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Zhang J, Dawkins A. Artificial Intelligence in Ultrasound Imaging: Where Are We Now? Ultrasound Q 2024; 40:93-97. [PMID: 38842384 DOI: 10.1097/ruq.0000000000000680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2024]
Affiliation(s)
- Jie Zhang
- From the Department of Radiology, University of Kentucky, Lexington, KY
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2
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Hoit BD. Deep Learning, Constrictive Pericarditis, and its Occasional Doppelganger: A Step Closer to Clinical Realization. JACC Cardiovasc Imaging 2024; 17:361-363. [PMID: 38032588 DOI: 10.1016/j.jcmg.2023.10.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 10/16/2023] [Indexed: 12/01/2023]
Affiliation(s)
- Brian D Hoit
- Harrington Heart and Vascular Institute, University Hospitals Cleveland Medical Center, Department of Medicine, Case Western Reserve University, Cleveland, Ohio, USA.
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3
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Alyami J. Computer-aided analysis of radiological images for cancer diagnosis: performance analysis on benchmark datasets, challenges, and directions. EJNMMI REPORTS 2024; 8:7. [PMID: 38748374 PMCID: PMC10982256 DOI: 10.1186/s41824-024-00195-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 02/05/2024] [Indexed: 05/19/2024]
Abstract
Radiological image analysis using machine learning has been extensively applied to enhance biopsy diagnosis accuracy and assist radiologists with precise cures. With improvements in the medical industry and its technology, computer-aided diagnosis (CAD) systems have been essential in detecting early cancer signs in patients that could not be observed physically, exclusive of introducing errors. CAD is a detection system that combines artificially intelligent techniques with image processing applications thru computer vision. Several manual procedures are reported in state of the art for cancer diagnosis. Still, they are costly, time-consuming and diagnose cancer in late stages such as CT scans, radiography, and MRI scan. In this research, numerous state-of-the-art approaches on multi-organs detection using clinical practices are evaluated, such as cancer, neurological, psychiatric, cardiovascular and abdominal imaging. Additionally, numerous sound approaches are clustered together and their results are assessed and compared on benchmark datasets. Standard metrics such as accuracy, sensitivity, specificity and false-positive rate are employed to check the validity of the current models reported in the literature. Finally, existing issues are highlighted and possible directions for future work are also suggested.
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Affiliation(s)
- Jaber Alyami
- Department of Radiological Sciences, Faculty of Applied Medical Sciences, King Abdulaziz University, 21589, Jeddah, Saudi Arabia.
- King Fahd Medical Research Center, King Abdulaziz University, 21589, Jeddah, Saudi Arabia.
- Smart Medical Imaging Research Group, King Abdulaziz University, 21589, Jeddah, Saudi Arabia.
- Medical Imaging and Artificial Intelligence Research Unit, Center of Modern Mathematical Sciences and its Applications, King Abdulaziz University, 21589, Jeddah, Saudi Arabia.
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Özbek Güven G, Yilmaz Ş, Inceoğlu F. Determining medical students' anxiety and readiness levels about artificial intelligence. Heliyon 2024; 10:e25894. [PMID: 38384508 PMCID: PMC10878911 DOI: 10.1016/j.heliyon.2024.e25894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 01/23/2024] [Accepted: 02/05/2024] [Indexed: 02/23/2024] Open
Abstract
The aim of this study is to determine the levels of anxiety and readiness among medical students regarding artificial intelligence (AI) and examine the relationship between these factors. The research was conducted on medical students, and the data was collected through face-to-face and online surveys between April and June 2022. The study utilized a socio-demographic information form, an AI anxiety scale, and a medical AI readiness scale. The data collected from a total of 542 students were analyzed using the Statistical Program for Social Sciences (SPSS) version 25. Cronbach's α coefficient was used for reliability analysis. A path diagram was created using AMOS 24, and structural equation modelling (SEM) analysis was applied. The findings of the study indicate that medical students have a moderate level of readiness and a high level of anxiety regarding AI. Furthermore, an inverse relationship was found between AI readiness and AI anxiety. These results highlight the importance of increasing the preparedness of medical students for AI applications and reducing their anxieties. The study suggests the inclusion of AI in the medical curriculum and the development of a standardized curriculum to facilitate its teaching.
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Affiliation(s)
- Gamze Özbek Güven
- Department of Medical History and Ethics, School of Medicine, Yuksek Ihtisas University, Ankara, Türkiye
| | - Şerife Yilmaz
- Department of Medical History and Ethics, School of Medicine, Harran University, Şanlıurfa, Türkiye
| | - Feyza Inceoğlu
- Department of Biostatistics, School of Medicine, Malatya Turgut Ozal University, Malatya, Türkiye
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Sandeep B, Liu X, Huang X, Wang X, Mao L, Xiao Z. Feasibility of artificial intelligence its current status, clinical applications, and future direction in cardiovascular disease. Curr Probl Cardiol 2024; 49:102349. [PMID: 38103818 DOI: 10.1016/j.cpcardiol.2023.102349] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Accepted: 12/13/2023] [Indexed: 12/19/2023]
Abstract
In routine clinical practice, the diagnosis and treatment of cardiovascular disease (CVD) rely on data in a variety of formats. These formats comprise invasive angiography, laboratory data, non-invasive imaging diagnostics, and patient history. Artificial intelligence (AI) is a field of computer science that aims to mimic human thought processes, learning capacity, and knowledge storage. In cardiovascular medicine, artificial intelligence (AI) algorithms have been used to discover novel genotypes and phenotypes in established diseases enhance patient care, enable cost effectiveness, and lower readmission and mortality rates. AI will lead to a paradigm change toward precision cardiovascular medicine in the near future. The promise application of AI in cardiovascular medicine is immense; however, failure to recognize and ignorance of the challenges may overshadow its potential clinical impact. AI can facilitate every stage in cardiology in the imaging process, from acquisition and reconstruction, to segmentation, measurement, interpretation, and subsequent clinical pathways. Along with new possibilities, new threats arise, acknowledging and understanding them is as important as understanding the machine learning (ML) methodology itself. Therefore, attention is also paid to the current opinions and guidelines regarding the validation and safety of AI. This paper provides a outline for clinicians on relevant aspects of AI and machine learning, selection of applications and methods in cardiology to date, and identifies how cardiovascular medicine could incorporate AI in the future. 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.
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Affiliation(s)
- Bhushan Sandeep
- Department of Cardio-Thoracic Surgery, Chengdu Second People's Hospital, Chengdu, Sichuan 610017, China.
| | - Xian Liu
- Department of Cardio-Thoracic Surgery, Chengdu Second People's Hospital, Chengdu, Sichuan 610017, China
| | - Xin Huang
- Department of Anesthesiology, West China Hospital of Medicine, Sichuan University, Chengdu, Sichuan 610017, China
| | - Xiaowei Wang
- Department of Cardio-Thoracic Surgery, Chengdu Second People's Hospital, Chengdu, Sichuan 610017, China
| | - Long Mao
- Department of Cardio-Thoracic Surgery, Chengdu Second People's Hospital, Chengdu, Sichuan 610017, China
| | - Zongwei Xiao
- Department of Cardio-Thoracic Surgery, Chengdu Second People's Hospital, Chengdu, Sichuan 610017, China
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Cau R, Pisu F, Suri JS, Montisci R, Gatti M, Mannelli L, Gong X, Saba L. Artificial Intelligence in the Differential Diagnosis of Cardiomyopathy Phenotypes. Diagnostics (Basel) 2024; 14:156. [PMID: 38248033 PMCID: PMC11154548 DOI: 10.3390/diagnostics14020156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 01/03/2024] [Accepted: 01/08/2024] [Indexed: 01/23/2024] Open
Abstract
Artificial intelligence (AI) is rapidly being applied to the medical field, especially in the cardiovascular domain. AI approaches have demonstrated their applicability in the detection, diagnosis, and management of several cardiovascular diseases, enhancing disease stratification and typing. Cardiomyopathies are a leading cause of heart failure and life-threatening ventricular arrhythmias. Identifying the etiologies is fundamental for the management and diagnostic pathway of these heart muscle diseases, requiring the integration of various data, including personal and family history, clinical examination, electrocardiography, and laboratory investigations, as well as multimodality imaging, making the clinical diagnosis challenging. In this scenario, AI has demonstrated its capability to capture subtle connections from a multitude of multiparametric datasets, enabling the discovery of hidden relationships in data and handling more complex tasks than traditional methods. This review aims to present a comprehensive overview of the main concepts related to AI and its subset. Additionally, we review the existing literature on AI-based models in the differential diagnosis of cardiomyopathy phenotypes, and we finally examine the advantages and limitations of these AI approaches.
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Affiliation(s)
- Riccardo Cau
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari-Polo di Monserrato s.s. 554 Monserrato, 09045 Cagliari, Italy; (R.C.); (F.P.)
| | - Francesco Pisu
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari-Polo di Monserrato s.s. 554 Monserrato, 09045 Cagliari, Italy; (R.C.); (F.P.)
| | - Jasjit S. Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoin™, Roseville, CA 95661, USA;
| | - Roberta Montisci
- Department of Cardiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari-Polo di Monserrato s.s. 554 Monserrato, 09045 Cagliari, Italy;
| | - Marco Gatti
- Department of Radiology, Università degli Studi di Torino, 10129 Turin, Italy;
| | | | - Xiangyang Gong
- Radiology Department, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou 310014, China;
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari-Polo di Monserrato s.s. 554 Monserrato, 09045 Cagliari, Italy; (R.C.); (F.P.)
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Truong NM, Vo TQ, Tran HTB, Nguyen HT, Pham VNH. Healthcare students' knowledge, attitudes, and perspectives toward artificial intelligence in the southern Vietnam. Heliyon 2023; 9:e22653. [PMID: 38107295 PMCID: PMC10724669 DOI: 10.1016/j.heliyon.2023.e22653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 11/15/2023] [Accepted: 11/16/2023] [Indexed: 12/19/2023] Open
Abstract
The application of new technologies in medical education still lags behind the extraordinary advances of AI. This study examined the understanding, attitudes, and perspectives of Vietnamese medical students toward AI and its consequences, as well as their knowledge of existing AI operations in Vietnam. A cross-sectional online survey was administered to 1142 students enrolled in undergraduate medicine and pharmacy programs. Most of the participants had no understanding of AI in healthcare (1053 or 92.2 %). The majority believed that AI would benefit their careers (890 or 77.9 %) and that such innovation will be used to oversee public health and epidemic prevention on their behalf (882 or 77.2 %). The proportion of students with satisfactory knowledge significantly differed depending on gender (P < 0.001), major (P = 0.003), experience (P < 0.001), and income (P = 0.011). The percentage of respondents with positive attitudes significantly differed by year level (P = 0.008) and income (P = 0.003), and the proportion with favorable perspectives regarding AI varied considerably by age (P = 0.046) and major (P < 0.001). Most of the participants wanted to integrate AI into radiology and digital imaging training (P = 0.283), while the fifth-year students wished to learn about AI in medical genetics and genomics (P < 0.001, 4.0 ± 0.8). The male students had 1.898 times more adequate knowledge of AI than their female counterparts, and those who had attended webinars/lectures/courses on AI in healthcare had 4.864 times more adequate knowledge than those having no such experiences. The majority believed that the barrier to implementing AI in healthcare is the lack of financial resources (83.54 %) and appropriate training (81.00 %). Participants saw AI as a "partner" rather than a "competitor", but the majority of low knowledge was recorded. Future research should take into account the way to integrate AI into medical training programs for healthcare students.
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Affiliation(s)
- Nguyen Minh Truong
- Faculty of Pharmacy, Pham Ngoc Thach University of Medicine, Ho Chi Minh City, 700000, Viet Nam
| | - Trung Quang Vo
- Faculty of Pharmacy, Pham Ngoc Thach University of Medicine, Ho Chi Minh City, 700000, Viet Nam
| | - Hien Thi Bich Tran
- Faculty of Pharmacy, Pham Ngoc Thach University of Medicine, Ho Chi Minh City, 700000, Viet Nam
| | - Hiep Thanh Nguyen
- Faculty of Medicine, Pham Ngoc Thach University of Medicine, Ho Chi Minh City, 700000, Viet Nam
| | - Van Nu Hanh Pham
- Faculty of Pharmaceutical Management and Economic, Hanoi University of Pharmacy, Hanoi, 100000, Viet Nam
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Jathanna N, Strachan K, Erhayiem B, Kamaruddin H, Swoboda P, Auer D, Chen X, Jamil-Copley S. The Nottingham Ischaemic Cardiovascular Magnetic Resonance resource (NotIs CMR): a prospective paired clinical and imaging scar database-protocol. J Cardiovasc Magn Reson 2023; 25:69. [PMID: 38008732 PMCID: PMC10680206 DOI: 10.1186/s12968-023-00978-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 11/12/2023] [Indexed: 11/28/2023] Open
Abstract
INTRODUCTION Research utilising artificial intelligence (AI) and cardiovascular magnetic resonance (CMR) is rapidly evolving with various objectives, however AI model development, generalisation and performance may be hindered by availability of robust training datasets including contrast enhanced images. METHODS NotIs CMR is a large UK, prospective, multicentre, observational cohort study to guide the development of a biventricular AI scar model. Patients with ischaemic heart disease undergoing clinically indicated contrast-enhanced cardiac magnetic resonance imaging will be recruited at Nottingham University Hospitals NHS Trust and Mid-Yorkshire Hospital NHS Trust. Baseline assessment will include cardiac magnetic resonance imaging, demographic data, medical history, electrocardiographic and serum biomarkers. Participants will undergo monitoring for a minimum of 5 years to document any major cardiovascular adverse events. The main objectives include (1) AI training, validation and testing to improve the performance, applicability and adaptability of an AI biventricular scar segmentation model being developed by the authors and (2) develop a curated, disease-specific imaging database to support future research and collaborations and, (3) to explore associations in clinical outcome for future risk prediction modelling studies. CONCLUSION NotIs CMR will collect and curate disease-specific, paired imaging and clinical datasets to develop an AI biventricular scar model whilst providing a database to support future research and collaboration in Artificial Intelligence and ischaemic heart disease.
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Affiliation(s)
- Nikesh Jathanna
- Department of Cardiology, Nottingham University Hospitals NHS Trust, Nottingham, UK
- Queen's Medical Centre, NIHR Nottingham Biomedical Research Centre, University of Nottingham, Nottingham, UK
| | - Kevin Strachan
- Department of Cardiology, Nottingham University Hospitals NHS Trust, Nottingham, UK
| | - Bara Erhayiem
- Department of Cardiology, Nottingham University Hospitals NHS Trust, Nottingham, UK
| | - Hazlyna Kamaruddin
- Department of Cardiology, Nottingham University Hospitals NHS Trust, Nottingham, UK
| | - Peter Swoboda
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK
| | - Dorothee Auer
- Queen's Medical Centre, NIHR Nottingham Biomedical Research Centre, University of Nottingham, Nottingham, UK
| | - Xin Chen
- Department of Computer Science, University of Nottingham, Nottingham, UK
| | - Shahnaz Jamil-Copley
- Department of Cardiology, Nottingham University Hospitals NHS Trust, Nottingham, UK.
- Queen's Medical Centre, NIHR Nottingham Biomedical Research Centre, University of Nottingham, Nottingham, UK.
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Alahdab F, El Shawi R, Ahmed AI, Han Y, Al-Mallah M. Patient-level explainable machine learning to predict major adverse cardiovascular events from SPECT MPI and CCTA imaging. PLoS One 2023; 18:e0291451. [PMID: 37967112 PMCID: PMC10651041 DOI: 10.1371/journal.pone.0291451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 08/30/2023] [Indexed: 11/17/2023] Open
Abstract
BACKGROUND Machine learning (ML) has shown promise in improving the risk prediction in non-invasive cardiovascular imaging, including SPECT MPI and coronary CT angiography. However, most algorithms used remain black boxes to clinicians in how they compute their predictions. Furthermore, objective consideration of the multitude of available clinical data, along with the visual and quantitative assessments from CCTA and SPECT, are critical for optimal patient risk stratification. We aim to provide an explainable ML approach to predict MACE using clinical, CCTA, and SPECT data. METHODS Consecutive patients who underwent clinically indicated CCTA and SPECT myocardial imaging for suspected CAD were included and followed up for MACEs. A MACE was defined as a composite outcome that included all-cause mortality, myocardial infarction, or late revascularization. We employed an Automated Machine Learning (AutoML) approach to predict MACE using clinical, CCTA, and SPECT data. Various mainstream models with different sets of hyperparameters have been explored, and critical predictors of risk are obtained using explainable techniques on the global and patient levels. Ten-fold cross-validation was used in training and evaluating the AutoML model. RESULTS A total of 956 patients were included (mean age 61.1 ±14.2 years, 54% men, 89% hypertension, 81% diabetes, 84% dyslipidemia). Obstructive CAD on CCTA and ischemia on SPECT were observed in 14% of patients, and 11% experienced MACE. ML prediction's sensitivity, specificity, and accuracy in predicting a MACE were 69.61%, 99.77%, and 96.54%, respectively. The top 10 global predictive features included 8 CCTA attributes (segment involvement score, number of vessels with severe plaque ≥70, ≥50% stenosis in the left marginal coronary artery, calcified plaque, ≥50% stenosis in the left circumflex coronary artery, plaque type in the left marginal coronary artery, stenosis degree in the second obtuse marginal of the left circumflex artery, and stenosis category in the marginals of the left circumflex artery) and 2 clinical features (past medical history of MI or left bundle branch block, being an ever smoker). CONCLUSION ML can accurately predict risk of developing a MACE in patients suspected of CAD undergoing SPECT MPI and CCTA. ML feature-ranking can also show, at a sample- as well as at a patient-level, which features are key in making such a prediction.
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Affiliation(s)
- Fares Alahdab
- Houston Methodist DeBakey Heart & Vascular Center, Houston, TX, United States of America
| | - Radwa El Shawi
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Ahmed Ibrahim Ahmed
- Houston Methodist DeBakey Heart & Vascular Center, Houston, TX, United States of America
| | - Yushui Han
- Houston Methodist DeBakey Heart & Vascular Center, Houston, TX, United States of America
| | - Mouaz Al-Mallah
- Houston Methodist DeBakey Heart & Vascular Center, Houston, TX, United States of America
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Amini M, Pursamimi M, Hajianfar G, Salimi Y, Saberi A, Mehri-Kakavand G, Nazari M, Ghorbani M, Shalbaf A, Shiri I, Zaidi H. Machine learning-based diagnosis and risk classification of coronary artery disease using myocardial perfusion imaging SPECT: A radiomics study. Sci Rep 2023; 13:14920. [PMID: 37691039 PMCID: PMC10493219 DOI: 10.1038/s41598-023-42142-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 09/06/2023] [Indexed: 09/12/2023] Open
Abstract
This study aimed to investigate the diagnostic performance of machine learning-based radiomics analysis to diagnose coronary artery disease status and risk from rest/stress Myocardial Perfusion Imaging (MPI) single-photon emission computed tomography (SPECT). A total of 395 patients suspicious of coronary artery disease who underwent 2-day stress-rest protocol MPI SPECT were enrolled in this study. The left ventricle myocardium, excluding the cardiac cavity, was manually delineated on rest and stress images to define a volume of interest. Added to clinical features (age, sex, family history, diabetes status, smoking, and ejection fraction), a total of 118 radiomics features, were extracted from rest and stress MPI SPECT images to establish different feature sets, including Rest-, Stress-, Delta-, and Combined-radiomics (all together) feature sets. The data were randomly divided into 80% and 20% subsets for training and testing, respectively. The performance of classifiers built from combinations of three feature selections, and nine machine learning algorithms was evaluated for two different diagnostic tasks, including 1) normal/abnormal (no CAD vs. CAD) classification, and 2) low-risk/high-risk CAD classification. Different metrics, including the area under the ROC curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE), were reported for models' evaluation. Overall, models built on the Stress feature set (compared to other feature sets), and models to diagnose the second task (compared to task 1 models) revealed better performance. The Stress-mRMR-KNN (feature set-feature selection-classifier) reached the highest performance for task 1 with AUC, ACC, SEN, and SPE equal to 0.61, 0.63, 0.64, and 0.6, respectively. The Stress-Boruta-GB model achieved the highest performance for task 2 with AUC, ACC, SEN, and SPE of 0.79, 0.76, 0.75, and 0.76, respectively. Diabetes status from the clinical feature family, and dependence count non-uniformity normalized, from the NGLDM family, which is representative of non-uniformity in the region of interest were the most frequently selected features from stress feature set for CAD risk classification. This study revealed promising results for CAD risk classification using machine learning models built on MPI SPECT radiomics. The proposed models are helpful to alleviate the labor-intensive MPI SPECT interpretation process regarding CAD status and can potentially expedite the diagnostic process.
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Affiliation(s)
- Mehdi Amini
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland
| | - Mohamad Pursamimi
- Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ghasem Hajianfar
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland
| | - Yazdan Salimi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland
| | - Abdollah Saberi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland
| | - Ghazal Mehri-Kakavand
- Department of Medical Physics, School of Medicine, Semnan University of Medical Sciences, Semnan, Iran
| | - Mostafa Nazari
- Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mahdi Ghorbani
- Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Ahmad Shalbaf
- Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland
- Department of Cardiology, Inselspital, University of Bern, Bern, Switzerland
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland.
- University Research and Innovation Center, Obuda University, Budapest, Hungary.
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University of Medical Center Groningen, Groningen, The Netherlands.
- Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark.
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Rezazadeh H, Ahmadipour H, Salajegheh M. Psychometric evaluation of Persian version of medical artificial intelligence readiness scale for medical students. BMC MEDICAL EDUCATION 2023; 23:527. [PMID: 37488522 PMCID: PMC10367280 DOI: 10.1186/s12909-023-04516-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Accepted: 07/18/2023] [Indexed: 07/26/2023]
Abstract
BACKGROUND Artificial intelligence's advancement in medicine and its worldwide implementation will be one of the main elements of medical education in the coming years. This study aimed to translate and psychometric evaluation of the Persian version of the medical artificial intelligence readiness scale for medical students. METHODS The questionnaire was translated according to a backward-forward translation procedure. Reliability was assessed by calculating Cronbach's alpha coefficient. Confirmatory Factor Analysis was conducted on 302 medical students. Content validity was evaluated using the Content Validity Index and Content Validity Ratio. RESULTS The Cronbach's alpha coefficient for the whole scale was found to be 0.94. The Content Validity Index was 0.92 and the Content Validity Ratio was 0.75. Confirmatory factor analysis revealed a fair fit for four factors: cognition, ability, vision, and ethics. CONCLUSION The Persian version of the medical artificial intelligence readiness scale for medical students consisting of four factors including cognition, ability, vision, and ethics appears to be an almost valid and reliable instrument for the evaluation of medical artificial intelligence readiness.
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Affiliation(s)
- Hossein Rezazadeh
- Student Committee of Medical Education Development, Education Development Center, Kerman University of Medical Sciences, Kerman, Iran
| | - Habibeh Ahmadipour
- Community Medicine Department, School of Medicine, Medical Education Leadership and Management Research Center, Kerman University of Medical Sciences, Kerman, Iran
| | - Mahla Salajegheh
- Department of Medical Education, Medical Education Development Center, Kerman University of Medical Sciences, Kerman, Iran.
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Cau R, Pisu F, Suri JS, Mannelli L, Scaglione M, Masala S, Saba L. Artificial Intelligence Applications in Cardiovascular Magnetic Resonance Imaging: Are We on the Path to Avoiding the Administration of Contrast Media? Diagnostics (Basel) 2023; 13:2061. [PMID: 37370956 PMCID: PMC10297403 DOI: 10.3390/diagnostics13122061] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 06/10/2023] [Accepted: 06/12/2023] [Indexed: 06/29/2023] Open
Abstract
In recent years, cardiovascular imaging examinations have experienced exponential growth due to technological innovation, and this trend is consistent with the most recent chest pain guidelines. Contrast media have a crucial role in cardiovascular magnetic resonance (CMR) imaging, allowing for more precise characterization of different cardiovascular diseases. However, contrast media have contraindications and side effects that limit their clinical application in determinant patients. The application of artificial intelligence (AI)-based techniques to CMR imaging has led to the development of non-contrast models. These AI models utilize non-contrast imaging data, either independently or in combination with clinical and demographic data, as input to generate diagnostic or prognostic algorithms. In this review, we provide an overview of the main concepts pertaining to AI, review the existing literature on non-contrast AI models in CMR, and finally, discuss the strengths and limitations of these AI models and their possible future development.
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Affiliation(s)
- Riccardo Cau
- Department of Radiology, University Hospital of Cagliari, 09042 Monserrato, Italy; (R.C.); (F.P.)
| | - Francesco Pisu
- Department of Radiology, University Hospital of Cagliari, 09042 Monserrato, Italy; (R.C.); (F.P.)
| | - Jasjit S. Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA;
| | | | - Mariano Scaglione
- Department of Radiology, University Hospital of Sassari, 07100 Sassari, Italy; (M.S.); (S.M.)
| | - Salvatore Masala
- Department of Radiology, University Hospital of Sassari, 07100 Sassari, Italy; (M.S.); (S.M.)
| | - Luca Saba
- Department of Radiology, University Hospital of Cagliari, 09042 Monserrato, Italy; (R.C.); (F.P.)
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Zair AM, Bouzouad Cherfa A, Cherfa Y, Belkhamsa N. An automated segmentation of coronary artery calcification using deep learning in specific region limitation. Med Biol Eng Comput 2023:10.1007/s11517-023-02797-z. [PMID: 36871109 DOI: 10.1007/s11517-023-02797-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 01/31/2023] [Indexed: 03/06/2023]
Abstract
Coronary artery calcification (CAC) is a frequent disease of the arteries that supply the surface of the heart muscle. Leaving a severe disease untreated can make it permanent. Computer tomography (CT), which is well known for its ability to quantify the Agatston score, is used to visualize high-resolution CACs. CAC segmentation is still an important topic. Our goal is to automatically segment CAC in a specific area and measure the Agatston score in 2D images. The heart region is limited using a threshold, unused structures are removed using 2D connectivity (muscle, lung, ribcage), the heart cavity is extracted using the convex hull of the lungs, and the CAC is then segmented in 2D using a convolutional neural network (U-Net models/SegNet-VGG16 with transfer learning). The Agatston score prediction is calculated for CAC quantification. The proposed strategy is tested through experiments, which yield encouraging outcomes. Graphical Abstract Deep learning for CAC segmentation in CT images.
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Affiliation(s)
- Asmae Mama Zair
- University of Blida 01, B.P 270, Soumaa road, Blida, Algeria.
| | | | - Yazid Cherfa
- University of Blida 01, B.P 270, Soumaa road, Blida, Algeria
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14
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Han X, He Y, Luo N, Zheng D, Hong M, Wang Z, Yang Z. The influence of artificial intelligence assistance on the diagnostic performance of CCTA for coronary stenosis for radiologists with different levels of experience. Acta Radiol 2023; 64:496-507. [PMID: 35389276 DOI: 10.1177/02841851221089263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND The interpretation of coronary computed tomography angiography (CCTA) stenosis may be difficult among radiologists of different experience levels. Artificial intelligence (AI) may improve the diagnostic performance. PURPOSE To investigate whether the diagnostic performance and time efficiency of radiologists with different levels of experience in interpreting CCTA images could be improved by using CCTA with AI assistance (CCTA-AI). MATERIAL AND METHODS This analysis included 200 patients with complete CCTA and invasive coronary angiography (ICA) data, using ICA results as the reference. Eighteen radiologists were divided into three levels based on experience (Levels I, II, and III), and the three levels were divided into groups without (Groups 1, 2, and 3) and with (Groups 4, 5, and 6) AI assistance, totaling six groups (to avoid reader recall bias). The average sensitivity, specificity, NPV, PPV, and AUC were reported for the six groups and CCTA-AI at the patient, vessel, and segment levels. The interpretation time in the groups with and without CCTA-AI was recorded. RESULTS Compared to the corresponding group without CCTA-AI, the Level I group with CCTA-AI had improved sensitivity (75.0% vs. 83.0% on patient-based; P = 0.003). At Level III, the specificity was better with CCTA-AI. The median interpretation times for the groups with and without CCTA-AI were 413 and 615 s, respectively (P < 0.001). CONCLUSION CCTA-AI could assist with and improve the diagnostic performance of radiologists with different experience levels, with Level I radiologists exhibiting improved sensitivity and Level III radiologists exhibiting improved specificity. The use of CCTA-AI could shorten the training time for radiologists.
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Affiliation(s)
- Xianjun Han
- Department of Radiology, Beijing Friendship Hospital, 535066Capital Medical University, Beijing, PR China
| | - Yi He
- Department of Radiology, Beijing Friendship Hospital, 535066Capital Medical University, Beijing, PR China
| | - Nan Luo
- Department of Radiology, Beijing Friendship Hospital, 535066Capital Medical University, Beijing, PR China
| | - Dandan Zheng
- Shukun (Beijing) Technology Co., Ltd., Beijing, PR China
| | - Min Hong
- Department of Computer Software Engineering, 37969Soonchunhyang University, Asan, Republic of Korea
| | - Zhenchang Wang
- Department of Radiology, Beijing Friendship Hospital, 535066Capital Medical University, Beijing, PR China
| | - Zhenghan Yang
- Department of Radiology, Beijing Friendship Hospital, 535066Capital Medical University, Beijing, PR China
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Nittas V, Daniore P, Landers C, Gille F, Amann J, Hubbs S, Puhan MA, Vayena E, Blasimme A. Beyond high hopes: A scoping review of the 2019-2021 scientific discourse on machine learning in medical imaging. PLOS DIGITAL HEALTH 2023; 2:e0000189. [PMID: 36812620 PMCID: PMC9931290 DOI: 10.1371/journal.pdig.0000189] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 01/02/2023] [Indexed: 02/04/2023]
Abstract
Machine learning has become a key driver of the digital health revolution. That comes with a fair share of high hopes and hype. We conducted a scoping review on machine learning in medical imaging, providing a comprehensive outlook of the field's potential, limitations, and future directions. Most reported strengths and promises included: improved (a) analytic power, (b) efficiency (c) decision making, and (d) equity. Most reported challenges included: (a) structural barriers and imaging heterogeneity, (b) scarcity of well-annotated, representative and interconnected imaging datasets (c) validity and performance limitations, including bias and equity issues, and (d) the still missing clinical integration. The boundaries between strengths and challenges, with cross-cutting ethical and regulatory implications, remain blurred. The literature emphasizes explainability and trustworthiness, with a largely missing discussion about the specific technical and regulatory challenges surrounding these concepts. Future trends are expected to shift towards multi-source models, combining imaging with an array of other data, in a more open access, and explainable manner.
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Affiliation(s)
- Vasileios Nittas
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
- Epidemiology, Biostatistics and Prevention Institute, Faculty of Medicine, Faculty of Science, University of Zurich, Zurich, Switzerland
| | - Paola Daniore
- Institute for Implementation Science in Health Care, Faculty of Medicine, University of Zurich, Switzerland
- Digital Society Initiative, University of Zurich, Switzerland
| | - Constantin Landers
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
| | - Felix Gille
- Institute for Implementation Science in Health Care, Faculty of Medicine, University of Zurich, Switzerland
- Digital Society Initiative, University of Zurich, Switzerland
| | - Julia Amann
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
| | - Shannon Hubbs
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
| | - Milo Alan Puhan
- Epidemiology, Biostatistics and Prevention Institute, Faculty of Medicine, Faculty of Science, University of Zurich, Zurich, Switzerland
| | - Effy Vayena
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
| | - Alessandro Blasimme
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
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16
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Wilson DU, Bailey MQ, Craig J. The role of artificial intelligence in clinical imaging and workflows. Vet Radiol Ultrasound 2022; 63 Suppl 1:897-902. [PMID: 36514227 DOI: 10.1111/vru.13157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 12/03/2021] [Accepted: 01/09/2022] [Indexed: 12/15/2022] Open
Abstract
Evidence-based medicine, outcomes management, and multidisciplinary systems are laying the foundation for radiology on the cusp of a new day. Environmental and operational forces coupled with technological advancements are redefining the veterinary radiologist of tomorrow. In the past several years, veterinary image volumes have exploded, and the scale of hardware and software required to support it seems boundless. The most dynamic trend within veterinary radiology is implementing digital information systems such as PACS, RIS, PIMS, and Voice Recognition systems. While the digitization of radiography imaging has significantly improved the workflow of the veterinary radiology assistant and radiologist, tedious, redundant tasks are abundant and mind-numbing. They can lead to errors with a significant impact on patient care. Today, these boring and repetitious tasks continue to bog down patient throughput and workflow. Artificial intelligence, particularly machine learning, shows much promise to rocket the workflow and veterinary clinical imaging into a new day where the AI management of mundane tasks allows for efficiency so the radiologist can better concentrate on the quality of patient care. In this article, we briefly discuss the major subsets of artificial intelligence (AI) workflow for the radiologist and veterinary radiology assistant including image acquisition, segmentation and mensuration, rotation and hanging protocol, detection and prioritization, monitoring and registration of lesions, implementation of these subsets, and the ethics of utilizing AI in veterinary medicine.
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Affiliation(s)
- Diane U Wilson
- Antech Imaging Services, Fountain Valley, California, USA
| | | | - John Craig
- EponaTech LLC, dba MetronMind, Paso Robles, California, USA
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17
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Asif A, Charters PFP, Thompson CAS, Komber HMEI, Hudson BJ, Rodrigues JCL. Artificial intelligence can detect left ventricular dilatation on contrast-enhanced thoracic computer tomography relative to cardiac magnetic resonance imaging. Br J Radiol 2022; 95:20210852. [PMID: 35286140 PMCID: PMC9815728 DOI: 10.1259/bjr.20210852] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 03/03/2022] [Accepted: 03/09/2022] [Indexed: 01/13/2023] Open
Abstract
OBJECTIVES To assess the diagnostic accuracy of an automated algorithm to detect left ventricular (LV) dilatation on non-ECG gated CT, using cardiac magnetic resonance (CMR) as reference standard. METHODS Consecutive patients with contrast-enhanced CT thorax and CMR within 31 days (2016-2020) were analysed (n = 84). LV dilatation was defined against age-, sex- and body surface area-specific values for CMR. CTs underwent automated artificial intelligence(AI)-derived analysis that segmented ventricular chambers, presenting maximal LV diameter and volume. Area under the receiver operator curve (AUC-ROC) analysis identified CT thresholds with ≥90% sensitivity and highest specificity and ≥90% specificity with highest sensitivity. Youden's Index was used to identify thresholds with optimised sensitivity and specificity. RESULTS Automated diameter analysis was feasible in 92% of cases (77/84; 45 men, age 61 ± 14 years, mean CT to CMR interval 10 ± 8 days). Relative to CMR as a reference standard, 45% had LV dilatation. In males, an automated LV diameter measurement of ≥55.5 mm was ≥90% specific for CMR-defined LV dilatation (positive predictive value (PPV) 85.7%, negative predictive value (NPV) 61.2%, accuracy 68.9%). In females, an LV diameter of ≥49.7 mm was ≥90% specific for CMR-defined LV dilatation (PPV 66.7%, NPV 73.1%, accuracy 71.9%). AI CT volumetry data did not significantly improve AUC performance. CONCLUSION Fully automated AI-derived analysis LV dilatation on routine unselected non-gated contrast-enhanced CT thorax studies is feasible. We have defined thresholds for the detection of LV dilatation on CT relative to CMR, which could be used to routinely screen for dilated cardiomyopathy at the time of CT. ADVANCES IN KNOWLEDGE We show, for the first time, that a fully-automated AI-derived analysis of maximal LV chamber axial diameter on non-ECG-gated thoracic CT is feasible in unselected real-world cases and that the derived measures can predict LV dilatation relative to cardiac magnetic resonance imaging, the non-invasive reference standard for determining cardiac chamber size. We have derived sex-specific cut-off values to screen for LV dilatation on routine contrast-enhanced thoracic CT. Future work should validate these thresholds and determine if technology can alter clinical outcomes in a cost-effective manner.
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Affiliation(s)
- Ashar Asif
- Medical School, University of Bristol, Bristol, UK
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18
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Lee HJ, Kim YW, Kim JH, Lee YJ, Moon J, Jeong P, Jeong J, Kim JS, Lee JS. Optimization of FFR prediction algorithm for gray zone by hemodynamic features with synthetic model and biometric data. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 220:106827. [PMID: 35500505 DOI: 10.1016/j.cmpb.2022.106827] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 03/31/2022] [Accepted: 04/19/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND Recent attempts on adopting artificial intelligence algorithm on coronary diagnosis had limitations on data quantity and quality. While most of previous studies only used vessel image as input data, flow features and biometric features should be also considered. Moreover, the accuracy should be optimized within gray zone as the purpose is to decide stent insertion with estimated fractional flow reserve. OBJECTIVES The main purpose of this study is to develop an artificial intelligence-based coronary vascular diagnosis system focused on performance in the gray zone, from CT image extraction to FFR estimation. Three main issues should be considered for an algorithm to be used for pre-screening: algorithm optimization in the gray zone, minimization of labor during image processing, and consideration of flow and biometric features. This paper introduces a full FFR pre-screening system from automatic image extraction to an algorithm for estimating the FFR value. METHOD The main techniques used in this study are an automatic image extraction algorithm, lattice Boltzmann method based computational fluid dynamics analysis of a synthetic model and patient data, and an AI algorithm optimization. For feature extraction, this study focused on an automatic process to reduce manual labor. The algorithm consisted of two steps: the first algorithm calculates flow features from geometrical features, and the second algorithm estimates the FFR value from flow features and patient biometric features. Algorithm selection, outlier elimination, and k-fold selection were included to optimize the algorithm. CONCLUSION Eight types of algorithms including two neural network models and six machine learning models were optimized and tested. The random forest model shows the highest performance before optimization, whereas the multilayer perceptron regressor shows the highest gray zone accuracy after optimization.
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Affiliation(s)
- Hyeong Jun Lee
- School of Mechanical Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, Korea
| | - Young Woo Kim
- School of Mechanical Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, Korea
| | - Jun Hong Kim
- School of Mechanical Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, Korea
| | - Yong-Joon Lee
- Division of Cardiology, Severance Hospital, Yonsei University College of Medicine, Korea
| | | | | | | | - Jung-Sun Kim
- Division of Cardiology, Severance Hospital, Yonsei University College of Medicine, Korea
| | - Joon Sang Lee
- School of Mechanical Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, Korea.
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19
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Wang S, Patel H, Miller T, Ameyaw K, Narang A, Chauhan D, Anand S, Anyanwu E, Besser SA, Kawaji K, Liu XP, Lang RM, Mor-Avi V, Patel AR. AI Based CMR Assessment of Biventricular Function: Clinical Significance of Intervendor Variability and Measurement Errors. JACC Cardiovasc Imaging 2022; 15:413-427. [PMID: 34656471 PMCID: PMC8917993 DOI: 10.1016/j.jcmg.2021.08.011] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 08/09/2021] [Accepted: 08/17/2021] [Indexed: 12/30/2022]
Abstract
OBJECTIVES The aim of this study was to determine whether left ventricular ejection fraction (LVEF) and right ventricular ejection fraction (RVEF) and left ventricular mass (LVM) measurements made using 3 fully automated deep learning (DL) algorithms are accurate and interchangeable and can be used to classify ventricular function and risk-stratify patients as accurately as an expert. BACKGROUND Artificial intelligence is increasingly used to assess cardiac function and LVM from cardiac magnetic resonance images. METHODS Two hundred patients were identified from a registry of individuals who underwent vasodilator stress cardiac magnetic resonance. LVEF, LVM, and RVEF were determined using 3 fully automated commercial DL algorithms and by a clinical expert (CLIN) using conventional methodology. Additionally, LVEF values were classified according to clinically important ranges: <35%, 35% to 50%, and ≥50%. Both ejection fraction values and classifications made by the DL ejection fraction approaches were compared against CLIN ejection fraction reference. Receiver-operating characteristic curve analysis was performed to evaluate the ability of CLIN and each of the DL classifications to predict major adverse cardiovascular events. RESULTS Excellent correlations were seen for each DL-LVEF compared with CLIN-LVEF (r = 0.83-0.93). Good correlations were present between DL-LVM and CLIN-LVM (r = 0.75-0.85). Modest correlations were observed between DL-RVEF and CLIN-RVEF (r = 0.59-0.68). A >10% error between CLIN and DL ejection fraction was present in 5% to 18% of cases for the left ventricle and 23% to 43% for the right ventricle. LVEF classification agreed with CLIN-LVEF classification in 86%, 80%, and 85% cases for the 3 DL-LVEF approaches. There were no differences among the 4 approaches in associations with major adverse cardiovascular events for LVEF, LVM, and RVEF. CONCLUSIONS This study revealed good agreement between automated and expert-derived LVEF and similarly strong associations with outcomes, compared with an expert. However, the ability of these automated measurements to accurately classify left ventricular function for treatment decision remains limited. DL-LVM showed good agreement with CLIN-LVM. DL-RVEF approaches need further refinements.
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Affiliation(s)
- Shuo Wang
- University of Chicago, Chicago, Illinois,Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Hena Patel
- University of Chicago, Chicago, Illinois
| | | | | | | | | | | | | | | | - Keigo Kawaji
- University of Chicago, Chicago, Illinois,Illinois Institute of Technology, Chicago, Illinois
| | - Xing-Peng Liu
- Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
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20
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Han X, Luo N, Xu L, Cao J, Guo N, He Y, Hong M, Jia X, Wang Z, Yang Z. Artificial intelligence stenosis diagnosis in coronary CTA: effect on the performance and consistency of readers with less cardiovascular experience. BMC Med Imaging 2022; 22:28. [PMID: 35177029 PMCID: PMC8851787 DOI: 10.1186/s12880-022-00756-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 02/11/2022] [Indexed: 11/25/2022] Open
Abstract
Background To investigate the influence of artificial intelligence (AI) based on deep learning on the diagnostic performance and consistency of inexperienced cardiovascular radiologists. Methods We enrolled 196 patents who had undergone both coronary computed tomography angiography (CCTA) and invasive coronary angiography (ICA) within 6 months. Four readers with less cardiovascular experience (Reader 1–Reader 4) and two cardiovascular radiologists (level II, Reader 5 and Reader 6) evaluated all images for ≥ 50% coronary artery stenosis, with ICA as the gold standard. Reader 3 and Reader 4 interpreted with AI system assistance, and the other four readers interpreted without the AI system. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy (area under the receiver operating characteristic curve (AUC)) of the six readers were calculated at the patient and vessel levels. Additionally, we evaluated the interobserver consistency between Reader 1 and Reader 2, Reader 3 and Reader 4, and Reader 5 and Reader 6. Results The AI system had 94% and 78% sensitivity at the patient and vessel levels, respectively, which were higher than that of Reader 5 and Reader 6. AI-assisted Reader 3 and Reader 4 had higher sensitivity (range + 7.2–+ 16.6% and + 5.9–+ 16.1%, respectively) and NPVs (range + 3.7–+ 13.4% and + 2.7–+ 4.2%, respectively) than Reader 1 and Reader 2 without AI. Good interobserver consistency was found between Reader 3 and Reader 4 in interpreting ≥ 50% stenosis (Kappa value = 0.75 and 0.80 at the patient and vessel levels, respectively). Only Reader 1 and Reader 2 showed poor interobserver consistency (Kappa value = 0.25 and 0.37). Reader 5 and Reader 6 showed moderate agreement (Kappa value = 0.55 and 0.61). Conclusions Our study showed that using AI could effectively increase the sensitivity of inexperienced readers and significantly improve the consistency of coronary stenosis diagnosis via CCTA. Trial registration Clinical trial registration number: ChiCTR1900021867. Name of registry: Diagnostic performance of artificial intelligence-assisted coronary computed tomography angiography for the assessment of coronary atherosclerotic stenosis. Supplementary Information The online version contains supplementary material available at 10.1186/s12880-022-00756-y.
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Affiliation(s)
- Xianjun Han
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 YongAn Road, Beijing, 100050, People's Republic of China
| | - Nan Luo
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 YongAn Road, Beijing, 100050, People's Republic of China
| | - Lixue Xu
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 YongAn Road, Beijing, 100050, People's Republic of China
| | - Jiaxin Cao
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 YongAn Road, Beijing, 100050, People's Republic of China
| | - Ning Guo
- Shukun (Beijing) Technology Co., Ltd., Jinhui Bd, Qiyang Rd, Beijing, 100102, People's Republic of China
| | - Yi He
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 YongAn Road, Beijing, 100050, People's Republic of China
| | - Min Hong
- Department of Computer Software Engineering, Soonchunhyang University, Asan, South Korea
| | - Xibin Jia
- Beijing University of Technology, Beijing, People's Republic of China
| | - Zhenchang Wang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 YongAn Road, Beijing, 100050, People's Republic of China
| | - Zhenghan Yang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 YongAn Road, Beijing, 100050, People's Republic of China.
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21
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Artificial Intelligence Advances in the World of Cardiovascular Imaging. Healthcare (Basel) 2022; 10:healthcare10010154. [PMID: 35052317 PMCID: PMC8776229 DOI: 10.3390/healthcare10010154] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 01/11/2022] [Accepted: 01/11/2022] [Indexed: 02/04/2023] Open
Abstract
The tremendous advances in digital information and communication technology have entered everything from our daily lives to the most intricate aspects of medical and surgical care. These advances are seen in electronic and mobile health and allow many new applications to further improve and make the diagnoses of patient diseases and conditions more precise. In the area of digital radiology with respect to diagnostics, the use of advanced imaging tools and techniques is now at the center of evaluation and treatment. Digital acquisition and analysis are central to diagnostic capabilities, especially in the field of cardiovascular imaging. Furthermore, the introduction of artificial intelligence (AI) into the world of digital cardiovascular imaging greatly broadens the capabilities of the field both with respect to advancement as well as with respect to complete and accurate diagnosis of cardiovascular conditions. The application of AI in recognition, diagnostics, protocol automation, and quality control for the analysis of cardiovascular imaging modalities such as echocardiography, nuclear cardiac imaging, cardiovascular computed tomography, cardiovascular magnetic resonance imaging, and other imaging, is a major advance that is improving rapidly and continuously. We document the innovations in the field of cardiovascular imaging that have been brought about by the acceptance and implementation of AI in relation to healthcare professionals and patients in the cardiovascular field.
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22
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Machine Learning in Medical Imaging – Clinical Applications and Challenges in Computer Vision. Artif Intell Med 2022. [DOI: 10.1007/978-981-19-1223-8_4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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23
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Aboalshamat K, Alhuzali R, Alalyani A, Alsharif S, Qadhi H, Almatrafi R, Ammash D, Alotaibi S. Medical and Dental Professionals Readiness for Artificial Intelligence for Saudi Arabia Vision 2030. INTERNATIONAL JOURNAL OF PHARMACEUTICAL RESEARCH AND ALLIED SCIENCES 2022. [DOI: 10.51847/nu8y6y6q1m] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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24
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Ben Ali W, Pesaranghader A, Avram R, Overtchouk P, Perrin N, Laffite S, Cartier R, Ibrahim R, Modine T, Hussin JG. Implementing Machine Learning in Interventional Cardiology: The Benefits Are Worth the Trouble. Front Cardiovasc Med 2021; 8:711401. [PMID: 34957230 PMCID: PMC8692711 DOI: 10.3389/fcvm.2021.711401] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 11/08/2021] [Indexed: 12/23/2022] Open
Abstract
Driven by recent innovations and technological progress, the increasing quality and amount of biomedical data coupled with the advances in computing power allowed for much progress in artificial intelligence (AI) approaches for health and biomedical research. In interventional cardiology, the hope is for AI to provide automated analysis and deeper interpretation of data from electrocardiography, computed tomography, magnetic resonance imaging, and electronic health records, among others. Furthermore, high-performance predictive models supporting decision-making hold the potential to improve safety, diagnostic and prognostic prediction in patients undergoing interventional cardiology procedures. These applications include robotic-assisted percutaneous coronary intervention procedures and automatic assessment of coronary stenosis during diagnostic coronary angiograms. Machine learning (ML) has been used in these innovations that have improved the field of interventional cardiology, and more recently, deep Learning (DL) has emerged as one of the most successful branches of ML in many applications. It remains to be seen if DL approaches will have a major impact on current and future practice. DL-based predictive systems also have several limitations, including lack of interpretability and lack of generalizability due to cohort heterogeneity and low sample sizes. There are also challenges for the clinical implementation of these systems, such as ethical limits and data privacy. This review is intended to bring the attention of health practitioners and interventional cardiologists to the broad and helpful applications of ML and DL algorithms to date in the field. Their implementation challenges in daily practice and future applications in the field of interventional cardiology are also discussed.
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Affiliation(s)
- Walid Ben Ali
- Service Médico-Chirurgical, Valvulopathies-Chirurgie Cardiaque-Cardiologie Interventionelle Structurelle, Hôpital Cardiologique de Haut Lévèque, Bordeaux, France.,Structural Heart Program and Interventional Cardiology, Université de Montréal, Montreal Heart Institute, Montréal, QC, Canada
| | - Ahmad Pesaranghader
- Faculty of Medicine, Research Center, Montreal Heart Institute, Université de Montréal, Montréal, QC, Canada.,Computer Science and Operations Research Department, Mila (Quebec Artificial Intelligence Institute), Montreal, QC, Canada
| | - Robert Avram
- Faculty of Medicine, Research Center, Montreal Heart Institute, Université de Montréal, Montréal, QC, Canada
| | - Pavel Overtchouk
- Interventional Cardiology and Cardiovascular Surgery Centre Hospitalier Regional Universitaire de Lille (CHRU de Lille), Lille, France
| | - Nils Perrin
- Structural Heart Program and Interventional Cardiology, Université de Montréal, Montreal Heart Institute, Montréal, QC, Canada
| | - Stéphane Laffite
- Service Médico-Chirurgical, Valvulopathies-Chirurgie Cardiaque-Cardiologie Interventionelle Structurelle, Hôpital Cardiologique de Haut Lévèque, Bordeaux, France
| | - Raymond Cartier
- Structural Heart Program and Interventional Cardiology, Université de Montréal, Montreal Heart Institute, Montréal, QC, Canada
| | - Reda Ibrahim
- Structural Heart Program and Interventional Cardiology, Université de Montréal, Montreal Heart Institute, Montréal, QC, Canada
| | - Thomas Modine
- Service Médico-Chirurgical, Valvulopathies-Chirurgie Cardiaque-Cardiologie Interventionelle Structurelle, Hôpital Cardiologique de Haut Lévèque, Bordeaux, France
| | - Julie G Hussin
- Faculty of Medicine, Research Center, Montreal Heart Institute, Université de Montréal, Montréal, QC, Canada
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25
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Zair AM, Bouzouad Cherfa A, Cherfa Y, Belkhamsa N. Machine learning for coronary artery calcification detection and labeling using only native computer tomography. Phys Eng Sci Med 2021; 45:49-61. [PMID: 34792761 DOI: 10.1007/s13246-021-01080-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2021] [Accepted: 11/10/2021] [Indexed: 11/26/2022]
Abstract
In recent decades, the World Health Organization has found an increase in the death rate due to cardiovascular disease. Calcifications of the coronary arteries are the main sign of any cardiovascular event. Each individual's calcium score helps estimate the severity of the disease. However, the score for each artery is more significant. This study aims to research the segmentation, the labeling, and then the complete and partial quantification of calcium using only native coronary computed tomography with the help of machine-learning algorithms. Our semi-automatic system limited the region of interest by applying a defined preprocessing step. We then implemented two random forest classifiers; the first separated true coronary artery calcification (CAC) from the noise, and the second labeled CAC into the right coronary artery, left coronary artery, left anterior descending artery, and left circumflex artery using specific features. Agatston score and volume score of each CAC, each artery, and all of the arteries were calculated. This method gave promising results, comparable to those found in the literature, with the accuracy of 99.98% and 100% for CAC detection and labeling respectively.
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Affiliation(s)
- Asmae Mama Zair
- LASICOM Laboratory, Department of Electronics, Faculty of Technology, University of Blida 1, 09000, Blida, Algeria.
| | - Assia Bouzouad Cherfa
- LASICOM Laboratory, Department of Electronics, Faculty of Technology, University of Blida 1, 09000, Blida, Algeria
| | - Yazid Cherfa
- LASICOM Laboratory, Department of Electronics, Faculty of Technology, University of Blida 1, 09000, Blida, Algeria
| | - Noureddine Belkhamsa
- LASICOM Laboratory, Department of Electronics, Faculty of Technology, University of Blida 1, 09000, Blida, Algeria
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Vergani V, Razavi R, Puyol-Antón E, Ruijsink B. Deep Learning for Classification and Selection of Cine CMR Images to Achieve Fully Automated Quality-Controlled CMR Analysis From Scanner to Report. Front Cardiovasc Med 2021; 8:742640. [PMID: 34722674 PMCID: PMC8551568 DOI: 10.3389/fcvm.2021.742640] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 09/09/2021] [Indexed: 01/03/2023] Open
Abstract
Introduction: Deep learning demonstrates great promise for automated analysis of CMR. However, existing limitations, such as insufficient quality control and selection of target acquisitions from the full CMR exam, are holding back the introduction of deep learning tools in the clinical environment. This study aimed to develop a framework for automated detection and quality-controlled selection of standard cine sequences images from clinical CMR exams, prior to analysis of cardiac function. Materials and Methods: Retrospective study of 3,827 subjects that underwent CMR imaging. We used a total of 119,285 CMR acquisitions, acquired with scanners of different magnetic field strengths and from different vendors (1.5T Siemens and 1.5T and 3.0T Phillips). We developed a framework to select one good acquisition for each conventional cine class. The framework consisted of a first pre-processing step to exclude still acquisitions; two sequential convolutional neural networks (CNN), the first (CNNclass) to classify acquisitions in standard cine views (2/3/4-chamber and short axis), the second (CNNQC) to classify acquisitions according to image quality and orientation; a final algorithm to select one good acquisition of each class. For each CNN component, 7 state-of-the-art architectures were trained for 200 epochs, with cross entropy loss and data augmentation. Data were divided into 80% for training, 10% for validation, and 10% for testing. Results: CNNclass selected cine CMR acquisitions with accuracy ranging from 0.989 to 0.998. Accuracy of CNNQC reached 0.861 for 2-chamber, 0.806 for 3-chamber, and 0.859 for 4-chamber. The complete framework was presented with 379 new full CMR studies, not used for CNN training/validation/testing, and selected one good 2-, 3-, and 4-chamber acquisition from each study with sensitivity to detect erroneous cases of 89.7, 93.2, and 93.9%, respectively. Conclusions: We developed an accurate quality-controlled framework for automated selection of cine acquisitions prior to image analysis. This framework is robust and generalizable as it was developed on multivendor data and could be used at the beginning of a pipeline for automated cine CMR analysis to obtain full automatization from scanner to report.
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Affiliation(s)
- Vittoria Vergani
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Reza Razavi
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,Department of Adult and Paediatric Cardiology, Guy's and St. Thomas' NHS Foundation Trust, London, United Kingdom
| | - Esther Puyol-Antón
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Bram Ruijsink
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,Department of Cardiology, Division of Heart and Lungs, University Medical Centre Utrecht, Utrecht, Netherlands
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Lang M, Bernier A, Knoppers BM. AI in Cardiovascular Imaging: "Unexplainable" Legal and Ethical Challenges? Can J Cardiol 2021; 38:225-233. [PMID: 34737036 DOI: 10.1016/j.cjca.2021.10.009] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 10/28/2021] [Accepted: 10/28/2021] [Indexed: 02/08/2023] Open
Abstract
Nowhere is the influence of artificial intelligence (AI) likely to be more profoundly felt than in healthcare, from patient triage and diagnosis to surgery and follow-up. Over the medium term, these impacts will be more acute in the cardiovascular imaging context, in which AI models are already successfully performing at roughly human levels of accuracy and efficiency in certain applications. Yet, the adoption of unexplainable AI systems for cardiovascular imaging still raises significant legal and ethical challenges. We focus in particular on challenges posed by the unexplainable character of deep learning and other forms of sophisticated AI modelling used for cardiovascular imaging by briefly outlining the systems being developed in this space, describing how they work, and considering how they might generate outputs that are not reviewable by physicians or system programmers. We suggest that an unexplainable tendency presents two specific ethico-legal concerns: (1) difficulty for health regulators and (2) confusion about the assignment of liability for error or fault in the use of AI systems. We suggest that addressing these concerns is critical for ensuring AI's successful implementation in cardiovascular imaging.
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Affiliation(s)
- Michael Lang
- Academic Associate, Centre of Genomics and Policy, McGill University Faculty of Medicine and Health Sciences
| | - Alexander Bernier
- Academic Associate, Centre of Genomics and Policy, McGill University Faculty of Medicine and Health Sciences
| | - Bartha Maria Knoppers
- Full Professor, Canada Research Chair in Law and Medicine and Director of the Centre of Genomics and Policy, McGill University Faculty of Medicine and Health Sciences.
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Balancing Speed and Accuracy in Cardiac Magnetic Resonance Function Post-Processing: Comparing 2 Levels of Automation in 3 Vendors to Manual Assessment. Diagnostics (Basel) 2021; 11:diagnostics11101758. [PMID: 34679457 PMCID: PMC8534796 DOI: 10.3390/diagnostics11101758] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 09/13/2021] [Accepted: 09/22/2021] [Indexed: 11/24/2022] Open
Abstract
Automating cardiac function assessment on cardiac magnetic resonance short-axis cines is faster and more reproducible than manual contour-tracing; however, accurately tracing basal contours remains challenging. Three automated post-processing software packages (Level 1) were compared to manual assessment. Subsequently, automated basal tracings were manually adjusted using a standardized protocol combined with software package-specific relative-to-manual standard error correction (Level 2). All post-processing was performed in 65 healthy subjects. Manual contour-tracing was performed separately from Level 1 and 2 automated analysis. Automated measurements were considered accurate when the difference was equal or less than the maximum manual inter-observer disagreement percentage. Level 1 (2.1 ± 1.0 min) and Level 2 automated (5.2 ± 1.3 min) were faster and more reproducible than manual (21.1 ± 2.9 min) post-processing, the maximum inter-observer disagreement was 6%. Compared to manual, Level 1 automation had wide limits of agreement. The most reliable software package obtained more accurate measurements in Level 2 compared to Level 1 automation: left ventricular end-diastolic volume, 98% and 53%; ejection fraction, 98% and 60%; mass, 70% and 3%; right ventricular end-diastolic volume, 98% and 28%; ejection fraction, 80% and 40%, respectively. Level 1 automated cardiac function post-processing is fast and highly reproducible with varying accuracy. Level 2 automation balances speed and accuracy.
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Sermesant M, Delingette H, Cochet H, Jaïs P, Ayache N. Applications of artificial intelligence in cardiovascular imaging. Nat Rev Cardiol 2021; 18:600-609. [PMID: 33712806 DOI: 10.1038/s41569-021-00527-2] [Citation(s) in RCA: 55] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/08/2021] [Indexed: 01/31/2023]
Abstract
Research into artificial intelligence (AI) has made tremendous progress over the past decade. In particular, the AI-powered analysis of images and signals has reached human-level performance in many applications owing to the efficiency of modern machine learning methods, in particular deep learning using convolutional neural networks. Research into the application of AI to medical imaging is now very active, especially in the field of cardiovascular imaging because of the challenges associated with acquiring and analysing images of this dynamic organ. In this Review, we discuss the clinical questions in cardiovascular imaging that AI can be used to address and the principal methodological AI approaches that have been developed to solve the related image analysis problems. Some approaches are purely data-driven and rely mainly on statistical associations, whereas others integrate anatomical and physiological information through additional statistical, geometric and biophysical models of the human heart. In a structured manner, we provide representative examples of each of these approaches, with particular attention to the underlying computational imaging challenges. Finally, we discuss the remaining limitations of AI approaches in cardiovascular imaging (such as generalizability and explainability) and how they can be overcome.
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Affiliation(s)
| | | | - Hubert Cochet
- IHU Liryc, CHU Bordeaux, Université Bordeaux, Inserm 1045, Pessac, France
| | - Pierre Jaïs
- IHU Liryc, CHU Bordeaux, Université Bordeaux, Inserm 1045, Pessac, France
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Yang DH. Application of Artificial Intelligence to Cardiovascular Computed Tomography. Korean J Radiol 2021; 22:1597-1608. [PMID: 34402240 PMCID: PMC8484158 DOI: 10.3348/kjr.2020.1314] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Revised: 03/26/2021] [Accepted: 05/14/2021] [Indexed: 11/15/2022] Open
Abstract
Cardiovascular computed tomography (CT) is among the most active fields with ongoing technical innovation related to image acquisition and analysis. Artificial intelligence can be incorporated into various clinical applications of cardiovascular CT, including imaging of the heart valves and coronary arteries, as well as imaging to evaluate myocardial function and congenital heart disease. This review summarizes the latest research on the application of deep learning to cardiovascular CT. The areas covered range from image quality improvement to automatic analysis of CT images, including methods such as calcium scoring, image segmentation, and coronary artery evaluation.
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Affiliation(s)
- Dong Hyun Yang
- Department of Radiology and Research Institute of Radiology, Cardiac Imaging Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
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31
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Danilov VV, Klyshnikov KY, Gerget OM, Skirnevsky IP, Kutikhin AG, Shilov AA, Ganyukov VI, Ovcharenko EA. Aortography Keypoint Tracking for Transcatheter Aortic Valve Implantation Based on Multi-Task Learning. Front Cardiovasc Med 2021; 8:697737. [PMID: 34350220 PMCID: PMC8326378 DOI: 10.3389/fcvm.2021.697737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 06/10/2021] [Indexed: 11/15/2022] Open
Abstract
Currently, transcatheter aortic valve implantation (TAVI) represents the most efficient treatment option for patients with aortic stenosis, yet its clinical outcomes largely depend on the accuracy of valve positioning that is frequently complicated when routine imaging modalities are applied. Therefore, existing limitations of perioperative imaging underscore the need for the development of novel visual assistance systems enabling accurate procedures. In this paper, we propose an original multi-task learning-based algorithm for tracking the location of anatomical landmarks and labeling critical keypoints on both aortic valve and delivery system during TAVI. In order to optimize the speed and precision of labeling, we designed nine neural networks and then tested them to predict 11 keypoints of interest. These models were based on a variety of neural network architectures, namely MobileNet V2, ResNet V2, Inception V3, Inception ResNet V2 and EfficientNet B5. During training and validation, ResNet V2 and MobileNet V2 architectures showed the best prediction accuracy/time ratio, predicting keypoint labels and coordinates with 97/96% accuracy and 4.7/5.6% mean absolute error, respectively. Our study provides evidence that neural networks with these architectures are capable to perform real-time predictions of aortic valve and delivery system location, thereby contributing to the proper valve positioning during TAVI.
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Affiliation(s)
- Viacheslav V. Danilov
- Research Laboratory for Processing and Analysis of Big Data, Tomsk Polytechnic University, Tomsk, Russia
| | - Kirill Yu. Klyshnikov
- Department of Experimental Medicine, Research Institute for Complex Issues of Cardiovascular Diseases, Kemerovo, Russia
| | - Olga M. Gerget
- Research Laboratory for Processing and Analysis of Big Data, Tomsk Polytechnic University, Tomsk, Russia
| | - Igor P. Skirnevsky
- Research Laboratory for Processing and Analysis of Big Data, Tomsk Polytechnic University, Tomsk, Russia
| | - Anton G. Kutikhin
- Department of Experimental Medicine, Research Institute for Complex Issues of Cardiovascular Diseases, Kemerovo, Russia
| | - Aleksandr A. Shilov
- Department of Experimental Medicine, Research Institute for Complex Issues of Cardiovascular Diseases, Kemerovo, Russia
| | - Vladimir I. Ganyukov
- Department of Experimental Medicine, Research Institute for Complex Issues of Cardiovascular Diseases, Kemerovo, Russia
| | - Evgeny A. Ovcharenko
- Department of Experimental Medicine, Research Institute for Complex Issues of Cardiovascular Diseases, Kemerovo, Russia
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Antwi WK, Akudjedu TN, Botwe BO. Artificial intelligence in medical imaging practice in Africa: a qualitative content analysis study of radiographers' perspectives. Insights Imaging 2021; 12:80. [PMID: 34132910 PMCID: PMC8206887 DOI: 10.1186/s13244-021-01028-z] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Accepted: 06/02/2021] [Indexed: 12/11/2022] Open
Abstract
Purpose Studies have documented the clinical potentials of artificial intelligence (AI) in medical imaging practice to improving patient care. This study aimed to qualitatively explore the perception of radiographers relating to the integration of AI in medical imaging practice in Africa. Methods The study employed a qualitative design using an open-ended online instrument administered between March and August 2020. Participants consisted of radiographers working within Africa during the time of the study. Data obtained were analysed using qualitative content analysis. Six themes of concerns were generated: expectant tool; career insecurity; cost of new technology, equipment preservation and data insecurity; service delivery quality; need for expanding AI awareness. Results A total of 475 valid responses were obtained. Participants demonstrated a positive outlook about AI in relation to clinical quality improvement, competent diagnosis, radiation dose reduction and improvement in research. They however expressed concerns relating to the implementation of this technology, including job security and loss of core professional radiographer skills and roles. In addition, concerns regarding AI equipment maintenance, lack of awareness about AI and education and training opportunities were evident. Conclusion Awareness of the importance of AI in medical imaging practice was acknowledged; however, concerns relating to job security, data protection must be given critical attention for successful implementation of these advanced technologies in medical imaging in Africa. Inclusion of AI modules in the training of future radiographers is highly recommended. Supplementary Information The online version contains supplementary material available at 10.1186/s13244-021-01028-z.
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Affiliation(s)
- William Kwadwo Antwi
- Department of Radiography, School of Biomedical and Allied Health Sciences, College of Health Sciences, University of Ghana, Korle Bu, Box KB143, Accra, Ghana
| | - Theophilus N Akudjedu
- Institute of Medical Imaging and Visualisation, Department of Medical Science and Public Health, Faculty of Health and Social Sciences, Bournemouth University, Bournemouth, UK
| | - Benard Ohene Botwe
- Department of Radiography, School of Biomedical and Allied Health Sciences, College of Health Sciences, University of Ghana, Korle Bu, Box KB143, Accra, Ghana
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Azarmehr N, Ye X, Howard JP, Lane ES, Labs R, Shun-Shin MJ, Cole GD, Bidaut L, Francis DP, Zolgharni M. Neural architecture search of echocardiography view classifiers. J Med Imaging (Bellingham) 2021; 8:034002. [PMID: 34179218 PMCID: PMC8217960 DOI: 10.1117/1.jmi.8.3.034002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2020] [Accepted: 06/04/2021] [Indexed: 11/14/2022] Open
Abstract
Purpose: Echocardiography is the most commonly used modality for assessing the heart in clinical practice. In an echocardiographic exam, an ultrasound probe samples the heart from different orientations and positions, thereby creating different viewpoints for assessing the cardiac function. The determination of the probe viewpoint forms an essential step in automatic echocardiographic image analysis. Approach: In this study, convolutional neural networks are used for the automated identification of 14 different anatomical echocardiographic views (larger than any previous study) in a dataset of 8732 videos acquired from 374 patients. Differentiable architecture search approach was utilized to design small neural network architectures for rapid inference while maintaining high accuracy. The impact of the image quality and resolution, size of the training dataset, and number of echocardiographic view classes on the efficacy of the models were also investigated. Results: In contrast to the deeper classification architectures, the proposed models had significantly lower number of trainable parameters (up to 99.9% reduction), achieved comparable classification performance (accuracy 88.4% to 96%, precision 87.8% to 95.2%, recall 87.1% to 95.1%) and real-time performance with inference time per image of 3.6 to 12.6 ms. Conclusion: Compared with the standard classification neural network architectures, the proposed models are faster and achieve comparable classification performance. They also require less training data. Such models can be used for real-time detection of the standard views.
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Affiliation(s)
- Neda Azarmehr
- University of Lincoln, School of Computer Science, Lincoln, United Kingdom
| | - Xujiong Ye
- University of Lincoln, School of Computer Science, Lincoln, United Kingdom
| | - James P. Howard
- Imperial College London, National Heart and Lung Institute, London, United Kingdom
| | - Elisabeth S. Lane
- University of West London, School of Computing and Engineering, London, United Kingdom
| | - Robert Labs
- University of West London, School of Computing and Engineering, London, United Kingdom
| | - Matthew J. Shun-Shin
- Imperial College London, National Heart and Lung Institute, London, United Kingdom
| | - Graham D. Cole
- Imperial College London, National Heart and Lung Institute, London, United Kingdom
| | - Luc Bidaut
- University of Lincoln, School of Computer Science, Lincoln, United Kingdom
| | - Darrel P. Francis
- Imperial College London, National Heart and Lung Institute, London, United Kingdom
| | - Massoud Zolgharni
- Imperial College London, National Heart and Lung Institute, London, United Kingdom
- University of West London, School of Computing and Engineering, London, United Kingdom
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Potential Role of Artificial Intelligence in Cardiac Magnetic Resonance Imaging: Can It Help Clinicians in Making a Diagnosis? J Thorac Imaging 2021; 36:142-148. [PMID: 33769416 DOI: 10.1097/rti.0000000000000584] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
In the era of modern medicine, artificial intelligence (AI) is a growing field of interest which is experiencing a steady development. Several applications of AI have been applied to various aspects of cardiac magnetic resonance to assist clinicians and engineers in reducing the costs of exams and, at the same time, to improve image acquisition and reconstruction, thus simplifying their analysis, interpretation, and decision-making process as well. In fact, the role of AI and machine learning in cardiovascular imaging relies on evaluating images more quickly, improving their quality, nulling intraobserver and interobserver variability in their interpretation, upgrading the understanding of the stage of the disease, and providing with a personalized approach to cardiovascular care. In addition, AI algorithm could be directed toward workflow management. This article presents an overview of the existing AI literature in cardiac magnetic resonance, with its strengths and limitations, recent applications, and promising developments. We conclude that AI is very likely be used in all the various process of diagnosis routine mode for cardiac care of patients.
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Schuuring MJ, Išgum I, Cosyns B, Chamuleau SAJ, Bouma BJ. Routine Echocardiography and Artificial Intelligence Solutions. Front Cardiovasc Med 2021; 8:648877. [PMID: 33708808 PMCID: PMC7940184 DOI: 10.3389/fcvm.2021.648877] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2021] [Accepted: 02/05/2021] [Indexed: 12/28/2022] Open
Abstract
Introduction: Echocardiography is widely used because of its portability, high temporal resolution, absence of radiation, and due to the low-costs. Over the past years, echocardiography has been recommended by the European Society of Cardiology in most cardiac diseases for both diagnostic and prognostic purposes. These recommendations have led to an increase in number of performed studies each requiring diligent processing and reviewing. The standard work pattern of image analysis including quantification and reporting has become highly resource intensive and time consuming. Existence of a large number of datasets with digital echocardiography images and recent advent of AI technology have created an environment in which artificial intelligence (AI) solutions can be developed successfully to automate current manual workflow. Methods and Results: We report on published AI solutions for echocardiography analysis on methods' performance, characteristics of the used data and imaged population. Contemporary AI applications are available for automation and advent in the image acquisition, analysis, reporting and education. AI solutions have been developed for both diagnostic and predictive tasks in echocardiography. Left ventricular function assessment and quantification have been most often performed. Performance of automated image view classification, image quality enhancement, cardiac function assessment, disease classification, and cardiac event prediction was overall good but most studies lack external evaluation. Conclusion: Contemporary AI solutions for image acquisition, analysis, reporting and education are developed for relevant tasks with promising performance. In the future major benefit of AI in echocardiography is expected from improvements in automated analysis and interpretation to reduce workload and improve clinical outcome. Some of the challenges have yet to be overcome, however, none of them are insurmountable.
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Affiliation(s)
- Mark J. Schuuring
- Amsterdam University Medical Centers -Location Academic Medical Center, Department of Cardiology, University of Amsterdam, Amsterdam, Netherlands
| | - Ivana Išgum
- Amsterdam University Medical Centers -Location Academic Medical Center, Department of Biomedical Engineering and Physics, University of Amsterdam, Amsterdam, Netherlands
- Amsterdam University Medical Centers -Location Academic Medical Center, Department of Radiology and Nuclear Medicine, University of Amsterdam, Amsterdam, Netherlands
- Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centers -Location Academic Medical Center, University of Amsterdam, Amsterdam, Netherlands
| | - Bernard Cosyns
- Department of Cardiology, University Hospital Brussel, Brussels, Belgium
| | - Steven A. J. Chamuleau
- Amsterdam University Medical Centers -Location Academic Medical Center, Department of Cardiology, University of Amsterdam, Amsterdam, Netherlands
- Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centers -Location Academic Medical Center, University of Amsterdam, Amsterdam, Netherlands
| | - Berto J. Bouma
- Amsterdam University Medical Centers -Location Academic Medical Center, Department of Cardiology, University of Amsterdam, Amsterdam, Netherlands
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Karaca O, Çalışkan SA, Demir K. Medical artificial intelligence readiness scale for medical students (MAIRS-MS) - development, validity and reliability study. BMC MEDICAL EDUCATION 2021; 21:112. [PMID: 33602196 PMCID: PMC7890640 DOI: 10.1186/s12909-021-02546-6] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 02/03/2021] [Indexed: 05/10/2023]
Abstract
BACKGROUND It is unlikely that applications of artificial intelligence (AI) will completely replace physicians. However, it is very likely that AI applications will acquire many of their roles and generate new tasks in medical care. To be ready for new roles and tasks, medical students and physicians will need to understand the fundamentals of AI and data science, mathematical concepts, and related ethical and medico-legal issues in addition with the standard medical principles. Nevertheless, there is no valid and reliable instrument available in the literature to measure medical AI readiness. In this study, we have described the development of a valid and reliable psychometric measurement tool for the assessment of the perceived readiness of medical students on AI technologies and its applications in medicine. METHODS To define medical students' required competencies on AI, a diverse set of experts' opinions were obtained by a qualitative method and were used as a theoretical framework, while creating the item pool of the scale. Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were applied. RESULTS A total of 568 medical students during the EFA phase and 329 medical students during the CFA phase, enrolled in two different public universities in Turkey participated in this study. The initial 27-items finalized with a 22-items scale in a four-factor structure (cognition, ability, vision, and ethics), which explains 50.9% cumulative variance that resulted from the EFA. Cronbach's alpha reliability coefficient was 0.87. CFA indicated appropriate fit of the four-factor model (χ2/df = 3.81, RMSEA = 0.094, SRMR = 0.057, CFI = 0.938, and NNFI (TLI) = 0.928). These values showed that the four-factor model has construct validity. CONCLUSIONS The newly developed Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) was found to be valid and reliable tool for evaluation and monitoring of perceived readiness levels of medical students on AI technologies and applications. Medical schools may follow 'a physician training perspective that is compatible with AI in medicine' to their curricula by using MAIRS-MS. This scale could be benefitted by medical and health science education institutions as a valuable curriculum development tool with its learner needs assessment and participants' end-course perceived readiness opportunities.
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Affiliation(s)
- Ozan Karaca
- Department of Medical Education, Ege University Faculty of Medicine, İzmir, Turkey
| | - S. Ayhan Çalışkan
- Department of Medical Education, Ege University Faculty of Medicine, İzmir, Turkey
| | - Kadir Demir
- Department of Computer Education and Instructional Technology, Dokuz Eylül University Buca Faculty of Education, İzmir, Turkey
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Tandon A, Mohan N, Jensen C, Burkhardt BEU, Gooty V, Castellanos DA, McKenzie PL, Zahr RA, Bhattaru A, Abdulkarim M, Amir-Khalili A, Sojoudi A, Rodriguez SM, Dillenbeck J, Greil GF, Hussain T. Retraining Convolutional Neural Networks for Specialized Cardiovascular Imaging Tasks: Lessons from Tetralogy of Fallot. Pediatr Cardiol 2021; 42:578-589. [PMID: 33394116 PMCID: PMC7990832 DOI: 10.1007/s00246-020-02518-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2020] [Accepted: 12/03/2020] [Indexed: 12/19/2022]
Abstract
Ventricular contouring of cardiac magnetic resonance imaging is the gold standard for volumetric analysis for repaired tetralogy of Fallot (rTOF), but can be time-consuming and subject to variability. A convolutional neural network (CNN) ventricular contouring algorithm was developed to generate contours for mostly structural normal hearts. We aimed to improve this algorithm for use in rTOF and propose a more comprehensive method of evaluating algorithm performance. We evaluated the performance of a ventricular contouring CNN, that was trained on mostly structurally normal hearts, on rTOF patients. We then created an updated CNN by adding rTOF training cases and evaluated the new algorithm's performance generating contours for both the left and right ventricles (LV and RV) on new testing data. Algorithm performance was evaluated with spatial metrics (Dice Similarity Coefficient (DSC), Hausdorff distance, and average Hausdorff distance) and volumetric comparisons (e.g., differences in RV volumes). The original Mostly Structurally Normal (MSN) algorithm was better at contouring the LV than the RV in patients with rTOF. After retraining the algorithm, the new MSN + rTOF algorithm showed improvements for LV epicardial and RV endocardial contours on testing data to which it was naïve (N = 30; e.g., DSC 0.883 vs. 0.905 for LV epicardium at end diastole, p < 0.0001) and improvements in RV end-diastolic volumetrics (median %error 8.1 vs 11.4, p = 0.0022). Even with a small number of cases, CNN-based contouring for rTOF can be improved. This work should be extended to other forms of congenital heart disease with more extreme structural abnormalities. Aspects of this work have already been implemented in clinical practice, representing rapid clinical translation. The combined use of both spatial and volumetric comparisons yielded insights into algorithm errors.
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Affiliation(s)
- Animesh Tandon
- Department of Pediatrics, UT Southwestern Medical Center, 1935 Medical District Dr, Dallas, TX 75235 USA
- Department of Radiology, UT Southwestern Medical Center, 1935 Medical District Dr, Dallas, TX 75235 USA
- Division of Cardiology, Children’s Health Children’s Medical Center Dallas, Dallas, TX USA
| | - Navina Mohan
- Department of Pediatrics, UT Southwestern Medical Center, 1935 Medical District Dr, Dallas, TX 75235 USA
| | - Cory Jensen
- Circle Cardiovascular Imaging, Calgary, AB Canada
| | - Barbara E. U. Burkhardt
- Department of Pediatrics, UT Southwestern Medical Center, 1935 Medical District Dr, Dallas, TX 75235 USA
- Division of Cardiology, Children’s Health Children’s Medical Center Dallas, Dallas, TX USA
- Pediatric Cardiology, Department of Surgery, Pediatric Heart Center, University Children’s- Hospital Zurich, Zurich, Switzerland
| | - Vasu Gooty
- Department of Pediatrics, LeBonheur Children’s Hospital and University of Tennessee, Memphis, TN USA
| | - Daniel A. Castellanos
- Department of Pediatrics, UT Southwestern Medical Center, 1935 Medical District Dr, Dallas, TX 75235 USA
- Division of Cardiology, Children’s Health Children’s Medical Center Dallas, Dallas, TX USA
| | - Paige L. McKenzie
- Department of Pediatrics, UT Southwestern Medical Center, 1935 Medical District Dr, Dallas, TX 75235 USA
| | - Riad Abou Zahr
- Department of Pediatrics, UT Southwestern Medical Center, 1935 Medical District Dr, Dallas, TX 75235 USA
- Division of Cardiology, Children’s Health Children’s Medical Center Dallas, Dallas, TX USA
- King Faisal Specialist Hospital and Research Centre, Jeddah, Saudi Arabia
| | - Abhijit Bhattaru
- Department of Pediatrics, UT Southwestern Medical Center, 1935 Medical District Dr, Dallas, TX 75235 USA
- Division of Cardiology, Children’s Health Children’s Medical Center Dallas, Dallas, TX USA
| | - Mubeena Abdulkarim
- Department of Pediatrics, UT Southwestern Medical Center, 1935 Medical District Dr, Dallas, TX 75235 USA
- Division of Cardiology, Children’s Health Children’s Medical Center Dallas, Dallas, TX USA
| | | | | | - Stephen M. Rodriguez
- Department of Pediatrics, UT Southwestern Medical Center, 1935 Medical District Dr, Dallas, TX 75235 USA
| | - Jeanne Dillenbeck
- Department of Radiology, UT Southwestern Medical Center, 1935 Medical District Dr, Dallas, TX 75235 USA
| | - Gerald F. Greil
- Department of Pediatrics, UT Southwestern Medical Center, 1935 Medical District Dr, Dallas, TX 75235 USA
- Department of Radiology, UT Southwestern Medical Center, 1935 Medical District Dr, Dallas, TX 75235 USA
- Division of Cardiology, Children’s Health Children’s Medical Center Dallas, Dallas, TX USA
| | - Tarique Hussain
- Department of Pediatrics, UT Southwestern Medical Center, 1935 Medical District Dr, Dallas, TX 75235 USA
- Department of Radiology, UT Southwestern Medical Center, 1935 Medical District Dr, Dallas, TX 75235 USA
- Division of Cardiology, Children’s Health Children’s Medical Center Dallas, Dallas, TX USA
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Jurtz VI, Skovbjerg G, Salinas CG, Roostalu U, Pedersen L, Hecksher-Sørensen J, Rolin B, Nyberg M, van de Bunt M, Ingvorsen C. Deep learning reveals 3D atherosclerotic plaque distribution and composition. Sci Rep 2020; 10:21523. [PMID: 33299076 PMCID: PMC7726562 DOI: 10.1038/s41598-020-78632-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Accepted: 11/26/2020] [Indexed: 12/14/2022] Open
Abstract
Complications of atherosclerosis are the leading cause of morbidity and mortality worldwide. Various genetically modified mouse models are used to investigate disease trajectory with classical histology, currently the preferred methodology to elucidate plaque composition. Here, we show the strength of light-sheet fluorescence microscopy combined with deep learning image analysis for characterising and quantifying plaque burden and composition in whole aorta specimens. 3D imaging is a non-destructive method that requires minimal ex vivo handling and can be up-scaled to large sample sizes. Combined with deep learning, atherosclerotic plaque in mice can be identified without any ex vivo staining due to the autofluorescent nature of the tissue. The aorta and its branches can subsequently be segmented to determine how anatomical position affects plaque composition and progression. Here, we find the highest plaque accumulation in the aortic arch and brachiocephalic artery. Simultaneously, aortas can be stained for markers of interest (for example the pan immune cell marker CD45) and quantified. In ApoE-/- mice we observe that levels of CD45 reach a plateau after which increases in plaque volume no longer correlate to immune cell infiltration. All underlying code is made publicly available to ease adaption of the method.
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MESH Headings
- Animals
- Aorta/pathology
- Aortic Diseases
- Apolipoproteins E/analysis
- Atherosclerosis/complications
- Atherosclerosis/pathology
- Deep Learning
- Disease Models, Animal
- Female
- Image Processing, Computer-Assisted/methods
- Imaging, Three-Dimensional/methods
- Male
- Mice
- Mice, Inbred C57BL
- Mice, Knockout
- Microscopy, Fluorescence/methods
- Plaque, Atherosclerotic/diagnostic imaging
- Plaque, Atherosclerotic/metabolism
- Plaque, Atherosclerotic/pathology
- Receptors, LDL/analysis
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Affiliation(s)
| | - Grethe Skovbjerg
- Novo Nordisk A/S, Novo Nordisk Park, 2760, Maaloev, Denmark
- Gubra, 2970, Hoersholm, Denmark
| | | | | | - Louise Pedersen
- Novo Nordisk A/S, Novo Nordisk Park, 2760, Maaloev, Denmark
- University of Copenhagen, 1017, Copenhagen, Denmark
| | | | - Bidda Rolin
- Novo Nordisk A/S, Novo Nordisk Park, 2760, Maaloev, Denmark
- Gubra, 2970, Hoersholm, Denmark
| | - Michael Nyberg
- Novo Nordisk A/S, Novo Nordisk Park, 2760, Maaloev, Denmark
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39
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Kwan AC, Pourmorteza A, Stutman D, Bluemke DA, Lima JAC. Next-Generation Hardware Advances in CT: Cardiac Applications. Radiology 2020; 298:3-17. [PMID: 33201793 DOI: 10.1148/radiol.2020192791] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Impending major hardware advances in cardiac CT include three areas: ultra-high-resolution (UHR) CT, photon-counting CT, and phase-contrast CT. Cardiac CT is a particularly demanding CT application that requires a high degree of temporal resolution, spatial resolution, and soft-tissue contrast in a moving structure. In this review, cardiac CT is used to highlight the strengths of these technical advances. UHR CT improves visualization of calcified and stented vessels but may result in increased noise and radiation exposure. Photon-counting CT uses multiple photon energies to reduce artifacts, improve contrast resolution, and perform material decomposition. Finally, phase-contrast CT uses x-ray refraction properties to improve spatial and soft-tissue contrast. This review describes these hardware advances in CT and their relevance to cardiovascular imaging.
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Affiliation(s)
- Alan C Kwan
- From the Smidt Heart Institute, Cedars-Sinai Medical Center, 127 S San Vicente Blvd, AHSP, Suite A3600, Los Angeles, CA 90048-0750 (A.C.K.); Department of Radiology and Imaging Sciences, Emory University, Atlanta, Ga (A.P.); Winship Cancer Institute, Emory University, Atlanta, Ga (A.P.); Department of Biomedical Engineering, Georgia Institute of Technology-Emory University, Atlanta, Ga (A.P.); Department of Physics and Astronomy, Johns Hopkins University, Baltimore, Md (D.S.); Extreme Light Infrastructure-Nuclear Physics, Bucharest-Magurele, Romania (D.S.); Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wis (D.A.B.); and Department of Cardiology, The Johns Hopkins Hospital, Baltimore, Md (J.A.C.L.)
| | - Amir Pourmorteza
- From the Smidt Heart Institute, Cedars-Sinai Medical Center, 127 S San Vicente Blvd, AHSP, Suite A3600, Los Angeles, CA 90048-0750 (A.C.K.); Department of Radiology and Imaging Sciences, Emory University, Atlanta, Ga (A.P.); Winship Cancer Institute, Emory University, Atlanta, Ga (A.P.); Department of Biomedical Engineering, Georgia Institute of Technology-Emory University, Atlanta, Ga (A.P.); Department of Physics and Astronomy, Johns Hopkins University, Baltimore, Md (D.S.); Extreme Light Infrastructure-Nuclear Physics, Bucharest-Magurele, Romania (D.S.); Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wis (D.A.B.); and Department of Cardiology, The Johns Hopkins Hospital, Baltimore, Md (J.A.C.L.)
| | - Dan Stutman
- From the Smidt Heart Institute, Cedars-Sinai Medical Center, 127 S San Vicente Blvd, AHSP, Suite A3600, Los Angeles, CA 90048-0750 (A.C.K.); Department of Radiology and Imaging Sciences, Emory University, Atlanta, Ga (A.P.); Winship Cancer Institute, Emory University, Atlanta, Ga (A.P.); Department of Biomedical Engineering, Georgia Institute of Technology-Emory University, Atlanta, Ga (A.P.); Department of Physics and Astronomy, Johns Hopkins University, Baltimore, Md (D.S.); Extreme Light Infrastructure-Nuclear Physics, Bucharest-Magurele, Romania (D.S.); Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wis (D.A.B.); and Department of Cardiology, The Johns Hopkins Hospital, Baltimore, Md (J.A.C.L.)
| | - David A Bluemke
- From the Smidt Heart Institute, Cedars-Sinai Medical Center, 127 S San Vicente Blvd, AHSP, Suite A3600, Los Angeles, CA 90048-0750 (A.C.K.); Department of Radiology and Imaging Sciences, Emory University, Atlanta, Ga (A.P.); Winship Cancer Institute, Emory University, Atlanta, Ga (A.P.); Department of Biomedical Engineering, Georgia Institute of Technology-Emory University, Atlanta, Ga (A.P.); Department of Physics and Astronomy, Johns Hopkins University, Baltimore, Md (D.S.); Extreme Light Infrastructure-Nuclear Physics, Bucharest-Magurele, Romania (D.S.); Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wis (D.A.B.); and Department of Cardiology, The Johns Hopkins Hospital, Baltimore, Md (J.A.C.L.)
| | - João A C Lima
- From the Smidt Heart Institute, Cedars-Sinai Medical Center, 127 S San Vicente Blvd, AHSP, Suite A3600, Los Angeles, CA 90048-0750 (A.C.K.); Department of Radiology and Imaging Sciences, Emory University, Atlanta, Ga (A.P.); Winship Cancer Institute, Emory University, Atlanta, Ga (A.P.); Department of Biomedical Engineering, Georgia Institute of Technology-Emory University, Atlanta, Ga (A.P.); Department of Physics and Astronomy, Johns Hopkins University, Baltimore, Md (D.S.); Extreme Light Infrastructure-Nuclear Physics, Bucharest-Magurele, Romania (D.S.); Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wis (D.A.B.); and Department of Cardiology, The Johns Hopkins Hospital, Baltimore, Md (J.A.C.L.)
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40
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Nakanishi R, Slomka PJ, Rios R, Betancur J, Blaha MJ, Nasir K, Miedema MD, Rumberger JA, Gransar H, Shaw LJ, Rozanski A, Budoff MJ, Berman DS. Machine Learning Adds to Clinical and CAC Assessments in Predicting 10-Year CHD and CVD Deaths. JACC Cardiovasc Imaging 2020; 14:615-625. [PMID: 33129741 DOI: 10.1016/j.jcmg.2020.08.024] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Revised: 07/01/2020] [Accepted: 08/06/2020] [Indexed: 12/23/2022]
Abstract
OBJECTIVES The aim of this study was to evaluate whether machine learning (ML) of noncontrast computed tomographic (CT) and clinical variables improves the prediction of atherosclerotic cardiovascular disease (ASCVD) and coronary heart disease (CHD) deaths compared with coronary artery calcium (CAC) Agatston scoring and clinical data. BACKGROUND The CAC score provides a measure of the global burden of coronary atherosclerosis, and its long-term prognostic utility has been consistently shown to have incremental value over clinical risk assessment. However, current approaches fail to integrate all available CT and clinical variables for comprehensive risk assessment. METHODS The study included data from 66,636 asymptomatic subjects (mean age 54 ± 11 years, 67% men) without established ASCVD undergoing CAC scanning and followed for cardiovascular disease (CVD) and CHD deaths at 10 years. Clinical risk assessment incorporated the ASCVD risk score. For ML, an ensemble boosting approach was used to fit a predictive classifier for outcomes, followed by automated feature selection using information gain ratio. The model-building process incorporated all available clinical and CT data, including the CAC score; the number, volume, and density of CAC plaques; and extracoronary scores; comprising a total of 77 variables. The overall proposed model (ML all) was evaluated using a 10-fold cross-validation framework on the population data and area under the curve (AUC) as metrics. The prediction performance was also compared with 2 traditional scores (ASCVD risk and CAC score) and 2 additional models that were trained using all the clinical data (ML clinical) and CT variables (ML CT). RESULTS The AUC by ML all (0.845) for predicting CVD death was superior compared with those obtained by ASCVD risk alone (0.821), CAC score alone (0.781), and ML CT alone (0.804) (p < 0.001 for all). Similarly, for predicting CHD death, AUC by ML all (0.860) was superior to the other analyses (0.835 for ASCVD risk, 0.816 for CAC, and 0.827 for ML CT; p < 0.001). CONCLUSIONS The comprehensive ML model was superior to ASCVD risk, CAC score, and an ML model fitted using CT variables alone in the prediction of both CVD and CHD death.
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Affiliation(s)
- Rine Nakanishi
- Department of Cardiovascular Medicine, Toho University Graduate School of Medicine, Tokyo, Japan; Los Angeles BioMedical Research Institute at Harbor UCLA Medical Center, Torrance, California, USA
| | - Piotr J Slomka
- Department of Imaging (Division of Nuclear Medicine), Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, USA; David Geffen School of Medicine at UCLA, Los Angeles, California, USA.
| | - Richard Rios
- Department of Imaging (Division of Nuclear Medicine), Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Julian Betancur
- Department of Imaging (Division of Nuclear Medicine), Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Michael J Blaha
- Prevention of Heart Disease, Division of Cardiology, Department of Medicine, Johns Hopkins Ciccarone Center, Baltimore, Maryland, USA
| | - Khurram Nasir
- Center for Prevention and Wellness Research, Baptist Health Medical Group, Miami Beach, Florida, USA
| | | | - John A Rumberger
- Department of Cardiac Imaging, The Princeton Longevity Center, Princeton, New Jersey, USA
| | - Heidi Gransar
- Department of Imaging (Division of Nuclear Medicine), Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Leslee J Shaw
- Weill Cornell Medical College, New York, New York, USA
| | - Alan Rozanski
- Division of Cardiology, Mount Sinai St. Luke's Hospital, Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Matthew J Budoff
- Los Angeles BioMedical Research Institute at Harbor UCLA Medical Center, Torrance, California, USA
| | - Daniel S Berman
- Department of Imaging (Division of Nuclear Medicine), Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, USA; David Geffen School of Medicine at UCLA, Los Angeles, California, USA
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41
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Machine Learning in Electrocardiography and Echocardiography: Technological Advances in Clinical Cardiology. Curr Cardiol Rep 2020; 22:161. [PMID: 33037949 DOI: 10.1007/s11886-020-01416-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/03/2020] [Indexed: 02/07/2023]
Abstract
PURPOSE OF REVIEW Electrocardiography (ECG) and echocardiography are the most widely used diagnostic tools in clinical cardiology. This review focuses on recent advancements in applying machine learning (ML) in ECG and echocardiography and potential synergistic ML integration of ECG and echocardiography. RECENT FINDINGS ML algorithms have been used in ECG for technical quality assurance, arrhythmia identification, and prognostic predictions, and in echocardiography to recognize image views, quantify measurements, and identify pathologic patterns. Synergistic application of ML in ECG and echocardiograph has demonstrated the potential to optimize therapeutic response, improve risk stratification, and generate new disease classification. There is mounting evidence that ML potentially outperforms in disease diagnoses and outcome prediction with ECG and echocardiography when compared with trained healthcare professionals. The applications of ML in ECG and echocardiography are playing increasingly greater roles in medical research and clinical practice, particularly for their contributions to developing novel diagnostic/prognostic prediction models. The automation in data acquisition, processing, and interpretation help streamline the workflows of ECG and echocardiography in contemporary cardiology practice.
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42
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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]
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43
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Kheradvar A, Jafarkhani H, Guy TS, Finn JP. Prospect of artificial intelligence for the assessment of cardiac function and treatment of cardiovascular disease. Future Cardiol 2020; 17:183-187. [PMID: 32933328 DOI: 10.2217/fca-2020-0128] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Affiliation(s)
- Arash Kheradvar
- The Edwards Lifesciences Center for Advanced Cardiovascular Technology, University of California, Irvine, Irvine, CA 92697, USA
| | - Hamid Jafarkhani
- Center for Pervasive Communications & Computing, University of California, Irvine, Irvine, CA 92697, USA
| | - Thomas Sloane Guy
- Division of Cardiac Surgery, Department of Surgery, Thomas Jefferson University, Philadelphia, PA 19107, USA
| | - John Paul Finn
- Department of Radiological Sciences, David Geffen School of Medicine, UCLA, Los Angeles, CA 90095, USA
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44
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de Jaegere P, de Ronde M, den Heijer P, Weger A, Baan J. The history of transcatheter aortic valve implantation: The role and contribution of an early believer and adopter, the Netherlands. Neth Heart J 2020; 28:128-135. [PMID: 32780343 PMCID: PMC7419393 DOI: 10.1007/s12471-020-01468-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022] Open
Abstract
This paper describes the history of transcatheter aortic valve implantation (TAVI) from its preclinical phase during which visionary pioneers developed its concept and prototype valves against strong head wind to first application in clinical practice (2002) and the clinical and scientific role of an early believer and adopter, the Netherlands (2005).
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Affiliation(s)
- P de Jaegere
- Department of Cardiology, Erasmus University, Rotterdam, The Netherlands.
| | - M de Ronde
- Department of Cardiology, Erasmus University, Rotterdam, The Netherlands
| | - P den Heijer
- Department of Cardiology, Amphia Hospital, Breda, The Netherlands
| | - A Weger
- Department of Cardiothoracic Surgery, Leiden University Medical Centre, Leiden, The Netherlands
| | - J Baan
- Department of Cardiology, Amsterdam AMC, University of Amsterdam, Amsterdam, The Netherlands
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45
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Wierda E, van Veghel D, Hirsch A, de Mol BAJM. Heart teams in the Netherlands: From teamwork to data‑driven decision-making. Neth Heart J 2020; 28:73-77. [PMID: 32780335 PMCID: PMC7419410 DOI: 10.1007/s12471-020-01452-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022] Open
Abstract
For all patients with cardiovascular disease requiring an intervention, this is a major life event. The heart team concept is one of the most exciting and effective team modalities to ensure cost-effective application of invasive cardiovascular care. It optimises patient selection in a complex decision-making process and identifies risk/benefit ratios of different interventions. Informed consent and patient safety should be at the centre of these decisions. To deal with increased load of medical data in the future, artificial intelligence could enable objective and effective interpretation of medical imaging and decision support. This technical support is indispensable to meet current patient and societal demands for informed consent, shared decision-making, outcome improvement and safety. The heart team should be restructured with clear leadership, accountability, and process and outcome measurement of interventions. In this way, the heart team concept in the Netherlands will be ready for the future.
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Affiliation(s)
- E Wierda
- Department of Cardiology, Dijklander Hospital, location Hoorn, Hoorn, The Netherlands.
| | - D van Veghel
- Department of Cardiology and Cardiothoracic Surgery, Catharina Hospital Eindhoven, Eindhoven, The Netherlands
| | - A Hirsch
- Department of Cardiology and Radiology and Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - B A J M de Mol
- Department of Cardiothoracic Surgery, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
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46
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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.
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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
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47
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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.
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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.
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48
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Gruson D, Bernardini S, Dabla PK, Gouget B, Stankovic S. Collaborative AI and Laboratory Medicine integration in precision cardiovascular medicine. Clin Chim Acta 2020; 509:67-71. [PMID: 32505771 DOI: 10.1016/j.cca.2020.06.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Revised: 05/30/2020] [Accepted: 06/01/2020] [Indexed: 12/11/2022]
Abstract
Artificial Intelligence (AI) is a broad term that combines computation with sophisticated mathematical models and in turn allows the development of complex algorithms which are capable to simulate human intelligence such as problem solving and learning. It is devised to promote a significant paradigm shift in the most diverse areas of medical knowledge. On the other hand, Cardiology is a vast field dealing with diseases relating to the heart, the circulatory system, and includes coronary heart disease, cerebrovascular disease, rheumatic heart disease and other conditions. AI has emerged as a promising tool in cardiovascular medicine which is aimed in augmenting the effectiveness of the cardiologist and to extend better quality to patients. It has the ability to support decision‑making and improve diagnostic and prognostic performance. Attempt has also been made to explore novel genotypes and phenotypes in existing cardiovascular diseases, improve the quality of patient care, to enablecost-effectiveness with reducereadmissionand mortality rates. Our review addresses the integration of AI and laboratory medicine as an accelerator of personalization care associated with the precision and the need of value creation services in cardiovascular medicine.
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Affiliation(s)
- Damien Gruson
- Department of Clinical Biochemistry, Cliniques Universitaires St-Luc and Université Catholique de Louvain, Brussels, Belgium; Pôle de recherche en Endocrinologie, Diabète et Nutrition, Institut de Recherche Expérimentale et Clinique, Cliniques Universitaires St-Luc and Université Catholique de Louvain, Brussels, Belgium; Emerging Technologies Division-MHBLM Committee, International Federation Clinical Chemistry and Laboratory Medicine (IFCC), Italy.
| | - Sergio Bernardini
- Department of Experimental Medicine, University of Tor Vergata, Rome, Italy; Emerging Technologies Division-MHBLM Committee, International Federation Clinical Chemistry and Laboratory Medicine (IFCC), Italy
| | - Pradeep Kumar Dabla
- Department of Biochemistry, G.B Pant Institute of Postgraduate Medical Education & Research, Associated to Maulana Azad Medical College, New Delhi, India; Emerging Technologies Division-MHBLM Committee, International Federation Clinical Chemistry and Laboratory Medicine (IFCC), Italy
| | - Bernard Gouget
- President-Healthcare Division Committee, Comité Français d'accréditation (Cofrac), 75012 Paris, France; Emerging Technologies Division-MHBLM Committee, International Federation Clinical Chemistry and Laboratory Medicine (IFCC), Italy
| | - Sanja Stankovic
- Center for Medical Biochemistry, Clinical Center of Serbia, Belgrade, Serbia; Emerging Technologies Division-MHBLM Committee, International Federation Clinical Chemistry and Laboratory Medicine (IFCC), Italy
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Vaishya R, Javaid M, Haleem A, Khan I, Vaish A. Extending capabilities of artificial intelligence for decision-making and healthcare education. APOLLO MEDICINE 2020. [DOI: 10.4103/am.am_10_20] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
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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.
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Affiliation(s)
- Lisa J. Lim
- UNIVERSITY OF CALIFORNIA SAN FRANCISCO, SAN FRANCISCO, CALIFORNIA
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