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Soh CH, Wright L, Baumann A, Seidel B, Yu C, Nolan M, Mylius T, Marwick TH. Use of artificial intelligence-guided echocardiography to detect cardiac dysfunction and heart valve disease in rural and remote areas: Rationale and design of the AGILE-echo trial. Am Heart J 2024; 277:11-19. [PMID: 39128659 DOI: 10.1016/j.ahj.2024.08.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/11/2024] [Revised: 08/01/2024] [Accepted: 08/06/2024] [Indexed: 08/13/2024]
Abstract
BACKGROUND Transthoracic echocardiography (TTE) is essential in the diagnosis of cardiovascular diseases (CVD), including but not limited to heart failure (HF) and heart valve disease (HVD). However, its dependence on expert acquisition means that its accessibility in rural areas may be limited, leading to delayed management decisions and potential missed diagnoses. Artificial intelligence-guided (AI)-TTE offers a solution by permitting non-expert image acquisition. The impact of AI-TTE on the timing of diagnosis and early initiation of cardioprotection is undefined. METHODS AGILE-Echo (use of Artificial intelligence-Guided echocardiography to assIst cardiovascuLar patient managEment) is a randomized-controlled trial conducted in 5 rural and remote areas around Australia. Adults with CV risk factors and exercise intolerance, or concerns regarding HVD are randomized into AI-TTE or usual care (UC). AI-TTE participants may have a cardiovascular problem excluded, identified (leading to AI-guided interventions) or unresolved (leading to conventional TTE). UC participants undergo usual management, including referral for standard TTE. The primary endpoint is a composite of HVD or HF diagnosis at 12-months. Subgroup analysis, stratified based on age range and sex, will be conducted. All statistical analyses will be conducted using R. RESULTS Of the first 157 participants, 78 have been randomized into AI-TTE (median age 68 [IQR 17]) and 79 to UC (median age 65 [IQR 17], P = .034). HVD was the primary concern in 37 participants (23.6%) while 84.7% (n = 133) experienced exercise intolerance. The overall 10-year HF incidence risk was 13.4% and 20.0% (P = .089) for UC and AI-TTE arm respectively. Atrial remodeling, left ventricular remodeling and valvular regurgitation were the most common findings. Thirty-three patients (42.3%) showed no abnormalities. CONCLUSIONS This randomized-controlled trial of AI-TTE will provide proof-of-concept for the role of AI-TTE in identifying pre-symptomatic HF or HVD when access to TTE is limited. Additionally, this could promote the usage of AI-TTE in rural or remote areas, ultimately improving health and quality of life of community dwelling adults with risks, signs or symptoms of cardiac dysfunction.
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Affiliation(s)
- Cheng Hwee Soh
- Imaging Research, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Leah Wright
- Imaging Research, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Angus Baumann
- Alice Springs Hospital, The Gap, Northern Territory, Australia
| | | | - Christopher Yu
- Walgett Aboriginal Medical Service Limited, Walgett, New South Wales, Australia; Nepean Clinical School, University of Sydney, Sydney, New South Wales, Australia
| | - Mark Nolan
- Imaging Research, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Tony Mylius
- Merredin District Hospital, Western Australian Country Health Service, Wheatbelt, Western Australia, Australia
| | - Thomas H Marwick
- Imaging Research, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, Victoria, Australia; Menzies Institute for Medical Research, Hobart, Tasmania, Australia.
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Mears J, Kaleem S, Panchamia R, Kamel H, Tam C, Thalappillil R, Murthy S, Merkler AE, Zhang C, Ch'ang JH. Leveraging the Capabilities of AI: Novice Neurology-Trained Operators Performing Cardiac POCUS in Patients with Acute Brain Injury. Neurocrit Care 2024; 41:523-532. [PMID: 38506968 DOI: 10.1007/s12028-024-01953-z] [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: 07/06/2023] [Accepted: 02/01/2024] [Indexed: 03/22/2024]
Abstract
BACKGROUND Cardiac point-of-care ultrasound (cPOCUS) can aid in the diagnosis and treatment of cardiac disorders. Such disorders can arise as complications of acute brain injury, but most neurologic intensive care unit (NICU) providers do not receive formal training in cPOCUS. Caption artificial intelligence (AI) uses a novel deep learning (DL) algorithm to guide novice cPOCUS users in obtaining diagnostic-quality cardiac images. The primary objective of this study was to determine how often NICU providers with minimal cPOCUS experience capture quality images using DL-guided cPOCUS as well as the association between DL-guided cPOCUS and change in management and time to formal echocardiograms in the NICU. METHODS From September 2020 to November 2021, neurology-trained physician assistants, residents, and fellows used DL software to perform clinically indicated cPOCUS scans in an academic tertiary NICU. Certified echocardiographers evaluated each scan independently to assess the quality of images and global interpretability of left ventricular function, right ventricular function, inferior vena cava size, and presence of pericardial effusion. Descriptive statistics with exact confidence intervals were used to calculate proportions of obtained images that were of adequate quality and that changed management. Time to first adequate cardiac images (either cPOCUS or formal echocardiography) was compared using a similar population from 2018. RESULTS In 153 patients, 184 scans were performed for a total of 943 image views. Three certified echocardiographers deemed 63.4% of scans as interpretable for a qualitative assessment of left ventricular size and function, 52.6% of scans as interpretable for right ventricular size and function, 34.8% of scans as interpretable for inferior vena cava size and variability, and 47.2% of scans as interpretable for the presence of pericardial effusion. Thirty-seven percent of screening scans changed management, most commonly adjusting fluid goals (81.2%). Time to first adequate cardiac images decreased significantly from 3.1 to 1.7 days (p < 0.001). CONCLUSIONS With DL guidance, neurology providers with minimal to no cPOCUS training were often able to obtain diagnostic-quality cardiac images, which informed management changes and significantly decreased time to cardiac imaging.
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Affiliation(s)
- Jennifer Mears
- Department of Neurology, Weill Cornell Medicine, New York, NY, USA
| | - Safa Kaleem
- Department of Neurology, Weill Cornell Medicine, New York, NY, USA
| | - Rohan Panchamia
- Department of Anesthesiology, Weill Cornell Medicine, New York, NY, USA
| | - Hooman Kamel
- Clinical and Translational Neuroscience Unit, Feil Family Brain and Mind Research Institute and Department of Neurology, Weill Cornell Medicine, 525 East 68th Street, F610, New York, NY, 10065, USA
| | - Chris Tam
- Department of Anesthesiology, Weill Cornell Medicine, New York, NY, USA
| | | | - Santosh Murthy
- Clinical and Translational Neuroscience Unit, Feil Family Brain and Mind Research Institute and Department of Neurology, Weill Cornell Medicine, 525 East 68th Street, F610, New York, NY, 10065, USA
| | - Alexander E Merkler
- Clinical and Translational Neuroscience Unit, Feil Family Brain and Mind Research Institute and Department of Neurology, Weill Cornell Medicine, 525 East 68th Street, F610, New York, NY, 10065, USA
| | - Cenai Zhang
- Clinical and Translational Neuroscience Unit, Feil Family Brain and Mind Research Institute and Department of Neurology, Weill Cornell Medicine, 525 East 68th Street, F610, New York, NY, 10065, USA
| | - Judy H Ch'ang
- Clinical and Translational Neuroscience Unit, Feil Family Brain and Mind Research Institute and Department of Neurology, Weill Cornell Medicine, 525 East 68th Street, F610, New York, NY, 10065, USA.
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Matasic DS, Zeitoun R, Fonarow GC, Razavi AC, Blumenthal RS, Gulati M. Advancements in Incident Heart Failure Risk Prediction and Screening Tools. Am J Cardiol 2024; 227:105-110. [PMID: 39029721 DOI: 10.1016/j.amjcard.2024.07.014] [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] [Received: 03/20/2024] [Revised: 07/08/2024] [Accepted: 07/11/2024] [Indexed: 07/21/2024]
Abstract
Heart failure (HF) is a major cause of mortality and morbidity in the United States that carries substantial healthcare costs. Multiple risk prediction models and strategies have been developed over the past 30 years with the aim of identifying those at high risk of developing HF and of implementing preventive therapies effectively. This review highlights recent developments in HF risk prediction tools including emerging risk factors, innovative risk prediction models, and novel screening strategies from artificial intelligence to biomarkers. These developments allow more accurate prediction, but their impact on clinical outcomes remains to be investigated. Implementation of these risk models in clinical practice is a considerable challenge, but HF risk prediction tools offer a promising opportunity to improve outcomes while maintaining value.
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Affiliation(s)
- Daniel S Matasic
- Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Johns Hopkins University, Baltimore, Maryland
| | - Ralph Zeitoun
- Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Johns Hopkins University, Baltimore, Maryland
| | - Gregg C Fonarow
- Division of Cardiology, University of California Los Angeles, Los Angeles, California
| | - Alexander C Razavi
- Division of Cardiology, Emory University School of Medicine, Atlanta, Georgia
| | - Roger S Blumenthal
- Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Johns Hopkins University, Baltimore, Maryland
| | - Martha Gulati
- Barbra Streisand Women's Heart Center, Cedars-Sinai Smidt Heart Institute, Los Angeles, California.
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Stefanini B, Giamperoli A, Terzi E, Piscaglia F. Artificial intelligence in Ultrasound: Pearls and pitfalls in 2024Künstliche Intelligenz im Ultraschall: Pearls and Pitfalls im Jahr 2024. ULTRASCHALL IN DER MEDIZIN (STUTTGART, GERMANY : 1980) 2024. [PMID: 39242087 DOI: 10.1055/a-2368-9201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/09/2024]
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5
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Honarvar M, Lobo J, Schneider C, Wolfe N, Gawrieh S, Loomba R, Ramji A, Hassanein T, Yoshida EM, Pang E, Curry MP, Afdhal NH. Deep Learning Based Shear Wave Detection and Segmentation Tool for Use in Point-of-Care for Chronic Liver Disease Assessments. ULTRASOUND IN MEDICINE & BIOLOGY 2024:S0301-5629(24)00299-0. [PMID: 39244483 DOI: 10.1016/j.ultrasmedbio.2024.08.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Revised: 07/31/2024] [Accepted: 08/01/2024] [Indexed: 09/09/2024]
Abstract
OBJECTIVE As metabolic dysfunction-associated steatotic liver disease (MASLD) becomes more prevalent worldwide, it is imperative to create more accurate technologies that make it easy to assess the liver in a point-of-care setting. The aim of this study is to test the performance of a new software tool implemented in Velacur (Sonic Incytes), a liver stiffness and ultrasound attenuation measurement device, on patients with MASLD. This tool employs a deep learning-based method to detect and segment shear waves in the liver tissue for subsequent analysis to improve tissue characterization for patient diagnosis. METHODS This new tool consists of a deep learning based algorithm, which was trained on 15,045 expert-segmented images from 103 patients, using a U-Net architecture. The algorithm was then tested on 4429 images from 36 volunteers and patients with MASLD. Test subjects were scanned at different clinics with different Velacur operators. Evaluation was performed on both individual images (image based) and averaged across all images collected from a patient (patient based). Ground truth was defined by expert segmentation of the shear waves within each image. For evaluation, sensitivity and specificity for correct wave detection in the image were calculated. For those images containing waves, the Dice coefficient was calculated. A prototype of the software tool was also implemented on Velacur and assessed by operators in real world settings. RESULTS The wave detection algorithm had a sensitivity of 81% and a specificity of 84%, with a Dice coefficient of 0.74 and 0.75 for image based and patient-based averages respectively. The implementation of this software tool as an overlay on the B-Mode ultrasound resulted in improved exam quality collected by operators. CONCLUSION The shear wave algorithm performed well on a test set of volunteers and patients with metabolic dysfunction-associated steatotic liver disease. The addition of this software tool, implemented on the Velacur system, improved the quality of the liver assessments performed in a real world, point of care setting.
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Affiliation(s)
| | - Julio Lobo
- Sonic Incytes Medical Corp., Vancouver, BC, Canada
| | | | | | - Samer Gawrieh
- Indiana University School of Medicine, Division of Gastroenterology and Hepatology, Indianapolis, IN, USA
| | - Rohit Loomba
- University of California San Diego, Division of Gastroenterology San Diego, CA, USA
| | - Alnoor Ramji
- University of British Columbia, Division of Gastroenterology, Vancouver, BC, Canada
| | | | - Eric M Yoshida
- University of British Columbia, Division of Gastroenterology, Vancouver, BC, Canada
| | - Emily Pang
- Vancouver General Hospital, Department of Radiology, Vancouver BC, Canada
| | - Michael P Curry
- Beth Israel Deaconess Medical Center, Division of Gastroenterology, Boston, MA, USA
| | - Nezam H Afdhal
- Beth Israel Deaconess Medical Center, Division of Gastroenterology, Boston, MA, USA
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Li X, Zhang H, Yue J, Yin L, Li W, Ding G, Peng B, Xie S. A multi-task deep learning approach for real-time view classification and quality assessment of echocardiographic images. Sci Rep 2024; 14:20484. [PMID: 39227373 PMCID: PMC11372079 DOI: 10.1038/s41598-024-71530-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2024] [Accepted: 08/28/2024] [Indexed: 09/05/2024] Open
Abstract
High-quality standard views in two-dimensional echocardiography are essential for accurate cardiovascular disease diagnosis and treatment decisions. However, the quality of echocardiographic images is highly dependent on the practitioner's experience. Ensuring timely quality control of echocardiographic images in the clinical setting remains a significant challenge. In this study, we aimed to propose new quality assessment criteria and develop a multi-task deep learning model for real-time multi-view classification and image quality assessment (six standard views and "others"). A total of 170,311 echocardiographic images collected between 2015 and 2022 were utilized to develop and evaluate the model. On the test set, the model achieved an overall classification accuracy of 97.8% (95%CI 97.7-98.0) and a mean absolute error of 6.54 (95%CI 6.43-6.66). A single-frame inference time of 2.8 ms was achieved, meeting real-time requirements. We also analyzed pre-stored images from three distinct groups of echocardiographers (junior, senior, and expert) to evaluate the clinical feasibility of the model. Our multi-task model can provide objective, reproducible, and clinically significant view quality assessment results for echocardiographic images, potentially optimizing the clinical image acquisition process and improving AI-assisted diagnosis accuracy.
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Affiliation(s)
- Xinyu Li
- School of Computer Science and Software Engineering, Southwest Petroleum University, Chengdu, 610500, China
| | - Hongmei Zhang
- Ultrasound in Cardiac Electrophysiology and Biomechanics Key Laboratory of Sichuan Province, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, 32# W. Sec 2, 1st Ring Rd., Chengdu, 610072, China
- Department of Cardiovascular Ultrasound & Noninvasive Cardiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, 32# W. Sec 2, 1st Ring Rd., Chengdu, 610072, China
| | - Jing Yue
- School of Computer Science and Software Engineering, Southwest Petroleum University, Chengdu, 610500, China
| | - Lixue Yin
- Ultrasound in Cardiac Electrophysiology and Biomechanics Key Laboratory of Sichuan Province, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, 32# W. Sec 2, 1st Ring Rd., Chengdu, 610072, China
- Department of Cardiovascular Ultrasound & Noninvasive Cardiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, 32# W. Sec 2, 1st Ring Rd., Chengdu, 610072, China
| | - Wenhua Li
- Ultrasound in Cardiac Electrophysiology and Biomechanics Key Laboratory of Sichuan Province, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, 32# W. Sec 2, 1st Ring Rd., Chengdu, 610072, China
- Department of Cardiovascular Ultrasound & Noninvasive Cardiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, 32# W. Sec 2, 1st Ring Rd., Chengdu, 610072, China
| | - Geqi Ding
- Ultrasound in Cardiac Electrophysiology and Biomechanics Key Laboratory of Sichuan Province, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, 32# W. Sec 2, 1st Ring Rd., Chengdu, 610072, China
- Department of Cardiovascular Ultrasound & Noninvasive Cardiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, 32# W. Sec 2, 1st Ring Rd., Chengdu, 610072, China
| | - Bo Peng
- School of Computer Science and Software Engineering, Southwest Petroleum University, Chengdu, 610500, China
| | - Shenghua Xie
- Ultrasound in Cardiac Electrophysiology and Biomechanics Key Laboratory of Sichuan Province, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, 32# W. Sec 2, 1st Ring Rd., Chengdu, 610072, China.
- Department of Cardiovascular Ultrasound & Noninvasive Cardiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, 32# W. Sec 2, 1st Ring Rd., Chengdu, 610072, China.
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Sengupta PP, Dey D, Davies RH, Duchateau N, Yanamala N. Challenges for augmenting intelligence in cardiac imaging. Lancet Digit Health 2024:S2589-7500(24)00142-0. [PMID: 39214759 DOI: 10.1016/s2589-7500(24)00142-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Revised: 05/15/2024] [Accepted: 06/17/2024] [Indexed: 09/04/2024]
Abstract
Artificial Intelligence (AI), through deep learning, has brought automation and predictive capabilities to cardiac imaging. However, despite considerable investment, tangible health-care cost reductions remain unproven. Although AI holds promise, there has been insufficient time for both methodological development and prospective clinical trials to establish its advantage over human interpretations in terms of its effect on patient outcomes. Challenges such as data scarcity, privacy issues, and ethical concerns impede optimal AI training. Furthermore, the absence of a unified model for the complex structure and function of the heart and evolving domain knowledge can introduce heuristic biases and influence underlying assumptions in model development. Integrating AI into diverse institutional picture archiving and communication systems and devices also presents a clinical hurdle. This hurdle is further compounded by an absence of high-quality labelled data, difficulty sharing data between institutions, and non-uniform and inadequate gold standards for external validations and comparisons of model performance in real-world settings. Nevertheless, there is a strong push in industry and academia for AI solutions in medical imaging. This Series paper reviews key studies and identifies challenges that require a pragmatic change in the approach for using AI for cardiac imaging, whereby AI is viewed as augmented intelligence to complement, not replace, human judgement. The focus should shift from isolated measurements to integrating non-linear and complex data towards identifying disease phenotypes-emphasising pattern recognition where AI excels. Algorithms should enhance imaging reports, enriching patients' understanding, communication between patients and clinicians, and shared decision making. The emergence of professional standards and guidelines is essential to address these developments and ensure the safe and effective integration of AI in cardiac imaging.
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Affiliation(s)
- Partho P Sengupta
- Division of Cardiovascular Disease and Hypertension, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA.
| | - Damini Dey
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Rhodri H Davies
- Institute of Cardiovascular Science, University College London, London, UK
| | - Nicolas Duchateau
- CREATIS, INSA, CNRS UMR 5220, INSERM U1294, Université Lyon 1, UJM Saint-Etienne, Lyon, France; Institut Universitaire de France, Paris, France
| | - Naveena Yanamala
- Division of Cardiovascular Disease and Hypertension, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA
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Pan Y, Jiao FY. Application of artificial intelligence in the diagnosis and treatment of Kawasaki disease. World J Clin Cases 2024; 12:5304-5307. [PMID: 39156094 PMCID: PMC11238697 DOI: 10.12998/wjcc.v12.i23.5304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Revised: 05/15/2024] [Accepted: 06/04/2024] [Indexed: 07/05/2024] Open
Abstract
This editorial provides commentary on an article titled "Potential and limitations of ChatGPT and generative artificial intelligence (AI) in medical safety education" recently published in the World Journal of Clinical Cases. AI has enormous potential for various applications in the field of Kawasaki disease (KD). One is machine learning (ML) to assist in the diagnosis of KD, and clinical prediction models have been constructed worldwide using ML; the second is using a gene signal calculation toolbox to identify KD, which can be used to monitor key clinical features and laboratory parameters of disease severity; and the third is using deep learning (DL) to assist in cardiac ultrasound detection. The performance of the DL algorithm is similar to that of experienced cardiac experts in detecting coronary artery lesions to promoting the diagnosis of KD. To effectively utilize AI in the diagnosis and treatment process of KD, it is crucial to improve the accuracy of AI decision-making using more medical data, while addressing issues related to patient personal information protection and AI decision-making responsibility. AI progress is expected to provide patients with accurate and effective medical services that will positively impact the diagnosis and treatment of KD in the future.
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Affiliation(s)
- Yan Pan
- Department of Pediatrics, The First Affiliated Hospital of Yangtze University, Jingzhou 434000, Hubei Province, China
| | - Fu-Yong Jiao
- Shaanxi Kawasaki Disease Diagnosis and Treatment Center, Shaanxi Provincial People's Hospital, Xi’an 710000, Shaanxi Province, China
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Łajczak PM, Jóźwik K. Artificial intelligence and myocarditis-a systematic review of current applications. Heart Fail Rev 2024:10.1007/s10741-024-10431-9. [PMID: 39138803 DOI: 10.1007/s10741-024-10431-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/30/2024] [Indexed: 08/15/2024]
Abstract
Myocarditis, marked by heart muscle inflammation, poses significant clinical challenges. This study, guided by PRISMA guidelines, explores the expanding role of artificial intelligence (AI) in myocarditis, aiming to consolidate current knowledge and guide future research. Following PRISMA guidelines, a systematic review was conducted across PubMed, Cochrane Reviews, Scopus, Embase, and Web of Science databases. MeSH terms including artificial intelligence, deep learning, machine learning, myocarditis, and inflammatory cardiomyopathy were used. Inclusion criteria involved original articles utilizing AI for myocarditis, while exclusion criteria eliminated reviews, editorials, and non-AI-focused studies. The search yielded 616 articles, with 42 meeting inclusion criteria after screening. The identified articles, spanning diagnostic, survival prediction, and molecular analysis aspects, were analyzed in each subsection. Diagnostic studies showcased the versatility of AI algorithms, achieving high accuracies in myocarditis detection. Survival prediction models exhibited robust discriminatory power, particularly in emergency settings and pediatric populations. Molecular analyses demonstrated AI's potential in deciphering complex immune interactions. This systematic review provides a comprehensive overview of AI applications in myocarditis, highlighting transformative potential in diagnostics, survival prediction, and molecular understanding. Collaborative efforts are crucial for overcoming limitations and realizing AI's full potential in improving myocarditis care.
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Affiliation(s)
- Paweł Marek Łajczak
- Zbigniew Religa Scientific Club at Biophysics Department, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Zabrze, Poland.
| | - Kamil Jóźwik
- Zbigniew Religa Scientific Club at Biophysics Department, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Zabrze, Poland
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Vega R, Kwok C, Rakkunedeth Hareendranathan A, Nagdev A, Jaremko JL. Assessment of an Artificial Intelligence Tool for Estimating Left Ventricular Ejection Fraction in Echocardiograms from Apical and Parasternal Long-Axis Views. Diagnostics (Basel) 2024; 14:1719. [PMID: 39202209 PMCID: PMC11353168 DOI: 10.3390/diagnostics14161719] [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: 07/23/2024] [Revised: 08/07/2024] [Accepted: 08/07/2024] [Indexed: 09/03/2024] Open
Abstract
This work aims to evaluate the performance of a new artificial intelligence tool (ExoAI) to compute the left ventricular ejection fraction (LVEF) in echocardiograms of the apical and parasternal long axis (PLAX) views. We retrospectively gathered echocardiograms from 441 individual patients (70% male, age: 67.3 ± 15.3, weight: 87.7 ± 25.4, BMI: 29.5 ± 7.4) and computed the ejection fraction in each echocardiogram using the ExoAI algorithm. We compared its performance against the ejection fraction from the clinical report. ExoAI achieved a root mean squared error of 7.58% in A2C, 7.45% in A4C, and 7.29% in PLAX, and correlations of 0.79, 0.75, and 0.89, respectively. As for the detection of low EF values (EF < 50%), ExoAI achieved an accuracy of 83% in A2C, 80% in A4C, and 91% in PLAX. Our results suggest that ExoAI effectively estimates the LVEF and it is an effective tool for estimating abnormal ejection fraction values (EF < 50%). Importantly, the PLAX view allows for the estimation of the ejection fraction when it is not feasible to acquire apical views (e.g., in ICU settings where it is not possible to move the patient to obtain an apical scan).
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Affiliation(s)
| | - Cherise Kwok
- Department of Radiology and Diagnostic Imaging, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB T6G 2R3, Canada; (C.K.); (A.R.H.); (J.L.J.)
| | - Abhilash Rakkunedeth Hareendranathan
- Department of Radiology and Diagnostic Imaging, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB T6G 2R3, Canada; (C.K.); (A.R.H.); (J.L.J.)
| | - Arun Nagdev
- Alameda Health System, Highland General Hospital, University of California San Francisco, San Francisco, CA 94143, USA;
| | - Jacob L. Jaremko
- Department of Radiology and Diagnostic Imaging, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB T6G 2R3, Canada; (C.K.); (A.R.H.); (J.L.J.)
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Sadeghpour A, Jiang Z, Hummel YM, Frost M, Lam CSP, Shah SJ, Lund LH, Stone GW, Swaminathan M, Weissman NJ, Asch FM. An Automated Machine Learning-Based Quantitative Multiparametric Approach for Mitral Regurgitation Severity Grading. JACC Cardiovasc Imaging 2024:S1936-878X(24)00247-X. [PMID: 39152959 DOI: 10.1016/j.jcmg.2024.06.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 05/16/2024] [Accepted: 06/20/2024] [Indexed: 08/19/2024]
Abstract
BACKGROUND Considering the high prevalence of mitral regurgitation (MR) and the highly subjective, variable MR severity reporting, an automated tool that could screen patients for clinically significant MR (≥ moderate) would streamline the diagnostic/therapeutic pathways and ultimately improve patient outcomes. OBJECTIVES The authors aimed to develop and validate a fully automated machine learning (ML)-based echocardiography workflow for grading MR severity. METHODS ML algorithms were trained on echocardiograms from 2 observational cohorts and validated in patients from 2 additional independent studies. Multiparametric echocardiography core laboratory MR assessment served as ground truth. The machine was trained to measure 16 MR-related parameters. Multiple ML models were developed to find the optimal parameters and preferred ML model for MR severity grading. RESULTS The preferred ML model used 9 parameters. Image analysis was feasible in 99.3% of cases and took 80 ± 5 seconds per case. The accuracy for grading MR severity (none to severe) was 0.80, and for significant (moderate or severe) vs nonsignificant MR was 0.97 with a sensitivity of 0.96 and specificity of 0.98. The model performed similarly in cases of eccentric and central MR. Patients graded as having severe MR had higher 1-year mortality (adjusted HR: 5.20 [95% CI: 1.24-21.9]; P = 0.025 compared with mild). CONCLUSIONS An automated multiparametric ML model for grading MR severity is feasible, fast, highly accurate, and predicts 1-year mortality. Its implementation in clinical practice could improve patient care by facilitating referral to specialized clinics and access to evidence-based therapies while improving quality and efficiency in the echocardiography laboratory.
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Affiliation(s)
- Anita Sadeghpour
- MedStar Health Research Institute and Georgetown University, Washington, District of Columbia, USA
| | | | | | | | - Carolyn S P Lam
- National Heart Centre Singapore, Duke-National University of Singapore, Singapore
| | - Sanjiv J Shah
- Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Lars H Lund
- Karolinska University Hospital, Stockholm, Sweden
| | - Gregg W Stone
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Madhav Swaminathan
- Department of Anesthesiology, Duke University School of Medicine, Durham, North Carolina, USA
| | - Neil J Weissman
- MedStar Health Research Institute and Georgetown University, Washington, District of Columbia, USA
| | - Federico M Asch
- MedStar Health Research Institute and Georgetown University, Washington, District of Columbia, USA.
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12
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Grenne B, Østvik A. Beyond Years: Is Artificial Intelligence Ready to Predict Biological Age and Cardiovascular Risk Using Echocardiography? J Am Soc Echocardiogr 2024; 37:736-739. [PMID: 38797330 DOI: 10.1016/j.echo.2024.05.013] [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: 05/18/2024] [Revised: 05/18/2024] [Accepted: 05/20/2024] [Indexed: 05/29/2024]
Affiliation(s)
- Bjørnar Grenne
- Clinic of Cardiology, St. Olav's University Hospital, Trondheim, Norway; Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway.
| | - Andreas Østvik
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway; Medical Image Analysis, Health Research, SINTEF Digital, Trondheim, Norway
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13
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Faierstein K, Fiman M, Loutati R, Rubin N, Manor U, Am-Shalom A, Cohen-Shelly M, Blank N, Lotan D, Zhao Q, Schwammenthal E, Klempfner R, Zimlichman E, Raanani E, Maor E. Artificial Intelligence Assessment of Biological Age From Transthoracic Echocardiography: Discrepancies with Chronologic Age Predict Significant Excess Mortality. J Am Soc Echocardiogr 2024; 37:725-735. [PMID: 38740271 DOI: 10.1016/j.echo.2024.04.017] [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] [Received: 03/04/2024] [Revised: 04/29/2024] [Accepted: 04/30/2024] [Indexed: 05/16/2024]
Abstract
BACKGROUND Age and sex can be estimated using artificial intelligence on the basis of various sources. The aims of this study were to test whether convolutional neural networks could be trained to estimate age and predict sex using standard transthoracic echocardiography and to evaluate the prognostic implications. METHODS The algorithm was trained on 76,342 patients, validated in 22,825 patients, and tested in 20,960 patients. It was then externally validated using data from a different hospital (n = 556). Finally, a prospective cohort of handheld point-of-care ultrasound devices (n = 319; ClinicalTrials.gov identifier NCT05455541) was used to confirm the findings. A multivariate Cox regression model was used to investigate the association between age estimation and chronologic age with overall survival. RESULTS The mean absolute error in age estimation was 4.9 years, with a Pearson correlation coefficient of 0.922. The probabilistic value of sex had an overall accuracy of 96.1% and an area under the curve of 0.993. External validation and prospective study cohorts yielded consistent results. Finally, survival analysis demonstrated that age prediction ≥5 years vs chronologic age was associated with an independent 34% increased risk for death during follow-up (P < .001). CONCLUSIONS Applying artificial intelligence to standard transthoracic echocardiography allows the prediction of sex and the estimation of age. Machine-based estimation is an independent predictor of overall survival and, with further evaluation, can be used for risk stratification and estimation of biological age.
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Affiliation(s)
- Kobi Faierstein
- Leviev Cardiovascular Institute, Sheba Medical Center, Ramat Gan, Israel; Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel.
| | | | - Ranel Loutati
- Leviev Cardiovascular Institute, Sheba Medical Center, Ramat Gan, Israel
| | | | - Uri Manor
- Leviev Cardiovascular Institute, Sheba Medical Center, Ramat Gan, Israel; Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel
| | | | | | - Nimrod Blank
- Echocardiography Unit, Division of Cardiovascular Medicine, Baruch-Padeh Medical Center, Poria, Israel
| | - Dor Lotan
- Division of Cardiology, Department of Medicine, NewYork-Presbyterian Hospital, Columbia University Irving Medical Center, New York, New York
| | - Qiong Zhao
- Inova Heart and Vascular Institute, Inova Fairfax Hospital, Falls Church, Virginia
| | - Ehud Schwammenthal
- Leviev Cardiovascular Institute, Sheba Medical Center, Ramat Gan, Israel; Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel; Aisap.ai, Ramat Gan, Israel
| | - Robert Klempfner
- Leviev Cardiovascular Institute, Sheba Medical Center, Ramat Gan, Israel; Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel; Aisap.ai, Ramat Gan, Israel
| | - Eyal Zimlichman
- Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Ehud Raanani
- Leviev Cardiovascular Institute, Sheba Medical Center, Ramat Gan, Israel; Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel; Aisap.ai, Ramat Gan, Israel
| | - Elad Maor
- Leviev Cardiovascular Institute, Sheba Medical Center, Ramat Gan, Israel; Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel; Aisap.ai, Ramat Gan, Israel
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14
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Tian Y, Qin W, Zhao Z, Wang C, Tian Y, Zhang Y, He K, Zhang Y, Shen L, Zhou Z, Yu C. Deep Learning Based Automatic Left Ventricle Segmentation from the Transgastric Short-Axis View on Transesophageal Echocardiography: A Feasibility Study. Diagnostics (Basel) 2024; 14:1655. [PMID: 39125530 PMCID: PMC11311555 DOI: 10.3390/diagnostics14151655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2024] [Revised: 07/26/2024] [Accepted: 07/30/2024] [Indexed: 08/12/2024] Open
Abstract
Segmenting the left ventricle from the transgastric short-axis views (TSVs) on transesophageal echocardiography (TEE) is the cornerstone for cardiovascular assessment during perioperative management. Even for seasoned professionals, the procedure remains time-consuming and experience-dependent. The current study aims to evaluate the feasibility of deep learning for automatic segmentation by assessing the validity of different U-Net algorithms. A large dataset containing 1388 TSV acquisitions was retrospectively collected from 451 patients (32% women, average age 53.42 years) who underwent perioperative TEE between July 2015 and October 2023. With image preprocessing and data augmentation, 3336 images were included in the training set, 138 images in the validation set, and 138 images in the test set. Four deep neural networks (U-Net, Attention U-Net, UNet++, and UNeXt) were employed for left ventricle segmentation and compared in terms of the Jaccard similarity coefficient (JSC) and Dice similarity coefficient (DSC) on the test set, as well as the number of network parameters, training time, and inference time. The Attention U-Net and U-Net++ models performed better in terms of JSC (the highest average JSC: 86.02%) and DSC (the highest average DSC: 92.00%), the UNeXt model had the smallest network parameters (1.47 million), and the U-Net model had the least training time (6428.65 s) and inference time for a single image (101.75 ms). The Attention U-Net model outperformed the other three models in challenging cases, including the impaired boundary of left ventricle and the artifact of the papillary muscle. This pioneering exploration demonstrated the feasibility of deep learning for the segmentation of the left ventricle from TSV on TEE, which will facilitate an accelerated and objective alternative of cardiovascular assessment for perioperative management.
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Affiliation(s)
- Yuan Tian
- Department of Anesthesiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China; (Y.T.); (C.W.); (Y.T.); (Y.Z.); (K.H.); (Y.Z.); (L.S.)
| | - Wenting Qin
- Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China; (W.Q.); (Z.Z.)
| | - Zihang Zhao
- Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China; (W.Q.); (Z.Z.)
| | - Chunrong Wang
- Department of Anesthesiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China; (Y.T.); (C.W.); (Y.T.); (Y.Z.); (K.H.); (Y.Z.); (L.S.)
| | - Yajie Tian
- Department of Anesthesiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China; (Y.T.); (C.W.); (Y.T.); (Y.Z.); (K.H.); (Y.Z.); (L.S.)
| | - Yuelun Zhang
- Department of Anesthesiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China; (Y.T.); (C.W.); (Y.T.); (Y.Z.); (K.H.); (Y.Z.); (L.S.)
| | - Kai He
- Department of Anesthesiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China; (Y.T.); (C.W.); (Y.T.); (Y.Z.); (K.H.); (Y.Z.); (L.S.)
| | - Yuguan Zhang
- Department of Anesthesiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China; (Y.T.); (C.W.); (Y.T.); (Y.Z.); (K.H.); (Y.Z.); (L.S.)
| | - Le Shen
- Department of Anesthesiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China; (Y.T.); (C.W.); (Y.T.); (Y.Z.); (K.H.); (Y.Z.); (L.S.)
| | - Zhuhuang Zhou
- Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China; (W.Q.); (Z.Z.)
| | - Chunhua Yu
- Department of Anesthesiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China; (Y.T.); (C.W.); (Y.T.); (Y.Z.); (K.H.); (Y.Z.); (L.S.)
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15
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Jaltotage B, Lu J, Dwivedi G. Use of Artificial Intelligence Including Multimodal Systems to Improve the Management of Cardiovascular Disease. Can J Cardiol 2024:S0828-282X(24)00566-X. [PMID: 39038650 DOI: 10.1016/j.cjca.2024.07.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 07/15/2024] [Accepted: 07/16/2024] [Indexed: 07/24/2024] Open
Abstract
The rising prevalence of cardiovascular disease presents an escalating challenge for current health services, which are grappling with increasing demands. Innovative changes are imperative to sustain the delivery of high-quality patient care. Recent technologic advances have resulted in the emergence of artificial intelligence as a viable solution. Advanced algorithms are now capable of performing complex analysis of large volumes of data rapidly and with exceptional accuracy. Multimodality artificial intelligence systems handle a diverse range of data including images, text, video, and audio. Compared with single-modality systems, multimodal artificial intelligence systems appear to hold promise for enhancing overall performance and enabling smoother integration into existing workflows. Such systems can empower physicians with clinical decision support and enhanced efficiency. Owing to the complexity of the field, however, truly multimodal artificial intelligence is still scarce in the management of cardiovascular disease. This article aims to cover current research, emerging trends, and the future utilisation of artificial intelligence in the management of cardiovascular disease, with a focus on multimodality systems.
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Affiliation(s)
- Biyanka Jaltotage
- Department of Cardiology, Fiona Stanley Hospital, Perth, Western Australia, Australia
| | - Juan Lu
- Harry Perkins Institute of Medical Research, Perth, Western Australia, Australia; School of Medicine, University of Western Australia, Perth, Western Australia, Australia
| | - Girish Dwivedi
- Department of Cardiology, Fiona Stanley Hospital, Perth, Western Australia, Australia; Harry Perkins Institute of Medical Research, Perth, Western Australia, Australia; School of Medicine, University of Western Australia, Perth, Western Australia, Australia.
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16
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Oikonomou EK, Khera R. Designing medical artificial intelligence systems for global use: focus on interoperability, scalability, and accessibility. Hellenic J Cardiol 2024:S1109-9666(24)00158-1. [PMID: 39025234 DOI: 10.1016/j.hjc.2024.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 06/21/2024] [Accepted: 07/02/2024] [Indexed: 07/20/2024] Open
Abstract
Advances in artificial intelligence (AI) and machine learning systems promise faster, more efficient, and more personalized care. While many of these models are built on the premise of improving access to the timely screening, diagnosis, and treatment of cardiovascular disease, their validity and accessibility across diverse and international cohorts remain unknown. In this mini-review article, we summarize key obstacles in the effort to design AI systems that will be scalable, accessible, and accurate across distinct geographical and temporal settings. We discuss representativeness, interoperability, quality assurance, and the importance of vendor-agnostic data types that will be available to end-users across the globe. These topics illustrate how the timely integration of these principles into AI development is crucial to maximizing the global benefits of AI in cardiology.
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Affiliation(s)
- Evangelos K Oikonomou
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
| | - Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA; Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT, USA; Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT, USA; Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
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17
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Kirkpatrick JN, Panebianco N, Díaz-Gómez JL, Adhikari S, Bremer ML, Bronshteyn YS, Damewood S, Jankowski M, Johri A, Kaplan JRH, Kimura BJ, Kort S, Labovitz A, Lu JC, Ma IWY, Mayo PH, Mulvagh SL, Nikravan S, Cole SP, Picard MH, Sorrell VL, Stainback R, Thamman R, Tucay ES, Via G, West FM. Recommendations for Cardiac Point-of-Care Ultrasound Nomenclature. J Am Soc Echocardiogr 2024; 37:S0894-7317(24)00222-0. [PMID: 39230540 DOI: 10.1016/j.echo.2024.05.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/05/2024]
Abstract
Point-of-care ultrasound (POCUS) involves the acquisition, interpretation, and immediate clinical integration of ultrasonographic imaging performed by a treating clinician. The current state of cardiac POCUS terminology is heterogeneous and ambiguous, in part because it evolved through siloed specialty practices. In particular, the medical literature and colloquial medical conversation contain a wide variety of terms that equate to cardiac POCUS. While diverse terminology aided in the development and dissemination of cardiac POCUS throughout multiple specialties, it also contributes to confusion and raises patient safety concerns. This statement is the product of a diverse and inclusive Writing Group from multiple specialties, including medical linguistics, that employed an iterative process to contextualize and standardize a nomenclature for cardiac POCUS. We sought to establish a deliberate vocabulary that is sufficiently unrelated to any specialty, ultrasound equipment, or clinical setting to enhance consistency throughout the academic literature and patient care settings. This statement (1) reviews the evolution of cardiac POCUS-related terms; (2) outlines specific recommendations, distinguishing between intrinsic and practical differences in terminology; (3) addresses the implications of these recommendations for current practice; and (4) discusses the implications for novel technologies and future research.
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Affiliation(s)
- James N Kirkpatrick
- Division of Cardiology and Department of Bioethics and Humanities, Department of Medicine, University of Washington, Seattle, Washington
| | - Nova Panebianco
- Department of Emergency Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - José L Díaz-Gómez
- Critical Care Institute, Integrated Hospital Care Institute, Cleveland Clinic Abu Dhabi, Abu Dhabi, United Arab Emirates
| | - Srikar Adhikari
- Department of Emergency Medicine, University of Arizona, Tucson, Arizona
| | | | - Yuriy S Bronshteyn
- Duke University School of Medicine, Durham Veterans Health Administrations, Durham, North Carolina
| | - Sara Damewood
- Department of Emergency Medicine, University of Wisconsin, Madison, Wisconsin
| | | | - Amer Johri
- Department of Medicine, Queen's University, Kingston, Ontario, Canada
| | | | - Bruce J Kimura
- Echocardiography and Vascular Ultrasound Lab, Scripps Mercy Hospital, San Diego, California
| | - Smadar Kort
- Division of Cardiology, Renaissance School of Medicine at Stony Brook University, Stony Brook, New York
| | - Arthur Labovitz
- Department of Cardiovascular Sciences, NCH Rooney Heart Institute, Naples, Florida
| | - Jimmy C Lu
- University of Michigan Congenital Heart Center, Ann Arbor, Michigan
| | - Irene W Y Ma
- Division of General Internal Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Paul H Mayo
- Division of Pulmonary, Critical Care, and Sleep Medicine LIJ/NSUH Medical Center, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York
| | - Sharon L Mulvagh
- Dalhousie University, Women's Heart Health Clinic, Halifax, Nova Scotia, Canada
| | - Sara Nikravan
- Department of Anesthesia and Pain Medicine, University of Washington, Seattle, Washington
| | - Sheela Pai Cole
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, California
| | - Michael H Picard
- Heart Center, Massachusetts General Hospital, and Harvard Medical School, Boston, Massachusetts
| | - Vincent L Sorrell
- University of Kentucky, Gill Heart and Vascular Institute, Lexington, Kentucky
| | | | - Ritu Thamman
- Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
| | | | - Gabriele Via
- Istituto Cardiocentro Ticino, Ente Ospedaliero Cantonale (EOC), Lugano, Switzerland
| | - Frances Mae West
- Division of Pulmonary & Critical Care Medicine, Thomas Jefferson University, Philadelphia, Pennsylvania
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18
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Oikonomou EK, Khera R. Artificial intelligence-enhanced patient evaluation: bridging art and science. Eur Heart J 2024:ehae415. [PMID: 38976371 DOI: 10.1093/eurheartj/ehae415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2024] [Revised: 04/23/2024] [Accepted: 06/18/2024] [Indexed: 07/10/2024] Open
Abstract
The advent of digital health and artificial intelligence (AI) has promised to revolutionize clinical care, but real-world patient evaluation has yet to witness transformative changes. As history taking and physical examination continue to rely on long-established practices, a growing pipeline of AI-enhanced digital tools may soon augment the traditional clinical encounter into a data-driven process. This article presents an evidence-backed vision of how promising AI applications may enhance traditional practices, streamlining tedious tasks while elevating diverse data sources, including AI-enabled stethoscopes, cameras, and wearable sensors, to platforms for personalized medicine and efficient care delivery. Through the lens of traditional patient evaluation, we illustrate how digital technologies may soon be interwoven into routine clinical workflows, introducing a novel paradigm of longitudinal monitoring. Finally, we provide a skeptic's view on the practical, ethical, and regulatory challenges that limit the uptake of such technologies.
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Affiliation(s)
- Evangelos K Oikonomou
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, 333 Cedar Street, PO Box 208017, New Haven, 06520-8017 CT, USA
| | - Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, 333 Cedar Street, PO Box 208017, New Haven, 06520-8017 CT, USA
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, 195 Church St, 6th Floor, New Haven, CT 06510, USA
- Section of Biomedical Informatics and Data Science, Yale School of Medicine, 100 College Street, New Haven, 06511 CT, USA
- Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, 06510 CT, USA
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19
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Kagiyama N, Abe Y, Kusunose K, Kato N, Kaneko T, Murata A, Ota M, Shibayama K, Izumo M, Watanabe H. Multicenter validation study for automated left ventricular ejection fraction assessment using a handheld ultrasound with artificial intelligence. Sci Rep 2024; 14:15359. [PMID: 38965290 PMCID: PMC11224326 DOI: 10.1038/s41598-024-65557-5] [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: 09/29/2023] [Accepted: 06/20/2024] [Indexed: 07/06/2024] Open
Abstract
We sought to validate the ability of a novel handheld ultrasound device with an artificial intelligence program (AI-POCUS) that automatically assesses left ventricular ejection fraction (LVEF). AI-POCUS was used to prospectively scan 200 patients in two Japanese hospitals. Automatic LVEF by AI-POCUS was compared to the standard biplane disk method using high-end ultrasound machines. After excluding 18 patients due to infeasible images for AI-POCUS, 182 patients (63 ± 15 years old, 21% female) were analyzed. The intraclass correlation coefficient (ICC) between the LVEF by AI-POCUS and the standard methods was good (0.81, p < 0.001) without clinically meaningful systematic bias (mean bias -1.5%, p = 0.008, limits of agreement ± 15.0%). Reduced LVEF < 50% was detected with a sensitivity of 85% (95% confidence interval 76%-91%) and specificity of 81% (71%-89%). Although the correlations between LV volumes by standard-echo and those by AI-POCUS were good (ICC > 0.80), AI-POCUS tended to underestimate LV volumes for larger LV (overall bias 42.1 mL for end-diastolic volume). These trends were mitigated with a newer version of the software tuned using increased data involving larger LVs, showing similar correlations (ICC > 0.85). In this real-world multicenter study, AI-POCUS showed accurate LVEF assessment, but careful attention might be necessary for volume assessment. The newer version, trained with larger and more heterogeneous data, demonstrated improved performance, underscoring the importance of big data accumulation in the field.
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Affiliation(s)
- Nobuyuki Kagiyama
- Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Tokyo, 113-0021, Japan.
- Department of Digital Health and Telemedicine R&D, Juntendo University, Tokyo, Japan.
| | - Yukio Abe
- Department of Cardiology, Osaka City General Hospital, Osaka, Japan
| | - Kenya Kusunose
- Department of Cardiovascular Medicine, Nephrology, and Neurology, University of the Ryukyus, Okinawa, Japan
| | - Nahoko Kato
- Department of Cardiology, Tokyo Bay Urayasu Ichikawa Medical Center, Urayasu, Japan
| | - Tomohiro Kaneko
- Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Tokyo, 113-0021, Japan
| | - Azusa Murata
- Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Tokyo, 113-0021, Japan
| | - Mitsuhiko Ota
- Department of Cardiovascular Center, Toranomon Hospital, Tokyo, Japan
| | - Kentaro Shibayama
- Department of Cardiovascular Medicine, Tokyo Cardiovascular and Internal Medicine Clinic, Tokyo, Japan
| | - Masaki Izumo
- Division of Cardiology, Department of Internal Medicine, St. Marianna University School of Medicine, Kawasaki, Japan
| | - Hiroyuki Watanabe
- Department of Cardiology, Tokyo Bay Urayasu Ichikawa Medical Center, Urayasu, Japan
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20
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Khera R, Oikonomou EK, Nadkarni GN, Morley JR, Wiens J, Butte AJ, Topol EJ. Transforming Cardiovascular Care With Artificial Intelligence: From Discovery to Practice: JACC State-of-the-Art Review. J Am Coll Cardiol 2024; 84:97-114. [PMID: 38925729 DOI: 10.1016/j.jacc.2024.05.003] [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] [Received: 04/29/2024] [Revised: 05/03/2024] [Accepted: 05/07/2024] [Indexed: 06/28/2024]
Abstract
Artificial intelligence (AI) has the potential to transform every facet of cardiovascular practice and research. The exponential rise in technology powered by AI is defining new frontiers in cardiovascular care, with innovations that span novel diagnostic modalities, new digital native biomarkers of disease, and high-performing tools evaluating care quality and prognosticating clinical outcomes. These digital innovations promise expanded access to cardiovascular screening and monitoring, especially among those without access to high-quality, specialized care historically. Moreover, AI is propelling biological and clinical discoveries that will make future cardiovascular care more personalized, precise, and effective. The review brings together these diverse AI innovations, highlighting developments in multimodal cardiovascular AI across clinical practice and biomedical discovery, and envisioning this new future backed by contemporary science and emerging discoveries. Finally, we define the critical path and the safeguards essential to realizing this AI-enabled future that helps achieve optimal cardiovascular health and outcomes for all.
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Affiliation(s)
- Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA; Center for Outcomes Research and Evaluation (CORE), New Haven, Connecticut, USA; Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, Connecticut, USA; Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA.
| | - Evangelos K Oikonomou
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Girish N Nadkarni
- The Samuel Bronfman Department of Medicine, Division of Data Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Jessica R Morley
- Digital Ethics Center, Yale University, New Haven, Connecticut, USA
| | - Jenna Wiens
- Electrical Engineering and Computer Science, Computer Science and Engineering, University of Michigan, Ann Arbor, Michigan, USA
| | - Atul J Butte
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, California, USA; Center for Data-Driven Insights and Innovation, University of California Health, Oakland, California, USA
| | - Eric J Topol
- Molecular Medicine, Scripps Research Translational Institute, Scripps Research, La Jolla, California, USA
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21
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Oikonomou EK, Vaid A, Holste G, Coppi A, McNamara RL, Baloescu C, Krumholz HM, Wang Z, Apakama DJ, Nadkarni GN, Khera R. Artificial intelligence-guided detection of under-recognized cardiomyopathies on point-of-care cardiac ultrasound: a multi-center study. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.10.24304044. [PMID: 38559021 PMCID: PMC10980112 DOI: 10.1101/2024.03.10.24304044] [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] [Indexed: 04/04/2024]
Abstract
Background Point-of-care ultrasonography (POCUS) enables cardiac imaging at the bedside and in communities but is limited by abbreviated protocols and variation in quality. We developed and tested artificial intelligence (AI) models to automate the detection of underdiagnosed cardiomyopathies from cardiac POCUS. Methods In a development set of 290,245 transthoracic echocardiographic videos across the Yale-New Haven Health System (YNHHS), we used augmentation approaches and a customized loss function weighted for view quality to derive a POCUS-adapted, multi-label, video-based convolutional neural network (CNN) that discriminates HCM (hypertrophic cardiomyopathy) and ATTR-CM (transthyretin amyloid cardiomyopathy) from controls without known disease. We evaluated the final model across independent, internal and external, retrospective cohorts of individuals who underwent cardiac POCUS across YNHHS and Mount Sinai Health System (MSHS) emergency departments (EDs) (2011-2024) to prioritize key views and validate the diagnostic and prognostic performance of single-view screening protocols. Findings We identified 33,127 patients (median age 61 [IQR: 45-75] years, n=17,276 [52·2%] female) at YNHHS and 5,624 (57 [IQR: 39-71] years, n=1,953 [34·7%] female) at MSHS with 78,054 and 13,796 eligible cardiac POCUS videos, respectively. An AI-enabled single-view screening approach successfully discriminated HCM (AUROC of 0·90 [YNHHS] & 0·89 [MSHS]) and ATTR-CM (YNHHS: AUROC of 0·92 [YNHHS] & 0·99 [MSHS]). In YNHHS, 40 (58·0%) HCM and 23 (47·9%) ATTR-CM cases had a positive screen at median of 2·1 [IQR: 0·9-4·5] and 1·9 [IQR: 1·0-3·4] years before clinical diagnosis. Moreover, among 24,448 participants without known cardiomyopathy followed over 2·2 [IQR: 1·1-5·8] years, AI-POCUS probabilities in the highest (vs lowest) quintile for HCM and ATTR-CM conferred a 15% (adj.HR 1·15 [95%CI: 1·02-1·29]) and 39% (adj.HR 1·39 [95%CI: 1·22-1·59]) higher age- and sex-adjusted mortality risk, respectively. Interpretation We developed and validated an AI framework that enables scalable, opportunistic screening of treatable cardiomyopathies wherever POCUS is used. Funding National Heart, Lung and Blood Institute, Doris Duke Charitable Foundation, BridgeBio.
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Affiliation(s)
- Evangelos K. Oikonomou
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Akhil Vaid
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Division of Data Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Gregory Holste
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA
| | - Andreas Coppi
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT, USA
| | - Robert L. McNamara
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Cristiana Baloescu
- Department of Emergency Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Harlan M. Krumholz
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT, USA
| | - Zhangyang Wang
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA
| | - Donald J. Apakama
- Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Girish N. Nadkarni
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Division of Data Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT, USA
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
- Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT, USA
- Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
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22
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Vrudhula A, Vukadinovic M, Haeffle C, Kwan AC, Berman D, Liang D, Siegel R, Cheng S, Ouyang D. Deep Learning Phenotyping of Tricuspid Regurgitation for Automated High Throughput Assessment of Transthoracic Echocardiography. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.06.22.24309332. [PMID: 38978651 PMCID: PMC11230333 DOI: 10.1101/2024.06.22.24309332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Background and Aims Diagnosis of tricuspid regurgitation (TR) requires careful expert evaluation. This study developed an automated deep learning pipeline for assessing TR from transthoracic echocardiography. Methods An automated deep learning workflow was developed using 47,312 studies (2,079,898 videos) from Cedars-Sinai Medical Center (CSMC) between 2011 and 2021. The pipeline was tested on a temporally distinct test set of 2,462 studies (108,138 videos) obtained in 2022 at CSMC and a geographically distinct cohort of 5,549 studies (278,377 videos) from Stanford Healthcare (SHC). Results In the CSMC test dataset, the view classifier demonstrated an AUC of 1.000 (0.999 - 1.000) and identified at least one A4C video with colour Doppler across the tricuspid valve in 2,410 of 2,462 studies with a sensitivity of 0.975 (0.968-0.982) and a specificity of 1.000 (1.00-1.000). In the CSMC test cohort, moderate-or-severe TR was detected with an AUC of 0.928 (0.913 - 0.943) and severe TR was detected with an AUC of 0.956 (0.940 - 0.969). In the SHC cohort, the view classifier correctly identified at least one TR colour Doppler video in 5,268 of the 5,549 studies, resulting in an AUC of 0.999 (0.998 - 0.999), a sensitivity of 0.949 (0.944 - 0.955) and specificity of 0.999 (0.999 - 0.999). The AI model detected moderate-or-severe TR with an AUC of 0.951 (0.938 - 0.962) and severe TR with an AUC of 0.980 (0.966 - 0.988). Conclusions We developed an automated pipeline to identify clinically significant TR with excellent performance. This approach carries potential for automated TR detection and stratification for surveillance and screening. Structured Graphical Abstract Key Question Can an automated deep learning model assess tricuspid regurgitation severity from echocardiography? Key Finding We developed and validated an automated tricuspid regurgitation detection algorithm pipeline across two healthcare systems with high volume echocardiography labs. The algorithm correctly identifies apical-4-chamber view videos with colour Doppler across the tricuspid valve and grades clinically significant TR with strong agreement to expert clinical readers. Take Home message A deep learning pipeline could automate TR screening, facilitating reproducible accurate assessment of TR severity, allowing rapid triage or re-review and expand access in low-resource or primary care settings.
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23
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Elias P, Jain SS, Poterucha T, Randazzo M, Lopez Jimenez F, Khera R, Perez M, Ouyang D, Pirruccello J, Salerno M, Einstein AJ, Avram R, Tison GH, Nadkarni G, Natarajan V, Pierson E, Beecy A, Kumaraiah D, Haggerty C, Avari Silva JN, Maddox TM. Artificial Intelligence for Cardiovascular Care-Part 1: Advances: JACC Review Topic of the Week. J Am Coll Cardiol 2024; 83:2472-2486. [PMID: 38593946 DOI: 10.1016/j.jacc.2024.03.400] [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: 03/01/2024] [Accepted: 03/14/2024] [Indexed: 04/11/2024]
Abstract
Recent artificial intelligence (AI) advancements in cardiovascular care offer potential enhancements in diagnosis, treatment, and outcomes. Innovations to date focus on automating measurements, enhancing image quality, and detecting diseases using novel methods. Applications span wearables, electrocardiograms, echocardiography, angiography, genetics, and more. AI models detect diseases from electrocardiograms at accuracy not previously achieved by technology or human experts, including reduced ejection fraction, valvular heart disease, and other cardiomyopathies. However, AI's unique characteristics necessitate rigorous validation by addressing training methods, real-world efficacy, equity concerns, and long-term reliability. Despite an exponentially growing number of studies in cardiovascular AI, trials showing improvement in outcomes remain lacking. A number are currently underway. Embracing this rapidly evolving technology while setting a high evaluation benchmark will be crucial for cardiology to leverage AI to enhance patient care and the provider experience.
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Affiliation(s)
- Pierre Elias
- Seymour, Paul and Gloria Milstein Division of Cardiology, Columbia University Irving Medical Center, New York, New York, USA; Department of Biomedical Informatics Columbia University Irving Medical Center, New York, New York, USA
| | - Sneha S Jain
- Division of Cardiology, Stanford University School of Medicine, Palo Alto, California, USA
| | - Timothy Poterucha
- Seymour, Paul and Gloria Milstein Division of Cardiology, Columbia University Irving Medical Center, New York, New York, USA
| | - Michael Randazzo
- Division of Cardiology, University of Chicago Medical Center, Chicago, Illinois, USA
| | | | - Rohan Khera
- Division of Cardiology, Yale School of Medicine, New Haven, Connecticut, USA
| | - Marco Perez
- Division of Cardiology, Stanford University School of Medicine, Palo Alto, California, USA
| | - David Ouyang
- Division of Cardiology, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - James Pirruccello
- Division of Cardiology, University of California-San Francisco, San Francisco, California, USA
| | - Michael Salerno
- Division of Cardiology, Stanford University School of Medicine, Palo Alto, California, USA
| | - Andrew J Einstein
- Seymour, Paul and Gloria Milstein Division of Cardiology, Columbia University Irving Medical Center, New York, New York, USA
| | - Robert Avram
- Division of Cardiology, Montreal Heart Institute, Montreal, Quebec, Canada
| | - Geoffrey H Tison
- Division of Cardiology, University of California-San Francisco, San Francisco, California, USA
| | - Girish Nadkarni
- Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | | | - Emma Pierson
- Department of Computer Science, Cornell Tech, New York, New York, USA
| | - Ashley Beecy
- NewYork-Presbyterian Health System, New York, New York, USA; Division of Cardiology, Weill Cornell Medical College, New York, New York, USA
| | - Deepa Kumaraiah
- Seymour, Paul and Gloria Milstein Division of Cardiology, Columbia University Irving Medical Center, New York, New York, USA; NewYork-Presbyterian Health System, New York, New York, USA
| | - Chris Haggerty
- Department of Biomedical Informatics Columbia University Irving Medical Center, New York, New York, USA; NewYork-Presbyterian Health System, New York, New York, USA
| | - Jennifer N Avari Silva
- Division of Cardiology, Washington University School of Medicine, St Louis, Missouri, USA
| | - Thomas M Maddox
- Division of Cardiology, Washington University School of Medicine, St Louis, Missouri, USA.
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24
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Oikonomou EK, Holste G, Yuan N, Coppi A, McNamara RL, Haynes NA, Vora AN, Velazquez EJ, Li F, Menon V, Kapadia SR, Gill TM, Nadkarni GN, Krumholz HM, Wang Z, Ouyang D, Khera R. A Multimodal Video-Based AI Biomarker for Aortic Stenosis Development and Progression. JAMA Cardiol 2024; 9:534-544. [PMID: 38581644 PMCID: PMC10999005 DOI: 10.1001/jamacardio.2024.0595] [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] [Received: 11/28/2023] [Accepted: 02/27/2024] [Indexed: 04/08/2024]
Abstract
Importance Aortic stenosis (AS) is a major public health challenge with a growing therapeutic landscape, but current biomarkers do not inform personalized screening and follow-up. A video-based artificial intelligence (AI) biomarker (Digital AS Severity index [DASSi]) can detect severe AS using single-view long-axis echocardiography without Doppler characterization. Objective To deploy DASSi to patients with no AS or with mild or moderate AS at baseline to identify AS development and progression. Design, Setting, and Participants This is a cohort study that examined 2 cohorts of patients without severe AS undergoing echocardiography in the Yale New Haven Health System (YNHHS; 2015-2021) and Cedars-Sinai Medical Center (CSMC; 2018-2019). A novel computational pipeline for the cross-modal translation of DASSi into cardiac magnetic resonance (CMR) imaging was further developed in the UK Biobank. Analyses were performed between August 2023 and February 2024. Exposure DASSi (range, 0-1) derived from AI applied to echocardiography and CMR videos. Main Outcomes and Measures Annualized change in peak aortic valve velocity (AV-Vmax) and late (>6 months) aortic valve replacement (AVR). Results A total of 12 599 participants were included in the echocardiographic study (YNHHS: n = 8798; median [IQR] age, 71 [60-80] years; 4250 [48.3%] women; median [IQR] follow-up, 4.1 [2.4-5.4] years; and CSMC: n = 3801; median [IQR] age, 67 [54-78] years; 1685 [44.3%] women; median [IQR] follow-up, 3.4 [2.8-3.9] years). Higher baseline DASSi was associated with faster progression in AV-Vmax (per 0.1 DASSi increment: YNHHS, 0.033 m/s per year [95% CI, 0.028-0.038] among 5483 participants; CSMC, 0.082 m/s per year [95% CI, 0.053-0.111] among 1292 participants), with values of 0.2 or greater associated with a 4- to 5-fold higher AVR risk than values less than 0.2 (YNHHS: 715 events; adjusted hazard ratio [HR], 4.97 [95% CI, 2.71-5.82]; CSMC: 56 events; adjusted HR, 4.04 [95% CI, 0.92-17.70]), independent of age, sex, race, ethnicity, ejection fraction, and AV-Vmax. This was reproduced across 45 474 participants (median [IQR] age, 65 [59-71] years; 23 559 [51.8%] women; median [IQR] follow-up, 2.5 [1.6-3.9] years) undergoing CMR imaging in the UK Biobank (for participants with DASSi ≥0.2 vs those with DASSi <.02, adjusted HR, 11.38 [95% CI, 2.56-50.57]). Saliency maps and phenome-wide association studies supported associations with cardiac structure and function and traditional cardiovascular risk factors. Conclusions and Relevance In this cohort study of patients without severe AS undergoing echocardiography or CMR imaging, a new AI-based video biomarker was independently associated with AS development and progression, enabling opportunistic risk stratification across cardiovascular imaging modalities as well as potential application on handheld devices.
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Affiliation(s)
- Evangelos K. Oikonomou
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
| | - Gregory Holste
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin
| | - Neal Yuan
- Department of Medicine, University of California, San Francisco
- Division of Cardiology, San Francisco Veterans Affairs Medical Center, San Francisco, California
| | - Andreas Coppi
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, Connecticut
| | - Robert L. McNamara
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
| | - Norrisa A. Haynes
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
| | - Amit N. Vora
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
| | - Eric J. Velazquez
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
| | - Fan Li
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut
- Center for Methods in Implementation and Prevention Science, Yale School of Public Health, New Haven, Connecticut
| | - Venu Menon
- Heart and Vascular Institute, Department of Cardiovascular Medicine, Cleveland Clinic Foundation, Cleveland, Ohio
| | - Samir R. Kapadia
- Heart and Vascular Institute, Department of Cardiovascular Medicine, Cleveland Clinic Foundation, Cleveland, Ohio
| | - Thomas M. Gill
- Section of Geriatrics, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
| | - Girish N. Nadkarni
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Harlan M. Krumholz
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, Connecticut
| | - Zhangyang Wang
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin
| | - David Ouyang
- Smidt Heart Institute, Department of Cardiology, Cedars-Sinai Medical Center, Los Angeles, California
- Division of Artificial Intelligence in Medicine, Cedars-Sinai Medical Center, Los Angeles, California
| | - Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, Connecticut
- Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, Connecticut
- Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut
- Associate Editor, JAMA
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25
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Qin Y, Qin X, Zhang J, Guo X. Artificial intelligence: The future for multimodality imaging of right ventricle. Int J Cardiol 2024; 404:131970. [PMID: 38490268 DOI: 10.1016/j.ijcard.2024.131970] [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] [Received: 11/26/2023] [Revised: 03/05/2024] [Accepted: 03/12/2024] [Indexed: 03/17/2024]
Abstract
The crucial pathophysiological and prognostic roles of the right ventricle in various diseases have been well-established. Nonetheless, conventional cardiovascular imaging modalities are frequently associated with intrinsic limitations when evaluating right ventricular (RV) morphology and function. The integration of artificial intelligence (AI) in multimodality imaging presents a promising avenue to circumvent these obstacles, paving the way for future fully automated imaging paradigms. This review aimed to address the current challenges faced by clinicians and researchers in integrating RV imaging and AI technology, to provide a comprehensive overview of the current applications of AI in RV imaging, and to offer insights into future directions, opportunities, and potential challenges in this rapidly advancing field.
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Affiliation(s)
- Yuhan Qin
- Department of Cardiology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Xiaohan Qin
- Department of Cardiology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Jing Zhang
- Department of Cardiology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Xiaoxiao Guo
- Department of Cardiology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.
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26
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Liu Y, Huang Q, Han X, Liang T, Zhang Z, Lu X, Dong B, Yuan J, Wang Y, Hu M, Wang J, Stefanidis A, Su J, Chen J, Li Q, Zhang Y. Atrial Septal Defect Detection in Children Based on Ultrasound Video Using Multiple Instances Learning. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:965-975. [PMID: 38347394 PMCID: PMC11169128 DOI: 10.1007/s10278-024-00987-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 12/09/2023] [Accepted: 12/12/2023] [Indexed: 06/13/2024]
Abstract
Thoracic echocardiography (TTE) can provide sufficient cardiac structure information, evaluate hemodynamics and cardiac function, and is an effective method for atrial septal defect (ASD) examination. This paper aims to study a deep learning method based on cardiac ultrasound video to assist in ASD diagnosis. We chose four standard views in pediatric cardiac ultrasound to identify atrial septal defects; the four standard views were as follows: subcostal sagittal view of the atrium septum (subSAS), apical four-chamber view (A4C), the low parasternal four-chamber view (LPS4C), and parasternal short-axis view of large artery (PSAX). We enlist data from 300 children patients as part of a double-blind experiment for five-fold cross-validation to verify the performance of our model. In addition, data from 30 children patients (15 positives and 15 negatives) are collected for clinician testing and compared to our model test results (these 30 samples do not participate in model training). In our model, we present a block random selection, maximal agreement decision, and frame sampling strategy for training and testing respectively, resNet18 and r3D networks are used to extract the frame features and aggregate them to build a rich video-level representation. We validate our model using our private dataset by five cross-validation. For ASD detection, we achieve 89.33 ± 3.13 AUC, 84.95 ± 3.88 accuracy, 85.70 ± 4.91 sensitivity, 81.51 ± 8.15 specificity, and 81.99 ± 5.30 F1 score. The proposed model is a multiple instances learning-based deep learning model for video atrial septal defect detection which effectively improves ASD detection accuracy when compared to the performances of previous networks and clinical doctors.
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Affiliation(s)
- Yiman Liu
- Department of Pediatric Cardiology, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
- Shanghai Engineering Research Center of Intelligence Pediatrics (SERCIP), Shanghai, 200127, People's Republic of China
- Shanghai Key Laboratory of Multidimensional Information Processing, school of communication and electronic engineering, East China Normal University, Shanghai, 200241, People's Republic of China
| | - Qiming Huang
- School of AI and Advanced Computing, Xi'an Jiao tong-Liverpool University, Taicang, 215028, People's Republic of China
| | - Xiaoxiang Han
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, People's Republic of China
| | - Tongtong Liang
- Shanghai Minhang Center for Disease Control and Prevention, Shanghai, 201101, People's Republic of China
| | - Zhifang Zhang
- Department of Pediatric Cardiology, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Xiuli Lu
- Department of Ultrasound, Jiaxing Xiuzhou District Maternal, Child Health Hospital, Jiaxing, Zhejiang, 314031, People's Republic of China
| | - Bin Dong
- Shanghai Engineering Research Center of Intelligence Pediatrics (SERCIP), Shanghai, 200127, People's Republic of China
| | - Jiajun Yuan
- Shanghai Engineering Research Center of Intelligence Pediatrics (SERCIP), Shanghai, 200127, People's Republic of China
| | - Yan Wang
- Shanghai Key Laboratory of Multidimensional Information Processing, school of communication and electronic engineering, East China Normal University, Shanghai, 200241, People's Republic of China
| | - Menghan Hu
- Shanghai Key Laboratory of Multidimensional Information Processing, school of communication and electronic engineering, East China Normal University, Shanghai, 200241, People's Republic of China
| | - Jinfeng Wang
- School of AI and Advanced Computing, Xi'an Jiao tong-Liverpool University, Taicang, 215028, People's Republic of China
| | - Angelos Stefanidis
- School of AI and Advanced Computing, Xi'an Jiao tong-Liverpool University, Taicang, 215028, People's Republic of China
| | - Jionglong Su
- School of AI and Advanced Computing, Xi'an Jiao tong-Liverpool University, Taicang, 215028, People's Republic of China.
| | - Jiangang Chen
- Shanghai Key Laboratory of Multidimensional Information Processing, school of communication and electronic engineering, East China Normal University, Shanghai, 200241, People's Republic of China.
| | - Qingli Li
- Shanghai Key Laboratory of Multidimensional Information Processing, school of communication and electronic engineering, East China Normal University, Shanghai, 200241, People's Republic of China.
| | - Yuqi Zhang
- Department of Pediatric Cardiology, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China.
- Shanghai Engineering Research Center of Intelligence Pediatrics (SERCIP), Shanghai, 200127, People's Republic of China.
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27
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Papadopoulou SL, Dionysopoulos D, Mentesidou V, Loga K, Michalopoulou S, Koukoutzeli C, Efthimiadis K, Kantartzi V, Timotheadou E, Styliadis I, Nihoyannopoulos P, Sachpekidis V. Artificial intelligence-assisted evaluation of cardiac function by oncology staff in chemotherapy patients. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2024; 5:278-287. [PMID: 38774364 PMCID: PMC11104473 DOI: 10.1093/ehjdh/ztae017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 02/03/2024] [Accepted: 02/21/2024] [Indexed: 05/24/2024]
Abstract
Aims Left ventricular ejection fraction (LVEF) calculation by echocardiography is pivotal in evaluating cancer patients' cardiac function. Artificial intelligence (AI) can facilitate the acquisition of optimal images and automated LVEF (autoEF) calculation. We sought to evaluate the feasibility and accuracy of LVEF calculation by oncology staff using an AI-enabled handheld ultrasound device (HUD). Methods and results We studied 115 patients referred for echocardiographic LVEF estimation. All patients were scanned by a cardiologist using standard echocardiography (SE), and biplane Simpson's LVEF was the reference standard. Hands-on training using the Kosmos HUD was provided to the oncology staff before the study. Each patient was scanned by a cardiologist, a senior oncologist, an oncology resident, and a nurse using the TRIO AI and KOSMOS EF deep learning algorithms to obtain autoEF. The correlation between autoEF and SE-ejection fraction (EF) was excellent for the cardiologist (r = 0.90), the junior oncologist (r = 0.82), and the nurse (r = 0.84), and good for the senior oncologist (r = 0.79). The Bland-Altman analysis showed a small underestimation by autoEF compared with SE-EF. Detection of impaired LVEF < 50% was feasible with a sensitivity of 95% and specificity of 94% for the cardiologist; sensitivity of 86% and specificity of 93% for the senior oncologist; sensitivity of 95% and specificity of 91% for the junior oncologist; and sensitivity of 94% and specificity of 87% for the nurse. Conclusion Automated LVEF calculation by oncology staff was feasible using AI-enabled HUD in a selected patient population. Detection of LVEF < 50% was possible with good accuracy. These findings show the potential to expedite the clinical workflow of cancer patients and speed up a referral when necessary.
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Affiliation(s)
- Stella-Lida Papadopoulou
- Department of Cardiology, Papageorgiou General Hospital, Ring Road, Nea Efkarpia, Thessaloniki 56403, Greece
| | - Dimitrios Dionysopoulos
- Department of Medical Oncology, Papageorgiou Hospital, Aristotle University of Thessaloniki, School of Health Sciences, Faculty of Medicine, Ring Road, Nea Efkarpia, Thessaloniki 56403, Greece
| | - Vaia Mentesidou
- Department of Medical Oncology, Papageorgiou Hospital, Aristotle University of Thessaloniki, School of Health Sciences, Faculty of Medicine, Ring Road, Nea Efkarpia, Thessaloniki 56403, Greece
| | - Konstantia Loga
- Department of Medical Oncology, Papageorgiou Hospital, Aristotle University of Thessaloniki, School of Health Sciences, Faculty of Medicine, Ring Road, Nea Efkarpia, Thessaloniki 56403, Greece
| | - Stella Michalopoulou
- Department of Medical Oncology, Papageorgiou Hospital, Aristotle University of Thessaloniki, School of Health Sciences, Faculty of Medicine, Ring Road, Nea Efkarpia, Thessaloniki 56403, Greece
| | - Chrysanthi Koukoutzeli
- Department of Medical Oncology, Papageorgiou Hospital, Aristotle University of Thessaloniki, School of Health Sciences, Faculty of Medicine, Ring Road, Nea Efkarpia, Thessaloniki 56403, Greece
| | - Konstantinos Efthimiadis
- Department of Medical Oncology, Papageorgiou Hospital, Aristotle University of Thessaloniki, School of Health Sciences, Faculty of Medicine, Ring Road, Nea Efkarpia, Thessaloniki 56403, Greece
| | - Vasiliki Kantartzi
- Department of Cardiology, Papageorgiou General Hospital, Ring Road, Nea Efkarpia, Thessaloniki 56403, Greece
| | - Eleni Timotheadou
- Department of Medical Oncology, Papageorgiou Hospital, Aristotle University of Thessaloniki, School of Health Sciences, Faculty of Medicine, Ring Road, Nea Efkarpia, Thessaloniki 56403, Greece
| | - Ioannis Styliadis
- Department of Cardiology, Papageorgiou General Hospital, Ring Road, Nea Efkarpia, Thessaloniki 56403, Greece
| | | | - Vasileios Sachpekidis
- Department of Cardiology, Papageorgiou General Hospital, Ring Road, Nea Efkarpia, Thessaloniki 56403, Greece
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Christensen M, Vukadinovic M, Yuan N, Ouyang D. Vision-language foundation model for echocardiogram interpretation. Nat Med 2024; 30:1481-1488. [PMID: 38689062 PMCID: PMC11108770 DOI: 10.1038/s41591-024-02959-y] [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: 08/17/2023] [Accepted: 03/28/2024] [Indexed: 05/02/2024]
Abstract
The development of robust artificial intelligence models for echocardiography has been limited by the availability of annotated clinical data. Here, to address this challenge and improve the performance of cardiac imaging models, we developed EchoCLIP, a vision-language foundation model for echocardiography, that learns the relationship between cardiac ultrasound images and the interpretations of expert cardiologists across a wide range of patients and indications for imaging. After training on 1,032,975 cardiac ultrasound videos and corresponding expert text, EchoCLIP performs well on a diverse range of benchmarks for cardiac image interpretation, despite not having been explicitly trained for individual interpretation tasks. EchoCLIP can assess cardiac function (mean absolute error of 7.1% when predicting left ventricular ejection fraction in an external validation dataset) and identify implanted intracardiac devices (area under the curve (AUC) of 0.84, 0.92 and 0.97 for pacemakers, percutaneous mitral valve repair and artificial aortic valves, respectively). We also developed a long-context variant (EchoCLIP-R) using a custom tokenizer based on common echocardiography concepts. EchoCLIP-R accurately identified unique patients across multiple videos (AUC of 0.86), identified clinical transitions such as heart transplants (AUC of 0.79) and cardiac surgery (AUC 0.77) and enabled robust image-to-text search (mean cross-modal retrieval rank in the top 1% of candidate text reports). These capabilities represent a substantial step toward understanding and applying foundation models in cardiovascular imaging for preliminary interpretation of echocardiographic findings.
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Affiliation(s)
- Matthew Christensen
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Milos Vukadinovic
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, USA
| | - Neal Yuan
- Department of Medicine, University of California San Francisco, San Francisco, CA, USA
- Division of Cardiology, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA
| | - David Ouyang
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
- Division of Artificial Intelligence in Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
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29
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Firnberg M, Addo N, Lin-Martore M, Shaahinfar A, Kornblith A. Evaluation of Focused Assessment With Sonography for Trauma Completeness of Children in the Clinical Setting. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2024; 43:873-879. [PMID: 38282464 PMCID: PMC11335411 DOI: 10.1002/jum.16417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 01/04/2024] [Accepted: 01/07/2024] [Indexed: 01/30/2024]
Abstract
OBJECTIVES We evaluated the completeness of real-world Focused Assessment with Sonography for Trauma (FAST) in children after blunt abdominal trauma by benchmarking against established expert guidelines. METHODS We conducted a retrospective cohort study, analyzing a random sample of FASTs from two urban pediatric emergency departments. Two experts reviewed and labeled all FASTs for completeness using a predefined guideline of 5 anatomic views and 30 landmarks. We compared frequencies of views and landmarks as medians with interquartile ranges. RESULTS We analyzed 200 FASTs, consisting of 1636 video clips, performed by 31 clinicians representing 198 children with a median age of 10 years (IQR 5,14). Over half of FASTs (52%) had all 5 views. The right upper quadrant view was most commonly visualized (96.5%), and suprapubic sagittal was least (65%). None of the FASTs included all 30 landmarks, ranging from 0 to 28 and median of 19 (IQR 15,23). The least visualized landmark of the right and left upper quadrants was caudal liver edge (60%) and splenic tip (64%), respectively. In the pericardial view, it was left atrium (45%). In both transverse and sagittal pelvic views, retro-uterine space was least visualized in girls, 21 and 29% respectively. CONCLUSIONS In our study, most FAST views and landmarks were visualized. However, the pelvic sagittal view was the least frequently visualized view, and caudal liver edge was the least visualized landmark. Future research should evaluate if variability in visualizing FAST views and landmarks correlates with inconsistencies in diagnostic test performance.
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Affiliation(s)
- Maytal Firnberg
- Department of Emergency Medicine, University of California San Francisco, San Francisco, California, USA
- Department of Pediatrics, University of California San Francisco, San Francisco, California, USA
| | - Newton Addo
- Department of Emergency Medicine, University of California San Francisco, San Francisco, California, USA
| | - Margaret Lin-Martore
- Department of Emergency Medicine, University of California San Francisco, San Francisco, California, USA
- Department of Pediatrics, University of California San Francisco, San Francisco, California, USA
| | - Ashkon Shaahinfar
- Department of Emergency Medicine, University of California San Francisco, San Francisco, California, USA
- Department of Pediatrics, University of California San Francisco, San Francisco, California, USA
| | - Aaron Kornblith
- Department of Emergency Medicine, University of California San Francisco, San Francisco, California, USA
- Department of Pediatrics, University of California San Francisco, San Francisco, California, USA
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30
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G S, Gopalakrishnan U, Parthinarupothi RK, Madathil T. Deep learning supported echocardiogram analysis: A comprehensive review. Artif Intell Med 2024; 151:102866. [PMID: 38593684 DOI: 10.1016/j.artmed.2024.102866] [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: 06/17/2023] [Revised: 03/20/2024] [Accepted: 03/30/2024] [Indexed: 04/11/2024]
Abstract
An echocardiogram is a sophisticated ultrasound imaging technique employed to diagnose heart conditions. The transthoracic echocardiogram, one of the most prevalent types, is instrumental in evaluating significant cardiac diseases. However, interpreting its results heavily relies on the clinician's expertise. In this context, artificial intelligence has emerged as a vital tool for helping clinicians. This study critically analyzes key state-of-the-art research that uses deep learning techniques to automate transthoracic echocardiogram analysis and support clinical judgments. We have systematically organized and categorized articles that proffer solutions for view classification, enhancement of image quality and dataset, segmentation and identification of cardiac structures, detection of cardiac function abnormalities, and quantification of cardiac functions. We compared the performance of various deep learning approaches within each category, identifying the most promising methods. Additionally, we highlight limitations in current research and explore promising avenues for future exploration. These include addressing generalizability issues, incorporating novel AI approaches, and tackling the analysis of rare cardiac diseases.
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Affiliation(s)
- Sanjeevi G
- Center for Wireless Networks & Applications (WNA), Amrita Vishwa Vidyapeetham, Amritapuri, India
| | - Uma Gopalakrishnan
- Center for Wireless Networks & Applications (WNA), Amrita Vishwa Vidyapeetham, Amritapuri, India.
| | | | - Thushara Madathil
- Department of Cardiac Anesthesiology, Amrita Institute of Medical Sciences and Research Center, Kochi, India
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31
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Sengupta PP, Kluin J, Lee SP, Oh JK, Smits AIPM. The future of valvular heart disease assessment and therapy. Lancet 2024; 403:1590-1602. [PMID: 38554727 DOI: 10.1016/s0140-6736(23)02754-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Revised: 08/15/2023] [Accepted: 12/06/2023] [Indexed: 04/02/2024]
Abstract
Valvular heart disease (VHD) is becoming more prevalent in an ageing population, leading to challenges in diagnosis and management. This two-part Series offers a comprehensive review of changing concepts in VHD, covering diagnosis, intervention timing, novel management strategies, and the current state of research. The first paper highlights the remarkable progress made in imaging and transcatheter techniques, effectively addressing the treatment paradox wherein populations at the highest risk of VHD often receive the least treatment. These advances have attracted the attention of clinicians, researchers, engineers, device manufacturers, and investors, leading to the exploration and proposal of treatment approaches grounded in pathophysiology and multidisciplinary strategies for VHD management. This Series paper focuses on innovations involving computational, pharmacological, and bioengineering approaches that are transforming the diagnosis and management of patients with VHD. Artificial intelligence and digital methods are enhancing screening, diagnosis, and planning procedures, and the integration of imaging and clinical data is improving the classification of VHD severity. The emergence of artificial intelligence techniques, including so-called digital twins-eg, computer-generated replicas of the heart-is aiding the development of new strategies for enhanced risk stratification, prognostication, and individualised therapeutic targeting. Various new molecular targets and novel pharmacological strategies are being developed, including multiomics-ie, analytical methods used to integrate complex biological big data to find novel pathways to halt the progression of VHD. In addition, efforts have been undertaken to engineer heart valve tissue and provide a living valve conduit capable of growth and biological integration. Overall, these advances emphasise the importance of early detection, personalised management, and cutting-edge interventions to optimise outcomes amid the evolving landscape of VHD. Although several challenges must be overcome, these breakthroughs represent opportunities to advance patient-centred investigations.
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Affiliation(s)
- Partho P Sengupta
- Division of Cardiovascular Diseases and Hypertension, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA; Cardiovascular Services, Robert Wood Johnson University Hospital, New Brunswick, NJ, USA.
| | - Jolanda Kluin
- Department of Cardiothoracic Surgery, Erasmus MC Rotterdam, Thorax Center, Rotterdam, Netherlands
| | - Seung-Pyo Lee
- Department of Internal Medicine, Seoul National University Hospital and Seoul National University College of Medicine, Seoul, South Korea
| | - Jae K Oh
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Anthal I P M Smits
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands; Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, Netherlands
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32
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Huang KC, Lin DSH, Jeng GS, Lin TT, Lin LY, Lee CK, Lin LC. Left Ventricular Segmentation, Warping, and Myocardial Registration for Automated Strain Measurement. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01119-5. [PMID: 38639806 DOI: 10.1007/s10278-024-01119-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 04/07/2024] [Accepted: 04/09/2024] [Indexed: 04/20/2024]
Abstract
The left ventricular global longitudinal strain (LVGLS) is a crucial prognostic indicator. However, inconsistencies in measurements due to the speckle tracking algorithm and manual adjustments have hindered its standardization and democratization. To solve this issue, we proposed a fully automated strain measurement by artificial intelligence-assisted LV segmentation contours. The LV segmentation model was trained from echocardiograms of 368 adults (11,125 frames). We compared the registration-like effects of dynamic time warping (DTW) with speckle tracking on a synthetic echocardiographic dataset in experiment-1. In experiment-2, we enrolled 80 patients to compare the DTW method with commercially available software. In experiment-3, we combined the segmentation model and DTW method to create the artificial intelligence (AI)-DTW method, which was then tested on 40 patients with general LV morphology, 20 with dilated cardiomyopathy (DCMP), and 20 with transthyretin-associated cardiac amyloidosis (ATTR-CA), 20 with severe aortic stenosis (AS), and 20 with severe mitral regurgitation (MR). Experiments-1 and -2 revealed that the DTW method is consistent with dedicated software. In experiment-3, the AI-DTW strain method showed comparable results for general LV morphology (bias - 0.137 ± 0.398%), DCMP (- 0.397 ± 0.607%), ATTR-CA (0.095 ± 0.581%), AS (0.334 ± 0.358%), and MR (0.237 ± 0.490%). Moreover, the strain curves showed a high correlation in their characteristics, with R-squared values of 0.8879-0.9452 for those LV morphology in experiment-3. Measuring LVGLS through dynamic warping of segmentation contour is a feasible method compared to traditional tracking techniques. This approach has the potential to decrease the need for manual demarcation and make LVGLS measurements more efficient and user-friendly for daily practice.
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Affiliation(s)
- Kuan-Chih Huang
- Graduate Institute of Clinical Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
- National Taiwan University Hospital, Hsin-Chu branch, Hsinchu, Taiwan
| | - Donna Shu-Han Lin
- Division of Cardiology, Department of Internal Medicine, Shin Kong Wu Ho-Su Memorial Hospital, Taipei, Taiwan
| | - Geng-Shi Jeng
- Institute of Electronics, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Ting-Tse Lin
- Section of Cardiology, Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Lian-Yu Lin
- Section of Cardiology, Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Chih-Kuo Lee
- National Taiwan University Hospital, Hsin-Chu branch, Hsinchu, Taiwan
| | - Lung-Chun Lin
- Section of Cardiology, Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan.
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33
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Deo RC. Artificial Intelligence and Machine Learning in Cardiology. Circulation 2024; 149:1235-1237. [PMID: 38620085 DOI: 10.1161/circulationaha.123.065469] [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] [Indexed: 04/17/2024]
Affiliation(s)
- Rahul C Deo
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA. Harvard Medical School, Boston, MA
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34
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Kapoor MC. Emerging Role of Artificial Intelligence in Echocardiography. Ann Card Anaesth 2024; 27:99-100. [PMID: 38607872 PMCID: PMC11095777 DOI: 10.4103/aca.aca_12_24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Accepted: 01/12/2024] [Indexed: 04/14/2024] Open
Affiliation(s)
- Mukul Chandra Kapoor
- Department of Anesthesiology and Critical Care, Amrita School of Medicine and Amrita Institute of Medical Sciences, Faridabad, Haryana, India
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35
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Aronovitz N, Hazan I, Jedwab R, Ben Shitrit I, Quinn A, Wacht O, Fuchs L. The effect of real-time EF automatic tool on cardiac ultrasound performance among medical students. PLoS One 2024; 19:e0299461. [PMID: 38547257 PMCID: PMC10977790 DOI: 10.1371/journal.pone.0299461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Accepted: 02/09/2024] [Indexed: 04/02/2024] Open
Abstract
PURPOSE Point-of-care ultrasound (POCUS) is a sensitive, safe, and efficient tool used in many clinical settings and is an essential part of medical education in the United States. Numerous studies present improved diagnostic performances and positive clinical outcomes among POCUS users. However, others stress the degree to which the modality is user-dependent, rendering high-quality POCUS training necessary in medical education. In this study, the authors aimed to investigate the potential of an artificial intelligence (AI) based quality indicator tool as a teaching device for cardiac POCUS performance. METHODS The authors integrated the quality indicator tool into the pre-clinical cardiac ultrasound course for 4th-year medical students and analyzed their performances. The analysis included 60 students who were assigned to one of two groups as follows: the intervention group using the AI-based quality indicator tool and the control group. Quality indicator users utilized the tool during both the course and the final test. At the end of the course, the authors tested the standard echocardiographic views, and an experienced clinician blindly graded the recorded clips. Results were analyzed and compared between the groups. RESULTS The results showed an advantage in quality indictor users' median overall scores (P = 0.002) with a relative risk of 2.3 (95% CI: 1.10, 4.93, P = 0.03) for obtaining correct cardiac views. In addition, quality indicator users also had a statistically significant advantage in the overall image quality in various cardiac views. CONCLUSIONS The AI-based quality indicator improved cardiac ultrasound performances among medical students who were trained with it compared to the control group, even in cardiac views in which the indicator was inactive. Performance scores, as well as image quality, were better in the AI-based group. Such tools can potentially enhance ultrasound training, warranting the expansion of the application to more views and prompting further studies on long-term learning effects.
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Affiliation(s)
- Noam Aronovitz
- Joyce and Irving Goldman Medical School, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Itai Hazan
- Joyce and Irving Goldman Medical School, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
- Clinical Research Center, Soroka University Medical Center and Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Roni Jedwab
- Joyce and Irving Goldman Medical School, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
- Clinical Research Center, Soroka University Medical Center and Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Itamar Ben Shitrit
- Joyce and Irving Goldman Medical School, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
- Clinical Research Center, Soroka University Medical Center and Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
- Department of Epidemiology, Biostatistics and Community Health, Faculty of Health Sciences, Ben-Gurion University, Beer-Sheva, Israel
| | - Anna Quinn
- Medical School for International Health in Beer-Sheva, Beer-Sheva, Israel
| | - Oren Wacht
- Department of Emergency Medicine, Ben Gurion University of the Negev in Beer- Sheva, Israel
| | - Lior Fuchs
- Clinical Research Center, Soroka University Medical Center and Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
- Department of Epidemiology, Biostatistics and Community Health, Faculty of Health Sciences, Ben-Gurion University, Beer-Sheva, Israel
- Medical Intensive Care Unit, Soroka University Medical Center, Beer-Sheva, Israel
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36
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Shiokawa N, Izumo M, Shimamura T, Kurosaka Y, Sato Y, Okamura T, Akashi YJ. Accuracy and Efficacy of Artificial Intelligence-Derived Automatic Measurements of Transthoracic Echocardiography in Routine Clinical Practice. J Clin Med 2024; 13:1861. [PMID: 38610628 PMCID: PMC11012797 DOI: 10.3390/jcm13071861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Revised: 03/22/2024] [Accepted: 03/22/2024] [Indexed: 04/14/2024] Open
Abstract
Background: Transthoracic echocardiography (TTE) is the gold standard modality for evaluating cardiac morphology, function, and hemodynamics in clinical practice. While artificial intelligence (AI) is expected to contribute to improved accuracy and is being applied clinically, its impact on daily clinical practice has not been fully evaluated. Methods: We retrospectively examined 30 consecutive patients who underwent AI-equipped TTE at a single institution. All patients underwent manual and automatic measurements of TTE parameters using the AI-equipped TTE. Measurements were performed by three sonographers with varying experience levels: beginner, intermediate, and expert. Results: A comparison between the manual and automatic measurements assessed by the experts showed extremely high agreement in the left ventricular (LV) filling velocities (E wave: r = 0.998, A wave: r = 0.996; both p < 0.001). The automated measurements of LV end-diastolic and end-systolic diameters were slightly smaller (-2.41 mm and -1.19 mm) than the manual measurements, although without significant differences, and both methods showing high agreement (r = 0.942 and 0.977, both p < 0.001). However, LV wall thickness showed low agreement between the automated and manual measurements (septum: r = 0.670, posterior: r = 0.561; both p < 0.01), with automated measurements tending to be larger. Regarding interobserver variabilities, statistically significant agreement was observed among the measurements of expert, intermediate, and beginner sonographers for all the measurements. In terms of measurement time, automatic measurement significantly reduced measurement time compared to manual measurement (p < 0.001). Conclusions: This preliminary study confirms the accuracy and efficacy of AI-equipped TTE in routine clinical practice. A multicenter study with a larger sample size is warranted.
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Affiliation(s)
- Noriko Shiokawa
- Ultrasound Center, St. Marianna University Hospital, 2-16-1 Sugao, Miyamae-ku, Kawasaki 216-8511, Japan; (N.S.); (T.S.); (Y.K.); (T.O.)
| | - Masaki Izumo
- Department of Cardiology, St. Marianna University School of Medicine, 2-16-1 Sugao, Miyamae-ku, Kawasaki 216-8511, Japan; (Y.S.); (Y.J.A.)
| | - Toshio Shimamura
- Ultrasound Center, St. Marianna University Hospital, 2-16-1 Sugao, Miyamae-ku, Kawasaki 216-8511, Japan; (N.S.); (T.S.); (Y.K.); (T.O.)
| | - Yui Kurosaka
- Ultrasound Center, St. Marianna University Hospital, 2-16-1 Sugao, Miyamae-ku, Kawasaki 216-8511, Japan; (N.S.); (T.S.); (Y.K.); (T.O.)
| | - Yukio Sato
- Department of Cardiology, St. Marianna University School of Medicine, 2-16-1 Sugao, Miyamae-ku, Kawasaki 216-8511, Japan; (Y.S.); (Y.J.A.)
| | - Takanori Okamura
- Ultrasound Center, St. Marianna University Hospital, 2-16-1 Sugao, Miyamae-ku, Kawasaki 216-8511, Japan; (N.S.); (T.S.); (Y.K.); (T.O.)
| | - Yoshihiro Johnny Akashi
- Department of Cardiology, St. Marianna University School of Medicine, 2-16-1 Sugao, Miyamae-ku, Kawasaki 216-8511, Japan; (Y.S.); (Y.J.A.)
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Barris B, Karp A, Jacobs M, Frishman WH. Harnessing the Power of AI: A Comprehensive Review of Left Ventricular Ejection Fraction Assessment With Echocardiography. Cardiol Rev 2024:00045415-990000000-00237. [PMID: 38520327 DOI: 10.1097/crd.0000000000000691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/25/2024]
Abstract
The quantification of left ventricular ejection fraction (LVEF) has important clinical utility in the assessment of cardiac function and is vital for the diagnosis of cardiovascular diseases. A transthoracic echocardiogram serves as the most commonly used tool for LVEF assessment for several reasons, including, its noninvasive nature, great safety profile, real-time image processing ability, portability, and cost-effectiveness. However, transthoracic echocardiogram is highly dependent on the clinical skill of the sonographer and interpreting physician. Moreover, even amongst well-trained clinicians, significant interobserver variability exists in the quantification of LVEF. In search of possible solutions, the usage of artificial intelligence (AI) has been increasingly tested in the clinical setting. While AI-derived ejection fraction is in the preliminary stages of development, it has shown promise in its ability to rapidly quantify LVEF, decrease variability, increase accuracy, and utilize higher-order processing capabilities. This review will delineate the latest advancements of AI in evaluating LVEF through echocardiography and explore the challenges and future trajectory of this emerging domain.
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Affiliation(s)
- Ben Barris
- From the Department of Medicine, Westchester Medical Center, Valhalla, NY
| | - Avrohom Karp
- From the Department of Medicine, Westchester Medical Center, Valhalla, NY
| | - Menachem Jacobs
- Department of Medicine, SUNY Downstate Medical Center, Brooklyn, NY
| | - William H Frishman
- From the Department of Medicine, Westchester Medical Center, Valhalla, NY
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38
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Yim D, Khuntia J, Parameswaran V, Meyers A. Preliminary Evidence of the Use of Generative AI in Health Care Clinical Services: Systematic Narrative Review. JMIR Med Inform 2024; 12:e52073. [PMID: 38506918 PMCID: PMC10993141 DOI: 10.2196/52073] [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: 08/21/2023] [Revised: 10/12/2023] [Accepted: 01/30/2024] [Indexed: 03/21/2024] Open
Abstract
BACKGROUND Generative artificial intelligence tools and applications (GenAI) are being increasingly used in health care. Physicians, specialists, and other providers have started primarily using GenAI as an aid or tool to gather knowledge, provide information, train, or generate suggestive dialogue between physicians and patients or between physicians and patients' families or friends. However, unless the use of GenAI is oriented to be helpful in clinical service encounters that can improve the accuracy of diagnosis, treatment, and patient outcomes, the expected potential will not be achieved. As adoption continues, it is essential to validate the effectiveness of the infusion of GenAI as an intelligent technology in service encounters to understand the gap in actual clinical service use of GenAI. OBJECTIVE This study synthesizes preliminary evidence on how GenAI assists, guides, and automates clinical service rendering and encounters in health care The review scope was limited to articles published in peer-reviewed medical journals. METHODS We screened and selected 0.38% (161/42,459) of articles published between January 1, 2020, and May 31, 2023, identified from PubMed. We followed the protocols outlined in the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to select highly relevant studies with at least 1 element on clinical use, evaluation, and validation to provide evidence of GenAI use in clinical services. The articles were classified based on their relevance to clinical service functions or activities using the descriptive and analytical information presented in the articles. RESULTS Of 161 articles, 141 (87.6%) reported using GenAI to assist services through knowledge access, collation, and filtering. GenAI was used for disease detection (19/161, 11.8%), diagnosis (14/161, 8.7%), and screening processes (12/161, 7.5%) in the areas of radiology (17/161, 10.6%), cardiology (12/161, 7.5%), gastrointestinal medicine (4/161, 2.5%), and diabetes (6/161, 3.7%). The literature synthesis in this study suggests that GenAI is mainly used for diagnostic processes, improvement of diagnosis accuracy, and screening and diagnostic purposes using knowledge access. Although this solves the problem of knowledge access and may improve diagnostic accuracy, it is oriented toward higher value creation in health care. CONCLUSIONS GenAI informs rather than assisting or automating clinical service functions in health care. There is potential in clinical service, but it has yet to be actualized for GenAI. More clinical service-level evidence that GenAI is used to streamline some functions or provides more automated help than only information retrieval is needed. To transform health care as purported, more studies related to GenAI applications must automate and guide human-performed services and keep up with the optimism that forward-thinking health care organizations will take advantage of GenAI.
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Affiliation(s)
- Dobin Yim
- Loyola University, Maryland, MD, United States
| | - Jiban Khuntia
- University of Colorado Denver, Denver, CO, United States
| | | | - Arlen Meyers
- University of Colorado Denver, Denver, CO, United States
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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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Firima E, Gonzalez L, Manthabiseng M, Bane M, Lukau B, Leigh B, Kaufmann BA, Weisser M, Amstutz A, Tromp J, Labhardt ND, Burkard T. Implementing focused echocardiography and AI-supported analysis in a population-based survey in Lesotho: implications for community-based cardiovascular disease care models. Hypertens Res 2024; 47:708-713. [PMID: 38228749 PMCID: PMC10912015 DOI: 10.1038/s41440-023-01559-6] [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: 08/24/2023] [Revised: 11/23/2023] [Accepted: 12/02/2023] [Indexed: 01/18/2024]
Abstract
In settings where access to expert echocardiography is limited, focused echocardiography, combined with artificial intelligence (AI)-supported analysis, may improve diagnosis and monitoring of left ventricular hypertrophy (LVH). Sixteen nurses/nurse-assistants without prior experience in echocardiography underwent a 2-day hands-on intensive training to learn how to assess parasternal long axis views (PLAX) using an inexpensive hand-held ultrasound device in Lesotho, Southern Africa. Loops were stored on a cloud-drive, analyzed using deep learning algorithms at the University Hospital Basel, and afterwards confirmed by a board-certified cardiologist. The nurses/nurse-assistants obtained 756 echocardiograms. Of the 754 uploaded image files, 628 (83.3%) were evaluable by deep learning algorithms. Of those, results of 514/628 (81.9%) were confirmed by a cardiologist. Of the 126 not evaluable by the AI algorithm, 46 (36.5%) were manually evaluable. Overall, 660 (87.5%) uploaded files were evaluable and confirmed. Following short-term training of nursing cadres, a high proportion of obtained PLAX was evaluable using AI-supported analysis. This could be a basis for AI- and telemedical support in hard-to-reach areas with minimal resources.
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Affiliation(s)
- Emmanuel Firima
- Division of Clinical Epidemiology, University Hospital Basel, Basel, Switzerland.
- University of Basel, Basel, Switzerland.
- Swiss Tropical and Public Health Institute, Basel, Switzerland.
| | - Lucia Gonzalez
- Division of Clinical Epidemiology, University Hospital Basel, Basel, Switzerland
- University of Basel, Basel, Switzerland
- Swiss Tropical and Public Health Institute, Basel, Switzerland
| | | | | | | | - Bailah Leigh
- University of Sierra Leone, Freetown, Sierra Leone
| | - Beat A Kaufmann
- Department of Cardiology, University Hospital Basel, Basel, Switzerland
| | - Maja Weisser
- University of Basel, Basel, Switzerland
- Division of Infectious Diseases and Hospital Epidemiology, University Hospital, Basel, Switzerland
- Ifakara Health Institute, Ifakara, Tanzania
| | - Alain Amstutz
- Division of Clinical Epidemiology, University Hospital Basel, Basel, Switzerland
- University of Basel, Basel, Switzerland
| | - Jasper Tromp
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - Niklaus Daniel Labhardt
- Division of Clinical Epidemiology, University Hospital Basel, Basel, Switzerland
- University of Basel, Basel, Switzerland
| | - Thilo Burkard
- Department of Cardiology, University Hospital Basel, Basel, Switzerland
- Medical Outpatient Department and Hypertension Clinic, ESH Hypertension Centre of Excellence, University Hospital Basel, Basel, Switzerland
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Bughrara NF, Neilson MR, Jones S, Workman L, Chopra A, Pustavoitau A. Is 1 Day of Focused Training in Echocardiographic Assessment Using Subxiphoid-Only (EASy) Examination Enough? A Tertiary Hospital Response to the COVID-19 Crisis and the Use of the EASy Examination to Support Unit-Wide Image Acquisition. Crit Care Explor 2024; 6:e1038. [PMID: 38415022 PMCID: PMC10898658 DOI: 10.1097/cce.0000000000001038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/29/2024] Open
Abstract
OBJECTIVES We assessed the efficacy of 1-day training in echocardiography assessment using subxiphoid-only (EASy) followed by supervised image interpretation and decision-making during patient rounds as a novel approach to scaling up the use of point-of-care ultrasound (POCUS) in critically ill patients. DESIGN Retrospective analysis of medical records and EASy examination images. SETTING Tertiary care academic hospital. PATIENTS A total of 14 adults (> 18 yr old) with COVID-19-associated respiratory failure under the care of Albany Medical Center's surge response team from April 6-17, 2020 who received at least one EASy examination. INTERVENTIONS Residents (previously novice sonographers) were trained in EASy examination using 1 day of didactic and hands-on training, followed by independent image acquisition and supervised image interpretation, identification of hemodynamic patterns, and clinical decision-making facilitated by an echocardiography-certified physician during daily rounds. MEASUREMENTS AND MAIN RESULTS We recorded the quality of resident-obtained EASy images, scanning time, and frequency with which the supervising physician had to repeat the examination or obtain additional images. A total of 63 EASy examinations were performed; average scanning time was 4.3 minutes. Resident-obtained images were sufficient for clinical decision-making on 55 occasions (87%), in the remaining 8 (13%) the supervising physician obtained further images. CONCLUSIONS EASy examination is an efficient, valuable tool under conditions of scarce resources. The educational model of 1-day training followed by supervised image interpretation and decision-making allows rapid expansion of the pool of sonographers and implementation of bedside echocardiography into routine ICU patient management.
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Affiliation(s)
- Nibras F Bughrara
- Department of Anesthesiology and Critical Care Medicine, Albany Medical Center, Albany, NY
- Department of Surgery, Albany Medical Center, Albany, NY
- Department of Anesthesiology and Critical Care Medicine, Albany Medical Center, Albany, NY
| | - Maegan R Neilson
- Department of Anesthesiology and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA
| | - Stephanie Jones
- Department of Anesthesiology and Critical Care Medicine, Albany Medical Center, Albany, NY
- Department of Surgery, Albany Medical Center, Albany, NY
- Department of Anesthesiology and Critical Care Medicine, Albany Medical Center, Albany, NY
| | - Lorna Workman
- Department of Anesthesiology and Critical Care Medicine, Wagga Wagga Base Hospital, Wagga, NSW, Australia
| | - Amit Chopra
- Department of Anesthesiology and Critical Care Medicine, Albany Medical Center, Albany, NY
- Department of Surgery, Albany Medical Center, Albany, NY
- Department of Internal Medicine, Albany Medical College, Albany Medical Center, Albany, NY
| | - Aliaksei Pustavoitau
- Department of Anesthesiology and Critical Care Medicine, Johns Hopkins Hospital, Baltimore, MD
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Morales-Ortega A, Duarte-Millán MÁ, Canora-Lebrato J, Zapatero-Gaviria A. [Point-of-care ultrasound: Indications and utility in internal medicine]. Med Clin (Barc) 2024; 162:190-196. [PMID: 38016854 DOI: 10.1016/j.medcli.2023.08.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 08/24/2023] [Accepted: 08/26/2023] [Indexed: 11/30/2023]
Affiliation(s)
- Alejandro Morales-Ortega
- Servicio de Medicina Interna, Hospital Universitario de Fuenlabrada, Madrid, España; Departamento de Medicina y Especialidades Médicas, Universidad de Alcalá, Madrid, España.
| | | | - Jesús Canora-Lebrato
- Servicio de Medicina Interna, Hospital Universitario de Fuenlabrada, Madrid, España
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Hanneman K, Playford D, Dey D, van Assen M, Mastrodicasa D, Cook TS, Gichoya JW, Williamson EE, Rubin GD. Value Creation Through Artificial Intelligence and Cardiovascular Imaging: A Scientific Statement From the American Heart Association. Circulation 2024; 149:e296-e311. [PMID: 38193315 DOI: 10.1161/cir.0000000000001202] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/10/2024]
Abstract
Multiple applications for machine learning and artificial intelligence (AI) in cardiovascular imaging are being proposed and developed. However, the processes involved in implementing AI in cardiovascular imaging are highly diverse, varying by imaging modality, patient subtype, features to be extracted and analyzed, and clinical application. This article establishes a framework that defines value from an organizational perspective, followed by value chain analysis to identify the activities in which AI might produce the greatest incremental value creation. The various perspectives that should be considered are highlighted, including clinicians, imagers, hospitals, patients, and payers. Integrating the perspectives of all health care stakeholders is critical for creating value and ensuring the successful deployment of AI tools in a real-world setting. Different AI tools are summarized, along with the unique aspects of AI applications to various cardiac imaging modalities, including cardiac computed tomography, magnetic resonance imaging, and positron emission tomography. AI is applicable and has the potential to add value to cardiovascular imaging at every step along the patient journey, from selecting the more appropriate test to optimizing image acquisition and analysis, interpreting the results for classification and diagnosis, and predicting the risk for major adverse cardiac events.
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Vrudhula A, Kwan AC, Ouyang D, Cheng S. Machine Learning and Bias in Medical Imaging: Opportunities and Challenges. Circ Cardiovasc Imaging 2024; 17:e015495. [PMID: 38377237 PMCID: PMC10883605 DOI: 10.1161/circimaging.123.015495] [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] [Indexed: 02/22/2024]
Abstract
Bias in health care has been well documented and results in disparate and worsened outcomes for at-risk groups. Medical imaging plays a critical role in facilitating patient diagnoses but involves multiple sources of bias including factors related to access to imaging modalities, acquisition of images, and assessment (ie, interpretation) of imaging data. Machine learning (ML) applied to diagnostic imaging has demonstrated the potential to improve the quality of imaging-based diagnosis and the precision of measuring imaging-based traits. Algorithms can leverage subtle information not visible to the human eye to detect underdiagnosed conditions or derive new disease phenotypes by linking imaging features with clinical outcomes, all while mitigating cognitive bias in interpretation. Importantly, however, the application of ML to diagnostic imaging has the potential to either reduce or propagate bias. Understanding the potential gain as well as the potential risks requires an understanding of how and what ML models learn. Common risks of propagating bias can arise from unbalanced training, suboptimal architecture design or selection, and uneven application of models. Notwithstanding these risks, ML may yet be applied to improve gain from imaging across all 3A's (access, acquisition, and assessment) for all patients. In this review, we present a framework for understanding the balance of opportunities and challenges for minimizing bias in medical imaging, how ML may improve current approaches to imaging, and what specific design considerations should be made as part of efforts to maximize the quality of health care for all.
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Affiliation(s)
- Amey Vrudhula
- Icahn School of Medicine at Mount Sinai, New York
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center
| | - Alan C Kwan
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center
| | - David Ouyang
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center
| | - Susan Cheng
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center
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Sahashi Y, Takeshita R, Watanabe T, Ishihara T, Sekine A, Watanabe D, Ishihara T, Ichiryu H, Endo S, Fukuoka D, Hara T, Okura H. Development of artificial intelligence-based slow-motion echocardiography and clinical usefulness for evaluating regional wall motion abnormalities. THE INTERNATIONAL JOURNAL OF CARDIOVASCULAR IMAGING 2024; 40:385-395. [PMID: 37940734 DOI: 10.1007/s10554-023-02997-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 10/27/2023] [Indexed: 11/10/2023]
Abstract
The diagnostic accuracy of exercise stress echocardiography (ESE) for myocardial ischemia requires improvement, given that it currently depends on the physicians' experience and image quality. To address this issue, we aimed to develop artificial intelligence (AI)-based slow-motion echocardiography using inter-image interpolation. The clinical usefulness of this method was evaluated for detecting regional wall-motion abnormalities (RWMAs). In this study, an AI-based echocardiographic image-interpolation pipeline was developed using optical flow calculation and prediction for in-between images. The accuracy for detecting RWMAs and image readability among 25 patients with RWMA and 25 healthy volunteers was compared between four cardiologists using slow-motion and conventional ESE. Slow-motion echocardiography was successfully developed for arbitrary time-steps (e.g., 0.125×, and 0.5×) using 1,334 videos. The RWMA detection accuracy showed a numerical improvement, but it was not statistically significant (87.5% in slow-motion echocardiography vs. 81.0% in conventional ESE; odds ratio: 1.43 [95% CI: 0.78-2.62], p = 0.25). Interreader agreement analysis (Fleiss's Kappa) for detecting RWMAs among the four cardiologists were 0.66 (95%CI: 0.55-0.77) for slow-motion ESE and 0.53 (95%CI: 0.42-0.65) for conventional ESE. Additionally, subjective evaluations of image readability using a four-point scale showed a significant improvement for slow-motion echocardiography (2.11 ± 0.73 vs. 1.70 ± 0.78, p < 0.001).In conclusion, we successfully developed slow-motion echocardiography using in-between echocardiographic image interpolation. Although the accuracy for detecting RWMAs did not show a significant improvement with this method, we observed enhanced image readability and interreader agreement. This AI-based approach holds promise in supporting physicians' evaluations.
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Affiliation(s)
- Yuki Sahashi
- Department of Cardiology, Gifu University Graduate School of Medicine, 1-1 Yanagido, Gifu City, Gifu, Japan.
| | - Ryo Takeshita
- Department of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu University Graduate School of Medicine, Gifu, Japan
| | - Takatomo Watanabe
- Department of Cardiology, Gifu University Graduate School of Medicine, 1-1 Yanagido, Gifu City, Gifu, Japan
- Division of Clinical Laboratory, Gifu University Hospital, Gifu, Japan
| | - Takuma Ishihara
- Innovative and Clinical Research Promotion Center, Gifu University Hospital, Gifu, Japan
| | - Ayako Sekine
- Division of Clinical Laboratory, Gifu University Hospital, Gifu, Japan
| | - Daichi Watanabe
- Division of Clinical Laboratory, Gifu University Hospital, Gifu, Japan
- Department of Pharmacy, Gifu University Hospital, Gifu, Japan
| | - Takeshi Ishihara
- Department of Cardiology, Gifu University Graduate School of Medicine, 1-1 Yanagido, Gifu City, Gifu, Japan
| | - Hajime Ichiryu
- Department of Cardiology, Gifu University Graduate School of Medicine, 1-1 Yanagido, Gifu City, Gifu, Japan
| | - Susumu Endo
- Department of Cardiology, Gifu University Graduate School of Medicine, 1-1 Yanagido, Gifu City, Gifu, Japan
| | - Daisuke Fukuoka
- Department of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu University Graduate School of Medicine, Gifu, Japan
- Faculty of Education, Gifu University, Gifu, Japan
| | - Takeshi Hara
- Department of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu University Graduate School of Medicine, Gifu, Japan
- Center for Research, Education, and Development for Healthcare Life Design (C-REX), Tokai National Higher Education and Research System, Gifu, Japan
| | - Hiroyuki Okura
- Department of Cardiology, Gifu University Graduate School of Medicine, 1-1 Yanagido, Gifu City, Gifu, Japan
- Division of Clinical Laboratory, Gifu University Hospital, Gifu, Japan
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Potter E, Huynh Q, Haji K, Wong C, Yang H, Wright L, Marwick TH. Use of Clinical and Echocardiographic Evaluation to Assess the Risk of Heart Failure. JACC. HEART FAILURE 2024; 12:275-286. [PMID: 37498272 DOI: 10.1016/j.jchf.2023.06.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 04/20/2023] [Accepted: 06/07/2023] [Indexed: 07/28/2023]
Abstract
BACKGROUND Clinical and echocardiographic features predict incident heart failure (HF), but the optimal strategy for combining them is unclear. OBJECTIVES This study sought to define an effective means of using echocardiography in HF risk evaluation. METHODS The same clinical and echocardiographic evaluation was obtained in 2 groups with HF risk factors: a training group (n = 926, followed to 7 years) and a validation group (n = 355, followed to 10 years). Clinical risk was categorized as low, intermediate, and high using 4-year ARIC (Atherosclerosis Risk In Communities) HF risk score cutpoints of 9% and 33%. A risk stratification algorithm based on clinical risk and echocardiographic markers of stage B HF (SBHF) (abnormal global longitudinal strain [GLS], diastolic dysfunction, or left ventricular hypertrophy) was developed using a classification and regression tree analysis and was validated. RESULTS HF developed in 12% of the training group, including 9%, 18%, and 73% of low-, intermediate-, and high-risk patients. HF occurred in 8.6% of stage A HF and 19.4% of SBHF (P < 0.001), but stage A HF with clinical risk of ≥9% had similar outcome to SBHF. Abnormal GLS (HR: 2.92 [95% CI: 1.95-4.37]; P < 0.001) was the strongest independent predictor of HF. Normal GLS and diastolic function reclassified 61% of the intermediate-risk group into the low-risk group (HF incidence: 12%). In the validation group, 11% developed HF over 4.5 years; 4%, 17%, and 39% of low-, intermediate-, and high-risk groups. Similar results were obtained after exclusion of patients with known coronary artery disease. The echocardiographic parameters also provided significant incremental value to the ARIC score in predicting new HF admission (C-statistic: 0.78 [95% CI: 0.71-0.84] vs 0.83 [95% CI: 0.77-0.88]; P = 0.027). CONCLUSIONS Clinical risk assessment is adequate to classify low and high HF risk. Echocardiographic evaluation reclassifies 61% of intermediate-risk patients.
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Affiliation(s)
- Elizabeth Potter
- Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Quan Huynh
- Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Kawa Haji
- Western Health, Melbourne, Victoria, Australia
| | - Chiew Wong
- Northern Health, Melbourne, Victoria, Australia
| | - Hong Yang
- Menzies Institute for Medical Research, Hobart, Tasmania, Australia
| | - Leah Wright
- Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Thomas H Marwick
- Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia; Western Health, Melbourne, Victoria, Australia; Menzies Institute for Medical Research, Hobart, Tasmania, Australia.
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47
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Panebianco N, Baston C. Training, Competency, and Interdisciplinary Collaboration in Point-of-Care Ultrasound. Semin Ultrasound CT MR 2024; 45:91-97. [PMID: 38056786 DOI: 10.1053/j.sult.2023.12.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/08/2023]
Abstract
Point-of-care ultrasound can provide critical diagnostic information and add to procedural safety. As with any clinical skill, however, it must be applied by an adequately trained provider, with systems to ensure safety. Training can include a mix of hands-on training with traditional didactics, online coursework, and simulation, but each carries its own costs and benefits worth of review. Following training it is essential to think about assessment of competency in point-of-care ultrasound to reflect the combination of cognitive and procedural skills that makes up the practice. Within the frameworks described, expansion can be expected across specialty and professional boundaries.
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Affiliation(s)
- Nova Panebianco
- Perelman School of Medicine at the University of Pennsylvania, Department of Emergency Medicine, Philadelphia, PA.
| | - Cameron Baston
- Perelman School of Medicine at the University of Pennsylvania, Department of Medicine, Philadelphia, PA
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Bullock-Palmer RP, Flores Rosario K, Douglas PS, Hahn RT, Lang RM, Chareonthaitawee P, Srichai MB, Ordovas KG, Baldassarre LA, Burroughs MS, Henderson CS, Woodard PK, Pressoir K, Swaminathan M, Blankstein R, Daubert MA. Multimodality Cardiac Imaging and the Imaging Workforce in the United States: Diversity, Disparities, and Future Directions. Circ Cardiovasc Imaging 2024; 17:e016409. [PMID: 38377238 DOI: 10.1161/circimaging.123.016409] [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] [Indexed: 02/22/2024]
Abstract
Innovations in cardiac imaging have fundamentally advanced the understanding and treatment of cardiovascular disease. These advances in noninvasive cardiac imaging have also expanded the role of the cardiac imager and dramatically increased the demand for imagers who are cross-trained in multiple modalities. However, we hypothesize that there is significant variation in the availability of cardiac imaging expertise and a disparity in the adoption of advanced imaging technologies across the United States. To evaluate this, we have brought together the leaders of cardiovascular imaging societies, imaging trainees, as well as collaborated with national imaging accreditation commissions and imaging certification boards to assess the state of cardiac imaging and the diversity of the imaging workforce in the United States. Aggregate data confirm the presence of critical gaps, such as limited access to imaging and imaging expertise in rural communities, as well as disparities in the imaging workforce, notably among women and underrepresented minorities. Based on these results, we have proposed solutions to promote and maintain a robust and diverse community of cardiac imagers and improve equity and accessibility for cardiac imaging technologies.
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Affiliation(s)
- Renee P Bullock-Palmer
- Clinical Associate Professor, Department of Medicine, Division of Cardiology, Thomas Jefferson University, Philadelphia, PA (R.P.B.P.)
- Department of Cardiology, Deborah Heart and Lung Center, Browns Mills, NJ (R.P.B.P., K.P.)
| | - Karen Flores Rosario
- Department of Medicine, Division of Cardiology (K.F.R., P.S.D., M.A.D.), Duke University Medical Center, Durham, NC
| | - Pamela S Douglas
- Department of Medicine, Division of Cardiology (K.F.R., P.S.D., M.A.D.), Duke University Medical Center, Durham, NC
| | - Rebecca T Hahn
- Department of Medicine, Columbia University Irving Medical Center, New York, NY (R.T.H.)
| | - Roberto M Lang
- Section of Cardiology, Heart and Vascular Center, University of Chicago, IL (R.M.L.)
| | | | - Monvadi B Srichai
- Department of Medicine and Radiology, Medstar Georgetown University Hospital, Medstar Heart and Vascular Institute, Washington, DC (M.B.S.)
| | - Karen G Ordovas
- Department of Radiology, University of Washington, Seattle, WA (K.G.O.)
| | - Lauren A Baldassarre
- Department of Medicine, Division of Cardiology, Yale School of Medicine, New Haven, CT (L.A.B.)
| | | | - Cory S Henderson
- Department of Medicine, Division of Cardiology, Department of Radiology, Boston Medical Center, MA (C.S.H.)
| | - Pamela K Woodard
- Department of Radiology, Mallinckrodt Institute of Radiology, Washington University, St Louis, MO (P.K.W.)
| | - Kathleen Pressoir
- Department of Cardiology, Deborah Heart and Lung Center, Browns Mills, NJ (R.P.B.P., K.P.)
| | - Madhav Swaminathan
- Department of Anesthesiology, Cardiothoracic Division (M.S.), Duke University Medical Center, Durham, NC
| | - Ron Blankstein
- Department of Medicine and Radiology, Brigham and Women's Hospital, Boston, MA (R.B.)
| | - Melissa A Daubert
- Department of Medicine, Division of Cardiology (K.F.R., P.S.D., M.A.D.), Duke University Medical Center, Durham, NC
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Fazlalizadeh H, Khan MS, Fox ER, Douglas PS, Adams D, Blaha MJ, Daubert MA, Dunn G, van den Heuvel E, Kelsey MD, Martin RP, Thomas JD, Thomas Y, Judd SE, Vasan RS, Budoff MJ, Bloomfield GS. Closing the Last Mile Gap in Access to Multimodality Imaging in Rural Settings: Design of the Imaging Core of the Risk Underlying Rural Areas Longitudinal Study. Circ Cardiovasc Imaging 2024; 17:e015496. [PMID: 38377236 PMCID: PMC10883604 DOI: 10.1161/circimaging.123.015496] [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] [Indexed: 02/22/2024]
Abstract
Achieving optimal cardiovascular health in rural populations can be challenging for several reasons including decreased access to care with limited availability of imaging modalities, specialist physicians, and other important health care team members. Therefore, innovative solutions are needed to optimize health care and address cardiovascular health disparities in rural areas. Mobile examination units can bring imaging technology to underserved or remote communities with limited access to health care services. Mobile examination units can be equipped with a wide array of assessment tools and multiple imaging modalities such as computed tomography scanning and echocardiography. The detailed structural assessment of cardiovascular and lung pathology, as well as the detection of extracardiac pathology afforded by computed tomography imaging combined with the functional and hemodynamic assessments acquired by echocardiography, yield deep phenotyping of heart and lung disease for populations historically underrepresented in epidemiological studies. Moreover, by bringing the mobile examination unit to local communities, innovative approaches are now possible including engagement with local professionals to perform these imaging assessments, thereby augmenting local expertise and experience. However, several challenges exist before mobile examination unit-based examinations can be effectively integrated into the rural health care setting including standardizing acquisition protocols, maintaining consistent image quality, and addressing ethical and privacy considerations. Herein, we discuss the potential importance of cardiac multimodality imaging to improve cardiovascular health in rural regions, outline the emerging experience in this field, highlight important current challenges, and offer solutions based on our experience in the RURAL (Risk Underlying Rural Areas Longitudinal) cohort study.
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Affiliation(s)
- Hooman Fazlalizadeh
- Lundquist Institute, Harbor-University of California Los Angeles Medical Center, Torrance (H.F., M.J.B.)
| | - Muhammad Shahzeb Khan
- Division of Cardiology, Department of Medicine (M.S.K., P.S.D., M.A.D., M.D.K., G.S.B.), Duke University, Durham, NC
| | - Ervin R Fox
- Division of Cardiology, Department of Medicine University of Mississippi Medical Center, Jackson, MS (E.R.F.)
| | - Pamela S Douglas
- Division of Cardiology, Department of Medicine (M.S.K., P.S.D., M.A.D., M.D.K., G.S.B.), Duke University, Durham, NC
- Duke Clinical Research Institute (P.S.D., M.A.D., G.D., M.D.K., G.S.B.), Duke University, Durham, NC
| | - David Adams
- Caption Health, Inc, San Francisco, CA (D.A., R.P.M., Y.T.)
| | - Michael J Blaha
- Lundquist Institute, Harbor-University of California Los Angeles Medical Center, Torrance (H.F., M.J.B.)
| | - Melissa A Daubert
- Division of Cardiology, Department of Medicine (M.S.K., P.S.D., M.A.D., M.D.K., G.S.B.), Duke University, Durham, NC
- Duke Clinical Research Institute (P.S.D., M.A.D., G.D., M.D.K., G.S.B.), Duke University, Durham, NC
| | - Gary Dunn
- Duke Clinical Research Institute (P.S.D., M.A.D., G.D., M.D.K., G.S.B.), Duke University, Durham, NC
| | - Edwin van den Heuvel
- Department of Mathematics and Computer Science, Eindhoven University of Technology, The Netherlands (E.v.d.H.)
| | - Michelle D Kelsey
- Division of Cardiology, Department of Medicine (M.S.K., P.S.D., M.A.D., M.D.K., G.S.B.), Duke University, Durham, NC
- Duke Clinical Research Institute (P.S.D., M.A.D., G.D., M.D.K., G.S.B.), Duke University, Durham, NC
| | | | - James D Thomas
- Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL (J.D.T.)
- Center for Artificial Intelligence, Northwestern Medicine Bluhm Cardiovascular Institute, Chicago, IL (J.D.T.)
| | - Yngvil Thomas
- Caption Health, Inc, San Francisco, CA (D.A., R.P.M., Y.T.)
| | - Suzanne E Judd
- Department of Biostatistics, University of Alabama at Birmingham (S.E.J.)
| | - Ramachandran S Vasan
- University of Texas Health Sciences Center, University of Texas School of Public Health, San Antonio (R.S.V.)
| | - Matthew J Budoff
- Division of Cardiology, John Hopkins University School of Medicine, Baltimore, MD (M.J.B.)
| | - Gerald S Bloomfield
- Division of Cardiology, Department of Medicine (M.S.K., P.S.D., M.A.D., M.D.K., G.S.B.), Duke University, Durham, NC
- Duke Clinical Research Institute (P.S.D., M.A.D., G.D., M.D.K., G.S.B.), Duke University, Durham, NC
- Duke Global Health Institute (G.S.B.), Duke University, Durham, NC
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Oppenheimer J, Mandegaran R, Staabs F, Adler A, Singöhl S, Kainz B, Heinrich M, Geroulakos G, Spiliopoulos S, Avgerinos E. Remote Expert DVT Triaging of Novice-User Compression Sonography with AI-Guidance. Ann Vasc Surg 2024; 99:272-279. [PMID: 37820986 DOI: 10.1016/j.avsg.2023.08.022] [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: 05/06/2023] [Revised: 07/27/2023] [Accepted: 08/08/2023] [Indexed: 10/13/2023]
Abstract
BACKGROUND Compression ultrasonography of the leg is established for triaging proximal lower extremity deep vein thrombosis (DVT). AutoDVT, a machine-learning software, provides a tool for nonspecialists in acquiring compression sequences to be reviewed by an expert for patient triage. The purpose of this study was to test image acquisition and remote triaging in a clinical setting. METHODS Patients with a suspected DVT were recruited at 2 centers in Germany and Greece. Enrolled patients underwent an artificial intelligence-guided two-point compression examination by a nonspecialist using a handheld ultrasound device prior to a standard scan. Images collected by the software were uploaded for blind review by 5 qualified physicians. All reviewers rated the quality of all sequences on the American College of Emergency Physicians (ACEP) image quality scale (score 1-5, ≥ 3 defined as adequate imaging quality) and for an ACEP score ≥3, chose "Compressible", "Incompressible", or "Other". Sensitivity and specificity were calculated for adequate quality scans with an assessment as "Compressible" or "Incompressible". We define this group as diagnostic quality. To simulate a triaging clinical algorithm, a post hoc analysis was performed merging the "incomplete", the "low quality", and the "Incompressible" into a high-risk group for proximal DVT. RESULTS Seventy-three patients (average age 64.2 years, 44% females) were eligible for inclusion and scanned by 3 nonultrasound-qualified healthcare professionals. Three patients were excluded from further analysis due to incomplete scans. Sixty two of 70 (88.57%) of the completed scans were judged to be of adequate image quality with an average ACEP score of 3.35. Forty seven of 62 adequate AutoDVT scans were assessed as diagnostic quality, of which 8 were interpreted as positive for proximal DVT by the reviewers resulting in a sensitivity of 100% and specificity of 95.12%. When simulating a triaging algorithm, 34/73 (46.58%) of patients would be triaged as high risk and 8 would be confirmed as positive for proximal DVT (6 in the diagnostic and 2 in the low-quality cohort). Of 39/73 patients triaged as low risk, all were negative for proximal DVT in standard duplex; thus, this triaging algorithm could potentially save 53.42% of standard duplex scans. CONCLUSIONS Machine learning software was able to aid nonexperts in acquiring valid ultrasound images of venous compressions and allowed remote triaging. This strategy allows faster diagnosis and treatment of high-risk patients and can spare the need for multiple unnecessary duplex scans, the vast majority being negative.
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Affiliation(s)
- Jonas Oppenheimer
- Klinik für Radiologie, Charité Universtitätsmedizin Berlin, Berlin, Germany.
| | - Ramin Mandegaran
- Central Alberta Medical Imaging Services, Red Deer, Alberta, Canada
| | | | - Andrea Adler
- Department for Emergency Medicine, Klinikum Magdeburg, Magdeburg, Germany
| | - Stephan Singöhl
- Department for Emergency Medicine, Klinikum Magdeburg, Magdeburg, Germany
| | - Bernhard Kainz
- FAU Erlangen-Nürnberg, Nürnberg, Germany; Department of Computing, Imperial College London, London, UK
| | - Matthias Heinrich
- University of Lübeck, Institute of Medical Informatics, Lübeck, Germany
| | - George Geroulakos
- Department of Vascular Surgery, Attikon Hospital, University of Athens, Athens, Greece
| | - Stavros Spiliopoulos
- 2nd Department of Radiology, Attikon Hospital, University of Athens, Athens, Greece
| | - Efthymios Avgerinos
- Department of Vascular Surgery, Attikon Hospital, University of Athens, Athens, Greece
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