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Gregory A, Ender J, Shaw AD, Denault A, Ibekwe S, Stoppe C, Alli A, Manning MW, Brodt JL, Galhardo C, Sander M, Zarbock A, Fletcher N, Ghadimi K, Grant MC. ERAS/STS 2024 Expert Consensus Statement on Perioperative Care in Cardiac Surgery: Continuing the Evolution of Optimized Patient Care and Recovery. J Cardiothorac Vasc Anesth 2024:S1053-0770(24)00399-9. [PMID: 39004570 DOI: 10.1053/j.jvca.2024.06.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/18/2024] [Accepted: 06/20/2024] [Indexed: 07/16/2024]
Affiliation(s)
- Alexander Gregory
- Department of Anesthesiology, Perioperative and Pain Medicine, Cumming School of Medicine and Libin Cardiovascular Institute, University of Calgary, Calgary, Canada
| | - Joerg Ender
- Department of Anesthesiology and Intensive Care Medicine, Heartcenter Leipzig GmbH, Leipzig, Germany
| | - Andrew D Shaw
- Department of Intensive Care and Resuscitation, Cleveland Clinic, Cleveland, OH
| | - André Denault
- Montreal Heart Institute, University of Montreal, Montreal, Quebec, Canada
| | - Stephanie Ibekwe
- Department of Anesthesiology, Baylor College of Medicine, Houston, TX
| | - Christian Stoppe
- Department of Cardiac Anesthesiology and Intensive Care Medicine, Charité Berlin, Berlin, Germany
| | - Ahmad Alli
- Department of Anesthesiology & Pain Medicine, St. Michael's Hospital, University of Toronto, Toronto, ON, Canada
| | | | - Jessica L Brodt
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto CA
| | - Carlos Galhardo
- Department of Anesthesia, McMaster University, Ontario, Canada
| | - Michael Sander
- Anesthesiology and Intensive Care Medicine, Justus Liebig University Giessen, University Hospital Giessen, Giessen, Germany
| | - Alexander Zarbock
- Department of Anesthesiology, Intensive Care and Pain Medicine, University Hospital Münster, Münster, Germany
| | - Nick Fletcher
- Institute of Anaesthesia and Critical Care, Cleveland Clinic London, London, UK
| | | | - Michael C Grant
- Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD
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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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Lu N, Vaseli H, Mahdavi M, Taheri Dezaki F, Luong C, Yeung D, Gin K, Tsang M, Nair P, Jue J, Barnes M, Behnami D, Abolmaesumi P, Tsang TSM. Automated Atrial Fibrillation Diagnosis by Echocardiography without ECG: Accuracy and Applications of a New Deep Learning Approach. Diseases 2024; 12:35. [PMID: 38391782 PMCID: PMC10888272 DOI: 10.3390/diseases12020035] [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: 01/11/2024] [Revised: 02/04/2024] [Accepted: 02/06/2024] [Indexed: 02/24/2024] Open
Abstract
BACKGROUND Automated rhythm detection on echocardiography through artificial intelligence (AI) has yet to be fully realized. We propose an AI model trained to identify atrial fibrillation (AF) using apical 4-chamber (AP4) cines without requiring electrocardiogram (ECG) data. METHODS Transthoracic echocardiography studies of consecutive patients ≥ 18 years old at our tertiary care centre were retrospectively reviewed for AF and sinus rhythm. The study was first interpreted by level III-trained echocardiography cardiologists as the gold standard for rhythm diagnosis based on ECG rhythm strip and imaging assessment, which was also verified with a 12-lead ECG around the time of the study. AP4 cines with three cardiac cycles were then extracted from these studies with the rhythm strip and Doppler information removed and introduced to the deep learning model ResNet(2+1)D with an 80:10:10 training-validation-test split ratio. RESULTS 634 patient studies (1205 cines) were included. After training, the AI model achieved high accuracy on validation for detection of both AF and sinus rhythm (mean F1-score = 0.92; AUROC = 0.95). Performance was consistent on the test dataset (mean F1-score = 0.94, AUROC = 0.98) when using the cardiologist's assessment of the ECG rhythm strip as the gold standard, who had access to the full study and external ECG data, while the AI model did not. CONCLUSIONS AF detection by AI on echocardiography without ECG appears accurate when compared to an echocardiography cardiologist's assessment of the ECG rhythm strip as the gold standard. This has potential clinical implications in point-of-care ultrasound and stroke risk stratification.
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Affiliation(s)
- Nelson Lu
- Division of Cardiology, Department of Medicine, University of British Columbia, Vancouver, BC V5Z 1M9, Canada
| | - Hooman Vaseli
- Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V5Z 1M9, Canada
| | - Mobina Mahdavi
- Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V5Z 1M9, Canada
| | - Fatemah Taheri Dezaki
- Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V5Z 1M9, Canada
| | - Christina Luong
- Division of Cardiology, Department of Medicine, University of British Columbia, Vancouver, BC V5Z 1M9, Canada
| | - Darwin Yeung
- Division of Cardiology, Department of Medicine, University of British Columbia, Vancouver, BC V5Z 1M9, Canada
| | - Ken Gin
- Division of Cardiology, Department of Medicine, University of British Columbia, Vancouver, BC V5Z 1M9, Canada
| | - Michael Tsang
- Division of Cardiology, Department of Medicine, University of British Columbia, Vancouver, BC V5Z 1M9, Canada
| | - Parvathy Nair
- Division of Cardiology, Department of Medicine, University of British Columbia, Vancouver, BC V5Z 1M9, Canada
| | - John Jue
- Division of Cardiology, Department of Medicine, University of British Columbia, Vancouver, BC V5Z 1M9, Canada
| | - Marion Barnes
- Division of Cardiology, Department of Medicine, University of British Columbia, Vancouver, BC V5Z 1M9, Canada
| | - Delaram Behnami
- Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V5Z 1M9, Canada
| | - Purang Abolmaesumi
- Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V5Z 1M9, Canada
| | - Teresa S M Tsang
- Division of Cardiology, Department of Medicine, University of British Columbia, Vancouver, BC V5Z 1M9, Canada
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Anand V, Weston AD, Scott CG, Kane GC, Pellikka PA, Carter RE. Machine Learning for Diagnosis of Pulmonary Hypertension by Echocardiography. Mayo Clin Proc 2024; 99:260-270. [PMID: 38309937 DOI: 10.1016/j.mayocp.2023.05.006] [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: 11/22/2022] [Revised: 03/23/2023] [Accepted: 05/02/2023] [Indexed: 02/05/2024]
Abstract
OBJECTIVE To evaluate a machine learning (ML)-based model for pulmonary hypertension (PH) prediction using measurements and impressions made during echocardiography. METHODS A total of 7853 consecutive patients with right-sided heart catheterization and transthoracic echocardiography performed within 1 week from January 1, 2012, through December 31, 2019, were included. The data were split into training (n=5024 [64%]), validation (n=1275 [16%]), and testing (n=1554 [20%]). A gradient boosting machine with enumerated grid search for optimization was selected to allow missing data in the boosted trees without imputation. The training target was PH, defined by right-sided heart catheterization as mean pulmonary artery pressure above 20 mm Hg; model performance was maximized relative to area under the receiver operating characteristic curve using 5-fold cross-validation. RESULTS Cohort age was 64±14 years; 3467 (44%) were female, and 81% (6323/7853) had PH. The final trained model included 19 characteristics, measurements, or impressions derived from the echocardiogram. In the testing data, the model had high discrimination for the detection of PH (area under the receiver operating characteristic curve, 0.83; 95% CI, 0.80 to 0.85). The model's accuracy, sensitivity, positive predictive value, and negative predictive value were 82% (1267/1554), 88% (1098/1242), 89% (1098/1241), and 54% (169/313), respectively. CONCLUSION By use of ML, PH could be predicted on the basis of clinical and echocardiographic variables, without tricuspid regurgitation velocity. Machine learning methods appear promising for identifying patients with low likelihood of PH.
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Affiliation(s)
- Vidhu Anand
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | - Alexander D Weston
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL; Digital Innovation Lab, Mayo Clinic, Jacksonville, FL
| | | | - Garvan C Kane
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | | | - Rickey E Carter
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL; Digital Innovation Lab, Mayo Clinic, Jacksonville, FL
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Cau R, Pisu F, Suri JS, Montisci R, Gatti M, Mannelli L, Gong X, Saba L. Artificial Intelligence in the Differential Diagnosis of Cardiomyopathy Phenotypes. Diagnostics (Basel) 2024; 14:156. [PMID: 38248033 PMCID: PMC11154548 DOI: 10.3390/diagnostics14020156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 01/03/2024] [Accepted: 01/08/2024] [Indexed: 01/23/2024] Open
Abstract
Artificial intelligence (AI) is rapidly being applied to the medical field, especially in the cardiovascular domain. AI approaches have demonstrated their applicability in the detection, diagnosis, and management of several cardiovascular diseases, enhancing disease stratification and typing. Cardiomyopathies are a leading cause of heart failure and life-threatening ventricular arrhythmias. Identifying the etiologies is fundamental for the management and diagnostic pathway of these heart muscle diseases, requiring the integration of various data, including personal and family history, clinical examination, electrocardiography, and laboratory investigations, as well as multimodality imaging, making the clinical diagnosis challenging. In this scenario, AI has demonstrated its capability to capture subtle connections from a multitude of multiparametric datasets, enabling the discovery of hidden relationships in data and handling more complex tasks than traditional methods. This review aims to present a comprehensive overview of the main concepts related to AI and its subset. Additionally, we review the existing literature on AI-based models in the differential diagnosis of cardiomyopathy phenotypes, and we finally examine the advantages and limitations of these AI approaches.
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Affiliation(s)
- Riccardo Cau
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari-Polo di Monserrato s.s. 554 Monserrato, 09045 Cagliari, Italy; (R.C.); (F.P.)
| | - Francesco Pisu
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari-Polo di Monserrato s.s. 554 Monserrato, 09045 Cagliari, Italy; (R.C.); (F.P.)
| | - Jasjit S. Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoin™, Roseville, CA 95661, USA;
| | - Roberta Montisci
- Department of Cardiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari-Polo di Monserrato s.s. 554 Monserrato, 09045 Cagliari, Italy;
| | - Marco Gatti
- Department of Radiology, Università degli Studi di Torino, 10129 Turin, Italy;
| | | | - Xiangyang Gong
- Radiology Department, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou 310014, China;
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari-Polo di Monserrato s.s. 554 Monserrato, 09045 Cagliari, Italy; (R.C.); (F.P.)
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7
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Mor-Avi V, Khandheria B, Klempfner R, Cotella JI, Moreno M, Ignatowski D, Guile B, Hayes HJ, Hipke K, Kaminski A, Spiegelstein D, Avisar N, Kezurer I, Mazursky A, Handel R, Peleg Y, Avraham S, Ludomirsky A, Lang RM. Real-Time Artificial Intelligence-Based Guidance of Echocardiographic Imaging by Novices: Image Quality and Suitability for Diagnostic Interpretation and Quantitative Analysis. Circ Cardiovasc Imaging 2023; 16:e015569. [PMID: 37955139 PMCID: PMC10659245 DOI: 10.1161/circimaging.123.015569] [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: 04/12/2023] [Accepted: 10/13/2023] [Indexed: 11/14/2023]
Abstract
BACKGROUND We aimed to assess in a prospective multicenter study the quality of echocardiographic exams performed by inexperienced users guided by a new artificial intelligence software and evaluate their suitability for diagnostic interpretation of basic cardiac pathology and quantitative analysis of cardiac chamber and function. METHODS The software (UltraSight, Ltd) was embedded into a handheld imaging device (Lumify; Philips). Six nurses and 3 medical residents, who underwent minimal training, scanned 240 patients (61±16 years; 63% with cardiac pathology) in 10 standard views. All patients were also scanned by expert sonographers using the same device without artificial intelligence guidance. Studies were reviewed by 5 certified echocardiographers blinded to the imager's identity, who evaluated the ability to assess left and right ventricular size and function, pericardial effusion, valve morphology, and left atrial and inferior vena cava sizes. Finally, apical 4-chamber images of adequate quality, acquired by novices and sonographers in 100 patients, were analyzed to measure left ventricular volumes, ejection fraction, and global longitudinal strain by an expert reader using conventional methodology. Measurements were compared between novices' and experts' images. RESULTS Of the 240 studies acquired by novices, 99.2%, 99.6%, 92.9%, and 100% had sufficient quality to assess left ventricular size and function, right ventricular size, and pericardial effusion, respectively. Valve morphology, right ventricular function, and left atrial and inferior vena cava size were visualized in 67% to 98% exams. Images obtained by novices and sonographers yielded concordant diagnostic interpretation in 83% to 96% studies. Quantitative analysis was feasible in 83% images acquired by novices and resulted in high correlations (r≥0.74) and small biases, compared with those obtained by sonographers. CONCLUSIONS After minimal training with the real-time guidance software, novice users can acquire images of diagnostic quality approaching that of expert sonographers in most patients. This technology may increase adoption and improve accuracy of point-of-care cardiac ultrasound.
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Affiliation(s)
- Victor Mor-Avi
- University of Chicago Medical Center, IL (V.M.-A., J.I.C., B.G., K.H., R.M.L.)
| | - Bijoy Khandheria
- Cardiovascular Research, Advocate Aurora Research, Milwaukee, WI (B.K., D.I., H.J.H., A.K.)
| | - Robert Klempfner
- Department of Cardiology, Cardiac Rehabilitation Institute, Sheba Medical Center, Israel (R.K., M.M.)
| | - Juan I. Cotella
- University of Chicago Medical Center, IL (V.M.-A., J.I.C., B.G., K.H., R.M.L.)
| | - Merav Moreno
- Department of Cardiology, Cardiac Rehabilitation Institute, Sheba Medical Center, Israel (R.K., M.M.)
| | - Denise Ignatowski
- Cardiovascular Research, Advocate Aurora Research, Milwaukee, WI (B.K., D.I., H.J.H., A.K.)
| | - Brittney Guile
- University of Chicago Medical Center, IL (V.M.-A., J.I.C., B.G., K.H., R.M.L.)
| | - Hailee J. Hayes
- Cardiovascular Research, Advocate Aurora Research, Milwaukee, WI (B.K., D.I., H.J.H., A.K.)
| | - Kyle Hipke
- University of Chicago Medical Center, IL (V.M.-A., J.I.C., B.G., K.H., R.M.L.)
| | - Abigail Kaminski
- Cardiovascular Research, Advocate Aurora Research, Milwaukee, WI (B.K., D.I., H.J.H., A.K.)
| | | | - Noa Avisar
- UltraSight, Ltd, Rehovot, Israel (D.S., N.A., I.K.)
| | - Itay Kezurer
- UltraSight, Ltd, Rehovot, Israel (D.S., N.A., I.K.)
| | - Asaf Mazursky
- Faculty of Medicine, Ben-Gurion University of the Negev, Beer-Sheva, Israel (A.M., S.A.)
| | - Ran Handel
- Azrieli Faculty of Medicine in the Galilee Bar-Ilan University, Safed, Israel (R.H., Y.P.)
| | - Yotam Peleg
- Azrieli Faculty of Medicine in the Galilee Bar-Ilan University, Safed, Israel (R.H., Y.P.)
| | - Shir Avraham
- Faculty of Medicine, Ben-Gurion University of the Negev, Beer-Sheva, Israel (A.M., S.A.)
| | | | - Roberto M. Lang
- University of Chicago Medical Center, IL (V.M.-A., J.I.C., B.G., K.H., R.M.L.)
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Motazedian P, Marbach JA, Prosperi-Porta G, Parlow S, Di Santo P, Abdel-Razek O, Jung R, Bradford WB, Tsang M, Hyon M, Pacifici S, Mohanty S, Ramirez FD, Huggins GS, Simard T, Hon S, Hibbert B. Diagnostic accuracy of point-of-care ultrasound with artificial intelligence-assisted assessment of left ventricular ejection fraction. NPJ Digit Med 2023; 6:201. [PMID: 37898711 PMCID: PMC10613290 DOI: 10.1038/s41746-023-00945-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 10/13/2023] [Indexed: 10/30/2023] Open
Abstract
Focused cardiac ultrasound (FoCUS) is becoming standard practice in a wide spectrum of clinical settings. There is limited data evaluating the real-world use of FoCUS with artificial intelligence (AI). Our objective was to determine the accuracy of FoCUS AI-assisted left ventricular ejection fraction (LVEF) assessment and compare its accuracy between novice and experienced users. In this prospective, multicentre study, participants requiring a transthoracic echocardiogram (TTE) were recruited to have a FoCUS done by a novice or experienced user. The AI-assisted device calculated LVEF at the bedside, which was subsequently compared to TTE. 449 participants were enrolled with 424 studies included in the final analysis. The overall intraclass coefficient was 0.904, and 0.921 in the novice (n = 208) and 0.845 in the experienced (n = 216) cohorts. There was a significant bias of 0.73% towards TTE (p = 0.005) with a level of agreement of 11.2%. Categorical grading of LVEF severity had excellent agreement to TTE (weighted kappa = 0.83). The area under the curve (AUC) was 0.98 for identifying an abnormal LVEF (<50%) with a sensitivity of 92.8%, specificity of 92.3%, negative predictive value (NPV) of 0.97 and a positive predictive value (PPV) of 0.83. In identifying severe dysfunction (<30%) the AUC was 0.99 with a sensitivity of 78.1%, specificity of 98.0%, NPV of 0.98 and PPV of 0.76. Here we report that FoCUS AI-assisted LVEF assessments provide highly reproducible LVEF estimations in comparison to formal TTE. This finding was consistent among senior and novice echocardiographers suggesting applicability in a variety of clinical settings.
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Affiliation(s)
- Pouya Motazedian
- CAPITAL Research Group, Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
- Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada
| | - Jeffrey A Marbach
- Division of Cardiology, Knight Cardiovascular Institute, Oregon Health and Sciences University, Portland, OR, USA
| | - Graeme Prosperi-Porta
- CAPITAL Research Group, Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
- Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
| | - Simon Parlow
- CAPITAL Research Group, Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
- Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
| | - Pietro Di Santo
- CAPITAL Research Group, Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada
- Department of Medicine, University of Ottawa, Ottawa, Ontario, Canada
| | - Omar Abdel-Razek
- CAPITAL Research Group, Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
- Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
| | - Richard Jung
- CAPITAL Research Group, Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
- Department of Medicine, University of Ottawa, Ottawa, Ontario, Canada
| | - William B Bradford
- Division of Cardiology, Tufts Medical Center and Tufts University School of Medicine, Boston, MA, USA
| | - Miranda Tsang
- Division of Cardiology, Tufts Medical Center and Tufts University School of Medicine, Boston, MA, USA
| | - Michael Hyon
- Division of Cardiology, Tufts Medical Center and Tufts University School of Medicine, Boston, MA, USA
| | - Stefano Pacifici
- Division of Cardiology, Tufts Medical Center and Tufts University School of Medicine, Boston, MA, USA
| | - Sharanya Mohanty
- Division of Cardiology, Tufts Medical Center and Tufts University School of Medicine, Boston, MA, USA
| | - F Daniel Ramirez
- Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
| | - Gordon S Huggins
- Division of Cardiology, Tufts Medical Center and Tufts University School of Medicine, Boston, MA, USA
| | - Trevor Simard
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Stephanie Hon
- Division of Pulmonary and Critical Care Medicine, Tufts Medical Center and Tufts University School of Medicine, Boston, MA, USA
| | - Benjamin Hibbert
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.
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Vasile CM, Iriart X. Embracing AI: The Imperative Tool for Echo Labs to Stay Ahead of the Curve. Diagnostics (Basel) 2023; 13:3137. [PMID: 37835880 PMCID: PMC10572870 DOI: 10.3390/diagnostics13193137] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Revised: 09/26/2023] [Accepted: 10/03/2023] [Indexed: 10/15/2023] Open
Abstract
Advancements in artificial intelligence (AI) have rapidly transformed various sectors, and the field of echocardiography is no exception. AI-driven technologies hold immense potential to revolutionize echo labs' diagnostic capabilities and improve patient care. This paper explores the importance for echo labs to embrace AI and stay ahead of the curve in harnessing its power. Our manuscript provides an overview of the growing impact of AI on medical imaging, specifically echocardiography. It highlights how AI-driven algorithms can enhance image quality, automate measurements, and accurately diagnose cardiovascular diseases. Additionally, we emphasize the importance of training echo lab professionals in AI implementation to optimize its integration into routine clinical practice. By embracing AI, echo labs can overcome challenges such as workload burden and diagnostic accuracy variability, improving efficiency and patient outcomes. This paper highlights the need for collaboration between echocardiography laboratory experts, AI researchers, and industry stakeholders to drive innovation and establish standardized protocols for implementing AI in echocardiography. In conclusion, this article emphasizes the importance of AI adoption in echocardiography labs, urging practitioners to proactively integrate AI technologies into their workflow and take advantage of their present opportunities. Embracing AI is not just a choice but an imperative for echo labs to maintain their leadership and excel in delivering state-of-the-art cardiac care in the era of advanced medical technologies.
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Affiliation(s)
- Corina Maria Vasile
- Department of Pediatric and Adult Congenital Cardiology, Bordeaux University Hospital, 33600 Pessac, France
| | - Xavier Iriart
- Department of Pediatric and Adult Congenital Cardiology, Bordeaux University Hospital, 33600 Pessac, France
- IHU Liryc—Electrophysiology and Heart Modelling Institute, Bordeaux University Foundation, 33600 Pessac, France
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10
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Vidal-Perez R, Grapsa J, Bouzas-Mosquera A, Fontes-Carvalho R, Vazquez-Rodriguez JM. Current role and future perspectives of artificial intelligence in echocardiography. World J Cardiol 2023; 15:284-292. [PMID: 37397831 PMCID: PMC10308270 DOI: 10.4330/wjc.v15.i6.284] [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: 04/11/2023] [Revised: 05/02/2023] [Accepted: 06/21/2023] [Indexed: 06/26/2023] Open
Abstract
Echocardiography is an essential tool in diagnostic cardiology and is fundamental to clinical care. Artificial intelligence (AI) can help health care providers serving as a valuable diagnostic tool for physicians in the field of echocardiography specially on the automation of measurements and interpretation of results. In addition, it can help expand the capabilities of research and discover alternative pathways in medical management specially on prognostication. In this review article, we describe the current role and future perspectives of AI in echocardiography.
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Affiliation(s)
- Rafael Vidal-Perez
- Servicio de Cardiología, Unidad de Imagen y Función Cardíaca, Complexo Hospitalario Universitario A Coruña Centro de Investigación Biomédica en Red-Instituto de Salud Carlos III, A Coruña 15006, Spain
| | - Julia Grapsa
- Department of Cardiology, Guys and St Thomas NHS Trust, London SE1 7EH, United Kingdom
| | - Alberto Bouzas-Mosquera
- Servicio de Cardiología, Unidad de Imagen y Función Cardíaca, Complexo Hospitalario Universitario A Coruña Centro de Investigación Biomédica en Red-Instituto de Salud Carlos III, A Coruña 15006, Spain
| | - Ricardo Fontes-Carvalho
- Cardiology Department, Centro Hospitalar de Vila Nova de Gaia/Espinho, Vilanova de Gaia 4434-502, Portugal
- Cardiovascular R&D Centre - UnIC@RISE, Department of Surgery and Physiology, Faculty of Medicine of the University of Porto, Porto 4200-319, Portugal
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11
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Namasivayam M, Meredith T, Muller DWM, Roy DA, Roy AK, Kovacic JC, Hayward CS, Feneley MP. Machine learning prediction of progressive subclinical myocardial dysfunction in moderate aortic stenosis. Front Cardiovasc Med 2023; 10:1153814. [PMID: 37324638 PMCID: PMC10266266 DOI: 10.3389/fcvm.2023.1153814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 05/15/2023] [Indexed: 06/17/2023] Open
Abstract
Background Moderate severity aortic stenosis (AS) is poorly understood, is associated with subclinical myocardial dysfunction, and can lead to adverse outcome rates that are comparable to severe AS. Factors associated with progressive myocardial dysfunction in moderate AS are not well described. Artificial neural networks (ANNs) can identify patterns, inform clinical risk, and identify features of importance in clinical datasets. Methods We conducted ANN analyses on longitudinal echocardiographic data collected from 66 individuals with moderate AS who underwent serial echocardiography at our institution. Image phenotyping involved left ventricular global longitudinal strain (GLS) and valve stenosis severity (including energetics) analysis. ANNs were constructed using two multilayer perceptron models. The first model was developed to predict change in GLS from baseline echocardiography alone and the second to predict change in GLS using data from baseline and serial echocardiography. ANNs used a single hidden layer architecture and a 70%:30% training/testing split. Results Over a median follow-up interval of 1.3 years, change in GLS (≤ or >median change) could be predicted with accuracy rates of 95% in training and 93% in testing using ANN with inputs from baseline echocardiogram data alone (AUC: 0.997). The four most important predictive baseline features (reported as normalized % importance relative to most important feature) were peak gradient (100%), energy loss (93%), GLS (80%), and DI < 0.25 (50%). When a further model was run including inputs from both baseline and serial echocardiography (AUC 0.844), the top four features of importance were change in dimensionless index between index and follow-up studies (100%), baseline peak gradient (79%), baseline energy loss (72%), and baseline GLS (63%). Conclusions Artificial neural networks can predict progressive subclinical myocardial dysfunction with high accuracy in moderate AS and identify features of importance. Key features associated with classifying progression in subclinical myocardial dysfunction included peak gradient, dimensionless index, GLS, and hydraulic load (energy loss), suggesting that these features should be closely evaluated and monitored in AS.
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Affiliation(s)
- Mayooran Namasivayam
- Department of Cardiology, St Vincent’s Hospital, Sydney, NSW, Australia
- Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia
- Heart Valve Disease and Artificial Intelligence Laboratory, Victor Chang Cardiac Research Institute, Sydney, NSW, Australia
| | - Thomas Meredith
- Department of Cardiology, St Vincent’s Hospital, Sydney, NSW, Australia
- Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia
- Heart Valve Disease and Artificial Intelligence Laboratory, Victor Chang Cardiac Research Institute, Sydney, NSW, Australia
| | - David W. M. Muller
- Department of Cardiology, St Vincent’s Hospital, Sydney, NSW, Australia
- Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia
| | - David A. Roy
- Department of Cardiology, St Vincent’s Hospital, Sydney, NSW, Australia
- Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia
| | - Andrew K. Roy
- Department of Cardiology, St Vincent’s Hospital, Sydney, NSW, Australia
| | - Jason C. Kovacic
- Department of Cardiology, St Vincent’s Hospital, Sydney, NSW, Australia
- Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia
- Vascular Biology Laboratory, Victor Chang Cardiac Research Institute, Sydney, NSW, Australia
- Icahn School of Medicine at Mount Sinai, Cardiovascular Research Institute, New York, NY, United States
| | - Christopher S. Hayward
- Department of Cardiology, St Vincent’s Hospital, Sydney, NSW, Australia
- Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia
- Cardiac Mechanics Laboratory, Victor Chang Cardiac Research Institute, Sydney, NSW, Australia
| | - Michael P. Feneley
- Department of Cardiology, St Vincent’s Hospital, Sydney, NSW, Australia
- Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia
- Cardiac Mechanics Laboratory, Victor Chang Cardiac Research Institute, Sydney, NSW, Australia
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12
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The Role of Artificial Intelligence in Echocardiography. J Imaging 2023; 9:jimaging9020050. [PMID: 36826969 PMCID: PMC9962859 DOI: 10.3390/jimaging9020050] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 02/03/2023] [Accepted: 02/15/2023] [Indexed: 02/22/2023] Open
Abstract
Echocardiography is an integral part of the diagnosis and management of cardiovascular disease. The use and application of artificial intelligence (AI) is a rapidly expanding field in medicine to improve consistency and reduce interobserver variability. AI can be successfully applied to echocardiography in addressing variance during image acquisition and interpretation. Furthermore, AI and machine learning can aid in the diagnosis and management of cardiovascular disease. In the realm of echocardiography, accurate interpretation is largely dependent on the subjective knowledge of the operator. Echocardiography is burdened by the high dependence on the level of experience of the operator, to a greater extent than other imaging modalities like computed tomography, nuclear imaging, and magnetic resonance imaging. AI technologies offer new opportunities for echocardiography to produce accurate, automated, and more consistent interpretations. This review discusses machine learning as a subfield within AI in relation to image interpretation and how machine learning can improve the diagnostic performance of echocardiography. This review also explores the published literature outlining the value of AI and its potential to improve patient care.
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13
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Ferraz S, Coimbra M, Pedrosa J. Assisted probe guidance in cardiac ultrasound: A review. Front Cardiovasc Med 2023; 10:1056055. [PMID: 36865885 PMCID: PMC9971589 DOI: 10.3389/fcvm.2023.1056055] [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: 09/28/2022] [Accepted: 01/24/2023] [Indexed: 02/16/2023] Open
Abstract
Echocardiography is the most frequently used imaging modality in cardiology. However, its acquisition is affected by inter-observer variability and largely dependent on the operator's experience. In this context, artificial intelligence techniques could reduce these variabilities and provide a user independent system. In recent years, machine learning (ML) algorithms have been used in echocardiography to automate echocardiographic acquisition. This review focuses on the state-of-the-art studies that use ML to automate tasks regarding the acquisition of echocardiograms, including quality assessment (QA), recognition of cardiac views and assisted probe guidance during the scanning process. The results indicate that performance of automated acquisition was overall good, but most studies lack variability in their datasets. From our comprehensive review, we believe automated acquisition has the potential not only to improve accuracy of diagnosis, but also help novice operators build expertise and facilitate point of care healthcare in medically underserved areas.
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Affiliation(s)
- Sofia Ferraz
- Institute for Systems and Computer Engineering, Technology and Science INESC TEC, Porto, Portugal,Faculty of Engineering of the University of Porto (FEUP), Porto, Portugal,*Correspondence: Sofia Ferraz,
| | - Miguel Coimbra
- Institute for Systems and Computer Engineering, Technology and Science INESC TEC, Porto, Portugal,Faculty of Sciences of the University of Porto (FCUP), Porto, Portugal
| | - João Pedrosa
- Institute for Systems and Computer Engineering, Technology and Science INESC TEC, Porto, Portugal,Faculty of Engineering of the University of Porto (FEUP), Porto, Portugal
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14
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Nittas V, Daniore P, Landers C, Gille F, Amann J, Hubbs S, Puhan MA, Vayena E, Blasimme A. Beyond high hopes: A scoping review of the 2019-2021 scientific discourse on machine learning in medical imaging. PLOS DIGITAL HEALTH 2023; 2:e0000189. [PMID: 36812620 PMCID: PMC9931290 DOI: 10.1371/journal.pdig.0000189] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 01/02/2023] [Indexed: 02/04/2023]
Abstract
Machine learning has become a key driver of the digital health revolution. That comes with a fair share of high hopes and hype. We conducted a scoping review on machine learning in medical imaging, providing a comprehensive outlook of the field's potential, limitations, and future directions. Most reported strengths and promises included: improved (a) analytic power, (b) efficiency (c) decision making, and (d) equity. Most reported challenges included: (a) structural barriers and imaging heterogeneity, (b) scarcity of well-annotated, representative and interconnected imaging datasets (c) validity and performance limitations, including bias and equity issues, and (d) the still missing clinical integration. The boundaries between strengths and challenges, with cross-cutting ethical and regulatory implications, remain blurred. The literature emphasizes explainability and trustworthiness, with a largely missing discussion about the specific technical and regulatory challenges surrounding these concepts. Future trends are expected to shift towards multi-source models, combining imaging with an array of other data, in a more open access, and explainable manner.
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Affiliation(s)
- Vasileios Nittas
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
- Epidemiology, Biostatistics and Prevention Institute, Faculty of Medicine, Faculty of Science, University of Zurich, Zurich, Switzerland
| | - Paola Daniore
- Institute for Implementation Science in Health Care, Faculty of Medicine, University of Zurich, Switzerland
- Digital Society Initiative, University of Zurich, Switzerland
| | - Constantin Landers
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
| | - Felix Gille
- Institute for Implementation Science in Health Care, Faculty of Medicine, University of Zurich, Switzerland
- Digital Society Initiative, University of Zurich, Switzerland
| | - Julia Amann
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
| | - Shannon Hubbs
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
| | - Milo Alan Puhan
- Epidemiology, Biostatistics and Prevention Institute, Faculty of Medicine, Faculty of Science, University of Zurich, Zurich, Switzerland
| | - Effy Vayena
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
| | - Alessandro Blasimme
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
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15
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Pastore MC, Ilardi F, Stefanini A, Mandoli GE, Palermi S, Bandera F, Benfari G, Esposito R, Lisi M, Pasquini A, Santoro C, Valente S, D’Andrea A, Cameli M. Bedside Ultrasound for Hemodynamic Monitoring in Cardiac Intensive Care Unit. J Clin Med 2022; 11:jcm11247538. [PMID: 36556154 PMCID: PMC9785677 DOI: 10.3390/jcm11247538] [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: 11/13/2022] [Revised: 12/03/2022] [Accepted: 12/15/2022] [Indexed: 12/23/2022] Open
Abstract
Thanks to the advances in medical therapy and assist devices, the management of patients hospitalized in cardiac intensive care unit (CICU) is becoming increasingly challenging. In fact, Patients in the cardiac intensive care unit are frequently characterized by dynamic and variable diseases, which may evolve into several clinical phenotypes based on underlying etiology and its complexity. Therefore, the use of noninvasive tools in order to provide a personalized approach to these patients, according to their phenotype, may help to optimize the therapeutic strategies towards the underlying etiology. Echocardiography is the most reliable and feasible bedside method to assess cardiac function repeatedly, assisting clinicians not only in characterizing hemodynamic disorders, but also in helping to guide interventions and monitor response to therapies. Beyond basic echocardiographic parameters, its application has been expanded with the introduction of new tools such as lung ultrasound (LUS), the Venous Excess UltraSound (VexUS) grading system, and the assessment of pulmonary hypertension, which is fundamental to guide oxygen therapy. The aim of this review is to provide an overview on the current knowledge about the pathophysiology and echocardiographic evaluation of perfusion and congestion in patients in CICU, and to provide practical indications for the use of echocardiography across clinical phenotypes and new applications in CICU.
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Affiliation(s)
- Maria Concetta Pastore
- Department of Medical Biotechnologies, Division of Cardiology, University of Siena, 53100 Siena, Italy
- Correspondence: (M.C.P.); (M.C.); Tel.: +39-057-758-5377 (M.C.P.)
| | - Federica Ilardi
- Department of Advanced Biomedical Sciences, University of Naples Federico II, 80138 Naples, Italy
- Mediterranea Cardiocentro, 80122 Naples, Italy
| | - Andrea Stefanini
- Department of Medical Biotechnologies, Division of Cardiology, University of Siena, 53100 Siena, Italy
| | - Giulia Elena Mandoli
- Department of Medical Biotechnologies, Division of Cardiology, University of Siena, 53100 Siena, Italy
| | - Stefano Palermi
- Public Health Department, University of Naples Federico II, 80131 Naples, Italy
| | - Francesco Bandera
- Cardiology University Department, Heart Failure Unit, IRCCS Policlinico San Donato, San Donato Milanese, 20097 Milan, Italy
- Department of Biomedical Sciences for Health, University of Milano, 20122 Milan, Italy
| | - Giovanni Benfari
- Section of Cardiology, Department of Medicine, University of Verona, 37129 Verona, Italy
| | - Roberta Esposito
- Department of Clinical Medicine and Surgery, Federico II University Hospital, 80131 Naples, Italy
| | - Matteo Lisi
- Department of Cardiovascular Disease—AUSL Romagna, Division of Cardiology, Ospedale S. Maria delle Croci, Viale Randi 5, 48121 Ravenna, Italy
| | - Annalisa Pasquini
- Department of Cardiovascular and Thoracic Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, 20123 Rome, Italy
| | - Ciro Santoro
- Department of Advanced Biomedical Sciences, University of Naples Federico II, 80138 Naples, Italy
| | - Serafina Valente
- Department of Medical Biotechnologies, Division of Cardiology, University of Siena, 53100 Siena, Italy
| | - Antonello D’Andrea
- Department of Cardiology, Umberto I Hospital, 84014 Nocera Inferiore, Italy
| | - Matteo Cameli
- Department of Medical Biotechnologies, Division of Cardiology, University of Siena, 53100 Siena, Italy
- Correspondence: (M.C.P.); (M.C.); Tel.: +39-057-758-5377 (M.C.P.)
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16
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Henry MP, Cotella JI, Slivnick JA, Yamat M, Hipke K, Johnson R, Mor-Avi V, Lang RM. Three-Dimensional Echocardiographic Deconstruction: Feasibility of Clinical Evaluation from Two-Dimensional Views Derived from a Three-Dimensional Data Set. J Am Soc Echocardiogr 2022; 35:1009-1017.e2. [PMID: 35835310 DOI: 10.1016/j.echo.2022.06.011] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 06/26/2022] [Accepted: 06/26/2022] [Indexed: 11/25/2022]
Abstract
BACKGROUND Three-dimensional echocardiography (3DE) makes it possible to capture the entire heart in a single data set that theoretically could be used to extract any two-dimensional (2D) views and potentially replace the standard practice of serial 2D acquisitions. The aim of this study was to test the hypothesis that the quality of 3DE-derived 2D images is sufficient to allow the visualization of the left ventricular (LV), right ventricular (RV), and left atrial (LA) endocardium, on par with images from conventional two-dimensional echocardiography (2DE), and potentially more accurate quantification of chamber size and function. METHODS First, the investigators prospectively studied 36 patients who underwent 2DE in 14 standard views, and full-volume data sets from 3DE, from which the same views were extracted offline. The ability to visualize the LV endocardium, RV free wall, and LA endocardium was scored. LV linear dimensions, LV volumes, and LV ejection fraction (LVEF), LA volume, and RV basal dimension were measured and compared between both types of images. Thereafter, 40 patients who underwent 2DE, 3DE, and cardiac magnetic resonance (CMR) imaging on the same day were retrospectively studied. LV volumes and LVEF derived from 2DE and 3DE were compared side by side against the CMR reference. RESULTS Intertechnique agreement in visualization scores was 87% for LV segments, 86% for the RV free wall, and 83% for the LA endocardium. The correlations between 2DE- and 3DE-derived measurements were 0.95, 0.97, and 0.97 for LV volumes and LVEF, respectively, and 0.88 for RV basal dimension. Three-dimensional echocardiography-derived measurements of LV volumes and LVEF were more similar to those on CMR than those obtained on conventional 2DE. CONCLUSIONS The feasibility of segmental assessment of cardiac chambers using 3DE-derived 2D views is similar to that using conventional 2DE. This approach provides similar quantitative information, including more accurate LV volumes and LVEF measurements compared with CMR, and thus promises to significantly shorten the duration of the echocardiographic examination.
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Affiliation(s)
- Michael P Henry
- Department of Medicine, Section of Cardiology, University of Chicago Medical Center, Chicago, Illinois
| | - Juan I Cotella
- Department of Medicine, Section of Cardiology, University of Chicago Medical Center, Chicago, Illinois
| | - Jeremy A Slivnick
- Department of Medicine, Section of Cardiology, University of Chicago Medical Center, Chicago, Illinois
| | - Megan Yamat
- Department of Medicine, Section of Cardiology, University of Chicago Medical Center, Chicago, Illinois
| | - Kyle Hipke
- Department of Medicine, Section of Cardiology, University of Chicago Medical Center, Chicago, Illinois
| | - Roydell Johnson
- Department of Medicine, Section of Cardiology, University of Chicago Medical Center, Chicago, Illinois
| | - Victor Mor-Avi
- Department of Medicine, Section of Cardiology, University of Chicago Medical Center, Chicago, Illinois
| | - Roberto M Lang
- Department of Medicine, Section of Cardiology, University of Chicago Medical Center, Chicago, Illinois.
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17
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Dell'Angela L, Nicolosi GL. Artificial intelligence applied to cardiovascular imaging, a critical focus on echocardiography: The point-of-view from "the other side of the coin". JOURNAL OF CLINICAL ULTRASOUND : JCU 2022; 50:772-780. [PMID: 35466409 DOI: 10.1002/jcu.23215] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 04/16/2022] [Accepted: 04/19/2022] [Indexed: 06/14/2023]
Abstract
Cardiovascular imaging has achieved a crucial role in the management of cardiovascular diseases. In this field, echocardiography advantages include wide availability, portability, and affordability, at a relatively low cost. However, echocardiographic assessment requires highly trained operators, and implies high observer variability, as compared with the other cardiac imaging modalities. Hence, artificial intelligence might be extremely helpful. From the point-of-view of the peripheral "Spoke" Hospital potential user ("the other side of the coin"), artificial intelligence development appears very slow in the clinical arena. Many limitations are still present, and require full involvement, cooperation, and coordination of professional operators into Hub-and-Spoke network.
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Affiliation(s)
- Luca Dell'Angela
- Emergency Department, Cardiology Division, Gorizia & Monfalcone Hospital, ASUGI, Gorizia, Italy
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18
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Zhang Z, Zhu Y, Liu M, Zhang Z, Zhao Y, Yang X, Xie M, Zhang L. Artificial Intelligence-Enhanced Echocardiography for Systolic Function Assessment. J Clin Med 2022; 11:jcm11102893. [PMID: 35629019 PMCID: PMC9143561 DOI: 10.3390/jcm11102893] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 05/06/2022] [Accepted: 05/18/2022] [Indexed: 11/16/2022] Open
Abstract
The accurate assessment of left ventricular systolic function is crucial in the diagnosis and treatment of cardiovascular diseases. Left ventricular ejection fraction (LVEF) and global longitudinal strain (GLS) are the most critical indexes of cardiac systolic function. Echocardiography has become the mainstay of cardiac imaging for measuring LVEF and GLS because it is non-invasive, radiation-free, and allows for bedside operation and real-time processing. However, the human assessment of cardiac function depends on the sonographer’s experience, and despite their years of training, inter-observer variability exists. In addition, GLS requires post-processing, which is time consuming and shows variability across different devices. Researchers have turned to artificial intelligence (AI) to address these challenges. The powerful learning capabilities of AI enable feature extraction, which helps to achieve accurate identification of cardiac structures and reliable estimation of the ventricular volume and myocardial motion. Hence, the automatic output of systolic function indexes can be achieved based on echocardiographic images. This review attempts to thoroughly explain the latest progress of AI in assessing left ventricular systolic function and differential diagnosis of heart diseases by echocardiography and discusses the challenges and promises of this new field.
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Affiliation(s)
- Zisang Zhang
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China; (Z.Z.); (Y.Z.); (M.L.); (Z.Z.); (Y.Z.)
- Clinical Research Center for Medical Imaging in Hubei Province, Wuhan 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
| | - Ye Zhu
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China; (Z.Z.); (Y.Z.); (M.L.); (Z.Z.); (Y.Z.)
- Clinical Research Center for Medical Imaging in Hubei Province, Wuhan 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
| | - Manwei Liu
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China; (Z.Z.); (Y.Z.); (M.L.); (Z.Z.); (Y.Z.)
- Clinical Research Center for Medical Imaging in Hubei Province, Wuhan 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
| | - Ziming Zhang
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China; (Z.Z.); (Y.Z.); (M.L.); (Z.Z.); (Y.Z.)
- Clinical Research Center for Medical Imaging in Hubei Province, Wuhan 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
| | - Yang Zhao
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China; (Z.Z.); (Y.Z.); (M.L.); (Z.Z.); (Y.Z.)
- Clinical Research Center for Medical Imaging in Hubei Province, Wuhan 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
| | - Xin Yang
- Media and Communication Lab (MC Lab), Electronics and Information Engineering Department, Huazhong University of Science and Technology, Wuhan 430022, China;
| | - Mingxing Xie
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China; (Z.Z.); (Y.Z.); (M.L.); (Z.Z.); (Y.Z.)
- Clinical Research Center for Medical Imaging in Hubei Province, Wuhan 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
- Correspondence: (M.X.); (L.Z.)
| | - Li Zhang
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China; (Z.Z.); (Y.Z.); (M.L.); (Z.Z.); (Y.Z.)
- Clinical Research Center for Medical Imaging in Hubei Province, Wuhan 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
- Correspondence: (M.X.); (L.Z.)
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19
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Zhang Z, Zhu Y, Liu M, Zhang Z, Zhao Y, Yang X, Xie M, Zhang L. Artificial Intelligence-Enhanced Echocardiography for Systolic Function Assessment. J Clin Med 2022; 11:2893. [PMID: 35629019 DOI: 10.1177/01410768221102064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 05/06/2022] [Accepted: 05/18/2022] [Indexed: 07/31/2024] Open
Abstract
The accurate assessment of left ventricular systolic function is crucial in the diagnosis and treatment of cardiovascular diseases. Left ventricular ejection fraction (LVEF) and global longitudinal strain (GLS) are the most critical indexes of cardiac systolic function. Echocardiography has become the mainstay of cardiac imaging for measuring LVEF and GLS because it is non-invasive, radiation-free, and allows for bedside operation and real-time processing. However, the human assessment of cardiac function depends on the sonographer's experience, and despite their years of training, inter-observer variability exists. In addition, GLS requires post-processing, which is time consuming and shows variability across different devices. Researchers have turned to artificial intelligence (AI) to address these challenges. The powerful learning capabilities of AI enable feature extraction, which helps to achieve accurate identification of cardiac structures and reliable estimation of the ventricular volume and myocardial motion. Hence, the automatic output of systolic function indexes can be achieved based on echocardiographic images. This review attempts to thoroughly explain the latest progress of AI in assessing left ventricular systolic function and differential diagnosis of heart diseases by echocardiography and discusses the challenges and promises of this new field.
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Affiliation(s)
- Zisang Zhang
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- Clinical Research Center for Medical Imaging in Hubei Province, Wuhan 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
| | - Ye Zhu
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- Clinical Research Center for Medical Imaging in Hubei Province, Wuhan 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
| | - Manwei Liu
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- Clinical Research Center for Medical Imaging in Hubei Province, Wuhan 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
| | - Ziming Zhang
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- Clinical Research Center for Medical Imaging in Hubei Province, Wuhan 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
| | - Yang Zhao
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- Clinical Research Center for Medical Imaging in Hubei Province, Wuhan 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
| | - Xin Yang
- Media and Communication Lab (MC Lab), Electronics and Information Engineering Department, Huazhong University of Science and Technology, Wuhan 430022, China
| | - Mingxing Xie
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- Clinical Research Center for Medical Imaging in Hubei Province, Wuhan 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
| | - Li Zhang
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- Clinical Research Center for Medical Imaging in Hubei Province, Wuhan 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
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20
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Kim S, Park HB, Jeon J, Arsanjani R, Heo R, Lee SE, Moon I, Yoo SK, Chang HJ. Fully automated quantification of cardiac chamber and function assessment in 2-D echocardiography: clinical feasibility of deep learning-based algorithms. Int J Cardiovasc Imaging 2022; 38:1047-1059. [PMID: 35152371 PMCID: PMC11143010 DOI: 10.1007/s10554-021-02482-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 11/24/2021] [Indexed: 12/20/2022]
Abstract
We aimed to compare the segmentation performance of the current prominent deep learning (DL) algorithms with ground-truth segmentations and to validate the reproducibility of the manually created 2D echocardiographic four cardiac chamber ground-truth annotation. Recently emerged DL based fully-automated chamber segmentation and function assessment methods have shown great potential for future application in aiding image acquisition, quantification, and suggestion for diagnosis. However, the performance of current DL algorithms have not previously been compared with each other. In addition, the reproducibility of ground-truth annotations which are the basis of these algorithms have not yet been fully validated. We retrospectively enrolled 500 consecutive patients who underwent transthoracic echocardiogram (TTE) from December 2019 to December 2020. Simple U-net, Res-U-net, and Dense-U-net algorithms were compared for the segmentation performances and clinical indices such as left atrial volume (LAV), left ventricular end diastolic volume (LVEDV), left ventricular end systolic volume (LVESV), LV mass, and ejection fraction (EF) were evaluated. The inter- and intra-observer variability analysis was performed by two expert sonographers for a randomly selected echocardiographic view in 100 patients (apical 2-chamber, apical 4-chamber, and parasternal short axis views). The overall performance of all DL methods was excellent [average dice similarity coefficient (DSC) 0.91 to 0.95 and average Intersection over union (IOU) 0.83 to 0.90], with the exception of LV wall area on PSAX view (average DSC of 0.83, IOU 0.72). In addition, there were no significant difference in clinical indices between ground truth and automated DL measurements. For inter- and intra-observer variability analysis, the overall intra observer reproducibility was excellent: LAV (ICC = 0.995), LVEDV (ICC = 0.996), LVESV (ICC = 0.997), LV mass (ICC = 0.991) and EF (ICC = 0.984). The inter-observer reproducibility was slightly lower as compared to intraobserver agreement: LAV (ICC = 0.976), LVEDV (ICC = 0.982), LVESV (ICC = 0.970), LV mass (ICC = 0.971), and EF (ICC = 0.899). The three current prominent DL-based fully automated methods are able to reliably perform four-chamber segmentation and quantification of clinical indices. Furthermore, we were able to validate the four cardiac chamber ground-truth annotation and demonstrate an overall excellent reproducibility, but still with some degree of inter-observer variability.
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Affiliation(s)
- Sekeun Kim
- CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, South Korea
- Graduate Program of Biomedical Engineering, Yonsei University College of Medicine, Seoul, South Korea
| | - Hyung-Bok Park
- CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, South Korea
- Department of Cardiology, Catholic Kwandong University International St. Mary's Hospital, Incheon, South Korea
| | - Jaeik Jeon
- CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, South Korea
| | - Reza Arsanjani
- Department of Cardiovascular Diseases, Mayo Clinic Arizona, Phoenix, AZ, USA
| | - Ran Heo
- CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, South Korea
- Department of Cardiology, Hanyang University Seoul Hospital, Hanyang University College of Medicine, Seoul, South Korea
| | - Sang-Eun Lee
- CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, South Korea
- Department of Cardiology, Ewha Womans University Seoul Hospital, Seoul, South Korea
| | - Inki Moon
- CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, South Korea
- Division of Cardiology, Department of Internal Medicine, Soonchunghyang University Bucheon Hospital, Bucheon, South Korea
| | - Sun Kook Yoo
- Department of Medical Engineering, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea.
| | - Hyuk-Jae Chang
- CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, South Korea.
- Division of Cardiology, Department of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Yonsei University Health System, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea.
- Ontact Health Co., Ltd., Seoul, South Korea.
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21
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Ndagire E, Ollberding N, Sarnacki R, Meghna M, Pulle J, Atala J, Agaba C, Kansiime R, Bowen A, Longenecker CT, Oyella L, Rwebembera J, Okello E, Parks T, Zang H, Carapetis J, Sable C, Beaton AZ. Modelling study of the ability to diagnose acute rheumatic fever at different levels of the Ugandan healthcare system. BMJ Open 2022; 12:e050478. [PMID: 35318227 PMCID: PMC8943770 DOI: 10.1136/bmjopen-2021-050478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Accepted: 01/28/2022] [Indexed: 11/03/2022] Open
Abstract
OBJECTIVE To determine the ability to accurately diagnose acute rheumatic fever (ARF) given the resources available at three levels of the Ugandan healthcare system. METHODS Using data obtained from a large epidemiological database on ARF conducted in three districts of Uganda, we selected variables that might positively or negatively predict rheumatic fever based on diagnostic capacity at three levels/tiers of the Ugandan healthcare system. Variables were put into three statistical models that were built sequentially. Multiple logistic regression was used to estimate ORs and 95% CI of predictors of ARF. Performance of the models was determined using Akaike information criterion, adjusted R2, concordance C statistic, Brier score and adequacy index. RESULTS A model with clinical predictor variables available at a lower-level health centre (tier 1) predicted ARF with an optimism corrected area under the curve (AUC) (c-statistic) of 0.69. Adding tests available at the district level (tier 2, ECG, complete blood count and malaria testing) increased the AUC to 0.76. A model that additionally included diagnostic tests available at the national referral hospital (tier 3, echocardiography, anti-streptolysin O titres, erythrocyte sedimentation rate/C-reactive protein) had the best performance with an AUC of 0.91. CONCLUSIONS Reducing the burden of rheumatic heart disease in low and middle-income countries requires overcoming challenges of ARF diagnosis. Ensuring that possible cases can be evaluated using electrocardiography and relatively simple blood tests will improve diagnostic accuracy somewhat, but access to echocardiography and tests to confirm recent streptococcal infection will have the greatest impact.
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Affiliation(s)
- Emma Ndagire
- Department of Pediatric Cardiology, Uganda Heart Institute, Kampala, Uganda
| | - Nicholas Ollberding
- Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
- Department of Pediatrics, School of Medicine, University of Cincinnati, Cincinnati, Ohio, USA
| | - Rachel Sarnacki
- Division of Cardiology, Children's National Hospital, Washington, DC, USA
| | - Murali Meghna
- Division of Cardiology, Children's National Hospital, Washington, DC, USA
| | - Jafesi Pulle
- Department of RHD Research, Uganda Heart Institute, Kampala, Uganda
| | - Jenifer Atala
- Department of RHD Research, Uganda Heart Institute, Kampala, Uganda
| | - Collins Agaba
- Department of RHD Research, Uganda Heart Institute, Kampala, Uganda
| | | | - Asha Bowen
- Telethon Kids Institute, Perth, Western Australia, Australia
| | | | - Linda Oyella
- Department of RHD Research, Uganda Heart Institute, Kampala, Uganda
| | | | - Emmy Okello
- Division of Adult Cardiology, Uganda Heart Institute, Kampala, Uganda
| | - Tom Parks
- London School of Hygiene & Tropical Medicine, London, UK
| | - Huaiyu Zang
- Division of Cardiology, The Heart Institute, Cincinnati Children's Medical Center, Cincinnati, Ohio, USA
| | | | - Craig Sable
- Division of Cardiology, Children's National Hospital, Washington, DC, USA
| | - Andrea Z Beaton
- Department of Pediatrics, School of Medicine, University of Cincinnati, Cincinnati, Ohio, USA
- Division of Cardiology, The Heart Institute, Cincinnati Children's Medical Center, Cincinnati, Ohio, USA
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22
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Coulter SA, Campos K. Artificial Intelligence in Echocardiography. Tex Heart Inst J 2022; 49:480954. [PMID: 35481864 DOI: 10.14503/thij-21-7671] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Artificial intelligence in diagnostic cardiac-imaging platforms is advancing rapidly. In particular, artificial intelligence algorithms have increased the efficiency and accuracy of echocardiographic cardiovascular imaging, resulting in more complex echocardiographic imaging techniques and expanded use among noncardiologists. Here, we provide an overview of real-world applications of artificial intelligence in echocardiography including automatic high-quality computer-optimized image acquisition sequences, automated measurements, and algorithms for the rapid and accurate interpretation of cardiac physiology. These advances will not replace physicians but will improve their productivity, workflow, and diagnostic performance.
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Affiliation(s)
- Stephanie A Coulter
- Center for Women's Heart and Vascular Health, Texas Heart Institute, Houston, Texas
| | - Karla Campos
- Center for Women's Heart and Vascular Health, Texas Heart Institute, Houston, Texas
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23
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D'Costa A, Zatale A. AI and the cardiologist: when mind, heart and machine unite. Open Heart 2021; 8:openhrt-2021-001874. [PMID: 34949649 PMCID: PMC8705226 DOI: 10.1136/openhrt-2021-001874] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 11/23/2021] [Indexed: 11/04/2022] Open
Abstract
Artificial intelligence (AI) and deep learning has made much headway in the consumer and advertising sector, not only affecting how and what people purchase these days, but also affecting behaviour and cultural attitudes. It is poised to influence nearly every aspect of our being, and the field of cardiology is not an exception. This paper aims to brief the clinician on the advances in AI and machine learning in the field of cardiology, its applications, while also recognising the potential for future development in these two mammoth fields. With the advent of big data, new opportunities are emerging to build AI tools, with better accuracy, that will directly aid not only the clinician but also allow nations to provide better healthcare to its citizens.
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Affiliation(s)
- Antonio D'Costa
- Paediatrics, Bai Jerbai Wadia Hospital for Children, Mumbai, Maharashtra, India
| | - Aishwarya Zatale
- Paediatrics, Bai Jerbai Wadia Hospital for Children, Mumbai, Maharashtra, India
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24
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Huang GS, Sheehan FH, Gill EA. Transesophageal echocardiography simulation: A review of current technology. Echocardiography 2021; 39:89-100. [PMID: 34913188 DOI: 10.1111/echo.15281] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 10/19/2021] [Accepted: 11/26/2021] [Indexed: 01/27/2023] Open
Abstract
Transesophageal echocardiography (TEE) has experienced tremendous increase in interest and demand alongside the rapid growth of transcatheter structural cardiac interventions. TEE instruction prolongs the procedure, increasing the risk of probe malfunction from overheating and patient complications from prolonged sedation. Echocardiographic simulation programs have been developed to hone the procedural skills of novice operators in a time-unrestricted, low-pressure environment before they perform TEEs on real patients. Simulators likely benefit training in interventional TEE for the same reasons. We searched PubMed, basic Google, and Google Scholar for currently marketed TEE simulators, including foreign as well as US companies. We queried the vendors regarding features of the simulators that pertain to effective instructional design for diagnostic TEE. We also queried regarding the simulators' applicability to training in interventional TEE. The vendors' responses are reported here. In addition, we discuss the specific training needs for structural heart interventions, for which echocardiographic simulation could be a powerful educational tool. Lastly, we discuss the role of simulation for formative and summative assessment, and the advances required to improve training in complex procedures within the field of interventional echocardiography.
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Affiliation(s)
- Gary S Huang
- Department of Medicine, Division of Cardiology, University of Washington, Seattle, Washington, USA
| | - Florence H Sheehan
- Department of Medicine, Division of Cardiology, University of Washington, Seattle, Washington, USA
| | - Edward A Gill
- Department of Medicine, Division of Cardiology, University of Colorado, Denver, Colorado, USA
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25
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Morita SX, Kusunose K, Haga A, Sata M, Hasegawa K, Raita Y, Reilly MP, Fifer MA, Maurer MS, Shimada YJ. Deep Learning Analysis of Echocardiographic Images to Predict Positive Genotype in Patients With Hypertrophic Cardiomyopathy. Front Cardiovasc Med 2021; 8:669860. [PMID: 34513940 PMCID: PMC8429777 DOI: 10.3389/fcvm.2021.669860] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Accepted: 08/09/2021] [Indexed: 11/26/2022] Open
Abstract
Genetic testing provides valuable insights into family screening strategies, diagnosis, and prognosis in patients with hypertrophic cardiomyopathy (HCM). On the other hand, genetic testing carries socio-economical and psychological burdens. It is therefore important to identify patients with HCM who are more likely to have positive genotype. However, conventional prediction models based on clinical and echocardiographic parameters offer only modest accuracy and are subject to intra- and inter-observer variability. We therefore hypothesized that deep convolutional neural network (DCNN, a type of deep learning) analysis of echocardiographic images improves the predictive accuracy of positive genotype in patients with HCM. In each case, we obtained parasternal short- and long-axis as well as apical 2-, 3-, 4-, and 5-chamber views. We employed DCNN algorithm to predict positive genotype based on the input echocardiographic images. We performed 5-fold cross-validations. We used 2 reference models—the Mayo HCM Genotype Predictor score (Mayo score) and the Toronto HCM Genotype score (Toronto score). We compared the area under the receiver-operating-characteristic curve (AUC) between a combined model using the reference model plus DCNN-derived probability and the reference model. We calculated the p-value by performing 1,000 bootstrapping. We calculated sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). In addition, we examined the net reclassification improvement. We included 99 adults with HCM who underwent genetic testing. Overall, 45 patients (45%) had positive genotype. The new model combining Mayo score and DCNN-derived probability significantly outperformed Mayo score (AUC 0.86 [95% CI 0.79–0.93] vs. 0.72 [0.61–0.82]; p < 0.001). Similarly, the new model combining Toronto score and DCNN-derived probability exhibited a higher AUC compared to Toronto score alone (AUC 0.84 [0.76–0.92] vs. 0.75 [0.65–0.85]; p = 0.03). An improvement in the sensitivity, specificity, PPV, and NPV was also achieved, along with significant net reclassification improvement. In conclusion, compared to the conventional models, our new model combining the conventional and DCNN-derived models demonstrated superior accuracy to predict positive genotype in patients with HCM.
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Affiliation(s)
- Sae X Morita
- Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, NY, United States
| | - Kenya Kusunose
- Department of Cardiovascular Medicine, Tokushima University, Tokushima, Japan
| | - Akihiro Haga
- Department of Medical Image Informatics, Graduate School of Biomedical Sciences, Tokushima University, Tokushima, Japan
| | - Masataka Sata
- Department of Cardiovascular Medicine, Tokushima University, Tokushima, Japan
| | - Kohei Hasegawa
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA, United States
| | - Yoshihiko Raita
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA, United States
| | - Muredach P Reilly
- Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, NY, United States.,Irving Institute for Clinical and Translational Research, Columbia University Irving Medical Center, New York, NY, United States
| | - Michael A Fifer
- Cardiology Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, United States
| | - Mathew S Maurer
- Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, NY, United States
| | - Yuichi J Shimada
- Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, NY, United States
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26
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Abstract
PURPOSE OF REVIEW Artificial intelligence is the ability for machines to perform intelligent tasks. Artificial intelligence is already penetrating many aspects of medicine including cardiac surgery. Here, we offer a platform introduction to artificial intelligence for cardiac surgeons to understand the implementations of this transformative tool. RECENT FINDINGS Artificial intelligence has contributed greatly to the automation of cardiac imaging, including echocardiography, cardiac computed tomography, cardiac MRI and most recently, in radiomics. There are also several artificial intelligence based clinical prediction tools that predict complex outcomes after cardiac surgery. Waveform analysis, specifically, automated electrocardiogram analysis, has seen significant strides with promise in wearables and remote monitoring. Experimentally, artificial intelligence has also entered the operating room in the form of augmented reality and automated robotic surgery. SUMMARY Artificial intelligence has many potential exciting applications in cardiac surgery. It can streamline physician workload and help make medicine more human again by placing the physician back at the bedside. Here, we offer cardiac surgeons an introduction to this transformative tool so that they may actively participate in creating clinically relevant implementations to improve our practice.
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27
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Abstract
Rapid development of artificial intelligence (AI) is gaining grounds in medicine. Its huge impact and inevitable necessity are also reflected in cardiovascular imaging. Although AI would probably never replace doctors, it can significantly support and improve their productivity and diagnostic performance. Many algorithms have already proven useful at all stages of the cardiac imaging chain. Their crucial practical applications include classification, automatic quantification, notification, diagnosis, and risk prediction. Consequently, more reproducible and repeatable studies are obtained, and personalized reports may be available to any patient. Utilization of AI also increases patient safety and decreases healthcare costs. Furthermore, AI is particularly useful for beginners in the field of cardiac imaging as it provides anatomic guidance and interpretation of complex imaging results. In contrast, lack of interpretability and explainability in AI carries a risk of harmful recommendations. This review was aimed at summarizing AI principles, essential execution requirements, and challenges as well as its recent applications in cardiovascular imaging.
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28
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Namasivayam M. Machine Learning in Cardiac Imaging: Exploring the Art of Cluster Analysis. J Am Soc Echocardiogr 2021; 34:913-915. [DOI: 10.1016/j.echo.2021.05.011] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Accepted: 05/17/2021] [Indexed: 01/31/2023]
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29
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Wood JR, Pedersen RC, Rooks VJ. Neuroimaging for the Primary Care Provider: A Review of Modalities, Indications, and Pitfalls. Pediatr Clin North Am 2021; 68:715-725. [PMID: 34247704 DOI: 10.1016/j.pcl.2021.04.014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
When evaluating a child with a potential neurologic or neurodevelopmental disorder, identifying indications for imaging and the correct imaging modality to order can be challenging. This article provides an overview of computed tomography, MRI, ultrasonography, and radiography with an emphasis on indications for use, pitfalls to be avoided, and recent advances. A discussion of the appropriate use of ionizing radiation, intravenous contrast, and sedation is also provided.
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Affiliation(s)
- Jonathan R Wood
- Department of Radiology, Tripler Army Medical Center, 1 Jarrett White Road, MCHK-DR, Honolulu, HI 96859, USA.
| | - Robert C Pedersen
- Department of Pediatrics, Hawaii Permanente Medical Group, 2828 Paa Street, Honolulu, HI 96819, USA
| | - Veronica J Rooks
- Department of Radiology, Tripler Army Medical Center, 1 Jarrett White Road, MCHK-DR, Honolulu, HI 96859, USA
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30
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Yoon YE, Kim S, Chang HJ. Artificial Intelligence and Echocardiography. J Cardiovasc Imaging 2021; 29:193-204. [PMID: 34080347 PMCID: PMC8318807 DOI: 10.4250/jcvi.2021.0039] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 03/30/2021] [Accepted: 04/05/2021] [Indexed: 12/13/2022] Open
Abstract
Artificial intelligence (AI) is evolving in the field of diagnostic medical imaging, including echocardiography. Although the dynamic nature of echocardiography presents challenges beyond those of static images from X-ray, computed tomography, magnetic resonance, and radioisotope imaging, AI has influenced all steps of echocardiography, from image acquisition to automatic measurement and interpretation. Considering that echocardiography often is affected by inter-observer variability and shows a strong dependence on the level of experience, AI could be extremely advantageous in minimizing observer variation and providing reproducible measures, enabling accurate diagnosis. Currently, most reported AI applications in echocardiographic measurement have focused on improved image acquisition and automation of repetitive and tedious tasks; however, the role of AI applications should not be limited to conventional processes. Rather, AI could provide clinically important insights from subtle and non-specific data, such as changes in myocardial texture in patients with myocardial disease. Recent initiatives to develop large echocardiographic databases can facilitate development of AI applications. The ultimate goal of applying AI to echocardiography is automation of the entire process of echocardiogram analysis. Once automatic analysis becomes reliable, workflows in clinical echocardiographic will change radically. The human expert will remain the master controlling the overall diagnostic process, will not be replaced by AI, and will obtain significant support from AI systems to guide acquisition, perform measurements, and integrate and compare data on request.
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Affiliation(s)
- Yeonyee E Yoon
- Cardiovascular Center, Seoul National University Bundang Hospital, Seongnam, Korea.,Department of Internal Medicine, Cardiovascular Center, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea
| | - Sekeun Kim
- Graduate School of Biomedical Engineering, Yonsei University College of Medicine, Seoul, Korea.,Ontact Health Co., Ltd., Seoul, Korea
| | - Hyuk Jae Chang
- CONNECT-AI Research Center, Yonsei University Health System, Yonsei University College of Medicine, Seoul, Korea.,Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University Health System, Yonsei University College of Medicine, Seoul, Korea.
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31
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Aortic Annular Sizing Using Novel Software in Three-Dimensional Transesophageal Echocardiography for Transcatheter Aortic Valve Replacement: A Systematic Review and Meta-Analysis. Diagnostics (Basel) 2021; 11:diagnostics11050751. [PMID: 33922239 PMCID: PMC8145366 DOI: 10.3390/diagnostics11050751] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 04/15/2021] [Accepted: 04/21/2021] [Indexed: 02/06/2023] Open
Abstract
(1) Background: We performed this study to evaluate the agreement between novel automated software of three-dimensional transesophageal echocardiography (3D-TEE) and multidetector computed tomography (MDCT) for aortic annular measurements of preprocedural transcatheter aortic valve replacement (TAVR); (2) Methods: PubMed, EMBASE, Web of Science, and Cochrane Library (Wiley) databases were systematically searched for studies that compared 3D-TEE and MDCT as the reference standard for aortic annular measurement of the following parameters: annular area, annular perimeter, area derived-diameter, perimeter derived-diameter, maximum and minimum diameter. Meta-analytic methods were utilized to determine the pooled correlations and mean differences between 3D-TEE and MDCT. Heterogeneity and publication bias were also assessed. Meta-regression analyses were performed based on the potential factors affecting the correlation of aortic annular area; (3) Results: A total of 889 patients from 10 studies were included in the meta-analysis. Pooled correlation coefficients between 3D-TEE and MDCT of annulus area, perimeter, area derived-diameter, perimeter derived-diameter, maximum and minimum diameter measurements were strong 0.89 (95% CI: 0.84–0.92), 0.88 (95% CI: 0.83–0.92), 0.87 (95% CI: 0.77–0.93), 0.87 (95% CI: 0.77–0.93), 0.79 (95% CI: 0.64–0.87), and 0.75 (95% CI: 0.61–0.84) (Overall p < 0.0001), respectively. Pooled mean differences between 3D-TEE and MDCT of annulus area, perimeter, area derived-diameter, perimeter derived-diameter, maximum and minimum diameter measurements were −20.01 mm2 ((95% CI: −35.37 to −0.64), p = 0.011), −2.31 mm ((95% CI: −3.31 to −1.31), p < 0.0001), −0.22 mm ((95% CI: −0.73 to 0.29), p = 0.40), −0.47 mm ((95% CI: −1.06 to 0.12), p = 0.12), −1.36 mm ((95% CI: −2.43 to −0.30), p = 0.012), and 0.31 mm ((95% CI: −0.15 to 0.77), p = 0.18), respectively. There were no statistically significant associations with the baseline patient characteristics of sex, age, left ventricular ejection fraction, mean transaortic gradient, and aortic valve area to the correlation between 3D-TEE and MDCT for aortic annular area sizing; (4) Conclusions: The present study implies that 3D-TEE using novel software tools, automatically analysis, is feasible to MDCT for annulus sizing in clinical practice.
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32
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Khoche S, Hashmi N, Bronshteyn YS, Choi C, Poorsattar S, Maus TM. The Year in Perioperative Echocardiography: Selected Highlights from 2020. J Cardiothorac Vasc Anesth 2021; 35:2559-2568. [PMID: 33934985 DOI: 10.1053/j.jvca.2021.03.038] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Accepted: 03/22/2021] [Indexed: 11/11/2022]
Abstract
This article is the fifth of an annual series reviewing the research highlights of the year pertaining to the subspecialty of perioperative echocardiography for the Journal of Cardiothoracic and Vascular Anesthesia. The authors thank Editor-in-Chief Dr. Kaplan and the editorial board for the opportunity to continue this series. In most cases, these will be research articles that are targeted at the perioperative echocardiography diagnosis and treatment of patients after cardiothoracic surgery; but in some cases, these articles will target the use of perioperative echocardiography in general.
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Affiliation(s)
- Swapnil Khoche
- Department of Anesthesiology, University of California San Diego Medical Center - Sulpizio Cardiovascular Center, La Jolla, CA
| | - Nazish Hashmi
- Department of Anesthesiology, Duke University, School of Medicine, Durham, NC
| | - Yuriy S Bronshteyn
- Department of Anesthesiology, Duke University, School of Medicine, Durham, NC
| | - Christine Choi
- Department of Anesthesiology, University of California San Diego Medical Center - Sulpizio Cardiovascular Center, La Jolla, CA
| | - Sophia Poorsattar
- Department of Anesthesiology and Perioperative Medicine, University of California, Los Angeles, CA
| | - Timothy M Maus
- Department of Anesthesiology, University of California San Diego Medical Center - Sulpizio Cardiovascular Center, La Jolla, CA.
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Cipolletta E, Fiorentino MC, Moccia S, Guidotti I, Grassi W, Filippucci E, Frontoni E. Artificial Intelligence for Ultrasound Informative Image Selection of Metacarpal Head Cartilage. A Pilot Study. Front Med (Lausanne) 2021; 8:589197. [PMID: 33732711 PMCID: PMC7956959 DOI: 10.3389/fmed.2021.589197] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Accepted: 01/19/2021] [Indexed: 12/12/2022] Open
Abstract
Objectives: This study aims to develop an automatic deep-learning algorithm, which is based on Convolutional Neural Networks (CNNs), for ultrasound informative-image selection of hyaline cartilage at metacarpal head level. The algorithm performance and that of three beginner sonographers were compared with an expert assessment, which was considered the gold standard. Methods: The study was divided into two steps. In the first one, an automatic deep-learning algorithm for image selection was developed using 1,600 ultrasound (US) images of the metacarpal head cartilage (MHC) acquired in 40 healthy subjects using a very high-frequency probe (up to 22 MHz). The algorithm task was to identify US images defined informative as they show enough information to fulfill the Outcome Measure in Rheumatology US definition of healthy hyaline cartilage. The algorithm relied on VGG16 CNN, which was fine-tuned to classify US images in informative and non-informative ones. A repeated leave-four-subject out cross-validation was performed using the expert sonographer assessment as gold-standard. In the second step, the expert assessed the algorithm and the beginner sonographers' ability to obtain US informative images of the MHC. Results: The VGG16 CNN showed excellent performance in the first step, with a mean area (AUC) under the receiver operating characteristic curve, computed among the 10 models obtained from cross-validation, of 0.99 ± 0.01. The model that reached the best AUC on the testing set, which we named “MHC identifier 1,” was then evaluated by the expert sonographer. The agreement between the algorithm, and the expert sonographer was almost perfect [Cohen's kappa: 0.84 (95% confidence interval: 0.71–0.98)], whereas the agreement between the expert and the beginner sonographers using conventional assessment was moderate [Cohen's kappa: 0.63 (95% confidence interval: 0.49–0.76)]. The conventional obtainment of US images by beginner sonographers required 6.0 ± 1.0 min, whereas US videoclip acquisition by a beginner sonographer lasted only 2.0 ± 0.8 min. Conclusion: This study paves the way for the automatic identification of informative US images for assessing MHC. This may redefine the US reliability in the evaluation of MHC integrity, especially in terms of intrareader reliability and may support beginner sonographers during US training.
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Affiliation(s)
- Edoardo Cipolletta
- Rheumatology Unit, Department of Clinical and Molecular Sciences, Polytechnic University of Marche, Ancona, Italy
| | | | - Sara Moccia
- Department of Information Engineering, Polytechnic University of Marche, Ancona, Italy.,Department of Advanced Robotics, Italian Institute of Technology, Genoa, Italy
| | - Irene Guidotti
- Department of Information Engineering, Polytechnic University of Marche, Ancona, Italy
| | - Walter Grassi
- Rheumatology Unit, Department of Clinical and Molecular Sciences, Polytechnic University of Marche, Ancona, Italy
| | - Emilio Filippucci
- Rheumatology Unit, Department of Clinical and Molecular Sciences, Polytechnic University of Marche, Ancona, Italy
| | - Emanuele Frontoni
- Department of Information Engineering, Polytechnic University of Marche, Ancona, Italy
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Schuuring MJ, Išgum I, Cosyns B, Chamuleau SAJ, Bouma BJ. Routine Echocardiography and Artificial Intelligence Solutions. Front Cardiovasc Med 2021; 8:648877. [PMID: 33708808 PMCID: PMC7940184 DOI: 10.3389/fcvm.2021.648877] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2021] [Accepted: 02/05/2021] [Indexed: 12/28/2022] Open
Abstract
Introduction: Echocardiography is widely used because of its portability, high temporal resolution, absence of radiation, and due to the low-costs. Over the past years, echocardiography has been recommended by the European Society of Cardiology in most cardiac diseases for both diagnostic and prognostic purposes. These recommendations have led to an increase in number of performed studies each requiring diligent processing and reviewing. The standard work pattern of image analysis including quantification and reporting has become highly resource intensive and time consuming. Existence of a large number of datasets with digital echocardiography images and recent advent of AI technology have created an environment in which artificial intelligence (AI) solutions can be developed successfully to automate current manual workflow. Methods and Results: We report on published AI solutions for echocardiography analysis on methods' performance, characteristics of the used data and imaged population. Contemporary AI applications are available for automation and advent in the image acquisition, analysis, reporting and education. AI solutions have been developed for both diagnostic and predictive tasks in echocardiography. Left ventricular function assessment and quantification have been most often performed. Performance of automated image view classification, image quality enhancement, cardiac function assessment, disease classification, and cardiac event prediction was overall good but most studies lack external evaluation. Conclusion: Contemporary AI solutions for image acquisition, analysis, reporting and education are developed for relevant tasks with promising performance. In the future major benefit of AI in echocardiography is expected from improvements in automated analysis and interpretation to reduce workload and improve clinical outcome. Some of the challenges have yet to be overcome, however, none of them are insurmountable.
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Affiliation(s)
- Mark J. Schuuring
- Amsterdam University Medical Centers -Location Academic Medical Center, Department of Cardiology, University of Amsterdam, Amsterdam, Netherlands
| | - Ivana Išgum
- Amsterdam University Medical Centers -Location Academic Medical Center, Department of Biomedical Engineering and Physics, University of Amsterdam, Amsterdam, Netherlands
- Amsterdam University Medical Centers -Location Academic Medical Center, Department of Radiology and Nuclear Medicine, University of Amsterdam, Amsterdam, Netherlands
- Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centers -Location Academic Medical Center, University of Amsterdam, Amsterdam, Netherlands
| | - Bernard Cosyns
- Department of Cardiology, University Hospital Brussel, Brussels, Belgium
| | - Steven A. J. Chamuleau
- Amsterdam University Medical Centers -Location Academic Medical Center, Department of Cardiology, University of Amsterdam, Amsterdam, Netherlands
- Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centers -Location Academic Medical Center, University of Amsterdam, Amsterdam, Netherlands
| | - Berto J. Bouma
- Amsterdam University Medical Centers -Location Academic Medical Center, Department of Cardiology, University of Amsterdam, Amsterdam, Netherlands
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Low-Cost Office-Based Cardiovascular Risk Stratification Using Machine Learning and Focused Carotid Ultrasound in an Asian-Indian Cohort. J Med Syst 2020; 44:208. [DOI: 10.1007/s10916-020-01675-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Accepted: 11/09/2020] [Indexed: 12/13/2022]
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Jenkins C, Tsang W. Three-dimensional echocardiographic acquisition and validity of left ventricular volumes and ejection fraction. Echocardiography 2020; 37:1646-1653. [PMID: 32976656 DOI: 10.1111/echo.14862] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Accepted: 08/11/2020] [Indexed: 12/12/2022] Open
Abstract
Transthoracic (TTE) and transesophageal (TEE) three-dimensional echocardiography (3DE) is now used in daily clinical practice. Advancements in technology have improved image acquisition with higher frame rates and increased resolution. Different 3DE acquisition techniques can be used depending upon the structure of interest and if volumetric analysis is required. Measurements of left ventricular (LV) volumes are the most common use of 3DE clinically but are highly dependent upon image quality. Three-dimensional LV function analysis has been made easier with the development of automated software, which has been found to be highly reproducible. However, further research is needed to develop normal reference range values of LV function for both 3D TTE and TEE.
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Affiliation(s)
- Carly Jenkins
- Cardiac Investigations, Logan Hospital, Meadowbrook, QLD, Australia
| | - Wendy Tsang
- Toronto General Hospital, University Health Network, University of Toronto, Toronto, ON, Canada
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Chen X, Owen CA, Huang EC, Maggard BD, Latif RK, Clifford SP, Li J, Huang J. Artificial Intelligence in Echocardiography for Anesthesiologists. J Cardiothorac Vasc Anesth 2020; 35:251-261. [PMID: 32962932 DOI: 10.1053/j.jvca.2020.08.048] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Accepted: 08/19/2020] [Indexed: 02/06/2023]
Abstract
Echocardiography is a unique diagnostic tool for intraoperative monitoring and assessment of patients with cardiovascular diseases. However, there are high levels of interoperator variations in echocardiography interpretations that could lead to inaccurate diagnosis and incorrect treatment. Furthermore, anesthesiologists are faced with the additional challenge to interpret echocardiography and make decisions in a limited timeframe from these complex data. The need for an automated, less operator-dependent process that enhances speed and accuracy of echocardiography analysis is crucial for anesthesiologists. Artificial intelligence is playing an increasingly important role in the medical field and could help anesthesiologists analyze complex echocardiographic data while adding increased accuracy and consistency to interpretation. This review aims to summarize practical use of artificial intelligence in echocardiography and discusses potential limitations and challenges in the future for anesthesiologists.
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Affiliation(s)
- Xia Chen
- Department of Anesthesiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | | | | | - Brittany D Maggard
- Department of Anesthesiology and Perioperative Medicine, University of Louisville, Louisville, KY
| | - Rana K Latif
- Department of Anesthesiology and Perioperative Medicine, University of Louisville, Louisville, KY; Outcomes Research Consortium, Cleveland, Ohio, USA
| | - Sean P Clifford
- Department of Anesthesiology and Perioperative Medicine, University of Louisville, Louisville, KY
| | - Jinbao Li
- Department of Anesthesiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jiapeng Huang
- Department of Anesthesiology and Perioperative Medicine, University of Louisville, Louisville, KY; Department of Cardiovascular and Thoracic Surgery, University of Louisville, Louisville, KY.
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