapse
|
291
|
Dockerill C, Lapidaire W, Lewandowski AJ, Leeson P. Cardiac remodelling and exercise: What happens with ultra-endurance exercise? Eur J Prev Cardiol 2020; 27:1464-1466. [PMID: 32053012 PMCID: PMC7521004 DOI: 10.1177/2047487320904511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Affiliation(s)
- Cameron Dockerill
- Oxford Cardiovascular Clinical Research Facility, University of Oxford, Oxford, UK
| | - Winok Lapidaire
- Oxford Cardiovascular Clinical Research Facility, University of Oxford, Oxford, UK
| | - Adam J Lewandowski
- Oxford Cardiovascular Clinical Research Facility, University of Oxford, Oxford, UK
| | - Paul Leeson
- Oxford Cardiovascular Clinical Research Facility, University of Oxford, Oxford, UK
| |
Collapse
|
292
|
Ordovas KG, Seo Y. Artificial Intelligence Pipeline for Risk Prediction in Cardiovascular Imaging. Circ Cardiovasc Imaging 2020; 13:e010427. [DOI: 10.1161/circimaging.120.010427] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Affiliation(s)
- Karen G. Ordovas
- Department of Radiology and Biomedical Imaging, University of California San Francisco
| | - Youngho Seo
- Department of Radiology and Biomedical Imaging, University of California San Francisco
| |
Collapse
|
293
|
Benincasa G, Marfella R, Della Mura N, Schiano C, Napoli C. Strengths and Opportunities of Network Medicine in Cardiovascular Diseases. Circ J 2020; 84:144-152. [DOI: 10.1253/circj.cj-19-0879] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Giuditta Benincasa
- Clinical Department of Internal Medicine and Specialistics, Department of Advanced Clinical and Surgical Sciences, University of Campania “Luigi Vanvitelli”
| | - Raffaele Marfella
- Clinical Department of Internal Medicine and Specialistics, Department of Advanced Clinical and Surgical Sciences, University of Campania “Luigi Vanvitelli”
| | | | - Concetta Schiano
- Clinical Department of Internal Medicine and Specialistics, Department of Advanced Clinical and Surgical Sciences, University of Campania “Luigi Vanvitelli”
| | - Claudio Napoli
- Clinical Department of Internal Medicine and Specialistics, Department of Advanced Clinical and Surgical Sciences, University of Campania “Luigi Vanvitelli”
- IRCCS-SDN
| |
Collapse
|
294
|
de Marvao A, Dawes TJ, Howard JP, O'Regan DP. Artificial intelligence and the cardiologist: what you need to know for 2020. Heart 2020; 106:399-400. [PMID: 31974212 PMCID: PMC7035692 DOI: 10.1136/heartjnl-2019-316033] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Affiliation(s)
- Antonio de Marvao
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
| | - Timothy Jw Dawes
- MRC London Institute of Medical Sciences, Imperial College London, London, UK.,National Heart and Lung Institute, Imperial College London, London, UK
| | | | - Declan P O'Regan
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
| |
Collapse
|
295
|
Abstract
Clinical decisions are based on a combination of inductive inference built on experience (ie, statistical models) and on deductions provided by our understanding of the workings of the cardiovascular system (ie, mechanistic models). In a similar way, computers can be used to discover new hidden patterns in the (big) data and to make predictions based on our knowledge of physiology or physics. Surprisingly, unlike humans through history, computers seldom combine inductive and deductive processes. An explosion of expectations surrounds the computer's inductive method, fueled by the "big data" and popular trends. This article reviews the risks and potential pitfalls of this computer approach, where the lack of generality, selection or confounding biases, overfitting, or spurious correlations are among the commonplace flaws. Recommendations to reduce these risks include an examination of data through the lens of causality, the careful choice and description of statistical techniques, and an open research culture with transparency. Finally, the synergy between mechanistic and statistical models (ie, the digital twin) is discussed as a promising pathway toward precision cardiology that mimics the human experience.
Collapse
Affiliation(s)
- Pablo Lamata
- Department of Biomedical Engineering, King's College London, UK
| |
Collapse
|
296
|
Artificial intelligence, machine learning, vascular surgery, automatic image processing. Implications for clinical practice. ANGIOLOGIA 2020. [DOI: 10.20960/angiologia.00177] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
|
297
|
Lim LJ, Tison GH, Delling FN. Artificial Intelligence in Cardiovascular Imaging. Methodist Debakey Cardiovasc J 2020; 16:138-145. [PMID: 32670474 PMCID: PMC7350824 DOI: 10.14797/mdcj-16-2-138] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
The number of cardiovascular imaging studies is growing exponentially, and so is the need to improve clinical workflow efficiency and avoid missed diagnoses. With the availability and use of large datasets, artificial intelligence (AI) has the potential to improve patient care at every stage of the imaging chain. Current literature indicates that in the short-term, AI has the capacity to reduce human error and save time in the clinical workflow through automated segmentation of cardiac structures. In the future, AI may expand the informational value of diagnostic images based on images alone or a combination of images and clinical variables, thus facilitating disease detection, prognosis, and decision making. This review describes the role of AI, specifically machine learning, in multimodality imaging, including echocardiography, nuclear imaging, computed tomography, and cardiac magnetic resonance, and highlights current uses of AI as well as potential challenges to its widespread implementation.
Collapse
Affiliation(s)
- Lisa J. Lim
- UNIVERSITY OF CALIFORNIA SAN FRANCISCO, SAN FRANCISCO, CALIFORNIA
| | | | | |
Collapse
|
298
|
Barone-Rochette G. Will artificial intelligence change the job of the cardiac imaging specialist? Arch Cardiovasc Dis 2019; 113:1-4. [PMID: 31899110 DOI: 10.1016/j.acvd.2019.11.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Revised: 11/18/2019] [Accepted: 11/19/2019] [Indexed: 10/25/2022]
Affiliation(s)
- Gilles Barone-Rochette
- Department of Cardiology, University Hospital, Grenoble Alpes, France; Inserm, U1039, Radiopharmaceutiques Biocliniques, Grenoble Alpes University, France; French Alliance Clinical Trial, French Clinical Research Infrastructure Network, France.
| |
Collapse
|
299
|
Raffort J, Adam C, Carrier M, Lareyre F. Fundamentals in Artificial Intelligence for Vascular Surgeons. Ann Vasc Surg 2019; 65:254-260. [PMID: 31857229 DOI: 10.1016/j.avsg.2019.11.037] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2019] [Revised: 11/17/2019] [Accepted: 11/21/2019] [Indexed: 12/31/2022]
Abstract
Artificial intelligence (AI) corresponds to a broad discipline that aims to design systems, which display properties of human intelligence. While it has led to many advances and applications in daily life, its introduction in medicine is still in its infancy. AI has created interesting perspectives for medical research and clinical practice but has been sometimes associated with hype leading to a misunderstanding of its real capabilities. Here, we aim to introduce the fundamental notions of AI and to bring an overview of its potential applications for medical and surgical practice. In the limelight of current knowledge, limits and challenges to face as well as future directions are discussed.
Collapse
Affiliation(s)
- Juliette Raffort
- Clinical Chemistry Laboratory, University Hospital of Nice, Nice, France; Université Côte d'Azur, CHU, Inserm U1065, C3M, Nice, France.
| | - Cédric Adam
- Laboratory of Applied Mathematics and Computer Science (MICS), CentraleSupélec, Université Paris-Saclay, Paris, France
| | - Marion Carrier
- Laboratory of Applied Mathematics and Computer Science (MICS), CentraleSupélec, Université Paris-Saclay, Paris, France
| | - Fabien Lareyre
- Université Côte d'Azur, CHU, Inserm U1065, C3M, Nice, France; Department of Vascular Surgery, University Hospital of Nice, Nice, France
| |
Collapse
|
300
|
von Knebel Doeberitz PL, De Cecco CN, Schoepf UJ, Albrecht MH, van Assen M, De Santis D, Gaskins J, Martin S, Bauer MJ, Ebersberger U, Giovagnoli DA, Varga-Szemes A, Bayer RR, Schönberg SO, Tesche C. Impact of Coronary Computerized Tomography Angiography-Derived Plaque Quantification and Machine-Learning Computerized Tomography Fractional Flow Reserve on Adverse Cardiac Outcome. Am J Cardiol 2019; 124:1340-1348. [PMID: 31481177 DOI: 10.1016/j.amjcard.2019.07.061] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/19/2019] [Revised: 07/16/2019] [Accepted: 07/18/2019] [Indexed: 12/16/2022]
Abstract
This study investigated the impact of coronary CT angiography (cCTA)-derived plaque markers and machine-learning-based CT-derived fractional flow reserve (CT-FFR) to identify adverse cardiac outcome. Data of 82 patients (60 ± 11 years, 62% men) who underwent cCTA and invasive coronary angiography (ICA) were analyzed in this single-center retrospective, institutional review board-approved, HIPAA-compliant study. Follow-up was performed to record major adverse cardiac events (MACE). Plaque quantification of lesions responsible for MACE and control lesions was retrospectively performed semiautomatically from cCTA together with machine-learning based CT-FFR. The discriminatory value of plaque markers and CT-FFR to predict MACE was evaluated. After a median follow-up of 18.5 months (interquartile range 11.5 to 26.6 months), MACE was observed in 18 patients (21%). In a multivariate analysis the following markers were predictors of MACE (odds ratio [OR]): lesion length (OR 1.16, p = 0.018), low-attenuation plaque (<30 HU) (OR 4.59, p = 0.003), Napkin ring sign (OR 2.71, p = 0.034), stenosis ≥50% (OR 3.83, p 0.042), and CT-FFR ≤0.80 (OR 7.78, p = 0.001). Receiver operating characteristics analysis including stenosis ≥50%, plaque markers and CT-FFR ≤0.80 (Area under the curve 0.94) showed incremental discriminatory power over stenosis ≥50% alone (Area under the curve 0.60, p <0.0001) for the prediction of MACE. cCTA-derived plaque markers and machine-learning CT-FFR demonstrate predictive value to identify MACE. In conclusion, combining plaque markers with machine-learning CT-FFR shows incremental discriminatory power over cCTA stenosis grading alone.
Collapse
Affiliation(s)
- Philipp L von Knebel Doeberitz
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina; Institute of Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim-Heidelberg University, Mannheim, Germany
| | - Carlo N De Cecco
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina; Division of Cardiothoracic Imaging, Nuclear Medicine and Molecular Imaging, Department of Radiology and Imaging Sciences, Emory University Hospital, Atlanta, Georgia
| | - U Joseph Schoepf
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina; Division of Cardiology, Department of Medicine, Medical University of South Carolina, Charleston, South Carolina.
| | - Moritz H Albrecht
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina; Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt, Germany
| | - Marly van Assen
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina; Center for Medical Imaging North East Netherlands, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Domenico De Santis
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina; Department of Radiological Sciences, Oncology and Pathology, University of Rome "Sapienza", Rome, Italy
| | - Jeffrey Gaskins
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina
| | - Simon Martin
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina; Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt, Germany
| | - Maximilian J Bauer
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina
| | - Ullrich Ebersberger
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina; Kardiologie MVZ München-Nord, Munich, Germany; Department of Cardiology, Munich University Clinic, Ludwig-Maximilians-University, Munich, Germany
| | - Dante A Giovagnoli
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina
| | - Akos Varga-Szemes
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina
| | - Richard R Bayer
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina; Division of Cardiology, Department of Medicine, Medical University of South Carolina, Charleston, South Carolina
| | - Stefan O Schönberg
- Institute of Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim-Heidelberg University, Mannheim, Germany
| | - Christian Tesche
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina; Department of Cardiology, Munich University Clinic, Ludwig-Maximilians-University, Munich, Germany; Department of Internal Medicine, St. Johannes-Hospital, Dortmund, Germany
| |
Collapse
|