1
|
Seetharam K, Thyagaturu H, Ferreira GL, Patel A, Patel C, Elahi A, Pachulski R, Shah J, Mir P, Thodimela A, Pala M, Thet Z, Hamirani Y. Broadening Perspectives of Artificial Intelligence in Echocardiography. Cardiol Ther 2024; 13:267-279. [PMID: 38703292 PMCID: PMC11093957 DOI: 10.1007/s40119-024-00368-3] [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: 11/13/2023] [Accepted: 04/11/2024] [Indexed: 05/06/2024] Open
Abstract
Echocardiography frequently serves as the first-line treatment of diagnostic imaging for several pathological entities in cardiology. Artificial intelligence (AI) has been growing substantially in information technology and various commercial industries. Machine learning (ML), a branch of AI, has been shown to expand the capabilities and potential of echocardiography. ML algorithms expand the field of echocardiography by automated assessment of the ejection fraction and left ventricular function, integrating novel approaches such as speckle tracking or tissue Doppler echocardiography or vector flow mapping, improved phenotyping, distinguishing between cardiac conditions, and incorporating information from mobile health and genomics. In this review article, we assess the impact of AI and ML in echocardiography.
Collapse
Affiliation(s)
- Karthik Seetharam
- Division of Cardiovascular Disease, West Virgina University, Heart and Vascular Institute, 1 Medical Center Drive, Morgantown, WV, 26506, USA.
- Wyckoff Heights Medical Center, Brooklyn, NY, USA.
| | - Harshith Thyagaturu
- Division of Cardiovascular Disease, West Virgina University, Heart and Vascular Institute, 1 Medical Center Drive, Morgantown, WV, 26506, USA
| | | | - Aditya Patel
- Wyckoff Heights Medical Center, Brooklyn, NY, USA
| | - Chinmay Patel
- University of Pittsburg Medical Center, Harrisburg, PA, USA
| | - Asim Elahi
- Wyckoff Heights Medical Center, Brooklyn, NY, USA
| | - Roman Pachulski
- St. John's Episcopal Hospital - South Shore, New York, NY, USA
| | - Jilan Shah
- Wyckoff Heights Medical Center, Brooklyn, NY, USA
| | - Parvez Mir
- Wyckoff Heights Medical Center, Brooklyn, NY, USA
| | | | - Manya Pala
- Wyckoff Heights Medical Center, Brooklyn, NY, USA
| | - Zeyar Thet
- Wyckoff Heights Medical Center, Brooklyn, NY, USA
| | - Yasmin Hamirani
- Robert Woods Johnson University Hospital/Rutgers University, New Brusnwick, NJ, USA
| |
Collapse
|
2
|
Pozza A, Zanella L, Castaldi B, Di Salvo G. How Will Artificial Intelligence Shape the Future of Decision-Making in Congenital Heart Disease? J Clin Med 2024; 13:2996. [PMID: 38792537 PMCID: PMC11122569 DOI: 10.3390/jcm13102996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 05/10/2024] [Accepted: 05/13/2024] [Indexed: 05/26/2024] Open
Abstract
Improvements in medical technology have significantly changed the management of congenital heart disease (CHD), offering novel tools to predict outcomes and personalize follow-up care. By using sophisticated imaging modalities, computational models and machine learning algorithms, clinicians can experiment with unprecedented insights into the complex anatomy and physiology of CHD. These tools enable early identification of high-risk patients, thus allowing timely, tailored interventions and improved outcomes. Additionally, the integration of genetic testing offers valuable prognostic information, helping in risk stratification and treatment optimisation. The birth of telemedicine platforms and remote monitoring devices facilitates customised follow-up care, enhancing patient engagement and reducing healthcare disparities. Taking into consideration challenges and ethical issues, clinicians can make the most of the full potential of artificial intelligence (AI) to further refine prognostic models, personalize care and improve long-term outcomes for patients with CHD. This narrative review aims to provide a comprehensive illustration of how AI has been implemented as a new technological method for enhancing the management of CHD.
Collapse
Affiliation(s)
- Alice Pozza
- Paediatric Cardiology Unit, Department of Women’s and Children’s Health, University of Padua, 35122 Padova, Italy; (A.P.)
| | - Luca Zanella
- Heart Surgery, Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy
- Cardiac Surgery Unit, Department of Cardiac-Thoracic-Vascular Diseases, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| | - Biagio Castaldi
- Paediatric Cardiology Unit, Department of Women’s and Children’s Health, University of Padua, 35122 Padova, Italy; (A.P.)
| | - Giovanni Di Salvo
- Paediatric Cardiology Unit, Department of Women’s and Children’s Health, University of Padua, 35122 Padova, Italy; (A.P.)
| |
Collapse
|
3
|
Mears J, Kaleem S, Panchamia R, Kamel H, Tam C, Thalappillil R, Murthy S, Merkler AE, Zhang C, Ch'ang JH. Leveraging the Capabilities of AI: Novice Neurology-Trained Operators Performing Cardiac POCUS in Patients with Acute Brain Injury. Neurocrit Care 2024:10.1007/s12028-024-01953-z. [PMID: 38506968 DOI: 10.1007/s12028-024-01953-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 02/01/2024] [Indexed: 03/22/2024]
Abstract
BACKGROUND Cardiac point-of-care ultrasound (cPOCUS) can aid in the diagnosis and treatment of cardiac disorders. Such disorders can arise as complications of acute brain injury, but most neurologic intensive care unit (NICU) providers do not receive formal training in cPOCUS. Caption artificial intelligence (AI) uses a novel deep learning (DL) algorithm to guide novice cPOCUS users in obtaining diagnostic-quality cardiac images. The primary objective of this study was to determine how often NICU providers with minimal cPOCUS experience capture quality images using DL-guided cPOCUS as well as the association between DL-guided cPOCUS and change in management and time to formal echocardiograms in the NICU. METHODS From September 2020 to November 2021, neurology-trained physician assistants, residents, and fellows used DL software to perform clinically indicated cPOCUS scans in an academic tertiary NICU. Certified echocardiographers evaluated each scan independently to assess the quality of images and global interpretability of left ventricular function, right ventricular function, inferior vena cava size, and presence of pericardial effusion. Descriptive statistics with exact confidence intervals were used to calculate proportions of obtained images that were of adequate quality and that changed management. Time to first adequate cardiac images (either cPOCUS or formal echocardiography) was compared using a similar population from 2018. RESULTS In 153 patients, 184 scans were performed for a total of 943 image views. Three certified echocardiographers deemed 63.4% of scans as interpretable for a qualitative assessment of left ventricular size and function, 52.6% of scans as interpretable for right ventricular size and function, 34.8% of scans as interpretable for inferior vena cava size and variability, and 47.2% of scans as interpretable for the presence of pericardial effusion. Thirty-seven percent of screening scans changed management, most commonly adjusting fluid goals (81.2%). Time to first adequate cardiac images decreased significantly from 3.1 to 1.7 days (p < 0.001). CONCLUSIONS With DL guidance, neurology providers with minimal to no cPOCUS training were often able to obtain diagnostic-quality cardiac images, which informed management changes and significantly decreased time to cardiac imaging.
Collapse
Affiliation(s)
- Jennifer Mears
- Department of Neurology, Weill Cornell Medicine, New York, NY, USA
| | - Safa Kaleem
- Department of Neurology, Weill Cornell Medicine, New York, NY, USA
| | - Rohan Panchamia
- Department of Anesthesiology, Weill Cornell Medicine, New York, NY, USA
| | - Hooman Kamel
- Clinical and Translational Neuroscience Unit, Feil Family Brain and Mind Research Institute and Department of Neurology, Weill Cornell Medicine, 525 East 68th Street, F610, New York, NY, 10065, USA
| | - Chris Tam
- Department of Anesthesiology, Weill Cornell Medicine, New York, NY, USA
| | | | - Santosh Murthy
- Clinical and Translational Neuroscience Unit, Feil Family Brain and Mind Research Institute and Department of Neurology, Weill Cornell Medicine, 525 East 68th Street, F610, New York, NY, 10065, USA
| | - Alexander E Merkler
- Clinical and Translational Neuroscience Unit, Feil Family Brain and Mind Research Institute and Department of Neurology, Weill Cornell Medicine, 525 East 68th Street, F610, New York, NY, 10065, USA
| | - Cenai Zhang
- Clinical and Translational Neuroscience Unit, Feil Family Brain and Mind Research Institute and Department of Neurology, Weill Cornell Medicine, 525 East 68th Street, F610, New York, NY, 10065, USA
| | - Judy H Ch'ang
- Clinical and Translational Neuroscience Unit, Feil Family Brain and Mind Research Institute and Department of Neurology, Weill Cornell Medicine, 525 East 68th Street, F610, New York, NY, 10065, USA.
| |
Collapse
|
4
|
Fazlalizadeh H, Khan MS, Fox ER, Douglas PS, Adams D, Blaha MJ, Daubert MA, Dunn G, van den Heuvel E, Kelsey MD, Martin RP, Thomas JD, Thomas Y, Judd SE, Vasan RS, Budoff MJ, Bloomfield GS. Closing the Last Mile Gap in Access to Multimodality Imaging in Rural Settings: Design of the Imaging Core of the Risk Underlying Rural Areas Longitudinal Study. Circ Cardiovasc Imaging 2024; 17:e015496. [PMID: 38377236 PMCID: PMC10883604 DOI: 10.1161/circimaging.123.015496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/22/2024]
Abstract
Achieving optimal cardiovascular health in rural populations can be challenging for several reasons including decreased access to care with limited availability of imaging modalities, specialist physicians, and other important health care team members. Therefore, innovative solutions are needed to optimize health care and address cardiovascular health disparities in rural areas. Mobile examination units can bring imaging technology to underserved or remote communities with limited access to health care services. Mobile examination units can be equipped with a wide array of assessment tools and multiple imaging modalities such as computed tomography scanning and echocardiography. The detailed structural assessment of cardiovascular and lung pathology, as well as the detection of extracardiac pathology afforded by computed tomography imaging combined with the functional and hemodynamic assessments acquired by echocardiography, yield deep phenotyping of heart and lung disease for populations historically underrepresented in epidemiological studies. Moreover, by bringing the mobile examination unit to local communities, innovative approaches are now possible including engagement with local professionals to perform these imaging assessments, thereby augmenting local expertise and experience. However, several challenges exist before mobile examination unit-based examinations can be effectively integrated into the rural health care setting including standardizing acquisition protocols, maintaining consistent image quality, and addressing ethical and privacy considerations. Herein, we discuss the potential importance of cardiac multimodality imaging to improve cardiovascular health in rural regions, outline the emerging experience in this field, highlight important current challenges, and offer solutions based on our experience in the RURAL (Risk Underlying Rural Areas Longitudinal) cohort study.
Collapse
Affiliation(s)
- Hooman Fazlalizadeh
- Lundquist Institute, Harbor-University of California Los Angeles Medical Center, Torrance (H.F., M.J.B.)
| | - Muhammad Shahzeb Khan
- Division of Cardiology, Department of Medicine (M.S.K., P.S.D., M.A.D., M.D.K., G.S.B.), Duke University, Durham, NC
| | - Ervin R Fox
- Division of Cardiology, Department of Medicine University of Mississippi Medical Center, Jackson, MS (E.R.F.)
| | - Pamela S Douglas
- Division of Cardiology, Department of Medicine (M.S.K., P.S.D., M.A.D., M.D.K., G.S.B.), Duke University, Durham, NC
- Duke Clinical Research Institute (P.S.D., M.A.D., G.D., M.D.K., G.S.B.), Duke University, Durham, NC
| | - David Adams
- Caption Health, Inc, San Francisco, CA (D.A., R.P.M., Y.T.)
| | - Michael J Blaha
- Lundquist Institute, Harbor-University of California Los Angeles Medical Center, Torrance (H.F., M.J.B.)
| | - Melissa A Daubert
- Division of Cardiology, Department of Medicine (M.S.K., P.S.D., M.A.D., M.D.K., G.S.B.), Duke University, Durham, NC
- Duke Clinical Research Institute (P.S.D., M.A.D., G.D., M.D.K., G.S.B.), Duke University, Durham, NC
| | - Gary Dunn
- Duke Clinical Research Institute (P.S.D., M.A.D., G.D., M.D.K., G.S.B.), Duke University, Durham, NC
| | - Edwin van den Heuvel
- Department of Mathematics and Computer Science, Eindhoven University of Technology, The Netherlands (E.v.d.H.)
| | - Michelle D Kelsey
- Division of Cardiology, Department of Medicine (M.S.K., P.S.D., M.A.D., M.D.K., G.S.B.), Duke University, Durham, NC
- Duke Clinical Research Institute (P.S.D., M.A.D., G.D., M.D.K., G.S.B.), Duke University, Durham, NC
| | | | - James D Thomas
- Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL (J.D.T.)
- Center for Artificial Intelligence, Northwestern Medicine Bluhm Cardiovascular Institute, Chicago, IL (J.D.T.)
| | - Yngvil Thomas
- Caption Health, Inc, San Francisco, CA (D.A., R.P.M., Y.T.)
| | - Suzanne E Judd
- Department of Biostatistics, University of Alabama at Birmingham (S.E.J.)
| | - Ramachandran S Vasan
- University of Texas Health Sciences Center, University of Texas School of Public Health, San Antonio (R.S.V.)
| | - Matthew J Budoff
- Division of Cardiology, John Hopkins University School of Medicine, Baltimore, MD (M.J.B.)
| | - Gerald S Bloomfield
- Division of Cardiology, Department of Medicine (M.S.K., P.S.D., M.A.D., M.D.K., G.S.B.), Duke University, Durham, NC
- Duke Clinical Research Institute (P.S.D., M.A.D., G.D., M.D.K., G.S.B.), Duke University, Durham, NC
- Duke Global Health Institute (G.S.B.), Duke University, Durham, NC
| |
Collapse
|
5
|
Xie Y, Zhong H, Wu J, Zhao W, Hou R, Zhao L, Xu X, Zhang M, Zhao J. Automatic classification of heart failure based on Cine-CMR images. Int J Comput Assist Radiol Surg 2024; 19:355-365. [PMID: 37921964 DOI: 10.1007/s11548-023-03028-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 10/03/2023] [Indexed: 11/05/2023]
Abstract
PURPOSE Heart failure (HF) is a serious and complex syndrome with a high mortality rate. In clinical diagnosis, the correct classification of HF is helpful. In our previous work, we proposed a self-supervised learning framework of HF classification (SSLHF) on cine cardiac magnetic resonance images (Cine-CMR). However, this method lacks the integration of three dimensions of spatial information and temporal information. Thus, this study aims at proposing an automatic 4D HF classification algorithm. METHODS To construct a 4D classification model, we proposed an extensional framework called 4D-SSLHF. It mainly consists of self-supervised image restoration and HF classification. The image restoration proxy task utilizes three image transformation methods to enhance the exploration of spatial and temporal information in the Cine-CMR. In the classification task, we proposed a Siamese Conv-LSTM network by combining the Siamese network and bi-directional Conv-LSTM to integrate the features of the four dimensions simultaneously. RESULTS Experimental results on 184 patients from Shanghai Chest Hospital achieved an AUC of 0.8794 and an ACC of 0.8402 in the five-fold cross-validation. Compared with our previous work, the improvements in AUC and ACC were 2.89 % and 1.94 %, respectively. CONCLUSIONS In this study, we proposed a novel self-supervised learning framework named 4D-SSLHF for HF classification based on Cine-CMR. The proposed 4D-SSLHF can mine 3D spatial information and temporal information in Cine-CMR images well and accurately classify different categories of HF. The good classification results show our method's potential to assist physicians in choosing personalized treatment.
Collapse
Affiliation(s)
- Yuan Xie
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Hai Zhong
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Jiaqi Wu
- Cardiology, Shanghai Chest Hospital, Shanghai, China
| | - Wangyuan Zhao
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Runping Hou
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Lu Zhao
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Xiaowei Xu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Min Zhang
- Cardiology, Shanghai Chest Hospital, Shanghai, China
| | - Jun Zhao
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
| |
Collapse
|
6
|
Brown K, Roshanitabrizi P, Rwebembera J, Okello E, Beaton A, Linguraru MG, Sable CA. Using Artificial Intelligence for Rheumatic Heart Disease Detection by Echocardiography: Focus on Mitral Regurgitation. J Am Heart Assoc 2024; 13:e031257. [PMID: 38226515 PMCID: PMC10926790 DOI: 10.1161/jaha.123.031257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 10/18/2023] [Indexed: 01/17/2024]
Abstract
BACKGROUND Identification of children with latent rheumatic heart disease (RHD) by echocardiography, before onset of symptoms, provides an opportunity to initiate secondary prophylaxis and prevent disease progression. There have been limited artificial intelligence studies published assessing the potential of machine learning to detect and analyze mitral regurgitation or to detect the presence of RHD on standard portable echocardiograms. METHODS AND RESULTS We used 511 echocardiograms in children, focusing on color Doppler images of the mitral valve. Echocardiograms were independently reviewed by an expert adjudication panel. Among 511 cases, 229 were normal, and 282 had RHD. Our automated method included harmonization of echocardiograms to localize the left atrium during systole using convolutional neural networks and RHD detection using mitral regurgitation jet analysis and deep learning models with an attention mechanism. We identified the correct view with an average accuracy of 0.99 and the correct systolic frame with an average accuracy of 0.94 (apical) and 0.93 (parasternal long axis). It localized the left atrium with an average Dice coefficient of 0.88 (apical) and 0.9 (parasternal long axis). Maximum mitral regurgitation jet measurements were similar to expert manual measurements (P value=0.83) and a 9-feature mitral regurgitation analysis showed an area under the receiver operating characteristics curve of 0.93, precision of 0.83, recall of 0.92, and F1 score of 0.87. Our deep learning model showed an area under the receiver operating characteristics curve of 0.84, precision of 0.78, recall of 0.98, and F1 score of 0.87. CONCLUSIONS Artificial intelligence has the potential to detect RHD as accurately as expert cardiologists and to improve with more data. These innovative approaches hold promise to scale echocardiography screening for RHD.
Collapse
Affiliation(s)
- Kelsey Brown
- Department of Pediatric CardiologyChildren’s National HospitalWashingtonDCUSA
| | - Pooneh Roshanitabrizi
- Sheikh Zayed Institute for Pediatric Surgical InnovationChildren’s National HospitalWashingtonDCUSA
| | | | | | - Andrea Beaton
- Department of Pediatric CardiologyCincinnati Children’s Hospital Medical CenterCincinnatiOHUSA
| | - Marius George Linguraru
- Sheikh Zayed Institute for Pediatric Surgical InnovationChildren’s National HospitalWashingtonDCUSA
- Departments of Radiology and Pediatrics, School of Medicine and Health SciencesGeorge Washington UniversityWashingtonDCUSA
| | - Craig A. Sable
- Department of Pediatric CardiologyChildren’s National HospitalWashingtonDCUSA
| |
Collapse
|
7
|
Marozzi MS, Cicco S, Mancini F, Corvasce F, Lombardi FA, Desantis V, Loponte L, Giliberti T, Morelli CM, Longo S, Lauletta G, Solimando AG, Ria R, Vacca A. A Novel Automatic Algorithm to Support Lung Ultrasound Non-Expert Physicians in Interstitial Pneumonia Evaluation: A Single-Center Study. Diagnostics (Basel) 2024; 14:155. [PMID: 38248032 PMCID: PMC10814651 DOI: 10.3390/diagnostics14020155] [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: 12/05/2023] [Revised: 01/06/2024] [Accepted: 01/07/2024] [Indexed: 01/23/2024] Open
Abstract
INTRODUCTION Lung ultrasound (LUS) is widely used in clinical practice for identifying interstitial lung diseases (ILDs) and assessing their progression. Although high-resolution computed tomography (HRCT) remains the gold standard for evaluating the severity of ILDs, LUS can be performed as a screening method or as a follow-up tool post-HRCT. Minimum training is needed to better identify typical lesions, and the integration of innovative artificial intelligence (AI) automatic algorithms may enhance diagnostic efficiency. AIM This study aims to assess the effectiveness of a novel AI algorithm in automatic ILD recognition and scoring in comparison to an expert LUS sonographer. The "SensUS Lung" device, equipped with an automatic algorithm, was employed for the automatic recognition of the typical ILD patterns and to calculate an index grading of the interstitial involvement. METHODS We selected 33 Caucasian patients in follow-up for ILDs exhibiting typical HRCT patterns (honeycombing, ground glass, fibrosis). An expert physician evaluated all patients with LUS on twelve segments (six per side). Next, blinded to the previous evaluation, an untrained operator, a non-expert in LUS, performed the exam with the SensUS device equipped with the automatic algorithm ("SensUS Lung") using the same protocol. Pulmonary functional tests (PFT) and DLCO were conducted for all patients, categorizing them as having reduced or preserved DLCO. The SensUS device indicated different grades of interstitial involvement named Lung Staging that were scored from 0 (absent) to 4 (peak), which was compared to the Lung Ultrasound Score (LUS score) by dividing it by the number of segments evaluated. Statistical analyses were done with Wilcoxon tests for paired values or Mann-Whitney for unpaired samples, and correlations were performed using Spearman analysis; p < 0.05 was considered significant. RESULTS Lung Staging was non-inferior to LUS score in identifying the risk of ILDs (median SensUS 1 [0-2] vs. LUS 0.67 [0.25-1.54]; p = 0.84). Furthermore, the grade of interstitial pulmonary involvement detected with the SensUS device is directly related to the LUS score (r = 0.607, p = 0.002). Lung Staging values were inversely correlated with forced expiratory volume at first second (FEV1%, r = -0.40, p = 0.027), forced vital capacity (FVC%, r = -0.39, p = 0.03) and forced expiratory flow (FEF) at 25th percentile (FEF25%, r = -0.39, p = 0.02) while results directly correlated with FEF25-75% (r = 0.45, p = 0.04) and FEF75% (r = 0.43, p = 0.01). Finally, in patients with reduced DLCO, the Lung Staging was significantly higher, overlapping the LUS (reduced median 1 [1-2] vs. preserved 0 [0-1], p = 0.001), and overlapping the LUS (reduced median 18 [4-20] vs. preserved 5.5 [2-9], p = 0.035). CONCLUSIONS Our data suggest that the considered AI automatic algorithm may assist non-expert physicians in LUS, resulting in non-inferior-to-expert LUS despite a tendency to overestimate ILD lesions. Therefore, the AI algorithm has the potential to support physicians, particularly non-expert LUS sonographers, in daily clinical practice to monitor patients with ILDs. The adopted device is user-friendly, offering a fully automatic real-time analysis. However, it needs proper training in basic skills.
Collapse
Affiliation(s)
- Marialuisa Sveva Marozzi
- Unit of Internal Medicine “G. Baccelli”, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro Medical School, 70124 Bari, Italy
| | - Sebastiano Cicco
- Unit of Internal Medicine “G. Baccelli”, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro Medical School, 70124 Bari, Italy
| | - Francesca Mancini
- Unit of Internal Medicine “G. Baccelli”, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro Medical School, 70124 Bari, Italy
| | - Francesco Corvasce
- Unit of Internal Medicine “G. Baccelli”, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro Medical School, 70124 Bari, Italy
| | | | - Vanessa Desantis
- Pharmacology Section, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro Medical School, 70124 Bari, Italy
- Interdepartmental Centre for Research in Telemedicine (CITEL), Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro Medical School, 70124 Bari, Italy
| | - Luciana Loponte
- Unit of Internal Medicine “G. Baccelli”, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro Medical School, 70124 Bari, Italy
| | - Tiziana Giliberti
- Unit of Internal Medicine “G. Baccelli”, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro Medical School, 70124 Bari, Italy
| | - Claudia Maria Morelli
- Unit of Internal Medicine “G. Baccelli”, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro Medical School, 70124 Bari, Italy
| | - Stefania Longo
- Unit of Internal Medicine “G. Baccelli”, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro Medical School, 70124 Bari, Italy
| | - Gianfranco Lauletta
- Unit of Internal Medicine “G. Baccelli”, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro Medical School, 70124 Bari, Italy
| | - Antonio G. Solimando
- Unit of Internal Medicine “G. Baccelli”, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro Medical School, 70124 Bari, Italy
- Interdepartmental Centre for Research in Telemedicine (CITEL), Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro Medical School, 70124 Bari, Italy
| | - Roberto Ria
- Unit of Internal Medicine “G. Baccelli”, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro Medical School, 70124 Bari, Italy
- Interdepartmental Centre for Research in Telemedicine (CITEL), Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro Medical School, 70124 Bari, Italy
| | - Angelo Vacca
- Unit of Internal Medicine “G. Baccelli”, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro Medical School, 70124 Bari, Italy
- Interdepartmental Centre for Research in Telemedicine (CITEL), Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro Medical School, 70124 Bari, Italy
| |
Collapse
|
8
|
Li Y, Li W, Wang L, Wang X, Gao S, Liao Y, Ji Y, Lin L, Liu Y, Chen J. Detecting anteriorly displaced temporomandibular joint discs using super-resolution magnetic resonance imaging: a multi-center study. Front Physiol 2024; 14:1272814. [PMID: 38250655 PMCID: PMC10796555 DOI: 10.3389/fphys.2023.1272814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 12/18/2023] [Indexed: 01/23/2024] Open
Abstract
Background: Magnetic resonance imaging (MRI) plays a crucial role in diagnosing anterior disc displacement (ADD) of the temporomandibular joint (TMJ). The primary objective of this study is to enhance diagnostic accuracy in two common disease subtypes of ADD of the TMJ on MRI, namely, ADD with reduction (ADDWR) and ADD without reduction (ADDWoR). To achieve this, we propose the development of transfer learning (TL) based on Convolutional Neural Network (CNN) models, which will aid in accurately identifying and distinguishing these subtypes. Methods: A total of 668 TMJ MRI scans were obtained from two medical centers. High-resolution (HR) MRI images were subjected to enhancement through a deep TL, generating super-resolution (SR) images. Naive Bayes (NB) and Logistic Regression (LR) models were applied, and performance was evaluated using receiver operating characteristic (ROC) curves. The model's outcomes in the test cohort were compared with diagnoses made by two clinicians. Results: The NB model utilizing SR reconstruction with 400 × 400 pixel images demonstrated superior performance in the validation cohort, exhibiting an area under the ROC curve (AUC) of 0.834 (95% CI: 0.763-0.904) and an accuracy rate of 0.768. Both LR and NB models, with 200 × 200 and 400 × 400 pixel images after SR reconstruction, outperformed the clinicians' diagnoses. Conclusion: The ResNet152 model's commendable AUC in detecting ADD highlights its potential application for pre-treatment assessment and improved diagnostic accuracy in clinical settings.
Collapse
Affiliation(s)
- Yang Li
- School and Hospital of Stomatology, Fujian Medical University, Fuzhou, China
- Fujian Key Laboratory of Oral Diseases, School and Hospital of Stomatology, Fujian Medical University, Fuzhou, China
- Department of Oral and Maxillofacial Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Wen Li
- School and Hospital of Stomatology, Fujian Medical University, Fuzhou, China
- Fujian Key Laboratory of Oral Diseases, School and Hospital of Stomatology, Fujian Medical University, Fuzhou, China
- Department of Oral and Maxillofacial Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Department of Oral and Maxillofacial Surgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Li Wang
- Department of Oral and Maxillofacial Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xinrui Wang
- Department of Oral and Maxillofacial Surgery, Shenzhen Stomatology Hospital, Shenzhen, China
| | - Shiyu Gao
- School of Mathematics and Statistics, Huazhong University of Science and Technology, Wuhan, China
| | - Yunyang Liao
- Department of Oral and Maxillofacial Surgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Yihan Ji
- Department of Oral and Maxillofacial Surgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Lisong Lin
- Department of Oral and Maxillofacial Surgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Yiming Liu
- Department of Oral and Maxillofacial Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jiang Chen
- School and Hospital of Stomatology, Fujian Medical University, Fuzhou, China
- Fujian Key Laboratory of Oral Diseases, School and Hospital of Stomatology, Fujian Medical University, Fuzhou, China
| |
Collapse
|
9
|
Sveric KM, Ulbrich S, Dindane Z, Winkler A, Botan R, Mierke J, Trausch A, Heidrich F, Linke A. Improved assessment of left ventricular ejection fraction using artificial intelligence in echocardiography: A comparative analysis with cardiac magnetic resonance imaging. Int J Cardiol 2024; 394:131383. [PMID: 37757986 DOI: 10.1016/j.ijcard.2023.131383] [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: 07/13/2023] [Revised: 09/10/2023] [Accepted: 09/18/2023] [Indexed: 09/29/2023]
Abstract
BACKGROUND Left ventricular ejection fraction (LVEF) measurement in echocardiography (Echo) using the recommended modified biplane Simpson (MBS) method is operator-dependent and exhibits variability. We aimed to assess the accuracy of a novel fully automated (Auto) artificial intelligence (AI) in view selection and biplane LVEF calculation compared to MBS-Echo, with cardiac magnetic resonance imaging (CMR) as reference. METHODS Each of the 301 consecutive patients underwent CMR and Echo on the same day. LVEF was measured independently by Auto-Echo, MBS-Echo and CMR. Interobserver (n = 40) and test-retest (n = 14) analysis followed. RESULTS A total of 229 patients (76%) underwent complete analysis. Auto-Echo and MBS-Echo showed high correlations with CMR (R = 0.89 and 0.89) and with each other (R = 0.93). Auto underestimated LVEF (bias: 2.2%; limits of agreement [LOA]: -13.5 to 17.9%), while MBS overestimated it (bias: -2.2%; LOA: 18.6 to 14.1%). Despite comparable areas under the curves of Auto- and MBS-Echo (0.93 and 0.92), 46% (n = 70) of MBS-Echo misclassified LVEF by ≥5% units in patients with a reduced CMR-LVEF <51%. Although LVEF bias variability across different LV function ranges was significant (p < 0.001), Auto-Echo was closer to CMR for patients with reduced LVEF, wall motion abnormalities, and poor image quality than MBS-Echo. The interobserver correlation coefficient of Auto-Echo was excellent compared to MBS-Echo (1.00 vs. <0.91) for different readers. True test-retest variability was higher for MBS-Echo than for Auto-Echo (7.9% vs. 2.5%). CONCLUSION The tested AI has the potential to improve the clinical utility of Echo by reducing user-related variability, providing more accurate and reliable results than MBS.
Collapse
Affiliation(s)
- Krunoslav Michael Sveric
- Department of Internal Medicine and Cardiology, Herzzentrum Dresden, Technische Universität Dresden, Fetscherstr. 76, Dresden 01307, Germany.
| | - Stefan Ulbrich
- Department of Internal Medicine and Cardiology, Herzzentrum Dresden, Technische Universität Dresden, Fetscherstr. 76, Dresden 01307, Germany
| | - Zouhir Dindane
- Department of Internal Medicine and Cardiology, Herzzentrum Dresden, Technische Universität Dresden, Fetscherstr. 76, Dresden 01307, Germany
| | - Anna Winkler
- Department of Internal Medicine and Cardiology, Herzzentrum Dresden, Technische Universität Dresden, Fetscherstr. 76, Dresden 01307, Germany
| | - Roxana Botan
- Department of Internal Medicine and Cardiology, Herzzentrum Dresden, Technische Universität Dresden, Fetscherstr. 76, Dresden 01307, Germany
| | - Johannes Mierke
- Department of Internal Medicine and Cardiology, Herzzentrum Dresden, Technische Universität Dresden, Fetscherstr. 76, Dresden 01307, Germany
| | - Anne Trausch
- Department of Internal Medicine and Cardiology, Herzzentrum Dresden, Technische Universität Dresden, Fetscherstr. 76, Dresden 01307, Germany
| | - Felix Heidrich
- Department of Internal Medicine and Cardiology, Herzzentrum Dresden, Technische Universität Dresden, Fetscherstr. 76, Dresden 01307, Germany
| | - Axel Linke
- Department of Internal Medicine and Cardiology, Herzzentrum Dresden, Technische Universität Dresden, Fetscherstr. 76, Dresden 01307, Germany
| |
Collapse
|
10
|
Hu J, Olaisen SH, Smistad E, Dalen H, Lovstakken L. Automated 2-D and 3-D Left Atrial Volume Measurements Using Deep Learning. ULTRASOUND IN MEDICINE & BIOLOGY 2024; 50:47-56. [PMID: 37813702 DOI: 10.1016/j.ultrasmedbio.2023.08.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 08/18/2023] [Accepted: 08/29/2023] [Indexed: 10/11/2023]
Abstract
OBJECTIVE Echocardiography, a critical tool for assessing left atrial (LA) volume, often relies on manual or semi-automated measurements. This study introduces a fully automated, real-time method for measuring LA volume in both 2-D and 3-D imaging, in the aim of offering accuracy comparable to that of expert assessments while saving time and reducing operator variability. METHODS We developed an automated pipeline comprising a network to identify the end-systole (ES) time point and robust 2-D and 3-D U-Nets for segmentation. We employed data sets of 789 2-D images and 286 3-D recordings and explored various training regimes, including recurrent networks and pseudo-labeling, to estimate volume curves. RESULTS Our baseline results revealed an average volume difference of 2.9 mL for 2-D and 7.8 mL for 3-D, respectively, compared with manual methods. The application of pseudo-labeling to all frames in the cine loop generally led to more robust volume curves and notably improved ES measurement in cases with limited data. CONCLUSION Our results highlight the potential of automated LA volume estimation in clinical practice. The proposed prototype application, capable of processing real-time data from a clinical ultrasound scanner, provides valuable temporal volume curve information in the echo lab.
Collapse
Affiliation(s)
- Jieyu Hu
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway.
| | - Sindre Hellum Olaisen
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Erik Smistad
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway; SINTEF Medical Technology, Trondheim, Norway
| | - Havard Dalen
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway; Clinic of Cardiology, St. Olav's University Hospital, Trondheim, Norway; Levanger Hospital, Nord-Trndelag Hospital Trust, Levanger, Norway
| | - Lasse Lovstakken
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| |
Collapse
|
11
|
Aziz D, Maganti K, Yanamala N, Sengupta P. The Role of Artificial Intelligence in Echocardiography: A Clinical Update. Curr Cardiol Rep 2023; 25:1897-1907. [PMID: 38091196 DOI: 10.1007/s11886-023-02005-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/21/2023] [Indexed: 01/26/2024]
Abstract
PURPOSE OF REVIEW In echocardiography, there has been robust development of artificial intelligence (AI) tools for image recognition, automated measurements, image segmentation, and patient prognostication that has created a monumental shift in the study of AI and machine learning models. However, integrating these measurements into complex disease recognition and therapeutic interventions remains challenging. While the tools have been developed, there is a lack of evidence regarding implementing heterogeneous systems for guiding clinical decision-making and therapeutic action. RECENT FINDINGS Newer AI modalities have shown concrete positive data in terms of user-guided image acquisition and processing, precise determination of both basic and advanced quantitative echocardiographic features, and the potential to construct predictive models, all with the possibility of seamless integration into clinical decision support systems. AI in echocardiography is a powerful and ever-growing tool with the potential for revolutionary effects on the practice of cardiology. In this review article, we explore the growth of AI and its applications in echocardiography, along with clinical implications and the associated regulatory, legal, and ethical considerations.
Collapse
Affiliation(s)
- Daniel Aziz
- Department of Internal Medicine, Rutgers - Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - Kameswari Maganti
- Division of Cardiology, Rutgers - Robert Wood Johnson Medical School & University Hospital, 1 Robert Wood Johnson Place, New Brunswick, NJ, 08901, USA
| | - Naveena Yanamala
- Division of Cardiology, Rutgers - Robert Wood Johnson Medical School & University Hospital, 1 Robert Wood Johnson Place, New Brunswick, NJ, 08901, USA
| | - Partho Sengupta
- Division of Cardiology, Rutgers - Robert Wood Johnson Medical School & University Hospital, 1 Robert Wood Johnson Place, New Brunswick, NJ, 08901, USA.
| |
Collapse
|
12
|
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.
Collapse
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.)
| |
Collapse
|
13
|
Wehbe RM. Echoing Errors: The Problem of Uncurated "Big Data" in Echocardiography. J Am Soc Echocardiogr 2023; 36:1201-1203. [PMID: 37747378 DOI: 10.1016/j.echo.2023.08.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Accepted: 08/13/2023] [Indexed: 09/26/2023]
Affiliation(s)
- Ramsey M Wehbe
- Division of Cardiology, Department of Medicine and Biomedical Informatics Center (BMIC), Medical University of South Carolina, Charleston, South Carolina.
| |
Collapse
|
14
|
Wehbe RM, Katsaggelos AK, Hammond KJ, Hong H, Ahmad FS, Ouyang D, Shah SJ, McCarthy PM, Thomas JD. Deep Learning for Cardiovascular Imaging: A Review. JAMA Cardiol 2023; 8:1089-1098. [PMID: 37728933 DOI: 10.1001/jamacardio.2023.3142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
Importance Artificial intelligence (AI), driven by advances in deep learning (DL), has the potential to reshape the field of cardiovascular imaging (CVI). While DL for CVI is still in its infancy, research is accelerating to aid in the acquisition, processing, and/or interpretation of CVI across various modalities, with several commercial products already in clinical use. It is imperative that cardiovascular imagers are familiar with DL systems, including a basic understanding of how they work, their relative strengths compared with other automated systems, and possible pitfalls in their implementation. The goal of this article is to review the methodology and application of DL to CVI in a simple, digestible fashion toward demystifying this emerging technology. Observations At its core, DL is simply the application of a series of tunable mathematical operations that translate input data into a desired output. Based on artificial neural networks that are inspired by the human nervous system, there are several types of DL architectures suited to different tasks; convolutional neural networks are particularly adept at extracting valuable information from CVI data. We survey some of the notable applications of DL to tasks across the spectrum of CVI modalities. We also discuss challenges in the development and implementation of DL systems, including avoiding overfitting, preventing systematic bias, improving explainability, and fostering a human-machine partnership. Finally, we conclude with a vision of the future of DL for CVI. Conclusions and Relevance Deep learning has the potential to meaningfully affect the field of CVI. Rather than a threat, DL could be seen as a partner to cardiovascular imagers in reducing technical burden and improving efficiency and quality of care. High-quality prospective evidence is still needed to demonstrate how the benefits of DL CVI systems may outweigh the risks.
Collapse
Affiliation(s)
- Ramsey M Wehbe
- Division of Cardiology, Department of Medicine & Biomedical Informatics Center, Medical University of South Carolina, Charleston
- Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Aggelos K Katsaggelos
- Department of Computer and Electrical Engineering, Northwestern University, Evanston, Illinois
| | - Kristian J Hammond
- Department of Computer Science, Northwestern University, Evanston, Illinois
| | - Ha Hong
- Medtronic, Minneapolis, Minnesota
| | - Faraz S Ahmad
- Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
- Center for Health Information Partnerships, Institute for Public Health and Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
- Center for Artificial Intelligence, Northwestern Medicine Bluhm Cardiovascular Institute, Chicago, Illinois
| | - David Ouyang
- Division of Cardiology, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California
| | - Sanjiv J Shah
- Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
- Center for Artificial Intelligence, Northwestern Medicine Bluhm Cardiovascular Institute, Chicago, Illinois
| | - Patrick M McCarthy
- Division of Cardiac Surgery, Department of Surgery, Northwestern University Feinberg School of Medicine, Chicago, Illinois
- Center for Artificial Intelligence, Northwestern Medicine Bluhm Cardiovascular Institute, Chicago, Illinois
| | - James D Thomas
- Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
- Center for Artificial Intelligence, Northwestern Medicine Bluhm Cardiovascular Institute, Chicago, Illinois
| |
Collapse
|
15
|
Meucci MC, Delgado V. Artificial Intelligence to Speed Up Training in Echocardiography: The Next Frontier. Circ Cardiovasc Imaging 2023; 16:e016148. [PMID: 37955173 DOI: 10.1161/circimaging.123.016148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2023]
Affiliation(s)
- Maria Chiara Meucci
- Department of Cardiovascular Medicine, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy (M.C.M.)
| | - Victoria Delgado
- Hospital Universitari Germans Trias i Pujol, Badalona, Spain (V.D.)
- Center of Comparative Medicine and Bioimaging (CMCIB), Research Institute Germans Trias i Pujol, Badalona, Spain (V.D.)
| |
Collapse
|
16
|
Shiina Y, Ishizu T, Nesaki S, Nakajima H, Iida N, Kawamatsu N, Sato K, Yamamoto M, Machino-Ohtsuka T, Ieda M, Kawakami Y. Using computed tomography fusion imaging as learning data for sonographer training in identification of left ventricular endocardial boundaries. J Cardiol 2023; 82:398-407. [PMID: 37100386 DOI: 10.1016/j.jjcc.2023.03.010] [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: 09/27/2022] [Revised: 03/26/2023] [Accepted: 03/29/2023] [Indexed: 04/28/2023]
Abstract
BACKGROUND We hypothesized that if computed tomography (CT) images were used as learning data, we could overcome volume underestimation by echocardiography, improving the accuracy of left ventricular (LV) volume measurements. METHODS We utilized a fusion imaging modality consisting of echocardiography with superimposed CT images for 37 consecutive patients to identify the endocardial boundary. We compared LV volumes obtained with and without CT learning trace-lines (TLs). Furthermore, 3D echocardiography was used to compare LV volumes obtained with and without CT learning for endocardial identification. The mean difference between the echocardiography and CT-derived LV volumes and the coefficient of variation were compared pre- and post-learning. Bland-Altman analysis was used to assess the differences in LV volume (mL) obtained from the 2D pre-learning TL and 3D post-learning TL. RESULTS The post-learning TL was located closer to the epicardium than the pre-learning TL. This trend was particularly pronounced in the lateral and the anterior wall. The post-learning TL was along the inner side of the high echoic layer in the basal-lateral wall in the four-chamber view. CT fusion imaging determined that the difference in LV volume between 2D echocardiography and CT was small (-25.6 ± 14.4 mL before learning, -6.9 ± 11.5 mL after learning) and that CT learning improved the coefficient of variation (10.9 % before learning, 7.8 % after learning). Significant improvements were observed during 3D echocardiography; the difference in LV volume between 3D echocardiography and CT was slight (-20.5 ± 15.1 mL before learning, 3.8 ± 15.7 mL after learning), and the coefficient of variation improved (11.5 % before learning, 9.3 % after learning). CONCLUSIONS Differences between the LV volumes obtained using CT and echocardiography either disappeared or were reduced after CT fusion imaging. Fusion imaging is useful in training regimens for accurate LV volume quantification using echocardiography and may contribute to quality control.
Collapse
Affiliation(s)
- Yoshiki Shiina
- Department of Clinical Laboratory, University of Tsukuba Hospital, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan
| | - Tomoko Ishizu
- Department of Cardiology, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan.
| | - Satomi Nesaki
- Department of Clinical Laboratory, University of Tsukuba Hospital, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan
| | - Hideki Nakajima
- Department of Clinical Laboratory, University of Tsukuba Hospital, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan
| | - Noriko Iida
- Department of Clinical Laboratory, University of Tsukuba Hospital, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan
| | - Naoto Kawamatsu
- Department of Cardiology, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan
| | - Kimi Sato
- Department of Cardiology, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan
| | - Masayoshi Yamamoto
- Department of Cardiology, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan
| | - Tomoko Machino-Ohtsuka
- Department of Clinical Laboratory Medicine, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan
| | - Masaki Ieda
- Department of Cardiology, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan
| | - Yasushi Kawakami
- Department of Clinical Laboratory Medicine, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan
| |
Collapse
|
17
|
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.
Collapse
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
| |
Collapse
|
18
|
Yeung DF, Abolmaesumi P, Tsang TSM. Artificial Intelligence for Left Ventricular Diastolic Function Assessment: A New Paradigm on the Horizon. J Am Soc Echocardiogr 2023; 36:1079-1082. [PMID: 37578403 DOI: 10.1016/j.echo.2023.07.006] [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: 07/10/2023] [Accepted: 07/12/2023] [Indexed: 08/15/2023]
Affiliation(s)
- Darwin F Yeung
- Division of Cardiology, University of British Columbia, Vancouver, British Columbia, Canada.
| | - Purang Abolmaesumi
- Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, British Columbia, Canada
| | - Teresa S M Tsang
- Division of Cardiology, University of British Columbia, Vancouver, British Columbia, Canada
| |
Collapse
|
19
|
Khan MS, Arshad MS, Greene SJ, Van Spall HGC, Pandey A, Vemulapalli S, Perakslis E, Butler J. Artificial intelligence and heart failure: A state-of-the-art review. Eur J Heart Fail 2023; 25:1507-1525. [PMID: 37560778 DOI: 10.1002/ejhf.2994] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 08/06/2023] [Accepted: 08/08/2023] [Indexed: 08/11/2023] Open
Abstract
Heart failure (HF) is a heterogeneous syndrome affecting more than 60 million individuals globally. Despite recent advancements in understanding of the pathophysiology of HF, many issues remain including residual risk despite therapy, understanding the pathophysiology and phenotypes of patients with HF and preserved ejection fraction, and the challenges related to integrating a large amount of disparate information available for risk stratification and management of these patients. Risk prediction algorithms based on artificial intelligence (AI) may have superior predictive ability compared to traditional methods in certain instances. AI algorithms can play a pivotal role in the evolution of HF care by facilitating clinical decision making to overcome various challenges such as allocation of treatment to patients who are at highest risk or are more likely to benefit from therapies, prediction of adverse outcomes, and early identification of patients with subclinical disease or worsening HF. With the ability to integrate and synthesize large amounts of data with multidimensional interactions, AI algorithms can supply information with which physicians can improve their ability to make timely and better decisions. In this review, we provide an overview of the AI algorithms that have been developed for establishing early diagnosis of HF, phenotyping HF with preserved ejection fraction, and stratifying HF disease severity. This review also discusses the challenges in clinical deployment of AI algorithms in HF, and the potential path forward for developing future novel learning-based algorithms to improve HF care.
Collapse
Affiliation(s)
| | | | - Stephen J Greene
- Division of Cardiology, Duke University School of Medicine, Durham, NC, USA
- Duke Clinical Research Institute, Durham, NC, USA
| | - Harriette G C Van Spall
- Department of Medicine and Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - Ambarish Pandey
- Canada Population Health Research Institute, Hamilton, ON, Canada
- Division of Cardiology, Department of Internal Medicine, UT Southwestern Medical Center, Dallas, TX, USA
| | - Sreekanth Vemulapalli
- Division of Cardiology, Duke University School of Medicine, Durham, NC, USA
- Duke Clinical Research Institute, Durham, NC, USA
| | | | - Javed Butler
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
- Baylor Scott and White Research Institute, Dallas, TX, USA
| |
Collapse
|
20
|
Howard-Quijano K, Saraf K, Borgstrom P, Baek C, Wasko M, Zhang X, Zheng Y, Saba S, Mukkamala R, Kaiser W, Mahajan A. Evaluation of Wearable Acoustic Sensors and Machine Learning Algorithms for Automated Measurement of Left Ventricular Ejection Fraction. Am J Cardiol 2023; 200:87-94. [PMID: 37307784 DOI: 10.1016/j.amjcard.2023.04.047] [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: 01/30/2023] [Revised: 04/12/2023] [Accepted: 04/28/2023] [Indexed: 06/14/2023]
Abstract
Left ventricular ejection fraction (EF) is a predictor of mortality and guides clinical decisions. Although transthoracic echocardiography (TTE) is commonly used for measuring EF, it has limitations, such as subjectivity and requires expert personnel. Advancements in biosensor technology and artificial intelligence are allowing systems capable of determining left ventricular function and providing automated measurement of EF. In this study, we tested new wearable automated real-time biosensors (Cardiac Performance System [CPS]) that compute EF using waveform machine learning on cardiac acoustic signals. The primary aim was to compare the accuracy of CPS EF with TTE EF. Adult patients presenting to cardiology, presurgical, and diagnostic radiology clinical settings in an academic center were enrolled. TTE examination was performed by a sonographer, followed immediately by a 3-minute recording of acoustic signals from CPS biosensors placed on the chest by nonexpert personnel. TTE EF was calculated offline using the Simpson biplane method. A total of 81 patients (aged 19 to 88 years, 27 women, 20% to 80% EF) were included. Deming regression and Bland-Altman analysis were performed to assess the accuracy of CPS EF against TTE EF. Both Deming regression (slope 0.9981; intercept 0.03415%) and Bland-Altman analysis (bias -0.0247%; limits of agreement [-11.65, 11.60]%) demonstrated equivalency between CPS EF and TTE EF. The receiver operating characteristic for measuring sensitivity and specificity of CPS in identifying subjects with abnormal EF showed an area under the curve value of 0.974 for identifying EF <35% and 0.916 for detecting EF <50% CPS EF intraoperator and interoperator assessments demonstrated low variability. In conclusion, this technology measuring cardiac function from noninvasive biosensors and machine learning on acoustic signals provides an accurate EF measurement that is automated, real-time, and acquired rapidly by personnel with minimal training.
Collapse
Affiliation(s)
| | - Kanav Saraf
- Samueli School of Engineering, University of California Los Angeles, Los Angeles, California
| | - Per Borgstrom
- Samueli School of Engineering, University of California Los Angeles, Los Angeles, California
| | - Christopher Baek
- Samueli School of Engineering, University of California Los Angeles, Los Angeles, California
| | - Michael Wasko
- Samueli School of Engineering, University of California Los Angeles, Los Angeles, California
| | - Xu Zhang
- Samueli School of Engineering, University of California Los Angeles, Los Angeles, California
| | - Yi Zheng
- Samueli School of Engineering, University of California Los Angeles, Los Angeles, California
| | - Samir Saba
- University of Pittsburgh Medical Center, Pittsburgh, Pensylvannia
| | - Rama Mukkamala
- University of Pittsburgh Medical Center, Pittsburgh, Pensylvannia
| | - William Kaiser
- Samueli School of Engineering, University of California Los Angeles, Los Angeles, California
| | - Aman Mahajan
- University of Pittsburgh Medical Center, Pittsburgh, Pensylvannia
| |
Collapse
|
21
|
Peck D, Rwebembera J, Nakagaayi D, Minja NW, Ollberding NJ, Pulle J, Klein J, Adams D, Martin R, Koepsell K, Sanyahumbi A, Beaton A, Okello E, Sable C. The Use of Artificial Intelligence Guidance for Rheumatic Heart Disease Screening by Novices. J Am Soc Echocardiogr 2023; 36:724-732. [PMID: 36906047 DOI: 10.1016/j.echo.2023.03.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 02/27/2023] [Accepted: 03/01/2023] [Indexed: 03/11/2023]
Abstract
INTRODUCTION A novel technology utilizing artificial intelligence (AI) to provide real-time image-acquisition guidance, enabling novices to obtain diagnostic echocardiographic images, holds promise to expand the reach of echo screening for rheumatic heart disease (RHD). We evaluated the ability of nonexperts to obtain diagnostic-quality images in patients with RHD using AI guidance with color Doppler. METHODS Novice providers without prior ultrasound experience underwent a 1-day training curriculum to complete a 7-view screening protocol using AI guidance in Kampala, Uganda. All trainees then scanned 8 to 10 volunteer patients using AI guidance, half RHD and half normal. The same patients were scanned by 2 expert sonographers without the use of AI guidance. Images were evaluated by expert blinded cardiologists to assess (1) diagnostic quality to determine presence/absence of RHD and (2) valvular function and (3) to assign an American College of Emergency Physicians score of 1 to 5 for each view. RESULTS Thirty-six novice participants scanned a total of 50 patients, resulting in a total of 462 echocardiogram studies, 362 obtained by nonexperts using AI guidance and 100 obtained by expert sonographers without AI guidance. Novice images enabled diagnostic interpretation in >90% of studies for presence/absence of RHD, abnormal MV morphology, and mitral regurgitation (vs 99% by experts, P ≤ .001). Images were less diagnostic for aortic valve disease (79% for aortic regurgitation, 50% for aortic stenosis, vs 99% and 91% by experts, P < .001). The American College of Emergency Physicians scores of nonexpert images were highest in the parasternal long-axis images (mean, 3.45; 81% ≥ 3) compared with lower scores for apical 4-chamber (mean, 3.20; 74% ≥ 3) and apical 5-chamber images (mean, 2.43; 38% ≥ 3). CONCLUSIONS Artificial intelligence guidance with color Doppler is feasible to enable RHD screening by nonexperts, performing significantly better for assessment of the mitral than aortic valve. Further refinement is needed to optimize acquisition of color Doppler apical views.
Collapse
Affiliation(s)
- Daniel Peck
- University of Minnesota, Minneapolis, Minnesota.
| | | | - Doreen Nakagaayi
- Uganda Heart Institute, Kampala, Uganda; The Heart Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - Neema W Minja
- Uganda Heart Institute, Kampala, Uganda; Department of Global Health, University of Washington, Seattle, Washington; Kilimanjaro Clinical Research Institute, Moshi, Tanzania
| | - Nicholas J Ollberding
- Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Centre, Cincinnati, Ohio; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio
| | | | - Jennifer Klein
- Children's National Hospital, Washington, District of Columbia
| | | | | | | | - Amy Sanyahumbi
- Baylor College of Medicine, Texas Children's Hospital, Houston, Texas
| | - Andrea Beaton
- The Heart Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | | | - Craig Sable
- Children's National Hospital, Washington, District of Columbia
| |
Collapse
|
22
|
Canning C, Guo J, Narang A, Thomas JD, Ahmad FS. The Emerging Role of Artificial Intelligence in Valvular Heart Disease. Heart Fail Clin 2023; 19:391-405. [PMID: 37230652 DOI: 10.1016/j.hfc.2023.03.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Valvular heart disease (VHD) is a morbid condition in which timely identification and evidence-based treatments can lead to improved outcomes. Artificial intelligence broadly refers to the ability for computers to perform tasks and problem solve like the human mind. Studies applying AI to VHD have used a variety of structured (eg, sociodemographic, clinical) and unstructured (eg, electrocardiogram, phonocardiogram, and echocardiograms) and machine learning modeling approaches. Additional researches in diverse populations, including prospective clinical trials, are needed to evaluate the effectiveness and value of AI-enabled medical technologies in clinical care for patients with VHD.
Collapse
Affiliation(s)
- Caroline Canning
- Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, 676 North St. Clair Street, Suite 600, Chicago, IL 60611, USA; Bluhm Cardiovascular Institute Center for Artificial Intelligence, Northwestern Medicine, Chicago, IL, USA. https://twitter.com/carolinecanning
| | - James Guo
- Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, 676 North St. Clair Street, Suite 600, Chicago, IL 60611, USA; Bluhm Cardiovascular Institute Center for Artificial Intelligence, Northwestern Medicine, Chicago, IL, USA
| | - Akhil Narang
- Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, 676 North St. Clair Street, Suite 600, Chicago, IL 60611, USA; Bluhm Cardiovascular Institute Center for Artificial Intelligence, Northwestern Medicine, Chicago, IL, USA. https://twitter.com/AkhilNarangMD
| | - James D Thomas
- Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, 676 North St. Clair Street, Suite 600, Chicago, IL 60611, USA; Bluhm Cardiovascular Institute Center for Artificial Intelligence, Northwestern Medicine, Chicago, IL, USA. https://twitter.com/jamesdthomasMD1
| | - Faraz S Ahmad
- Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, 676 North St. Clair Street, Suite 600, Chicago, IL 60611, USA; Bluhm Cardiovascular Institute Center for Artificial Intelligence, Northwestern Medicine, Chicago, IL, USA; Division of Health and Biomedical informatics, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
| |
Collapse
|
23
|
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.
Collapse
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
| | | |
Collapse
|
24
|
Batool S, Taj IA, Ghafoor M. Ejection Fraction Estimation from Echocardiograms Using Optimal Left Ventricle Feature Extraction Based on Clinical Methods. Diagnostics (Basel) 2023; 13:2155. [PMID: 37443550 DOI: 10.3390/diagnostics13132155] [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: 05/16/2023] [Revised: 06/10/2023] [Accepted: 06/21/2023] [Indexed: 07/15/2023] Open
Abstract
Echocardiography is one of the imaging systems most often utilized for assessing heart anatomy and function. Left ventricle ejection fraction (LVEF) is an important clinical variable assessed from echocardiography via the measurement of left ventricle (LV) parameters. Significant inter-observer and intra-observer variability is seen when LVEF is quantified by cardiologists using huge echocardiography data. Machine learning algorithms have the capability to analyze such extensive datasets and identify intricate patterns of structure and function of the heart that highly skilled observers might overlook, hence paving the way for computer-assisted diagnostics in this field. In this study, LV segmentation is performed on echocardiogram data followed by feature extraction from the left ventricle based on clinical methods. The extracted features are then subjected to analysis using both neural networks and traditional machine learning algorithms to estimate the LVEF. The results indicate that employing machine learning techniques on the extracted features from the left ventricle leads to higher accuracy than the utilization of Simpson's method for estimating the LVEF. The evaluations are performed on a publicly available echocardiogram dataset, EchoNet-Dynamic. The best results are obtained when DeepLab, a convolutional neural network architecture, is used for LV segmentation along with Long Short-Term Memory Networks (LSTM) for the regression of LVEF, obtaining a dice similarity coefficient of 0.92 and a mean absolute error of 5.736%.
Collapse
Affiliation(s)
- Samana Batool
- Electrical Engineering, Capital University of Science and Technology, Islamabad Expressway, Kahuta Road, Islamabad 44000, Pakistan
| | - Imtiaz Ahmad Taj
- Electrical Engineering, Capital University of Science and Technology, Islamabad Expressway, Kahuta Road, Islamabad 44000, Pakistan
| | - Mubeen Ghafoor
- School of Computer Science, University of Lincoln, Brayford Way, Brayford, Pool, Lincoln LN6 7TS, UK
| |
Collapse
|
25
|
Sveric KM, Botan R, Dindane Z, Winkler A, Nowack T, Heitmann C, Schleußner L, Linke A. Single-Site Experience with an Automated Artificial Intelligence Application for Left Ventricular Ejection Fraction Measurement in Echocardiography. Diagnostics (Basel) 2023; 13:diagnostics13071298. [PMID: 37046515 PMCID: PMC10093353 DOI: 10.3390/diagnostics13071298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 03/28/2023] [Accepted: 03/28/2023] [Indexed: 03/31/2023] Open
Abstract
Left ventricular ejection fraction (LVEF) is a key parameter in evaluating left ventricular (LV) function using echocardiography (Echo), but its manual measurement by the modified biplane Simpson (MBS) method is time consuming and operator dependent. We investigated the feasibility of a server-based, commercially available and ready-to use-artificial intelligence (AI) application based on convolutional neural network methods that integrate fully automatic view selection and measurement of LVEF from an entire Echo exam into a single workflow. We prospectively enrolled 1083 consecutive patients who had been referred to Echo for diagnostic or therapeutic purposes. LVEF was measured independently using MBS and AI. Test–retest variability was assessed in 40 patients. The reliability, repeatability, and time efficiency of LVEF measurements were compared between the two methods. Overall, 889 Echos were analyzed by cardiologists with the MBS method and by the AI. Over the study period of 10 weeks, the feasibility of both automatic view classification and seamlessly measured LVEF rose to 81% without user involvement. LVEF, LV end-diastolic and end-systolic volumes correlated strongly between MBS and AI (R = 0.87, 0.89 and 0.93, p < 0.001 for all) with a mean bias of +4.5% EF, −12 mL and −11 mL, respectively, due to impaired image quality and the extent of LV function. Repeatability and reliability of LVEF measurement (n = 40, test–retest) by AI was excellent compared to MBS (coefficient of variation: 3.2% vs. 5.9%), although the median analysis time of the AI was longer than that of the operator-dependent MBS method (258 s vs. 171 s). This AI has succeeded in identifying apical LV views and measuring EF in one workflow with comparable results to the MBS method and shows excellent reproducibility. It offers realistic perspectives for fully automated AI-based measurement of LVEF in routine clinical settings.
Collapse
|
26
|
Kresoja KP, Unterhuber M, Wachter R, Thiele H, Lurz P. A cardiologist's guide to machine learning in cardiovascular disease prognosis prediction. Basic Res Cardiol 2023; 118:10. [PMID: 36939941 PMCID: PMC10027799 DOI: 10.1007/s00395-023-00982-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 02/21/2023] [Accepted: 02/26/2023] [Indexed: 03/21/2023]
Abstract
A modern-day physician is faced with a vast abundance of clinical and scientific data, by far surpassing the capabilities of the human mind. Until the last decade, advances in data availability have not been accompanied by analytical approaches. The advent of machine learning (ML) algorithms might improve the interpretation of complex data and should help to translate the near endless amount of data into clinical decision-making. ML has become part of our everyday practice and might even further change modern-day medicine. It is important to acknowledge the role of ML in prognosis prediction of cardiovascular disease. The present review aims on preparing the modern physician and researcher for the challenges that ML might bring, explaining basic concepts but also caveats that might arise when using these methods. Further, a brief overview of current established classical and emerging concepts of ML disease prediction in the fields of omics, imaging and basic science is presented.
Collapse
Affiliation(s)
- Karl-Patrik Kresoja
- Department of Internal Medicine/Cardiology, Heart Center Leipzig at University of Leipzig, Struempellstr. 39, 04289, Leipzig, Germany
- Leipzig Heart Institute, Leipzig Heart Science at Heart Center Leipzig, Leipzig, Germany
| | - Matthias Unterhuber
- Department of Internal Medicine/Cardiology, Heart Center Leipzig at University of Leipzig, Struempellstr. 39, 04289, Leipzig, Germany
- Leipzig Heart Institute, Leipzig Heart Science at Heart Center Leipzig, Leipzig, Germany
| | - Rolf Wachter
- Department of Cardiology, University Hospital Leipzig, Leipzig, Germany
- Clinic for Cardiology and Pneumology, University Medicine Göttingen, Göttingen, Germany
- German Cardiovascular Research Center (DZHK), Partner Site Göttingen, Göttingen, Germany
| | - Holger Thiele
- Department of Internal Medicine/Cardiology, Heart Center Leipzig at University of Leipzig, Struempellstr. 39, 04289, Leipzig, Germany.
- Leipzig Heart Institute, Leipzig Heart Science at Heart Center Leipzig, Leipzig, Germany.
| | - Philipp Lurz
- Department of Internal Medicine/Cardiology, Heart Center Leipzig at University of Leipzig, Struempellstr. 39, 04289, Leipzig, Germany.
- Leipzig Heart Institute, Leipzig Heart Science at Heart Center Leipzig, Leipzig, Germany.
| |
Collapse
|
27
|
Sorrell VL, Lindner JR, Pellikka PA, Kirkpatrick JN, Muraru D. Recognized and Unrecognized Value of Echocardiography in Guideline and Consensus Documents Regarding Patients With Chest Pain. J Am Soc Echocardiogr 2023; 36:146-153. [PMID: 36375734 DOI: 10.1016/j.echo.2022.10.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Revised: 10/21/2022] [Accepted: 10/26/2022] [Indexed: 11/13/2022]
Abstract
Guideline and consensus documents have recently been published on the important topic of the noninvasive evaluation of patients presenting with chest pain (CP) or patients with known acute or chronic coronary syndromes. Authors for these documents have included members representing multispecialty imaging societies, yet the process of generating consensus and the need to produce concise written documents have led to a situation where the particular advantages of echocardiography are overlooked. Broad guidelines such as these can be helpful when it comes to "when to do" noninvasive cardiac testing, but they do not pretend to offer nuances on "how to do" noninvasive cardiac testing. This report details the particular value of echocardiography and potential explanations for its understated role in recent guidelines. This report is categorized into the following sections: (1) impact of the level of evidence on guideline creation; (2) versatility of echocardiography in the assessment of CP and the inimitable role for echo Doppler echocardiography in the assessment of dyspnea; (3) value of point-of-care ultrasound in assessing CP and dyspnea; and (4) the future role of echocardiography in ischemic heart disease.
Collapse
Affiliation(s)
- Vincent L Sorrell
- Division of Cardiovascular Medicine, University of Kentucky, Lexington, Kentucky.
| | - Jonathan R Lindner
- Vice-chief for Research in the Cardiology Division, Department of Medicine, University of Virginia, Charlottesville, Virginia
| | | | - James N Kirkpatrick
- Division of Cardiology, Department of Medicine, University of Washington, Seattle, Washington
| | - Denisa Muraru
- Department of Cardiology, Istituto Auxologico Italiano, IRCCS, San Luca Hospital, Milan, Italy; Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
| |
Collapse
|
28
|
Chiarito M, Luceri L, Oliva A, Stefanini G, Condorelli G. Artificial Intelligence and Cardiovascular Risk Prediction: All That Glitters is not Gold. Eur Cardiol 2022; 17:e29. [PMID: 36845218 PMCID: PMC9947926 DOI: 10.15420/ecr.2022.11] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 06/30/2022] [Indexed: 12/24/2022] Open
Abstract
Artificial intelligence (AI) is a broad term referring to any automated systems that need 'intelligence' to carry out specific tasks. During the last decade, AI-based techniques have been gaining popularity in a vast range of biomedical fields, including the cardiovascular setting. Indeed, the dissemination of cardiovascular risk factors and the better prognosis of patients experiencing cardiovascular events resulted in an increase in the prevalence of cardiovascular disease (CVD), eliciting the need for precise identification of patients at increased risk for development and progression of CVD. AI-based predictive models may overcome some of the limitations that hinder the performance of classic regression models. Nonetheless, the successful application of AI in this field requires knowledge of the potential pitfalls of the AI techniques, to guarantee their safe and effective use in daily clinical practice. The aim of the present review is to summarise the pros and cons of different AI methods and their potential application in the cardiovascular field, with a focus on the development of predictive models and risk assessment tools.
Collapse
Affiliation(s)
- Mauro Chiarito
- Department of Biomedical Sciences, Humanitas UniversityPieve Emanuele, Milan, Italy,Center for Interventional Cardiovascular Research and Clinical Trials, The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount SinaiNew York, US
| | - Luca Luceri
- Institute of Information Systems and Networking, University of Applied Sciences and Arts of Southern SwitzerlandLugano, Switzerland
| | - Angelo Oliva
- Department of Biomedical Sciences, Humanitas UniversityPieve Emanuele, Milan, Italy,Cardio Center, Humanitas Research Hospital IRCCSRozzano, Milan, Italy
| | - Giulio Stefanini
- Department of Biomedical Sciences, Humanitas UniversityPieve Emanuele, Milan, Italy,Cardio Center, Humanitas Research Hospital IRCCSRozzano, Milan, Italy
| | - Gianluigi Condorelli
- Department of Biomedical Sciences, Humanitas UniversityPieve Emanuele, Milan, Italy,Cardio Center, Humanitas Research Hospital IRCCSRozzano, Milan, Italy
| |
Collapse
|
29
|
Varudo R, Gonzalez FA, Leote J, Martins C, Bacariza J, Fernandes A, Michard F. Machine learning for the real-time assessment of left ventricular ejection fraction in critically ill patients: a bedside evaluation by novices and experts in echocardiography. Crit Care 2022; 26:386. [PMID: 36517906 PMCID: PMC9749290 DOI: 10.1186/s13054-022-04269-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Accepted: 10/07/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Machine learning algorithms have recently been developed to enable the automatic and real-time echocardiographic assessment of left ventricular ejection fraction (LVEF) and have not been evaluated in critically ill patients. METHODS Real-time LVEF was prospectively measured in 95 ICU patients with a machine learning algorithm installed on a cart-based ultrasound system. Real-time measurements taken by novices (LVEFNov) and by experts (LVEFExp) were compared with LVEF reference measurements (LVEFRef) taken manually by echo experts. RESULTS LVEFRef ranged from 26 to 80% (mean 54 ± 12%), and the reproducibility of measurements was 9 ± 6%. Thirty patients (32%) had a LVEFRef < 50% (left ventricular systolic dysfunction). Real-time LVEFExp and LVEFNov measurements ranged from 31 to 68% (mean 54 ± 10%) and from 28 to 70% (mean 54 ± 9%), respectively. The reproducibility of measurements was comparable for LVEFExp (5 ± 4%) and for LVEFNov (6 ± 5%) and significantly better than for reference measurements (p < 0.001). We observed a strong relationship between LVEFRef and both real-time LVEFExp (r = 0.86, p < 0.001) and LVEFNov (r = 0.81, p < 0.001). The average difference (bias) between real time and reference measurements was 0 ± 6% for LVEFExp and 0 ± 7% for LVEFNov. The sensitivity to detect systolic dysfunction was 70% for real-time LVEFExp and 73% for LVEFNov. The specificity to detect systolic dysfunction was 98% both for LVEFExp and LVEFNov. CONCLUSION Machine learning-enabled real-time measurements of LVEF were strongly correlated with manual measurements obtained by experts. The accuracy of real-time LVEF measurements was excellent, and the precision was fair. The reproducibility of LVEF measurements was better with the machine learning system. The specificity to detect left ventricular dysfunction was excellent both for experts and for novices, whereas the sensitivity could be improved. TRIAL REGISTRATION NCT05336448. Retrospectively registered on April 19, 2022.
Collapse
Affiliation(s)
- Rita Varudo
- grid.414708.e0000 0000 8563 4416Intensive Care Department, Hospital Garcia de Orta, Almada, Portugal
| | - Filipe A. Gonzalez
- grid.414708.e0000 0000 8563 4416Intensive Care Department, Hospital Garcia de Orta, Almada, Portugal ,grid.9983.b0000 0001 2181 4263Faculdade de Medicina da Universidade de Lisboa, Lisbon, Portugal
| | - João Leote
- grid.414708.e0000 0000 8563 4416Intensive Care Department, Hospital Garcia de Orta, Almada, Portugal
| | - Cristina Martins
- grid.414708.e0000 0000 8563 4416Intensive Care Department, Hospital Garcia de Orta, Almada, Portugal
| | - Jacobo Bacariza
- grid.414708.e0000 0000 8563 4416Intensive Care Department, Hospital Garcia de Orta, Almada, Portugal
| | - Antero Fernandes
- grid.414708.e0000 0000 8563 4416Intensive Care Department, Hospital Garcia de Orta, Almada, Portugal ,grid.9983.b0000 0001 2181 4263Faculdade de Medicina da Universidade de Lisboa, Lisbon, Portugal ,grid.7427.60000 0001 2220 7094Faculdade de Ciencias da Saude da Universidade da Beira Interior, Covilha, Portugal
| | | |
Collapse
|
30
|
Nobre Menezes M, Lourenço-Silva J, Silva B, Rodrigues O, Francisco ARG, Carrilho Ferreira P, Oliveira AL, Pinto FJ. Development of deep learning segmentation models for coronary X-ray angiography: Quality assessment by a new global segmentation score and comparison with human performance. Rev Port Cardiol 2022; 41:1011-1021. [PMID: 36511271 DOI: 10.1016/j.repc.2022.04.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 04/03/2022] [Indexed: 01/11/2023] Open
Abstract
INTRODUCTION AND OBJECTIVES Although automatic artificial intelligence (AI) coronary angiography (CAG) segmentation is arguably the first step toward future clinical application, it is underexplored. We aimed to (1) develop AI models for CAG segmentation and (2) assess the results using similarity scores and a set of criteria defined by expert physicians. METHODS Patients undergoing CAG were randomly selected in a retrospective study at a single center. Per incidence, an ideal frame was segmented, forming a baseline human dataset (BH), used for training a baseline AI model (BAI). Enhanced human segmentation (EH) was created by combining the best of both. An enhanced AI model (EAI) was trained using the EH. Results were assessed by experts using 11 weighted criteria, combined into a Global Segmentation Score (GSS: 0-100 points). Generalized Dice Score (GDS) and Dice Similarity Coefficient (DSC) were also used for AI models assessment. RESULTS 1664 processed images were generated. GSS for BH, EH, BAI and EAI were 96.9+/-5.7; 98.9+/-3.1; 86.1+/-10.1 and 90+/-7.6, respectively (95% confidence interval, p<0.001 for both paired and global differences). The GDS for the BAI and EAI was 0.9234±0.0361 and 0.9348±0.0284, respectively. The DSC for the coronary tree was 0.8904±0.0464 and 0.9134±0.0410 for the BAI and EAI, respectively. The EAI outperformed the BAI in all coronary segmentation tasks, but performed less well in some catheter segmentation tasks. CONCLUSIONS We successfully developed AI models capable of CAG segmentation, with good performance as assessed by all scores.
Collapse
Affiliation(s)
- Miguel Nobre Menezes
- Structural and Coronary Heart Disease Unit, Cardiovascular Center of the University of Lisbon, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal; Serviço de Cardiologia, Departamento de Coração e Vasos, CHULN Hospital de Santa Maria, Lisboa, Portugal.
| | | | - Beatriz Silva
- Structural and Coronary Heart Disease Unit, Cardiovascular Center of the University of Lisbon, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal; Serviço de Cardiologia, Departamento de Coração e Vasos, CHULN Hospital de Santa Maria, Lisboa, Portugal
| | - Oliveira Rodrigues
- Structural and Coronary Heart Disease Unit, Cardiovascular Center of the University of Lisbon, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal; Serviço de Cardiologia, Departamento de Coração e Vasos, CHULN Hospital de Santa Maria, Lisboa, Portugal
| | - Ana Rita G Francisco
- Structural and Coronary Heart Disease Unit, Cardiovascular Center of the University of Lisbon, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal; Serviço de Cardiologia, Departamento de Coração e Vasos, CHULN Hospital de Santa Maria, Lisboa, Portugal
| | - Pedro Carrilho Ferreira
- Structural and Coronary Heart Disease Unit, Cardiovascular Center of the University of Lisbon, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal; Serviço de Cardiologia, Departamento de Coração e Vasos, CHULN Hospital de Santa Maria, Lisboa, Portugal
| | | | - Fausto J Pinto
- Structural and Coronary Heart Disease Unit, Cardiovascular Center of the University of Lisbon, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal; Serviço de Cardiologia, Departamento de Coração e Vasos, CHULN Hospital de Santa Maria, Lisboa, Portugal
| |
Collapse
|
31
|
Tromp J, Bauer D, Claggett BL, Frost M, Iversen MB, Prasad N, Petrie MC, Larson MG, Ezekowitz JA, Solomon SD. A formal validation of a deep learning-based automated workflow for the interpretation of the echocardiogram. Nat Commun 2022; 13:6776. [PMID: 36351912 PMCID: PMC9646849 DOI: 10.1038/s41467-022-34245-1] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 10/19/2022] [Indexed: 11/11/2022] Open
Abstract
This study compares a deep learning interpretation of 23 echocardiographic parameters-including cardiac volumes, ejection fraction, and Doppler measurements-with three repeated measurements by core lab sonographers. The primary outcome metric, the individual equivalence coefficient (IEC), compares the disagreement between deep learning and human readers relative to the disagreement among human readers. The pre-determined non-inferiority criterion is 0.25 for the upper bound of the 95% confidence interval. Among 602 anonymised echocardiographic studies from 600 people (421 with heart failure, 179 controls, 69% women), the point estimates of IEC are all <0 and the upper bound of the 95% confidence intervals below 0.25, indicating that the disagreement between the deep learning and human measures is lower than the disagreement among three core lab readers. These results highlight the potential of deep learning algorithms to improve efficiency and reduce the costs of echocardiography.
Collapse
Affiliation(s)
- Jasper Tromp
- grid.4280.e0000 0001 2180 6431Saw Swee Hock School of Public Health, National University of Singapore & National University Health System, Singapore, Singapore ,grid.428397.30000 0004 0385 0924Duke-NUS Medical School, Singapore, Singapore
| | - David Bauer
- grid.38142.3c000000041936754XCardiovascular Division, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA USA
| | - Brian L. Claggett
- grid.38142.3c000000041936754XCardiovascular Division, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA USA
| | | | | | - Narayana Prasad
- grid.38142.3c000000041936754XCardiovascular Division, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA USA
| | - Mark C. Petrie
- grid.8756.c0000 0001 2193 314XBritish Heart Foundation Cardiovascular Research Centre, University of Glasgow, Glasgow, UK
| | - Martin G. Larson
- grid.189504.10000 0004 1936 7558Department of Biostatistics, School of Public Health, Boston University, Boston, MA USA
| | - Justin A. Ezekowitz
- grid.17089.370000 0001 2190 316XDivision of Cardiology and Mazankowski Alberta Heart Institute, University of Alberta, Edmonton, AB Canada ,grid.17089.370000 0001 2190 316XCanadian Vigour Centre, University of Alberta, Edmonton, AB Canada
| | - Scott D. Solomon
- grid.38142.3c000000041936754XCardiovascular Division, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA USA
| |
Collapse
|
32
|
Szabo L, Raisi-Estabragh Z, Salih A, McCracken C, Ruiz Pujadas E, Gkontra P, Kiss M, Maurovich-Horvath P, Vago H, Merkely B, Lee AM, Lekadir K, Petersen SE. Clinician's guide to trustworthy and responsible artificial intelligence in cardiovascular imaging. Front Cardiovasc Med 2022; 9:1016032. [PMID: 36426221 PMCID: PMC9681217 DOI: 10.3389/fcvm.2022.1016032] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 10/11/2022] [Indexed: 12/01/2023] Open
Abstract
A growing number of artificial intelligence (AI)-based systems are being proposed and developed in cardiology, driven by the increasing need to deal with the vast amount of clinical and imaging data with the ultimate aim of advancing patient care, diagnosis and prognostication. However, there is a critical gap between the development and clinical deployment of AI tools. A key consideration for implementing AI tools into real-life clinical practice is their "trustworthiness" by end-users. Namely, we must ensure that AI systems can be trusted and adopted by all parties involved, including clinicians and patients. Here we provide a summary of the concepts involved in developing a "trustworthy AI system." We describe the main risks of AI applications and potential mitigation techniques for the wider application of these promising techniques in the context of cardiovascular imaging. Finally, we show why trustworthy AI concepts are important governing forces of AI development.
Collapse
Affiliation(s)
- Liliana Szabo
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom
- Semmelweis University Heart and Vascular Center, Budapest, Hungary
| | - Zahra Raisi-Estabragh
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom
| | - Ahmed Salih
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom
| | - Celeste McCracken
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, National Institute for Health Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, University of Oxford, Oxford, United Kingdom
| | - Esmeralda Ruiz Pujadas
- Departament de Matemàtiques i Informàtica, Artificial Intelligence in Medicine Lab (BCN-AIM), Universitat de Barcelona, Barcelona, Spain
| | - Polyxeni Gkontra
- Departament de Matemàtiques i Informàtica, Artificial Intelligence in Medicine Lab (BCN-AIM), Universitat de Barcelona, Barcelona, Spain
| | - Mate Kiss
- Siemens Healthcare Hungary, Budapest, Hungary
| | - Pal Maurovich-Horvath
- Department of Radiology, Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| | - Hajnalka Vago
- Semmelweis University Heart and Vascular Center, Budapest, Hungary
| | - Bela Merkely
- Semmelweis University Heart and Vascular Center, Budapest, Hungary
| | - Aaron M. Lee
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom
| | - Karim Lekadir
- Departament de Matemàtiques i Informàtica, Artificial Intelligence in Medicine Lab (BCN-AIM), Universitat de Barcelona, Barcelona, Spain
| | - Steffen E. Petersen
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom
- Health Data Research UK, London, United Kingdom
- Alan Turing Institute, London, United Kingdom
| |
Collapse
|
33
|
Karužas A, Balčiūnas J, Fukson M, Verikas D, Matuliauskas D, Platūkis T, Vaičiulienė D, Jurgaitytė J, Kupstytė-Krištaponė N, Dirsienė R, Jaruševičius G, Šakalytė G, Plisienė J, Lesauskaitė V. Artificial intelligence for automated evaluation of aortic measurements in 2D echocardiography: Feasibility, accuracy, and reproducibility. Echocardiography 2022; 39:1439-1445. [PMID: 36266744 DOI: 10.1111/echo.15475] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 09/14/2022] [Accepted: 10/02/2022] [Indexed: 12/01/2022] Open
Abstract
AIMS This study sought to examine the feasibility, accuracy and reproducibility of a novel, fully automated 2D transthoracic echocardiography (2D TTE) parasternal long axis (PLAX) view aortic measurements quantification software compared to board-certified cardiologists in controlled clinical setting. METHODS AND RESULTS Aortic Annulus (AoA), Aortic Sinus (AoS), Sinotubular Junction (STJ) and Proximal Ascending Aorta (AAo) diameter measurements were performed retrospectively on each of 58 subjects in two different ways: twice using a fully automated software (Ligence Heart version 2) and twice manually by three cardiologists (ORG) and one expert cardiologist (EC). Out of 58 studies AoA was measured in 54 (93%), AoS in 55 (95%), STJ in 55 (95%) and AAo in 54 (93%) studies. Automated measurements had a stronger correlation with EC when compared to ORG with the largest correlation difference of .1 for STJ measurements and lowest difference of .01 for AoS measurements. Automated software was in higher agreement with ground truth intervals (ORG measurements mean +- SEM) in three out of four measurements. CONCLUSION Fully automated 2D TTE PLAX view aortic measurements using a novel AI-based quantification software are feasible and yield results that are in close agreement with what experienced readers measure manually while providing better reproducibility. This approach may prove to have important clinical implications in the automation of the aortic root and ascending aorta assessment to improve workflow efficiency.
Collapse
Affiliation(s)
- Arnas Karužas
- Institute of Cardiology, Lithuanian University of Health Sciences, 15 Sukileliu street, Kaunas, 50103, Lithuania.,Ligence, UAB, Goštauto Str. 8, Vilnius, 01108, Lithuania
| | | | - Mark Fukson
- Ligence, UAB, Goštauto Str. 8, Vilnius, 01108, Lithuania
| | - Dovydas Verikas
- Institute of Cardiology, Lithuanian University of Health Sciences, 15 Sukileliu street, Kaunas, 50103, Lithuania.,Ligence, UAB, Goštauto Str. 8, Vilnius, 01108, Lithuania
| | | | - Tautvydas Platūkis
- Republican Siauliai Hospital, 99 V. Kudirkos street, Siauliai, 76231, Lithuania
| | - Dovilė Vaičiulienė
- Republican Siauliai Hospital, 99 V. Kudirkos street, Siauliai, 76231, Lithuania
| | - Julija Jurgaitytė
- Republican Siauliai Hospital, 99 V. Kudirkos street, Siauliai, 76231, Lithuania
| | | | - Rūta Dirsienė
- Cardiology clinic, Lithuanian University of Health Sciences, 2 Eiveniu street, Kaunas, Lithuania
| | - Gediminas Jaruševičius
- Institute of Cardiology, Lithuanian University of Health Sciences, 15 Sukileliu street, Kaunas, 50103, Lithuania
| | - Gintarė Šakalytė
- Institute of Cardiology, Lithuanian University of Health Sciences, 15 Sukileliu street, Kaunas, 50103, Lithuania
| | - Jurgita Plisienė
- Cardiology clinic, Lithuanian University of Health Sciences, 2 Eiveniu street, Kaunas, Lithuania
| | - Vaiva Lesauskaitė
- Institute of Cardiology, Lithuanian University of Health Sciences, 15 Sukileliu street, Kaunas, 50103, Lithuania
| |
Collapse
|
34
|
Pellikka PA, Strom JB, Pajares-Hurtado GM, Keane MG, Khazan B, Qamruddin S, Tutor A, Gul F, Peterson E, Thamman R, Watson S, Mandale D, Scott CG, Naqvi T, Woodward GM, Hawkes W. Automated analysis of limited echocardiograms: Feasibility and relationship to outcomes in COVID-19. Front Cardiovasc Med 2022; 9:937068. [PMID: 35935624 PMCID: PMC9353267 DOI: 10.3389/fcvm.2022.937068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 06/27/2022] [Indexed: 11/19/2022] Open
Abstract
Background As automated echocardiographic analysis is increasingly utilized, continued evaluation within hospital settings is important to further understand its potential value. The importance of cardiac involvement in patients hospitalized with COVID-19 provides an opportunity to evaluate the feasibility and clinical relevance of automated analysis applied to limited echocardiograms. Methods In this multisite US cohort, the feasibility of automated AI analysis was evaluated on 558 limited echocardiograms in patients hospitalized with COVID-19. Reliability of automated assessment of left ventricular (LV) volumes, ejection fraction (EF), and LV longitudinal strain (LS) was assessed against clinically obtained measures and echocardiographic findings. Automated measures were evaluated against patient outcomes using ROC analysis, survival modeling, and logistic regression for the outcomes of 30-day mortality and in-hospital sequelae. Results Feasibility of automated analysis for both LVEF and LS was 87.5% (488/558 patients). AI analysis was performed with biplane method in 300 (61.5%) and single plane apical 4- or 2-chamber analysis in 136 (27.9%) and 52 (10.7%) studies, respectively. Clinical LVEF was assessed using visual estimation in 192 (39.3%), biplane in 163 (33.4%), and single plane or linear methods in 104 (21.2%) of the 488 studies; 29 (5.9%) studies did not have clinically reported LVEF. LV LS was clinically reported in 80 (16.4%). Consistency between automated and clinical values demonstrated Pearson's R, root mean square error (RMSE) and intraclass correlation coefficient (ICC) of 0.61, 11.3% and 0.72, respectively, for LVEF; 0.73, 3.9% and 0.74, respectively for LS; 0.76, 24.4ml and 0.87, respectively, for end-diastolic volume; and 0.82, 12.8 ml, and 0.91, respectively, for end-systolic volume. Abnormal automated measures of LVEF and LS were associated with LV wall motion abnormalities, left atrial enlargement, and right ventricular dysfunction. Automated analysis was associated with outcomes, including survival. Conclusion Automated analysis was highly feasible on limited echocardiograms using abbreviated protocols, consistent with equivalent clinically obtained metrics, and associated with echocardiographic abnormalities and patient outcomes.
Collapse
Affiliation(s)
- Patricia A. Pellikka
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States
- *Correspondence: Patricia A. Pellikka
| | - Jordan B. Strom
- Richard A. and Susan F. Smith Center for Outcomes Research in Cardiology, Beth Israel Deaconess Medical Center, Boston, MA, United States
| | - Gabriel M. Pajares-Hurtado
- Richard A. and Susan F. Smith Center for Outcomes Research in Cardiology, Beth Israel Deaconess Medical Center, Boston, MA, United States
| | - Martin G. Keane
- Temple Heart and Vascular Center, Philadelphia, PA, United States
| | - Benjamin Khazan
- Temple Heart and Vascular Center, Philadelphia, PA, United States
| | | | - Austin Tutor
- Ochsner Health System, New Orleans, LA, United States
| | - Fahad Gul
- Einstein Medical Center, Philadelphia, PA, United States
| | - Eric Peterson
- Einstein Medical Center, Philadelphia, PA, United States
| | - Ritu Thamman
- Department of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | - Shivani Watson
- Department of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | - Deepa Mandale
- Department of Cardiovascular Medicine, Mayo Clinic, Scottsdale, AZ, United States
| | - Christopher G. Scott
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States
| | - Tasneem Naqvi
- Department of Cardiovascular Medicine, Mayo Clinic, Scottsdale, AZ, United States
| | | | | |
Collapse
|
35
|
Human vs Artificial Intelligence-Based Echocardiography Analysis as Predictor of Outcomes: An analysis from the World Alliance Societies of Echocardiography COVID study. J Am Soc Echocardiogr 2022; 35:1226-1237.e7. [PMID: 35863542 PMCID: PMC9293371 DOI: 10.1016/j.echo.2022.07.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Revised: 05/15/2022] [Accepted: 07/07/2022] [Indexed: 11/23/2022]
Abstract
Background Transthoracic echocardiography is the leading cardiac imaging modality for patients admitted with COVID-19, a condition of high short-term mortality. The aim of this study was to test the hypothesis that artificial intelligence (AI)–based analysis of echocardiographic images could predict mortality more accurately than conventional analysis by a human expert. Methods Patients admitted to 13 hospitals for acute COVID-19 who underwent transthoracic echocardiography were included. Left ventricular ejection fraction (LVEF) and left ventricular longitudinal strain (LVLS) were obtained manually by multiple expert readers and by automated AI software. The ability of the manual and AI analyses to predict all-cause mortality was compared. Results In total, 870 patients were enrolled. The mortality rate was 27.4% after a mean follow-up period of 230 ± 115 days. AI analysis had lower variability than manual analysis for both LVEF (P = .003) and LVLS (P = .005). AI-derived LVEF and LVLS were predictors of mortality in univariable and multivariable regression analysis (odds ratio, 0.974 [95% CI, 0.956-0.991; P = .003] for LVEF; odds ratio, 1.060 [95% CI, 1.019-1.105; P = .004] for LVLS), but LVEF and LVLS obtained by manual analysis were not. Direct comparison of the predictive value of AI versus manual measurements of LVEF and LVLS showed that AI was significantly better (P = .005 and P = .003, respectively). In addition, AI-derived LVEF and LVLS had more significant and stronger correlations to other objective biomarkers of acute disease than manual reads. Conclusions AI-based analysis of LVEF and LVLS had similar feasibility as manual analysis, minimized variability, and consequently increased the statistical power to predict mortality. AI-based, but not manual, analyses were a significant predictor of in-hospital and follow-up mortality.
Collapse
|
36
|
Karakuş G, Değirmencioğlu A, Nanda NC. Artificial intelligence in echocardiography: Review and limitations including epistemological concerns. Echocardiography 2022; 39:1044-1053. [PMID: 35808922 DOI: 10.1111/echo.15417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 06/01/2022] [Accepted: 06/13/2022] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND AND PURPOSE In this review we describe the use of artificial intelligence in the field of echocardiography. Various aspects and terminologies used in artificial intelligence are explained in an easy-to-understand manner and supplemented with illustrations related to echocardiography. Limitations of artificial intelligence, including epistemologic concerns from a philosophical standpoint, are also discussed. METHODS A narrative review of relevant papers was conducted. CONCLUSION We provide an overview of the usefulness of artificial intelligence in echocardiography and focus on how it can supplement current day-to-day clinical practice in the assessment of various cardiovascular disease entities. On the other hand, there are significant limitations, including epistemological concerns, which need to be kept in perspective.
Collapse
Affiliation(s)
- Gültekin Karakuş
- Department of Cardiology, School of Medicine, Acibadem University, Istanbul, Turkey
| | - Aleks Değirmencioğlu
- Department of Cardiology, School of Medicine, Acibadem University, Istanbul, Turkey
| | - Navin C Nanda
- Division of Cardiology, Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA
| |
Collapse
|
37
|
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.
Collapse
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.)
| |
Collapse
|
38
|
Moal O, Roger E, Lamouroux A, Younes C, Bonnet G, Moal B, Lafitte S. Explicit and automatic ejection fraction assessment on 2D cardiac ultrasound with a deep learning-based approach. Comput Biol Med 2022; 146:105637. [PMID: 35617727 DOI: 10.1016/j.compbiomed.2022.105637] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 03/01/2022] [Accepted: 04/29/2022] [Indexed: 12/16/2022]
Abstract
BACKGROUND Ejection fraction (EF) is a key parameter for assessing cardiovascular functions in cardiac ultrasound, but its manual assessment is time-consuming and subject to high inter and intra-observer variability. Deep learning-based methods have the potential to perform accurate fully automatic EF predictions but suffer from a lack of explainability and interpretability. This study proposes a fully automatic method to reliably and explicitly evaluate the biplane left ventricular EF on 2D echocardiography following the recommended modified Simpson's rule. METHODS A deep learning model was trained on apical 4 and 2-chamber echocardiography to segment the left ventricle and locate the mitral valve. Predicted segmentations are then validated with a statistical shape model, which detects potential failures that could impact the EF evaluation. Finally, the end-diastolic and end-systolic frames are identified based on the remaining LV segmentations' areas and EF is estimated on all available cardiac cycles. RESULTS Our approach was trained on a dataset of 783 patients. Its performances were evaluated on an internal and external dataset of respectively 200 and 450 patients. On the internal dataset, EF assessment achieved a mean absolute error of 6.10% and a bias of 1.56 ± 7.58% using multiple cardiac cycles. The approach evaluated EF with a mean absolute error of 5.39% and a bias of -0.74 ± 7.12% on the external dataset. CONCLUSION Following the recommended guidelines, we proposed an end-to-end fully automatic approach that achieves state-of-the-art performance in biplane EF evaluation while giving explicit details to clinicians.
Collapse
Affiliation(s)
| | | | | | | | - Guillaume Bonnet
- Hôpital Cardiologique Haut Lévêque, CHU de Bordeaux, CIC 0005, Pessac, France.
| | | | - Stephane Lafitte
- Hôpital Cardiologique Haut Lévêque, CHU de Bordeaux, CIC 0005, Pessac, France.
| |
Collapse
|
39
|
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.
Collapse
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
| |
Collapse
|
40
|
Kagiyama N, Tokodi M, Sengupta PP. Machine Learning in Cardiovascular Imaging. Heart Fail Clin 2022; 18:245-258. [DOI: 10.1016/j.hfc.2021.11.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
|
41
|
Samtani R, Bienstock S, Lai AC, Liao S, Baber U, Croft L, Stern E, Beerkens F, Ting P, Goldman ME. Assessment and validation of a novel fast fully automated artificial intelligence left ventricular ejection fraction quantification software. Echocardiography 2022; 39:473-482. [PMID: 35178746 DOI: 10.1111/echo.15318] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 01/11/2022] [Accepted: 01/27/2022] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND Quantification of left ventricular ejection fraction (LVEF) by transthoracic echocardiography (TTE) is operator-dependent, time-consuming, and error-prone. LVivoEF by DIA is a new artificial intelligence (AI) software, which displays the tracking of endocardial borders and rapidly quantifies LVEF. We sought to assess the accuracy of LVivoEF compared to cardiac magnetic resonance imaging (cMRI) as the reference standard and to compare LVivoEF to the standard-of-care physician-measured LVEF (MD-EF) including studies with ultrasound enhancing agents (UEAs). METHODS In 273 consecutive patients, we compared MD-EF and AI-derived LVEF to cMRI. AI-derived LVEF was obtained from a non-UEA four-chamber view without manual correction. Thirty-one patients were excluded: 25 had interval interventions or incomplete TTE or cMRI studies and six had uninterpretable non-UEA apical views. RESULTS In the 242 subjects, the correlation between AI and cMRI was r = .890, similar to MD-EF and cMRI with r = .891 (p = 0.48). Of the 126 studies performed with UEAs, the correlation of AI using the unenhanced four-chamber view was r = .89, similar to MD-EF with r = .90. In the 116 unenhanced studies, AI correlation was r = .87, similar to MD-EF with r = .84. From Bland-Altman analysis, LVivoEF underreported the LVEF with a bias of 3.63 ± 7.40% EF points compared to cMRI while MD-EF to cMRI had a bias of .33 ± 7.52% (p = 0.80). CONCLUSIONS Compared to cMRI, LVivoEF can accurately quantify LVEF from a standard apical four-chamber view without manual correction. Thus, LVivoEF has the ability to improve and expedite LVEF quantification.
Collapse
Affiliation(s)
- Rajeev Samtani
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York City, New York, USA
| | - Solomon Bienstock
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York City, New York, USA
| | - Ashton C Lai
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York City, New York, USA
| | - Steve Liao
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York City, New York, USA
| | - Usman Baber
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York City, New York, USA
| | - Lori Croft
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York City, New York, USA
| | - Eric Stern
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York City, New York, USA
| | - Frans Beerkens
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York City, New York, USA
| | - Peter Ting
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York City, New York, USA
| | - Martin E Goldman
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York City, New York, USA
| |
Collapse
|
42
|
Nedadur R, Wang B, Tsang W. Artificial intelligence for the echocardiographic assessment of valvular heart disease. Heart 2022; 108:1592-1599. [PMID: 35144983 PMCID: PMC9554049 DOI: 10.1136/heartjnl-2021-319725] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 12/29/2021] [Indexed: 11/18/2022] Open
Abstract
Developments in artificial intelligence (AI) have led to an explosion of studies exploring its application to cardiovascular medicine. Due to the need for training and expertise, one area where AI could be impactful would be in the diagnosis and management of valvular heart disease. This is because AI can be applied to the multitude of data generated from clinical assessments, imaging and biochemical testing during the care of the patient. In the area of valvular heart disease, the focus of AI has been on the echocardiographic assessment and phenotyping of patient populations to identify high-risk groups. AI can assist image acquisition, view identification for review, and segmentation of valve and cardiac structures for automated analysis. Using image recognition algorithms, aortic and mitral valve disease states have been directly detected from the images themselves. Measurements obtained during echocardiographic valvular assessment have been integrated with other clinical data to identify novel aortic valve disease subgroups and describe new predictors of aortic valve disease progression. In the future, AI could integrate echocardiographic parameters with other clinical data for precision medical management of patients with valvular heart disease.
Collapse
Affiliation(s)
- Rashmi Nedadur
- Division of Cardiac Surgery, University of Toronto, Toronto, Ontario, Canada.,Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
| | - Bo Wang
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada.,Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.,Vector Institute of Artificial Intelligence, University of Toronto, Toronto, Ontario, Canada.,Peter Munk Cardiac Center, University Health Network, Toronto, Ontario, Canada
| | - Wendy Tsang
- Peter Munk Cardiac Center, University Health Network, Toronto, Ontario, Canada .,Division of Cardiology, University of Toronto, Toronto, Ontario, Canada
| |
Collapse
|
43
|
Radiomics in Cardiovascular Disease Imaging: from Pixels to the Heart of the Problem. CURRENT CARDIOVASCULAR IMAGING REPORTS 2022. [DOI: 10.1007/s12410-022-09563-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Abstract
Purpose of Review
This review of the literature aims to present potential applications of radiomics in cardiovascular radiology and, in particular, in cardiac imaging.
Recent Findings
Radiomics and machine learning represent a technological innovation which may be used to extract and analyze quantitative features from medical images. They aid in detecting hidden pattern in medical data, possibly leading to new insights in pathophysiology of different medical conditions. In the recent literature, radiomics and machine learning have been investigated for numerous potential applications in cardiovascular imaging. They have been proposed to improve image acquisition and reconstruction, for anatomical structure automated segmentation or automated characterization of cardiologic diseases.
Summary
The number of applications for radiomics and machine learning is continuing to rise, even though methodological and implementation issues still limit their use in daily practice. In the long term, they may have a positive impact in patient management.
Collapse
|
44
|
Current and Future Applications of Artificial Intelligence in Coronary Artery Disease. Healthcare (Basel) 2022; 10:healthcare10020232. [PMID: 35206847 PMCID: PMC8872080 DOI: 10.3390/healthcare10020232] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 01/19/2022] [Accepted: 01/24/2022] [Indexed: 02/07/2023] Open
Abstract
Cardiovascular diseases (CVDs) carry significant morbidity and mortality and are associated with substantial economic burden on healthcare systems around the world. Coronary artery disease, as one disease entity under the CVDs umbrella, had a prevalence of 7.2% among adults in the United States and incurred a financial burden of 360 billion US dollars in the years 2016–2017. The introduction of artificial intelligence (AI) and machine learning over the last two decades has unlocked new dimensions in the field of cardiovascular medicine. From automatic interpretations of heart rhythm disorders via smartwatches, to assisting in complex decision-making, AI has quickly expanded its realms in medicine and has demonstrated itself as a promising tool in helping clinicians guide treatment decisions. Understanding complex genetic interactions and developing clinical risk prediction models, advanced cardiac imaging, and improving mortality outcomes are just a few areas where AI has been applied in the domain of coronary artery disease. Through this review, we sought to summarize the advances in AI relating to coronary artery disease, current limitations, and future perspectives.
Collapse
|
45
|
Papadopoulou SL, Sachpekidis V, Kantartzi V, Styliadis I, Nihoyannopoulos P. Clinical validation of an artificial intelligence-assisted algorithm for automated quantification of left ventricular ejection fraction in real time by a novel handheld ultrasound device. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2022; 3:29-37. [PMID: 36713988 PMCID: PMC9707920 DOI: 10.1093/ehjdh/ztac001] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 12/20/2021] [Accepted: 01/10/2022] [Indexed: 02/01/2023]
Abstract
Aims We sought to evaluate the reliability and diagnostic accuracy of a novel handheld ultrasound device (HUD) with artificial intelligence (AI) assisted algorithm to automatically calculate ejection fraction (autoEF) in a real-world patient population. Methods and results We studied 100 consecutive patients (57 ± 15 years old, 61% male), including 38 with abnormal left ventricular (LV) function [LV ejection fraction (LVEF) < 50%]. The autoEF results acquired using the HUD were independently compared with manually traced biplane Simpson's rule measurements on cart-based systems to assess method agreement using intra-class correlation coefficient (ICC), linear regression analysis, and Bland-Altman analysis. The diagnostic accuracy for the detection of LVEF <50% was also calculated. Test-retest reliability of measured EF by the HUD was assessed by calculating the ICC and the minimal detectable change (MDC). The ICC, linear regression analysis, and Bland-Altman analysis revealed good agreement between autoEF and reference manual EF (ICC = 0.85; r = 0.87, P < 0.001; mean bias -1.42% with limits of agreement 14.5%, respectively). Detection of abnormal LV function (EF < 50%) by autoEF algorithm was feasible with sensitivity 90% (95% CI 75-97%), specificity 87% (95% CI 76-94%), PPV 81% (95% CI 66-91%), NPV 93% (95% CI 83-98%), and a total diagnostic accuracy of 88%. Test-retest reliability was excellent (ICC = 0.91, P < 0.001; r = 0.91, P < 0.001; mean difference ± SD: 0.54% ± 5.27%, P = 0.308) and MDC for LVEF measurement by autoEF was calculated at 4.38%. Conclusion Use of a novel HUD with AI-enabled capabilities provided similar LVEF results with those derived by manual biplane Simpson's method on cart-based systems and shows clinical potential.
Collapse
Affiliation(s)
| | | | - Vasiliki Kantartzi
- Department of Cardiology, Papageorgiou General Hospital, Ring Road, Nea Efkarpia, Thessaloniki 56403, Greece
| | - Ioannis Styliadis
- Department of Cardiology, Papageorgiou General Hospital, Ring Road, Nea Efkarpia, Thessaloniki 56403, Greece
| | - Petros Nihoyannopoulos
- Imperial College London, National Heart & Lung Institute, The Hammersmith Hospital, Du Cane Road, London W120NN, UK,First Cardiology Department, Medical School, University of Athens, Hippokration Hospital, 114 Vasilissis Sofias Avenue, 11527 Athens, Greece
| |
Collapse
|
46
|
Deng Y, Cai P, Zhang L, Cao X, Chen Y, Jiang S, Zhuang Z, Wang B. Myocardial strain analysis of echocardiography based on deep learning. Front Cardiovasc Med 2022; 9:1067760. [PMID: 36588559 PMCID: PMC9800889 DOI: 10.3389/fcvm.2022.1067760] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 11/30/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Strain analysis provides more thorough spatiotemporal signatures for myocardial contraction, which is helpful for early detection of cardiac insufficiency. The use of deep learning (DL) to automatically measure myocardial strain from echocardiogram videos has garnered recent attention. However, the development of key techniques including segmentation and motion estimation remains a challenge. In this work, we developed a novel DL-based framework for myocardial segmentation and motion estimation to generate strain measures from echocardiogram videos. METHODS Three-dimensional (3D) Convolutional Neural Network (CNN) was developed for myocardial segmentation and optical flow network for motion estimation. The segmentation network was used to define the region of interest (ROI), and the optical flow network was used to estimate the pixel motion in the ROI. We performed a model architecture search to identify the optimal base architecture for motion estimation. The final workflow design and associated hyperparameters are the result of a careful implementation. In addition, we compared the DL model with a traditional speck tracking algorithm on an independent, external clinical data. Each video was double-blind measured by an ultrasound expert and a DL expert using speck tracking echocardiography (STE) and DL method, respectively. RESULTS The DL method successfully performed automatic segmentation, motion estimation, and global longitudinal strain (GLS) measurements in all examinations. The 3D segmentation has better spatio-temporal smoothness, average dice correlation reaches 0.82, and the effect of target frame is better than that of previous 2D networks. The best motion estimation network achieved an average end-point error of 0.05 ± 0.03 mm per frame, better than previously reported state-of-the-art. The DL method showed no significant difference relative to the traditional method in GLS measurement, Spearman correlation of 0.90 (p < 0.001) and mean bias -1.2 ± 1.5%. CONCLUSION In conclusion, our method exhibits better segmentation and motion estimation performance and demonstrates the feasibility of DL method for automatic strain analysis. The DL approach helps reduce time consumption and human effort, which holds great promise for translational research and precision medicine efforts.
Collapse
Affiliation(s)
- Yinlong Deng
- Department of Cardiology, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
- Department of Preventive Medicine, Shantou University Medical College, Shantou, China
| | - Peiwei Cai
- Ultrasound Division, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Li Zhang
- Department of Cardiology, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Xiongcheng Cao
- Department of Cardiology, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
- Department of Preventive Medicine, Shantou University Medical College, Shantou, China
| | - Yequn Chen
- Department of Cardiology, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Shiyan Jiang
- Department of Cardiology, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Zhemin Zhuang
- Department of Electronic Information Engineering, College of Engineering, Shantou University, Shantou, China
- Zhemin Zhuang,
| | - Bin Wang
- Department of Cardiology, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
- *Correspondence: Bin Wang,
| |
Collapse
|
47
|
Tromp J, Seekings PJ, Hung CL, Iversen MB, Frost MJ, Ouwerkerk W, Jiang Z, Eisenhaber F, Goh RSM, Zhao H, Huang W, Ling LH, Sim D, Cozzone P, Richards AM, Lee HK, Solomon SD, Lam CSP, Ezekowitz JA. Automated interpretation of systolic and diastolic function on the echocardiogram: a multicohort study. Lancet Digit Health 2021; 4:e46-e54. [PMID: 34863649 DOI: 10.1016/s2589-7500(21)00235-1] [Citation(s) in RCA: 55] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 06/24/2021] [Accepted: 10/07/2021] [Indexed: 12/21/2022]
Abstract
BACKGROUND Echocardiography is the diagnostic modality for assessing cardiac systolic and diastolic function to diagnose and manage heart failure. However, manual interpretation of echocardiograms can be time consuming and subject to human error. Therefore, we developed a fully automated deep learning workflow to classify, segment, and annotate two-dimensional (2D) videos and Doppler modalities in echocardiograms. METHODS We developed the workflow using a training dataset of 1145 echocardiograms and an internal test set of 406 echocardiograms from the prospective heart failure research platform (Asian Network for Translational Research and Cardiovascular Trials; ATTRaCT) in Asia, with previous manual tracings by expert sonographers. We validated the workflow against manual measurements in a curated dataset from Canada (Alberta Heart Failure Etiology and Analysis Research Team; HEART; n=1029 echocardiograms), a real-world dataset from Taiwan (n=31 241), the US-based EchoNet-Dynamic dataset (n=10 030), and in an independent prospective assessment of the Asian (ATTRaCT) and Canadian (Alberta HEART) datasets (n=142) with repeated independent measurements by two expert sonographers. FINDINGS In the ATTRaCT test set, the automated workflow classified 2D videos and Doppler modalities with accuracies (number of correct predictions divided by the total number of predictions) ranging from 0·91 to 0·99. Segmentations of the left ventricle and left atrium were accurate, with a mean Dice similarity coefficient greater than 93% for all. In the external datasets (n=1029 to 10 030 echocardiograms used as input), automated measurements showed good agreement with locally measured values, with a mean absolute error range of 9-25 mL for left ventricular volumes, 6-10% for left ventricular ejection fraction (LVEF), and 1·8-2·2 for the ratio of the mitral inflow E wave to the tissue Doppler e' wave (E/e' ratio); and reliably classified systolic dysfunction (LVEF <40%, area under the receiver operating characteristic curve [AUC] range 0·90-0·92) and diastolic dysfunction (E/e' ratio ≥13, AUC range 0·91-0·91), with narrow 95% CIs for AUC values. Independent prospective evaluation confirmed less variance of automated compared with human expert measurements, with all individual equivalence coefficients being less than 0 for all measurements. INTERPRETATION Deep learning algorithms can automatically annotate 2D videos and Doppler modalities with similar accuracy to manual measurements by expert sonographers. Use of an automated workflow might accelerate access, improve quality, and reduce costs in diagnosing and managing heart failure globally. FUNDING A*STAR Biomedical Research Council and A*STAR Exploit Technologies.
Collapse
Affiliation(s)
- Jasper Tromp
- National Heart Centre Singapore, Singapore; Duke-NUS Medical School, Singapore; Saw Swee Hock School of Public Health, National University of Singapore & National University Health System, Singapore
| | - Paul J Seekings
- Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), Singapore; Us2.ai, Singapore
| | - Chung-Lieh Hung
- Department of Medicine and Institute of Biomedical Sciences, Mackay Medical College, Taipei, Taiwan; Cardiovascular Division, Department of Internal Medicine, Mackay Memorial Hospital, Taipei, Taiwan
| | | | | | - Wouter Ouwerkerk
- National Heart Centre Singapore, Singapore; Department of Dermatology, Amsterdam UMC, University of Amsterdam, Amsterdam Infection and Immunity Institute, Amsterdam, Netherlands
| | | | - Frank Eisenhaber
- Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), Singapore; Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), Singapore; School of Biological Science, Nanyang Technological University, Singapore
| | - Rick S M Goh
- Institute of High Performance Computing, Agency for Science, Technology and Research (A*STAR), Singapore
| | - Heng Zhao
- Institute of High Performance Computing, Agency for Science, Technology and Research (A*STAR), Singapore
| | - Weimin Huang
- Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR), Singapore
| | - Lieng-Hsi Ling
- National University Heart Centre, Singapore; Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - David Sim
- National Heart Centre Singapore, Singapore
| | - Patrick Cozzone
- Singapore Bioimaging Consortium, Biomedical Sciences Institutes, Agency for Science, Technology and Research (A*STAR), Singapore
| | - A Mark Richards
- National University Heart Centre, Singapore; Cardiovascular Research Institute, National University Health System, Singapore; Christchurch Heart Institute, University of Otago, Christchurch, New Zealand
| | - Hwee Kuan Lee
- Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), Singapore; Image and Pervasive Access Lab, CNRS UMI 2955, Singapore; Singapore Eye Research Institute, Singapore
| | - Scott D Solomon
- Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Carolyn S P Lam
- National Heart Centre Singapore, Singapore; Duke-NUS Medical School, Singapore; Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
| | | |
Collapse
|
48
|
Echocardiographic Advances in Dilated Cardiomyopathy. J Clin Med 2021; 10:jcm10235518. [PMID: 34884220 PMCID: PMC8658091 DOI: 10.3390/jcm10235518] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 11/05/2021] [Accepted: 11/23/2021] [Indexed: 12/29/2022] Open
Abstract
Although the overall survival of patients with dilated cardiomyopathy (DCM) has improved significantly in the last decades, a non-negligible proportion of DCM patients still shows an unfavorable prognosis. DCM patients not only need imaging techniques that are effective in diagnosis, but also suitable for long-term follow-up with frequent re-evaluations. The exponential growth of echocardiography’s technology and performance in recent years has resulted in improved diagnostic accuracy, stratification, management and follow-up of patients with DCM. This review summarizes some new developments in echocardiography and their promising applications in DCM. Although nowadays cardiac magnetic resonance (CMR) remains the gold standard technique in DCM, the echocardiographic advances and novelties proposed in the manuscript, if properly integrated into clinical practice, could bring echocardiography closer to CMR in terms of accuracy and may certify ultrasound as the technique of choice in the follow-up of DCM patients. The application in DCM patients of novel echocardiographic techniques represents an interesting emergent research area for scholars in the near future.
Collapse
|
49
|
Italiano G, Tamborini G, Fusini L, Mantegazza V, Doldi M, Celeste F, Gripari P, Muratori M, Lang RM, Pepi M. Feasibility and Accuracy of the Automated Software for Dynamic Quantification of Left Ventricular and Atrial Volumes and Function in a Large Unselected Population. J Clin Med 2021; 10:jcm10215030. [PMID: 34768549 PMCID: PMC8584703 DOI: 10.3390/jcm10215030] [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: 09/23/2021] [Revised: 10/22/2021] [Accepted: 10/26/2021] [Indexed: 11/24/2022] Open
Abstract
We aimed to evaluate the feasibility and accuracy of machine learning-based automated dynamic quantification of left ventricular (LV) and left atrial (LA) volumes in an unselected population. We enrolled 600 unselected patients (12% in atrial fibrillation) clinically referred for transthoracic echocardiography (2DTTE), who also underwent 3D echocardiography (3DE) imaging. LV ejection fraction (EF), LV, and LA volumes were obtained from 2D images; 3D images were analyzed using dynamic heart model (DHM) software (Philips) resulting in LV and LA volume–time curves. A subgroup of 140 patients also underwent cardiac magnetic resonance (CMR) imaging. Average time of analysis, feasibility, and image quality were recorded, and results were compared between 2DTTE, DHM, and CMR. The use of DHM was feasible in 522/600 cases (87%). When feasible, the boundary position was considered accurate in 335/522 patients (64%), while major (n = 38) or minor (n = 149) border corrections were needed. The overall time required for DHM datasets was approximately 40 seconds. As expected, DHM LV volumes were larger than 2D ones (end-diastolic volume: 173 ± 64 vs. 142 ± 58 mL, respectively), while no differences were found for LV EF and LA volumes (EF: 55% ± 12 vs. 56% ± 14; LA volume 89 ± 36 vs. 89 ± 38 mL, respectively). The comparison between DHM and CMR values showed a high correlation for LV volumes (r = 0.70 and r = 0.82, p < 0.001 for end-diastolic and end-systolic volume, respectively) and an excellent correlation for EF (r = 0.82, p < 0.001) and LA volumes. The DHM software is feasible, accurate, and quick in a large series of unselected patients, including those with suboptimal 2D images or in atrial fibrillation.
Collapse
Affiliation(s)
- Gianpiero Italiano
- Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy; (G.T.); (L.F.); (V.M.); (M.D.); (F.C.); (P.G.); (M.M.); (M.P.)
- Correspondence:
| | - Gloria Tamborini
- Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy; (G.T.); (L.F.); (V.M.); (M.D.); (F.C.); (P.G.); (M.M.); (M.P.)
| | - Laura Fusini
- Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy; (G.T.); (L.F.); (V.M.); (M.D.); (F.C.); (P.G.); (M.M.); (M.P.)
| | - Valentina Mantegazza
- Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy; (G.T.); (L.F.); (V.M.); (M.D.); (F.C.); (P.G.); (M.M.); (M.P.)
| | - Marco Doldi
- Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy; (G.T.); (L.F.); (V.M.); (M.D.); (F.C.); (P.G.); (M.M.); (M.P.)
| | - Fabrizio Celeste
- Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy; (G.T.); (L.F.); (V.M.); (M.D.); (F.C.); (P.G.); (M.M.); (M.P.)
| | - Paola Gripari
- Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy; (G.T.); (L.F.); (V.M.); (M.D.); (F.C.); (P.G.); (M.M.); (M.P.)
| | - Manuela Muratori
- Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy; (G.T.); (L.F.); (V.M.); (M.D.); (F.C.); (P.G.); (M.M.); (M.P.)
| | - Roberto M. Lang
- Department of Medicine, University of Chicago Medical Center, Chicago, IL 60637, USA;
| | - Mauro Pepi
- Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy; (G.T.); (L.F.); (V.M.); (M.D.); (F.C.); (P.G.); (M.M.); (M.P.)
| |
Collapse
|
50
|
de Siqueira VS, Borges MM, Furtado RG, Dourado CN, da Costa RM. Artificial intelligence applied to support medical decisions for the automatic analysis of echocardiogram images: A systematic review. Artif Intell Med 2021; 120:102165. [PMID: 34629153 DOI: 10.1016/j.artmed.2021.102165] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 08/07/2021] [Accepted: 08/31/2021] [Indexed: 12/16/2022]
Abstract
The echocardiogram is a test that is widely used in Heart Disease Diagnoses. However, its analysis is largely dependent on the physician's experience. In this regard, artificial intelligence has become an essential technology to assist physicians. This study is a Systematic Literature Review (SLR) of primary state-of-the-art studies that used Artificial Intelligence (AI) techniques to automate echocardiogram analyses. Searches on the leading scientific article indexing platforms using a search string returned approximately 1400 articles. After applying the inclusion and exclusion criteria, 118 articles were selected to compose the detailed SLR. This SLR presents a thorough investigation of AI applied to support medical decisions for the main types of echocardiogram (Transthoracic, Transesophageal, Doppler, Stress, and Fetal). The article's data extraction indicated that the primary research interest of the studies comprised four groups: 1) Improvement of image quality; 2) identification of the cardiac window vision plane; 3) quantification and analysis of cardiac functions, and; 4) detection and classification of cardiac diseases. The articles were categorized and grouped to show the main contributions of the literature to each type of ECHO. The results indicate that the Deep Learning (DL) methods presented the best results for the detection and segmentation of the heart walls, right and left atrium and ventricles, and classification of heart diseases using images/videos obtained by echocardiography. The models that used Convolutional Neural Network (CNN) and its variations showed the best results for all groups. The evidence produced by the results presented in the tabulation of the studies indicates that the DL contributed significantly to advances in echocardiogram automated analysis processes. Although several solutions were presented regarding the automated analysis of ECHO, this area of research still has great potential for further studies to improve the accuracy of results already known in the literature.
Collapse
Affiliation(s)
- Vilson Soares de Siqueira
- Federal Institute of Tocantins, Av. Bernado Sayão, S/N, Santa Maria, Colinas do Tocantins, TO, Brazil; Federal University of Goias, Alameda Palmeiras, Quadra D, Câmpus Samambaia, Goiânia, GO, Brazil.
| | - Moisés Marcos Borges
- Diagnostic Imaging Center - CDI, Av. Portugal, 1155, St. Marista, Goiânia, GO, Brazil
| | - Rogério Gomes Furtado
- Diagnostic Imaging Center - CDI, Av. Portugal, 1155, St. Marista, Goiânia, GO, Brazil
| | - Colandy Nunes Dourado
- Diagnostic Imaging Center - CDI, Av. Portugal, 1155, St. Marista, Goiânia, GO, Brazil. http://www.cdigoias.com.br
| | - Ronaldo Martins da Costa
- Federal University of Goias, Alameda Palmeiras, Quadra D, Câmpus Samambaia, Goiânia, GO, Brazil.
| |
Collapse
|