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Mathis M, Steffner KR, Subramanian H, Gill GP, Girardi NI, Bansal S, Bartels K, Khanna AK, Huang J. Overview and Clinical Applications of Artificial Intelligence and Machine Learning in Cardiac Anesthesiology. J Cardiothorac Vasc Anesth 2024; 38:1211-1220. [PMID: 38453558 PMCID: PMC10999327 DOI: 10.1053/j.jvca.2024.02.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Revised: 01/30/2024] [Accepted: 02/05/2024] [Indexed: 03/09/2024]
Abstract
Artificial intelligence- (AI) and machine learning (ML)-based applications are becoming increasingly pervasive in the healthcare setting. This has in turn challenged clinicians, hospital administrators, and health policymakers to understand such technologies and develop frameworks for safe and sustained clinical implementation. Within cardiac anesthesiology, challenges and opportunities for AI/ML to support patient care are presented by the vast amounts of electronic health data, which are collected rapidly, interpreted, and acted upon within the periprocedural area. To address such challenges and opportunities, in this article, the authors review 3 recent applications relevant to cardiac anesthesiology, including depth of anesthesia monitoring, operating room resource optimization, and transthoracic/transesophageal echocardiography, as conceptual examples to explore strengths and limitations of AI/ML within healthcare, and characterize this evolving landscape. Through reviewing such applications, the authors introduce basic AI/ML concepts and methodologies, as well as practical considerations and ethical concerns for initiating and maintaining safe clinical implementation of AI/ML-based algorithms for cardiac anesthesia patient care.
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Affiliation(s)
- Michael Mathis
- Department of Anesthesiology, University of Michigan Medicine, Ann Arbor, MI
| | - Kirsten R Steffner
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA
| | - Harikesh Subramanian
- Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh, Pittsburgh, PA
| | - George P Gill
- Department of Anesthesiology, Cedars Sinai, Los Angeles, CA
| | | | - Sagar Bansal
- Department of Anesthesiology and Perioperative Medicine, University of Missouri School of Medicine, Columbia, MO
| | - Karsten Bartels
- Department of Anesthesiology, University of Nebraska Medical Center, Omaha, NE
| | - Ashish K Khanna
- Department of Anesthesiology, Section on Critical Care Medicine, School of Medicine, Wake Forest University, Atrium Health Wake Forest Baptist Medical Center, Winston-Salem, NC
| | - Jiapeng Huang
- Department of Anesthesiology and Perioperative Medicine, University of Louisville, Louisville, KY.
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Grzyb C, Du D, Nair N. Artificial Intelligence Approaches for Predicting the Risks of Durable Mechanical Circulatory Support Therapy and Cardiac Transplantation. J Clin Med 2024; 13:2076. [PMID: 38610843 PMCID: PMC11013005 DOI: 10.3390/jcm13072076] [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: 02/19/2024] [Revised: 03/24/2024] [Accepted: 03/30/2024] [Indexed: 04/14/2024] Open
Abstract
Background: The use of AI-driven technologies in probing big data to generate better risk prediction models has been an ongoing and expanding area of investigation. The AI-driven models may perform better as compared to linear models; however, more investigations are needed in this area to refine their predictability and applicability to the field of durable MCS and cardiac transplantation. Methods: A literature review was carried out using Google Scholar/PubMed from 2000 to 2023. Results: This review defines the knowledge gaps and describes different AI-driven approaches that may be used to further our understanding. Conclusions: The limitations of current models are due to missing data, data imbalances, and the uneven distribution of variables in the datasets from which the models are derived. There is an urgent need for predictive models that can integrate a large number of clinical variables from multicenter data to account for the variability in patient characteristics that influence patient selection, outcomes, and survival for both durable MCS and HT; this may be fulfilled by AI-driven risk prediction models.
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Affiliation(s)
- Chloe Grzyb
- PennState College of Medicine, Heart and Vascular Institute, Milton S. Hershey Medical Center, 500 University Dr, Hershey, PA 17033, USA;
| | - Dongping Du
- Department of Industrial and Structural Engineering, Texas Tech University, Lubbock, TX 79409, USA;
| | - Nandini Nair
- PennState College of Medicine, Heart and Vascular Institute, Milton S. Hershey Medical Center, 500 University Dr, Hershey, PA 17033, USA;
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Chang A, Wu X, Liu K. Deep learning from latent spatiotemporal information of the heart: Identifying advanced bioimaging markers from echocardiograms. BIOPHYSICS REVIEWS 2024; 5:011304. [PMID: 38559589 PMCID: PMC10978053 DOI: 10.1063/5.0176850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Accepted: 03/01/2024] [Indexed: 04/04/2024]
Abstract
A key strength of echocardiography lies in its integration of comprehensive spatiotemporal cardiac imaging data in real-time, to aid frontline or bedside patient risk stratification and management. Nonetheless, its acquisition, processing, and interpretation are known to all be subject to heterogeneity from its reliance on manual and subjective human tracings, which challenges workflow and protocol standardization and final interpretation accuracy. In the era of advanced computational power, utilization of machine learning algorithms for big data analytics in echocardiography promises reduction in cost, cognitive errors, and intra- and inter-observer variability. Novel spatiotemporal deep learning (DL) models allow the integration of temporal arm information based on unlabeled pixel echocardiographic data for convolution of an adaptive semantic spatiotemporal calibration to construct personalized 4D heart meshes, assess global and regional cardiac function, detect early valve pathology, and differentiate uncommon cardiovascular disorders. Meanwhile, data visualization on spatiotemporal DL prediction models helps extract latent temporal imaging features to develop advanced imaging biomarkers in early disease stages and advance our understanding of pathophysiology to support the development of personalized prevention or treatment strategies. Since portable echocardiograms have been increasingly used as point-of-care imaging tools to aid rural care delivery, the application of these new spatiotemporal DL techniques show the potentials in streamlining echocardiographic acquisition, processing, and data analysis to improve workflow standardization and efficiencies, and provide risk stratification and decision supporting tools in real-time, to prompt the building of new imaging diagnostic networks to enhance rural healthcare engagement.
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Affiliation(s)
- Amanda Chang
- Division of Cardiovascular Medicine, Department of Internal Medicine, University of Iowa, Iowa City, Iowa 52242, USA
| | - Xiaodong Wu
- Department of Electrical and Computer Engineering, College of Engineering, University of Iowa, Iowa City, Iowa 52242, USA
| | - Kan Liu
- Division of Cardiology, Department of Internal Medicine, Washington University in St. Louis, St. Louis, Missouri 63110, USA
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Zaman F, Isom N, Chang A, Wang YG, Abdelhamid A, Khan A, Makan M, Abdelghany M, Wu X, Liu K. Deep learning from atrioventricular plane displacement in patients with Takotsubo syndrome: lighting up the black-box. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2024; 5:134-143. [PMID: 38505490 PMCID: PMC10944681 DOI: 10.1093/ehjdh/ztad077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Revised: 11/21/2023] [Accepted: 12/01/2023] [Indexed: 03/21/2024]
Abstract
Aims The spatiotemporal deep convolutional neural network (DCNN) helps reduce echocardiographic readers' erroneous 'judgement calls' on Takotsubo syndrome (TTS). The aim of this study was to improve the interpretability of the spatiotemporal DCNN to discover latent imaging features associated with causative TTS pathophysiology. Methods and results We applied gradient-weighted class activation mapping analysis to visualize an established spatiotemporal DCNN based on the echocardiographic videos to differentiate TTS (150 patients) from anterior wall ST-segment elevation myocardial infarction (STEMI, 150 patients). Forty-eight human expert readers interpreted the same echocardiographic videos and prioritized the regions of interest on myocardium for the differentiation. Based on visualization results, we completed optical flow measurement, myocardial strain, and Doppler/tissue Doppler echocardiography studies to investigate regional myocardial temporal dynamics and diastology. While human readers' visualization predominantly focused on the apex of the heart in TTS patients, the DCNN temporal arm's saliency visualization was attentive on the base of the heart, particularly at the atrioventricular (AV) plane. Compared with STEMI patients, TTS patients consistently showed weaker peak longitudinal displacement (in pixels) in the basal inferoseptal (systolic: 2.15 ± 1.41 vs. 3.10 ± 1.66, P < 0.001; diastolic: 2.36 ± 1.71 vs. 2.97 ± 1.69, P = 0.004) and basal anterolateral (systolic: 2.70 ± 1.96 vs. 3.44 ± 2.13, P = 0.003; diastolic: 2.73 ± 1.70 vs. 3.45 ± 2.20, P = 0.002) segments, and worse longitudinal myocardial strain in the basal inferoseptal (-8.5 ± 3.8% vs. -9.9 ± 4.1%, P = 0.013) and basal anterolateral (-8.6 ± 4.2% vs. -10.4 ± 4.1%, P = 0.006) segments. Meanwhile, TTS patients showed worse diastolic mechanics than STEMI patients (E'/septal: 5.1 ± 1.2 cm/s vs. 6.3 ± 1.5 cm/s, P < 0.001; S'/septal: 5.8 ± 1.3 cm/s vs. 6.8 ± 1.4 cm/s, P < 0.001; E'/lateral: 6.0 ± 1.4 cm/s vs. 7.9 ± 1.6 cm/s, P < 0.001; S'/lateral: 6.3 ± 1.4 cm/s vs. 7.3 ± 1.5 cm/s, P < 0.001; E/E': 15.5 ± 5.6 vs. 12.5 ± 3.5, P < 0.001). Conclusion The spatiotemporal DCNN saliency visualization helps identify the pattern of myocardial temporal dynamics and navigates the quantification of regional myocardial mechanics. Reduced AV plane displacement in TTS patients likely correlates with impaired diastolic mechanics.
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Affiliation(s)
- Fahim Zaman
- Department of Electrical and Computer Engineering, University of Iowa, 103 S. Capitol St., 3318 SC, Iowa City, IA 52242, USA
| | - Nicholas Isom
- Division of Cardiology, Department of Internal Medicine, University of Iowa, 200 Hawkins Drive, Iowa City, IA 52242, USA
| | - Amanda Chang
- Division of Cardiology, Department of Internal Medicine, University of Iowa, 200 Hawkins Drive, Iowa City, IA 52242, USA
| | - Yi Grace Wang
- Department of Mathematics, California State University Dominguez Hills, 1000 E. Victoria Street, Carson, CA 90747, USA
| | - Ahmed Abdelhamid
- Division of Cardiology, Department of Internal Medicine, University of Iowa, 200 Hawkins Drive, Iowa City, IA 52242, USA
| | - Arooj Khan
- Division of Cardiology, Department of Internal Medicine, University of Iowa, 200 Hawkins Drive, Iowa City, IA 52242, USA
| | - Majesh Makan
- Division of Cardiology, Department of Internal Medicine, Washington University, 4940 Parkview Place, St Louis, MO 63110, USA
| | - Mahmoud Abdelghany
- Department of Cardiovascular Medicine, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH 44195, USA
| | - Xiaodong Wu
- Department of Electrical and Computer Engineering, University of Iowa, 103 S. Capitol St., 3318 SC, Iowa City, IA 52242, USA
| | - Kan Liu
- Division of Cardiology, Department of Internal Medicine, University of Iowa, 200 Hawkins Drive, Iowa City, IA 52242, USA
- Division of Cardiology, Department of Internal Medicine, Washington University, 4940 Parkview Place, St Louis, MO 63110, USA
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Miller T, Lang FM, Rahbari A, Theodoropoulos K, Topkara VK. Right heart failure after durable left ventricular assist device implantation. Expert Rev Med Devices 2024; 21:197-206. [PMID: 38214584 DOI: 10.1080/17434440.2024.2305362] [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: 07/28/2023] [Accepted: 01/10/2024] [Indexed: 01/13/2024]
Abstract
INTRODUCTION Right heart failure (RHF) is a well-known complication after left ventricular assist device (LVAD) implantation and portends increased morbidity and mortality. Understanding the mechanisms and predictors of RHF in this clinical setting may offer ideas for early identification and aggressive management to minimize poor outcomes. A variety of medical therapies and mechanical circulatory support options are currently available for the management of post-LVAD RHF. AREAS COVERED We reviewed the existing definitions of RHF including its potential mechanisms in the context of durable LVAD implantation and currently available medical and device therapies. We performed a literature search using PubMed (from 2010 to 2023). EXPERT OPINION RHF remains a common complication after LVAD implantation. However, existing knowledge gaps limit clinicians' ability to adequately address its consequences. Early identification and management are crucial to reducing the risk of poor outcomes, but existing risk stratification tools perform poorly and have limited clinical applicability. This is an area ripe for investigation with the potential for major improvements in identification and targeted therapy in an effort to improve outcomes.
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Affiliation(s)
- Tamari Miller
- Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
| | - Frederick M Lang
- Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
| | - Ashkon Rahbari
- Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
| | - Kleanthis Theodoropoulos
- Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
| | - Veli K Topkara
- Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
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Makimoto H, Kohro T. Adopting artificial intelligence in cardiovascular medicine: a scoping review. Hypertens Res 2024; 47:685-699. [PMID: 37907600 DOI: 10.1038/s41440-023-01469-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Revised: 09/03/2023] [Accepted: 09/26/2023] [Indexed: 11/02/2023]
Abstract
Recent years have witnessed significant transformations in cardiovascular medicine, driven by the rapid evolution of artificial intelligence (AI). This scoping review was conducted to capture the breadth of AI applications within cardiovascular science. Employing a structured approach, we sourced relevant articles from PubMed, with an emphasis on journals encompassing general cardiology and digital medicine. We applied filters to highlight cardiovascular articles published in journals focusing on general internal medicine, cardiology and digital medicine, thereby identifying the prevailing trends in the field. Following a comprehensive full-text screening, a total of 140 studies were identified. Over the preceding 5 years, cardiovascular medicine's interplay with AI has seen an over tenfold augmentation. This expansive growth encompasses multiple cardiovascular subspecialties, including but not limited to, general cardiology, ischemic heart disease, heart failure, and arrhythmia. Deep learning emerged as the predominant methodology. The majority of AI endeavors in this domain have been channeled toward enhancing diagnostic and prognostic capabilities, utilizing resources such as hospital datasets, electrocardiograms, and echocardiography. A significant uptrend was observed in AI's application for omics data analysis. However, a clear gap persists in AI's full-scale integration into the clinical decision-making framework. AI, particularly deep learning, has demonstrated robust applications across cardiovascular subspecialties, indicating its transformative potential in this field. As we continue on this trajectory, ensuring the alignment of technological progress with medical ethics becomes crucial. The abundant digital health data today further accentuates the need for meticulous systematic reviews, tailoring them to each cardiovascular subspecialty.
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Affiliation(s)
- Hisaki Makimoto
- Data Science Center/Cardiovascular Center, Jichi Medical University, Shimotsuke, Japan.
| | - Takahide Kohro
- Data Science Center/Cardiovascular Center, Jichi Medical University, Shimotsuke, Japan
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Balcioglu O, Ozgocmen C, Ozsahin DU, Yagdi T. The Role of Artificial Intelligence and Machine Learning in the Prediction of Right Heart Failure after Left Ventricular Assist Device Implantation: A Comprehensive Review. Diagnostics (Basel) 2024; 14:380. [PMID: 38396419 PMCID: PMC10888030 DOI: 10.3390/diagnostics14040380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2024] [Revised: 02/05/2024] [Accepted: 02/07/2024] [Indexed: 02/25/2024] Open
Abstract
One of the most challenging and prevalent side effects of LVAD implantation is that of right heart failure (RHF) that may develop afterwards. The purpose of this study is to review and highlight recent advances in the uses of AI in evaluating RHF after LVAD implantation. The available literature was scanned using certain key words (artificial intelligence, machine learning, left ventricular assist device, prediction of right heart failure after LVAD) was scanned within Pubmed, Web of Science, and Google Scholar databases. Conventional risk scoring systems were also summarized, with their pros and cons being included in the results section of this study in order to provide a useful contrast with AI-based models. There are certain interesting and innovative ML approaches towards RHF prediction among the studies reviewed as well as more straightforward approaches that identified certain important predictive clinical parameters. Despite their accomplishments, the resulting AUC scores were far from ideal for these methods to be considered fully sufficient. The reasons for this include the low number of studies, standardized data availability, and lack of prospective studies. Another topic briefly discussed in this study is that relating to the ethical and legal considerations of using AI-based systems in healthcare. In the end, we believe that it would be beneficial for clinicians to not ignore these developments despite the current research indicating more time is needed for AI-based prediction models to achieve a better performance.
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Affiliation(s)
- Ozlem Balcioglu
- Department of Cardiovascular Surgery, Faculty of Medicine, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey;
- Operational Research Center in Healthcare, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey;
| | - Cemre Ozgocmen
- Department of Biomedical Engineering, Faculty of Engineering, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey;
| | - Dilber Uzun Ozsahin
- Operational Research Center in Healthcare, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey;
- Medical Diagnostic Imaging Department, College of Health Sciences, University of Sharjah, Sharjah 27272, United Arab Emirates
| | - Tahir Yagdi
- Department of Cardiovascular Surgery, Faculty of Medicine, Ege University, Izmir 35100, Turkey
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Steffner KR, Christensen M, Gill G, Bowdish M, Rhee J, Kumaresan A, He B, Zou J, Ouyang D. Deep learning for transesophageal echocardiography view classification. Sci Rep 2024; 14:11. [PMID: 38167849 PMCID: PMC10761863 DOI: 10.1038/s41598-023-50735-8] [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: 06/27/2023] [Accepted: 12/24/2023] [Indexed: 01/05/2024] Open
Abstract
Transesophageal echocardiography (TEE) imaging is a vital tool used in the evaluation of complex cardiac pathology and the management of cardiac surgery patients. A key limitation to the application of deep learning strategies to intraoperative and intraprocedural TEE data is the complexity and unstructured nature of these images. In the present study, we developed a deep learning-based, multi-category TEE view classification model that can be used to add structure to intraoperative and intraprocedural TEE imaging data. More specifically, we trained a convolutional neural network (CNN) to predict standardized TEE views using labeled intraoperative and intraprocedural TEE videos from Cedars-Sinai Medical Center (CSMC). We externally validated our model on intraoperative TEE videos from Stanford University Medical Center (SUMC). Accuracy of our model was high across all labeled views. The highest performance was achieved for the Trans-Gastric Left Ventricular Short Axis View (area under the receiver operating curve [AUC] = 0.971 at CSMC, 0.957 at SUMC), the Mid-Esophageal Long Axis View (AUC = 0.954 at CSMC, 0.905 at SUMC), the Mid-Esophageal Aortic Valve Short Axis View (AUC = 0.946 at CSMC, 0.898 at SUMC), and the Mid-Esophageal 4-Chamber View (AUC = 0.939 at CSMC, 0.902 at SUMC). Ultimately, we demonstrate that our deep learning model can accurately classify standardized TEE views, which will facilitate further downstream deep learning analyses for intraoperative and intraprocedural TEE imaging.
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Affiliation(s)
- Kirsten R Steffner
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, 300 Pasteur Drive, Stanford, CA, 94305, USA.
| | - Matthew Christensen
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, USA
| | - George Gill
- Department of Cardiac Surgery, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, USA
| | - Michael Bowdish
- Department of Cardiac Surgery, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, USA
| | - Justin Rhee
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, USA
| | - Abirami Kumaresan
- Department of Cardiac Surgery, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, USA
- Department of Anesthesiology, Cedars-Sinai Medical Center, Los Angeles, USA
| | - Bryan He
- Department of Computer Science, Stanford University, Stanford, USA
| | - James Zou
- Department of Biomedical Data Science, Stanford University, Stanford, USA
| | - David Ouyang
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, USA
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O'Donnell C, Sanchez PA, Celestin B, McConnell MV, Haddad F. The Echocardiographic Evaluation of the Right Heart: Current and Future Advances. Curr Cardiol Rep 2023; 25:1883-1896. [PMID: 38041726 DOI: 10.1007/s11886-023-02001-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/17/2023] [Indexed: 12/03/2023]
Abstract
PURPOSE OF REVIEW To discuss physiologic and methodologic advances in the echocardiographic assessment of right heart (RH) function, including the emergence of artificial intelligence (AI) and point-of-care ultrasound. RECENT FINDINGS Recent studies have highlighted the prognostic value of right ventricular (RV) longitudinal strain, RV end-systolic dimensions, and right atrial (RA) size and function in pulmonary hypertension and heart failure. While RA pressure is a central marker of right heart diastolic function, the recent emphasis on venous excess imaging (VExUS) has provided granularity to the systemic consequences of RH failure. Several methodological advances are also changing the landscape of RH imaging including post-processing 3D software to delineate the non-longitudinal (radial, anteroposterior, and circumferential) components of RV function, as well as AI segmentation- and non-segmentation-based quantification. Together with recent guidelines and advances in AI technology, the field is shifting from specific RV functional metrics to integrated RH disease-specific phenotypes. A modern echocardiographic evaluation of RH function should focus on the entire cardiopulmonary venous unit-from the venous to the pulmonary arterial system. Together, a multi-parametric approach, guided by physiology and AI algorithms, will help define novel integrated RH profiles for improved disease detection and monitoring.
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Affiliation(s)
- Christian O'Donnell
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA.
| | - Pablo Amador Sanchez
- Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Bettia Celestin
- Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, USA
- Vera Moulton Wall Center for Pulmonary Vascular Disease, Stanford University School of Medicine, Stanford, CA, USA
| | - Michael V McConnell
- Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Francois Haddad
- Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, USA
- Vera Moulton Wall Center for Pulmonary Vascular Disease, Stanford University School of Medicine, Stanford, CA, USA
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Tokodi M, Kovács A. A New Hope for Deep Learning-Based Echocardiogram Interpretation: The DROIDs You Were Looking For. J Am Coll Cardiol 2023; 82:1949-1952. [PMID: 37940232 DOI: 10.1016/j.jacc.2023.09.799] [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: 09/07/2023] [Accepted: 09/13/2023] [Indexed: 11/10/2023]
Affiliation(s)
- Márton Tokodi
- Heart and Vascular Center, Semmelweis University, Budapest, Hungary.
| | - Attila Kovács
- Heart and Vascular Center, Semmelweis University, Budapest, Hungary. https://twitter.com/kovatti87
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11
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Rostami B, Fetterly K, Attia Z, Challa A, Lopez-Jimenez F, Thaden J, Asirvatham S, Friedman P, Gulati R, Alkhouli M. Deep Learning to Estimate Left Ventricular Ejection Fraction From Routine Coronary Angiographic Images. JACC. ADVANCES 2023; 2:100632. [PMID: 38938722 PMCID: PMC11198437 DOI: 10.1016/j.jacadv.2023.100632] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 07/04/2023] [Accepted: 07/26/2023] [Indexed: 06/29/2024]
Abstract
Background Cine images during coronary angiography contain a wealth of information besides the assessment of coronary stenosis. We hypothesized that deep learning (DL) can discern moderate-severe left ventricular dysfunction among patients undergoing coronary angiography. Objectives The purpose of this study was to assess the ability of machine learning models in estimating left ventricular ejection fraction (LVEF) from routine coronary angiographic images. Methods We developed a combined 3D-convolutional neural network (CNN) and transformer to estimate LVEF for diagnostic coronary angiograms of the left coronary artery (LCA). Two angiograms, left anterior oblique (LAO)-caudal and right anterior oblique (RAO)-cranial projections, were fed into the model simultaneously. The model classified LVEF as significantly reduced (LVEF ≤40%) vs normal or mildly reduced (LVEF>40%). Echocardiogram performed within 30 days served as the gold standard for LVEF. Results A collection of 18,809 angiograms from 17,346 patients from Mayo Clinic were included (mean age 67.29; 35% women). Each patient appeared only in the training (70%), validation (10%), or testing set (20%). The model exhibited excellent performance (area under the receiver operator curve [AUC] 0.87; sensitivity 0.77; specificity 0.80) in the training set. The model's performance exceeded human expert assessment (AUC, sensitivity, and specificity of 0.86, 0.76, and 0.77, respectively) vs (AUC, sensitivity, and specificity of 0.76-0.77, 0.50-0.44, and 0.90-0.93, respectively). In additional sensitivity analyses, combining the LAO and RAO views yielded a higher AUC, sensitivity, and specificity than utilizing either LAO or RAO individually. The original model combining CNN and transformer was superior to DL models using either 3D-CNN or transformers. Conclusions A novel DL algorithm demonstrated rapid and accurate assessment of LVEF from routine coronary angiography. The algorithm can be used to support clinical decision-making and form the foundation for future models that could extract meaningful data from routine angiography studies.
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Affiliation(s)
- Behrouz Rostami
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Kenneth Fetterly
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Zachi Attia
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Apurva Challa
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | | | - Jeremy Thaden
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Samuel Asirvatham
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Paul Friedman
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Rajiv Gulati
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Mohamad Alkhouli
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
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Wehbe RM, Katsaggelos AK, Hammond KJ, Hong H, Ahmad FS, Ouyang D, Shah SJ, McCarthy PM, Thomas JD. Deep Learning for Cardiovascular Imaging: A Review. JAMA Cardiol 2023; 8:1089-1098. [PMID: 37728933 DOI: 10.1001/jamacardio.2023.3142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
Importance Artificial intelligence (AI), driven by advances in deep learning (DL), has the potential to reshape the field of cardiovascular imaging (CVI). While DL for CVI is still in its infancy, research is accelerating to aid in the acquisition, processing, and/or interpretation of CVI across various modalities, with several commercial products already in clinical use. It is imperative that cardiovascular imagers are familiar with DL systems, including a basic understanding of how they work, their relative strengths compared with other automated systems, and possible pitfalls in their implementation. The goal of this article is to review the methodology and application of DL to CVI in a simple, digestible fashion toward demystifying this emerging technology. Observations At its core, DL is simply the application of a series of tunable mathematical operations that translate input data into a desired output. Based on artificial neural networks that are inspired by the human nervous system, there are several types of DL architectures suited to different tasks; convolutional neural networks are particularly adept at extracting valuable information from CVI data. We survey some of the notable applications of DL to tasks across the spectrum of CVI modalities. We also discuss challenges in the development and implementation of DL systems, including avoiding overfitting, preventing systematic bias, improving explainability, and fostering a human-machine partnership. Finally, we conclude with a vision of the future of DL for CVI. Conclusions and Relevance Deep learning has the potential to meaningfully affect the field of CVI. Rather than a threat, DL could be seen as a partner to cardiovascular imagers in reducing technical burden and improving efficiency and quality of care. High-quality prospective evidence is still needed to demonstrate how the benefits of DL CVI systems may outweigh the risks.
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Affiliation(s)
- Ramsey M Wehbe
- Division of Cardiology, Department of Medicine & Biomedical Informatics Center, Medical University of South Carolina, Charleston
- Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Aggelos K Katsaggelos
- Department of Computer and Electrical Engineering, Northwestern University, Evanston, Illinois
| | - Kristian J Hammond
- Department of Computer Science, Northwestern University, Evanston, Illinois
| | - Ha Hong
- Medtronic, Minneapolis, Minnesota
| | - Faraz S Ahmad
- Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
- Center for Health Information Partnerships, Institute for Public Health and Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
- Center for Artificial Intelligence, Northwestern Medicine Bluhm Cardiovascular Institute, Chicago, Illinois
| | - David Ouyang
- Division of Cardiology, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California
| | - Sanjiv J Shah
- Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
- Center for Artificial Intelligence, Northwestern Medicine Bluhm Cardiovascular Institute, Chicago, Illinois
| | - Patrick M McCarthy
- Division of Cardiac Surgery, Department of Surgery, Northwestern University Feinberg School of Medicine, Chicago, Illinois
- Center for Artificial Intelligence, Northwestern Medicine Bluhm Cardiovascular Institute, Chicago, Illinois
| | - James D Thomas
- Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
- Center for Artificial Intelligence, Northwestern Medicine Bluhm Cardiovascular Institute, Chicago, Illinois
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13
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Haddad F, Gomes B. Automation for Right Heart Analysis: The Start of a New Era. JACC Cardiovasc Imaging 2023; 16:1019-1021. [PMID: 37227331 DOI: 10.1016/j.jcmg.2023.03.018] [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: 03/20/2023] [Accepted: 03/22/2023] [Indexed: 05/26/2023]
Affiliation(s)
- Francois Haddad
- Department of Medicine, Cardiovascular Institute, Stanford University, Stanford, California, USA.
| | - Bruna Gomes
- Departments of Medicine, Genetics, Computer Science, and Biomedical Data Science, Stanford University, Stanford, California, USA
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14
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Tokodi M, Magyar B, Soós A, Takeuchi M, Tolvaj M, Lakatos BK, Kitano T, Nabeshima Y, Fábián A, Szigeti MB, Horváth A, Merkely B, Kovács A. Deep Learning-Based Prediction of Right Ventricular Ejection Fraction Using 2D Echocardiograms. JACC Cardiovasc Imaging 2023; 16:1005-1018. [PMID: 37178072 DOI: 10.1016/j.jcmg.2023.02.017] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 01/25/2023] [Accepted: 02/17/2023] [Indexed: 05/15/2023]
Abstract
BACKGROUND Evidence has shown the independent prognostic value of right ventricular (RV) function, even in patients with left-sided heart disease. The most widely used imaging technique to measure RV function is echocardiography; however, conventional 2-dimensional (2D) echocardiographic assessment is unable to leverage the same clinical information that 3-dimensional (3D) echocardiography-derived right ventricular ejection fraction (RVEF) can provide. OBJECTIVES The authors aimed to implement a deep learning (DL)-based tool to estimate RVEF from 2D echocardiographic videos. In addition, they benchmarked the tool's performance against human expert reading and evaluated the prognostic power of the predicted RVEF values. METHODS The authors retrospectively identified 831 patients with RVEF measured by 3D echocardiography. All 2D apical 4-chamber view echocardiographic videos of these patients were retrieved (n = 3,583), and each subject was assigned to either the training or the internal validation set (80:20 ratio). Using the videos, several spatiotemporal convolutional neural networks were trained to predict RVEF. The 3 best-performing networks were combined into an ensemble model, which was further evaluated in an external data set containing 1,493 videos of 365 patients with a median follow-up time of 1.9 years. RESULTS The ensemble model predicted RVEF with a mean absolute error of 4.57 percentage points in the internal and 5.54 percentage points in the external validation set. In the latter, the model identified RV dysfunction (defined as RVEF <45%) with an accuracy of 78.4%, which was comparable to an expert reader's visual assessment (77.0%; P = 0.678). The DL-predicted RVEF values were associated with major adverse cardiac events independent of age, sex, and left ventricular systolic function (HR: 0.924 [95% CI: 0.862-0.990]; P = 0.025). CONCLUSIONS Using 2D echocardiographic videos alone, the proposed DL-based tool can accurately assess RV function, with similar diagnostic and prognostic power as 3D imaging.
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Affiliation(s)
- Márton Tokodi
- Heart and Vascular Center, Semmelweis University, Budapest, Hungary.
| | - Bálint Magyar
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| | - András Soós
- Heart and Vascular Center, Semmelweis University, Budapest, Hungary; Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| | - Masaaki Takeuchi
- Department of Laboratory and Transfusion Medicine, University Hospital, University of Occupational and Environmental Health, Kitakyushu, Japan
| | - Máté Tolvaj
- Heart and Vascular Center, Semmelweis University, Budapest, Hungary
| | | | - Tetsuji Kitano
- Department of Cardiology and Nephrology, Wakamatsu Hospital, University of Occupational and Environmental Health, Kitakyushu, Japan
| | - Yosuke Nabeshima
- Second Department of Internal Medicine, School of Medicine, University of Occupational and Environmental Health, Kitakyushu, Japan
| | - Alexandra Fábián
- Heart and Vascular Center, Semmelweis University, Budapest, Hungary
| | - Mark Bence Szigeti
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| | - András Horváth
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| | - Béla Merkely
- Heart and Vascular Center, Semmelweis University, Budapest, Hungary
| | - Attila Kovács
- Heart and Vascular Center, Semmelweis University, Budapest, Hungary.
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15
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Ro SK, Sato K, Ijuin S, Sela D, Fior G, Heinsar S, Kim JY, Chan J, Nonaka H, Lin ACW, Bassi GL, Platts DG, Obonyo NG, Suen JY, Fraser JF. Assessment and diagnosis of right ventricular failure-retrospection and future directions. Front Cardiovasc Med 2023; 10:1030864. [PMID: 37324632 PMCID: PMC10268009 DOI: 10.3389/fcvm.2023.1030864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 05/17/2023] [Indexed: 06/17/2023] Open
Abstract
The right ventricle (RV) has a critical role in hemodynamics and right ventricular failure (RVF) often leads to poor clinical outcome. Despite the clinical importance of RVF, its definition and recognition currently rely on patients' symptoms and signs, rather than on objective parameters from quantifying RV dimensions and function. A key challenge is the geometrical complexity of the RV, which often makes it difficult to assess RV function accurately. There are several assessment modalities currently utilized in the clinical settings. Each diagnostic investigation has both advantages and limitations according to its characteristics. The purpose of this review is to reflect on the current diagnostic tools, consider the potential technological advancements and propose how to improve the assessment of right ventricular failure. Advanced technique such as automatic evaluation with artificial intelligence and 3-dimensional assessment for the complex RV structure has a potential to improve RV assessment by increasing accuracy and reproducibility of the measurements. Further, noninvasive assessments for RV-pulmonary artery coupling and right and left ventricular interaction are also warranted to overcome the load-related limitations for the accurate evaluation of RV contractile function. Future studies to cross-validate the advanced technologies in various populations are required.
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Affiliation(s)
- Sun Kyun Ro
- Department of Thoracic and Cardiovascular Surgery, Hanyang University Guri Hospital, Hanyang University College of Medicine, Seoul, Republic of Korea
- Critical Care Research Group, The Prince Charles Hospital, University of Queensland, Brisbane, QLD, Australia
- Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
| | - Kei Sato
- Critical Care Research Group, The Prince Charles Hospital, University of Queensland, Brisbane, QLD, Australia
- Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
| | - Shinichi Ijuin
- Critical Care Research Group, The Prince Charles Hospital, University of Queensland, Brisbane, QLD, Australia
- Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
- Department of Emergency and Critical Care Medicine, Hyogo Emergency Medical Center, Kobe, Japan
| | - Declan Sela
- Critical Care Research Group, The Prince Charles Hospital, University of Queensland, Brisbane, QLD, Australia
- Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
| | - Gabriele Fior
- Critical Care Research Group, The Prince Charles Hospital, University of Queensland, Brisbane, QLD, Australia
- Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
| | - Silver Heinsar
- Critical Care Research Group, The Prince Charles Hospital, University of Queensland, Brisbane, QLD, Australia
- Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
- Intensive Care Unit, St. Andrews War Memorial Hospital, Brisbane, QLD, Australia
- Department of Intensive Care, North Estonia Medical Centre, Tallinn, Estonia
| | - Ji Young Kim
- Department of Nuclear Medicine, Hanyang University Guri Hospital, Hanyang University College of Medicine, Seoul, Republic of Korea
| | - Jonathan Chan
- Division of Cardiology, The Prince Charles Hospital, Brisbane, QLD, Australia
- School of Medicine, Griffith University, Gold Coast, QLD, Australia
| | - Hideaki Nonaka
- Division of Cardiology, Mitsui Memorial Hospital, Tokyo, Japan
| | - Aaron C. W. Lin
- Division of Cardiology, The Prince Charles Hospital, Brisbane, QLD, Australia
- School of Medicine, Griffith University, Gold Coast, QLD, Australia
| | - Gianluigi Li Bassi
- Critical Care Research Group, The Prince Charles Hospital, University of Queensland, Brisbane, QLD, Australia
- Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
- Intensive Care Unit, St. Andrews War Memorial Hospital, Brisbane, QLD, Australia
| | - David G. Platts
- Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
- Division of Cardiology, The Prince Charles Hospital, Brisbane, QLD, Australia
| | - Nchafatso G. Obonyo
- Critical Care Research Group, The Prince Charles Hospital, University of Queensland, Brisbane, QLD, Australia
- Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
- Wellcome Trust Centre for Global Health Research, Imperial College London, London, United Kingdom
- Initiative to Develop African Research Leaders (IDeAL)/KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
| | - Jacky Y. Suen
- Critical Care Research Group, The Prince Charles Hospital, University of Queensland, Brisbane, QLD, Australia
- Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
| | - John F. Fraser
- Critical Care Research Group, The Prince Charles Hospital, University of Queensland, Brisbane, QLD, Australia
- Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
- Intensive Care Unit, St. Andrews War Memorial Hospital, Brisbane, QLD, Australia
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16
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Krittanawong C, Omar AMS, Narula S, Sengupta PP, Glicksberg BS, Narula J, Argulian E. Deep Learning for Echocardiography: Introduction for Clinicians and Future Vision: State-of-the-Art Review. Life (Basel) 2023; 13:life13041029. [PMID: 37109558 PMCID: PMC10145844 DOI: 10.3390/life13041029] [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: 02/17/2023] [Revised: 03/30/2023] [Accepted: 04/03/2023] [Indexed: 04/29/2023] Open
Abstract
Exponential growth in data storage and computational power is rapidly narrowing the gap between translating findings from advanced clinical informatics into cardiovascular clinical practice. Specifically, cardiovascular imaging has the distinct advantage in providing a great quantity of data for potentially rich insights, but nuanced interpretation requires a high-level skillset that few individuals possess. A subset of machine learning, deep learning (DL), is a modality that has shown promise, particularly in the areas of image recognition, computer vision, and video classification. Due to a low signal-to-noise ratio, echocardiographic data tend to be challenging to classify; however, utilization of robust DL architectures may help clinicians and researchers automate conventional human tasks and catalyze the extraction of clinically useful data from the petabytes of collected imaging data. The promise is extending far and beyond towards a contactless echocardiographic exam-a dream that is much needed in this time of uncertainty and social distancing brought on by a stunning pandemic culture. In the current review, we discuss state-of-the-art DL techniques and architectures that can be used for image and video classification, and future directions in echocardiographic research in the current era.
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Affiliation(s)
- Chayakrit Krittanawong
- Cardiology Division, NYU Langone Health, NYU School of Medicine, New York, NY 10016, USA
- Icahn School of Medicine at Mount Sinai, Mount Sinai Heart, New York, NY 10029, USA
| | - Alaa Mabrouk Salem Omar
- Icahn School of Medicine at Mount Sinai, Mount Sinai Heart, New York, NY 10029, USA
- Division of Cardiovascular Medicine, Icahn School of Medicine at Mount Sinai Morningside, Mount Sinai Heart, New York, NY 10029, USA
| | - Sukrit Narula
- Department of Medicine, Yale School of Medicine, New Haven, CT 06512, USA
| | - Partho P Sengupta
- Robert Wood Johnson University Hospital, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ 08901, USA
| | - Benjamin S Glicksberg
- Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Jagat Narula
- Icahn School of Medicine at Mount Sinai, Mount Sinai Heart, New York, NY 10029, USA
- Division of Cardiovascular Medicine, Icahn School of Medicine at Mount Sinai Morningside, Mount Sinai Heart, New York, NY 10029, USA
| | - Edgar Argulian
- Icahn School of Medicine at Mount Sinai, Mount Sinai Heart, New York, NY 10029, USA
- Division of Cardiovascular Medicine, Icahn School of Medicine at Mount Sinai Morningside, Mount Sinai Heart, New York, NY 10029, USA
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17
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Ni C, Feng B, Yao J, Zhou X, Shen J, Ou D, Peng C, Xu D. Value of deep learning models based on ultrasonic dynamic videos for distinguishing thyroid nodules. Front Oncol 2023; 12:1066508. [PMID: 36733368 PMCID: PMC9887311 DOI: 10.3389/fonc.2022.1066508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 12/28/2022] [Indexed: 01/18/2023] Open
Abstract
Objective This study was designed to distinguish benign and malignant thyroid nodules by using deep learning(DL) models based on ultrasound dynamic videos. Methods Ultrasound dynamic videos of 1018 thyroid nodules were retrospectively collected from 657 patients in Zhejiang Cancer Hospital from January 2020 to December 2020 for the tests with 5 DL models. Results In the internal test set, the area under the receiver operating characteristic curve (AUROC) was 0.929(95% CI: 0.888,0.970) for the best-performing model LSTM Two radiologists interpreted the dynamic video with AUROC values of 0.760 (95% CI: 0.653, 0.867) and 0.815 (95% CI: 0.778, 0.853). In the external test set, the best-performing DL model had AUROC values of 0.896(95% CI: 0.847,0.945), and two ultrasound radiologist had AUROC values of 0.754 (95% CI: 0.649,0.850) and 0.833 (95% CI: 0.797,0.869). Conclusion This study demonstrates that the DL model based on ultrasound dynamic videos performs better than the ultrasound radiologists in distinguishing thyroid nodules.
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Affiliation(s)
- Chen Ni
- The Second Clinical School of Zhejiang Chinese Medical University, Hangzhou, China
| | - Bojian Feng
- Key Laboratory of Head and Neck Cancer Translational Research of Zhejiang Province, Hangzhou, China
| | - Jincao Yao
- Department of Ultrasonography, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer, Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Xueqin Zhou
- Clinical Research Department, Esaote (Shenzhen) Medical Equipment Co., Ltd., Xinyilingyu Research Center, Shenzhen, China
| | - Jiafei Shen
- Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China; Key Laboratory of Head and Neck Cancer Translational Research of Zhejiang Province, Hangzhou, China
| | - Di Ou
- Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China; Key Laboratory of Head and Neck Cancer Translational Research of Zhejiang Province, Hangzhou, China
| | - Chanjuan Peng
- Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China; Key Laboratory of Head and Neck Cancer Translational Research of Zhejiang Province, Hangzhou, China
| | - Dong Xu
- Key Laboratory of Head and Neck Cancer Translational Research of Zhejiang Province, Hangzhou, China,Department of Ultrasonography, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer, Chinese Academy of Sciences, Hangzhou, Zhejiang, China,*Correspondence: Dong Xu,
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18
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Dai W, Nazzari H, Namasivayam M, Hung J, Stultz CM. Identifying Aortic Stenosis With a Single Parasternal Long-Axis Video Using Deep Learning. J Am Soc Echocardiogr 2023; 36:116-118. [PMID: 36323367 DOI: 10.1016/j.echo.2022.10.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 10/13/2022] [Accepted: 10/21/2022] [Indexed: 11/06/2022]
Affiliation(s)
- Wangzhi Dai
- Department of Electrical Engineering and Computer Science, Research Laboratory of Electronics, and Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Hamed Nazzari
- Department of Cardiology, Surrey Memorial Hospital, Surrey, British Columbia, Canada; University of British Columbia, Vancouver, British Columbia, Canada
| | - Mayooran Namasivayam
- Department of Cardiology, St. Vincent's Hospital, Sydney, Australia; Faculty of Medicine and Health, University of New South Wales, Sydney, Australia; Heart Valve Disease and Artificial Intelligence Laboratory, Victor Chang Cardiac Research Institute, Sydney, Australia
| | - Judy Hung
- Division of Cardiology, Massachusetts General Hospital, Boston, Massachusetts
| | - Collin M Stultz
- Department of Electrical Engineering and Computer Science, Research Laboratory of Electronics, and Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts; Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts; Division of Cardiology, Massachusetts General Hospital, Boston, Massachusetts.
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19
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Al-Ani MA, Bai C, Hashky A, Parker AM, Vilaro JR, Aranda JM, Shickel B, Rashidi P, Bihorac A, Ahmed MM, Mardini MT. Artificial intelligence guidance of advanced heart failure therapies: A systematic scoping review. Front Cardiovasc Med 2023; 10:1127716. [PMID: 36910520 PMCID: PMC9999024 DOI: 10.3389/fcvm.2023.1127716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 02/07/2023] [Indexed: 03/14/2023] Open
Abstract
Introduction Artificial intelligence can recognize complex patterns in large datasets. It is a promising technology to advance heart failure practice, as many decisions rely on expert opinions in the absence of high-quality data-driven evidence. Methods We searched Embase, Web of Science, and PubMed databases for articles containing "artificial intelligence," "machine learning," or "deep learning" and any of the phrases "heart transplantation," "ventricular assist device," or "cardiogenic shock" from inception until August 2022. We only included original research addressing post heart transplantation (HTx) or mechanical circulatory support (MCS) clinical care. Review and data extraction were performed in accordance with PRISMA-Scr guidelines. Results Of 584 unique publications detected, 31 met the inclusion criteria. The majority focused on outcome prediction post HTx (n = 13) and post durable MCS (n = 7), as well as post HTx and MCS management (n = 7, n = 3, respectively). One study addressed temporary mechanical circulatory support. Most studies advocated for rapid integration of AI into clinical practice, acknowledging potential improvements in management guidance and reliability of outcomes prediction. There was a notable paucity of external data validation and integration of multiple data modalities. Conclusion Our review showed mounting innovation in AI application in management of MCS and HTx, with the largest evidence showing improved mortality outcome prediction.
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Affiliation(s)
- Mohammad A Al-Ani
- Division of Cardiovascular Medicine, University of Florida, Gainesville, FL, United States
| | - Chen Bai
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, United States
| | - Amal Hashky
- Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL, United States
| | - Alex M Parker
- Division of Cardiovascular Medicine, University of Florida, Gainesville, FL, United States
| | - Juan R Vilaro
- Division of Cardiovascular Medicine, University of Florida, Gainesville, FL, United States
| | - Juan M Aranda
- Division of Cardiovascular Medicine, University of Florida, Gainesville, FL, United States
| | - Benjamin Shickel
- Department of Medicine, University of Florida, Gainesville, FL, United States.,Intelligent Critical Care Center (IC3), University of Florida, Gainesville, FL, United States
| | - Parisa Rashidi
- Intelligent Critical Care Center (IC3), University of Florida, Gainesville, FL, United States.,Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
| | - Azra Bihorac
- Department of Medicine, University of Florida, Gainesville, FL, United States.,Intelligent Critical Care Center (IC3), University of Florida, Gainesville, FL, United States
| | - Mustafa M Ahmed
- Division of Cardiovascular Medicine, University of Florida, Gainesville, FL, United States
| | - Mamoun T Mardini
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, United States
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20
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Gautam N, Ghanta SN, Clausen A, Saluja P, Sivakumar K, Dhar G, Chang Q, DeMazumder D, Rabbat MG, Greene SJ, Fudim M, Al'Aref SJ. Contemporary Applications of Machine Learning for Device Therapy in Heart Failure. JACC. HEART FAILURE 2022; 10:603-622. [PMID: 36049812 DOI: 10.1016/j.jchf.2022.06.011] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 05/31/2022] [Accepted: 06/16/2022] [Indexed: 06/15/2023]
Abstract
Despite a better understanding of the underlying pathogenesis of heart failure (HF), pharmacotherapy, surgical, and percutaneous interventions do not prevent disease progression in all patients, and a significant proportion of patients end up requiring advanced therapies. Machine learning (ML) is gaining wider acceptance in cardiovascular medicine because of its ability to incorporate large, complex, and multidimensional data and to potentially facilitate the creation of predictive models not constrained by many of the limitations of traditional statistical approaches. With the coexistence of "big data" and novel advanced analytic techniques using ML, there is ever-increasing research into applying ML in the context of HF with the goal of improving patient outcomes. Through this review, the authors describe the basics of ML and summarize the existing published reports regarding contemporary applications of ML in device therapy for HF while highlighting the limitations to widespread implementation and its future promises.
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Affiliation(s)
- Nitesh Gautam
- Department of Internal Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
| | - Sai Nikhila Ghanta
- Department of Internal Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
| | - Alex Clausen
- Department of Internal Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
| | - Prachi Saluja
- Department of Internal Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
| | - Kalai Sivakumar
- Division of Cardiology, Department of Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
| | - Gaurav Dhar
- Division of Cardiology, Department of Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
| | - Qi Chang
- Department of Computer Science, Rutgers University, The State University of New Jersey, Newark, New Jersey, USA
| | | | - Mark G Rabbat
- Department of Cardiology, Loyola University Medical Center, Maywood, Illinois, USA
| | - Stephen J Greene
- Department of Cardiology, Duke University Medical Center, Durham, North Carolina, USA; Duke Clinical Research Institute, Durham, North Carolina, USA
| | - Marat Fudim
- Department of Cardiology, Duke University Medical Center, Durham, North Carolina, USA; Duke Clinical Research Institute, Durham, North Carolina, USA
| | - Subhi J Al'Aref
- Division of Cardiology, Department of Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA.
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Machine learning-based 3D modeling and volumetry of human posterior vitreous cavity of optical coherence tomographic images. Sci Rep 2022; 12:13836. [PMID: 35974072 PMCID: PMC9381727 DOI: 10.1038/s41598-022-17615-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 07/28/2022] [Indexed: 11/12/2022] Open
Abstract
The structure of the human vitreous varies considerably because of age-related liquefactions of the vitreous gel. These changes are poorly studied in vivo mainly because their high transparency and mobility make it difficult to obtain reliable and repeatable images of the vitreous. Optical coherence tomography can detect the boundaries between the vitreous gel and vitreous fluid, but it is difficult to obtain high resolution images that can be used to convert the images to three-dimensional (3D) images. Thus, the purpose of this study was to determine the shape and characteristics of the vitreous fluid using machine learning-based 3D modeling in which manually labelled fluid areas were used to train deep convolutional neural network (DCNN). The trained DCNN labelled vitreous fluid automatically and allowed us to obtain 3D vitreous model and to quantify the vitreous fluidic cavities. The mean volume and surface area of posterior vitreous fluidic cavities are 19.6 ± 7.8 mm3 and 104.0 ± 18.9 mm2 in eyes of 17 school children. The results suggested that vitreous fluidic cavities expanded as the cavities connects with each other, and this modeling system provided novel imaging markers for aging and eye diseases.
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Karakuş G, Değirmencioğlu A, Nanda NC. Artificial intelligence in echocardiography: Review and limitations including epistemological concerns. Echocardiography 2022; 39:1044-1053. [PMID: 35808922 DOI: 10.1111/echo.15417] [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: 04/08/2022] [Revised: 06/01/2022] [Accepted: 06/13/2022] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND AND PURPOSE In this review we describe the use of artificial intelligence in the field of echocardiography. Various aspects and terminologies used in artificial intelligence are explained in an easy-to-understand manner and supplemented with illustrations related to echocardiography. Limitations of artificial intelligence, including epistemologic concerns from a philosophical standpoint, are also discussed. METHODS A narrative review of relevant papers was conducted. CONCLUSION We provide an overview of the usefulness of artificial intelligence in echocardiography and focus on how it can supplement current day-to-day clinical practice in the assessment of various cardiovascular disease entities. On the other hand, there are significant limitations, including epistemological concerns, which need to be kept in perspective.
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Affiliation(s)
- Gültekin Karakuş
- Department of Cardiology, School of Medicine, Acibadem University, Istanbul, Turkey
| | - Aleks Değirmencioğlu
- Department of Cardiology, School of Medicine, Acibadem University, Istanbul, Turkey
| | - Navin C Nanda
- Division of Cardiology, Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA
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23
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Sakai A, Komatsu M, Komatsu R, Matsuoka R, Yasutomi S, Dozen A, Shozu K, Arakaki T, Machino H, Asada K, Kaneko S, Sekizawa A, Hamamoto R. Medical professional enhancement using explainable artificial intelligence in fetal cardiac ultrasound screening (supplementary). Biomedicines 2022; 10:biomedicines10030551. [PMID: 35327353 PMCID: PMC8945208 DOI: 10.3390/biomedicines10030551] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 02/18/2022] [Accepted: 02/21/2022] [Indexed: 12/10/2022] Open
Abstract
Diagnostic support tools based on artificial intelligence (AI) have exhibited high performance in various medical fields. However, their clinical application remains challenging because of the lack of explanatory power in AI decisions (black box problem), making it difficult to build trust with medical professionals. Nevertheless, visualizing the internal representation of deep neural networks will increase explanatory power and improve the confidence of medical professionals in AI decisions. We propose a novel deep learning-based explainable representation “graph chart diagram” to support fetal cardiac ultrasound screening, which has low detection rates of congenital heart diseases due to the difficulty in mastering the technique. Screening performance improves using this representation from 0.966 to 0.975 for experts, 0.829 to 0.890 for fellows, and 0.616 to 0.748 for residents in the arithmetic mean of area under the curve of a receiver operating characteristic curve. This is the first demonstration wherein examiners used deep learning-based explainable representation to improve the performance of fetal cardiac ultrasound screening, highlighting the potential of explainable AI to augment examiner capabilities.
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Affiliation(s)
- Akira Sakai
- Artificial Intelligence Laboratory, Research Unit, Fujitsu Research, Fujitsu Ltd., 4-1-1 Kamikodanaka, Nakahara-ku, Kawasaki 211-8588, Japan; (A.S.); (S.Y.)
- RIKEN AIP-Fujitsu Collaboration Center, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (R.K.); (R.M.)
- Department of NCC Cancer Science, Biomedical Science and Engineering Track, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.D.); (K.S.); (H.M.); (K.A.); (S.K.)
| | - Masaaki Komatsu
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
- Correspondence: (M.K.); (R.H.); Tel.: +81-3-3547-5271 (R.H.)
| | - Reina Komatsu
- RIKEN AIP-Fujitsu Collaboration Center, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (R.K.); (R.M.)
- Department of Obstetrics and Gynecology, School of Medicine, Showa University, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo 142-8666, Japan; (T.A.); (A.S.)
| | - Ryu Matsuoka
- RIKEN AIP-Fujitsu Collaboration Center, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (R.K.); (R.M.)
- Department of Obstetrics and Gynecology, School of Medicine, Showa University, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo 142-8666, Japan; (T.A.); (A.S.)
| | - Suguru Yasutomi
- Artificial Intelligence Laboratory, Research Unit, Fujitsu Research, Fujitsu Ltd., 4-1-1 Kamikodanaka, Nakahara-ku, Kawasaki 211-8588, Japan; (A.S.); (S.Y.)
- RIKEN AIP-Fujitsu Collaboration Center, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (R.K.); (R.M.)
| | - Ai Dozen
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.D.); (K.S.); (H.M.); (K.A.); (S.K.)
| | - Kanto Shozu
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.D.); (K.S.); (H.M.); (K.A.); (S.K.)
| | - Tatsuya Arakaki
- Department of Obstetrics and Gynecology, School of Medicine, Showa University, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo 142-8666, Japan; (T.A.); (A.S.)
| | - Hidenori Machino
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.D.); (K.S.); (H.M.); (K.A.); (S.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Ken Asada
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.D.); (K.S.); (H.M.); (K.A.); (S.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Syuzo Kaneko
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.D.); (K.S.); (H.M.); (K.A.); (S.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Akihiko Sekizawa
- Department of Obstetrics and Gynecology, School of Medicine, Showa University, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo 142-8666, Japan; (T.A.); (A.S.)
| | - Ryuji Hamamoto
- Department of NCC Cancer Science, Biomedical Science and Engineering Track, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.D.); (K.S.); (H.M.); (K.A.); (S.K.)
- Correspondence: (M.K.); (R.H.); Tel.: +81-3-3547-5271 (R.H.)
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24
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Designing clinically translatable artificial intelligence systems for high-dimensional medical imaging. NAT MACH INTELL 2021. [DOI: 10.1038/s42256-021-00399-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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25
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Kodera S, Akazawa H, Morita H, Komuro I. Prospects for cardiovascular medicine using artificial intelligence. J Cardiol 2021; 79:319-325. [PMID: 34772574 DOI: 10.1016/j.jjcc.2021.10.016] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Accepted: 10/19/2021] [Indexed: 12/19/2022]
Abstract
As the importance of artificial intelligence (AI) in the clinical setting increases, the need for clinicians to understand AI is also increasing. This review focuses on the fundamental principles of AI and the current state of cardiovascular AI. Various types of cardiovascular AI have been developed for evaluating examinations such as X-rays, electrocardiogram, echocardiography, computed tomography, and magnetic resonance imaging. Cardiovascular AI achieves high accuracy in diagnostic support and prognosis prediction. Furthermore, it can even detect abnormalities that were previously difficult for cardiologists to detect. Randomized controlled trials begin to be reported to verify the usefulness of cardiovascular AI. The day is approaching when cardiovascular AI will be commonly used in clinical practice. Various types of medical AI will be used for cardiovascular care; however, it will not replace medical doctors. We need to understand the strengths and weaknesses of medical AI so that cardiologists can effectively use AI to improve the medical care of patients.
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Affiliation(s)
- Satoshi Kodera
- Department of Cardiovascular Medicine, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan.
| | - Hiroshi Akazawa
- Department of Cardiovascular Medicine, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Hiroyuki Morita
- Department of Cardiovascular Medicine, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Issei Komuro
- Department of Cardiovascular Medicine, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
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