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Lopez Santi R, Gupta S, Baranchuk A. Artificial intelligence, the challenge of maintaining an active role. J Electrocardiol 2024; 86:153757. [PMID: 39126970 DOI: 10.1016/j.jelectrocard.2024.07.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Revised: 06/21/2024] [Accepted: 07/01/2024] [Indexed: 08/12/2024]
Affiliation(s)
| | - Shyla Gupta
- Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada
| | - Adrian Baranchuk
- Division of Cardiology, Queen's University, Kingston, Ontario, Canada
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Abdollahi A, Kato Y, Bakhshi H, Varadarajan V, Chehab O, Zeitoun R, Ostovaneh MR, Wu CO, Bertoni AG, Shah SJ, Ambale-Venkatesh B, Bluemke DA, Lima JAC, Panzer A. Differential Stroke Volume between Left and Right Ventricles as a Predictor of Clinical Outcomes: The MESA Study. Radiology 2024; 312:e232973. [PMID: 39041933 PMCID: PMC11294760 DOI: 10.1148/radiol.232973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Revised: 04/09/2023] [Accepted: 04/15/2023] [Indexed: 07/24/2024]
Abstract
Background Valvular heart disease and intracardiac shunts can disrupt the balance between left ventricular (LV) and right ventricular (RV) stroke volumes. However, the prognostic value of such imbalances has not been established among asymptomatic individuals. Purpose To assess the association between differential ventricular stroke volumes quantified using cardiac MRI and clinical outcomes in individuals without cardiovascular disease. Materials and Methods This secondary analysis of a prospective study included participants without cardiovascular disease at enrollment (July 2000 to July 2002) who underwent cardiac MRI. Differences in stroke volume were calculated as LV stroke volume minus RV stroke volume, and participants were categorized as having balanced (greater than or equal to -30 mL to ≤30 mL), negative (less than -30 mL), or positive (>30 mL) differential stroke volumes. Multivariable Cox proportional hazard regression models were used to test the association between differences in stroke volume and adverse outcomes. Results A cohort of 4058 participants (mean age, 61.4 years ± 10 [SD]; 2120 female) were included and followed up for a median of 18.4 years (IQR, 18.3-18.5 years). During follow-up, 1006 participants died, 235 participants developed heart failure, and 764 participants developed atrial fibrillation. Compared with participants who had a balanced differential stroke volume, those with an increased differential stroke volume showed a higher risk of mortality (hazard ratio [HR], 1.73 [95% CI: 1.12, 2.67]; P = .01), heart failure (HR, 2.40 [95% CI: 1.11, 5.20]; P = .03), and atrial fibrillation (HR, 1.89 [95% CI: 1.16, 3.08]; P = .01) in adjusted models. Participants in the negative group, with a decreased differential stroke volume, showed an increased risk of heart failure compared with those in the balanced group (HR, 2.09 [95% CI: 1.09, 3.99]; P = .03); however, this was no longer observed after adjusting for baseline LV function (P = .34). Conclusion Participants without cardiovascular disease at the time of study enrollment who had an LV stroke volume exceeding the RV stroke volume by greater than 30 mL had an increased risk of mortality, heart failure, and atrial fibrillation compared with those with balanced stroke volumes. ClinicalTrials.gov Identifier: NCT00005487 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Almeida in this issue.
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Affiliation(s)
- Ashkan Abdollahi
- From the Division of Cardiology, Department of Medicine (A.A., Y.K.,
H.B., V.V., O.C., R.Z., M.R.O., J.A.C.L.), and Department of Radiology (B.A.V.),
Johns Hopkins University, 600 N Wolfe St, Baltimore, MD 21287-0409; Office of
Biostatistics Research, National Heart, Lung, and Blood Institute, National
Institutes of Health, Bethesda, Md (C.O.W.); Department of Epidemiology and
Prevention, Wake Forest School of Medicine, Winston-Salem, NC (A.G.B.); Division
of Cardiology, Department of Medicine, Northwestern University Feinberg School
of Medicine, Chicago, Ill (S.J.S.); and Department of Radiology, University of
Wisconsin School of Medicine and Public Health, Madison, Wis (D.A.B.)
| | - Yoko Kato
- From the Division of Cardiology, Department of Medicine (A.A., Y.K.,
H.B., V.V., O.C., R.Z., M.R.O., J.A.C.L.), and Department of Radiology (B.A.V.),
Johns Hopkins University, 600 N Wolfe St, Baltimore, MD 21287-0409; Office of
Biostatistics Research, National Heart, Lung, and Blood Institute, National
Institutes of Health, Bethesda, Md (C.O.W.); Department of Epidemiology and
Prevention, Wake Forest School of Medicine, Winston-Salem, NC (A.G.B.); Division
of Cardiology, Department of Medicine, Northwestern University Feinberg School
of Medicine, Chicago, Ill (S.J.S.); and Department of Radiology, University of
Wisconsin School of Medicine and Public Health, Madison, Wis (D.A.B.)
| | - Hooman Bakhshi
- From the Division of Cardiology, Department of Medicine (A.A., Y.K.,
H.B., V.V., O.C., R.Z., M.R.O., J.A.C.L.), and Department of Radiology (B.A.V.),
Johns Hopkins University, 600 N Wolfe St, Baltimore, MD 21287-0409; Office of
Biostatistics Research, National Heart, Lung, and Blood Institute, National
Institutes of Health, Bethesda, Md (C.O.W.); Department of Epidemiology and
Prevention, Wake Forest School of Medicine, Winston-Salem, NC (A.G.B.); Division
of Cardiology, Department of Medicine, Northwestern University Feinberg School
of Medicine, Chicago, Ill (S.J.S.); and Department of Radiology, University of
Wisconsin School of Medicine and Public Health, Madison, Wis (D.A.B.)
| | - Vinithra Varadarajan
- From the Division of Cardiology, Department of Medicine (A.A., Y.K.,
H.B., V.V., O.C., R.Z., M.R.O., J.A.C.L.), and Department of Radiology (B.A.V.),
Johns Hopkins University, 600 N Wolfe St, Baltimore, MD 21287-0409; Office of
Biostatistics Research, National Heart, Lung, and Blood Institute, National
Institutes of Health, Bethesda, Md (C.O.W.); Department of Epidemiology and
Prevention, Wake Forest School of Medicine, Winston-Salem, NC (A.G.B.); Division
of Cardiology, Department of Medicine, Northwestern University Feinberg School
of Medicine, Chicago, Ill (S.J.S.); and Department of Radiology, University of
Wisconsin School of Medicine and Public Health, Madison, Wis (D.A.B.)
| | - Omar Chehab
- From the Division of Cardiology, Department of Medicine (A.A., Y.K.,
H.B., V.V., O.C., R.Z., M.R.O., J.A.C.L.), and Department of Radiology (B.A.V.),
Johns Hopkins University, 600 N Wolfe St, Baltimore, MD 21287-0409; Office of
Biostatistics Research, National Heart, Lung, and Blood Institute, National
Institutes of Health, Bethesda, Md (C.O.W.); Department of Epidemiology and
Prevention, Wake Forest School of Medicine, Winston-Salem, NC (A.G.B.); Division
of Cardiology, Department of Medicine, Northwestern University Feinberg School
of Medicine, Chicago, Ill (S.J.S.); and Department of Radiology, University of
Wisconsin School of Medicine and Public Health, Madison, Wis (D.A.B.)
| | - Ralph Zeitoun
- From the Division of Cardiology, Department of Medicine (A.A., Y.K.,
H.B., V.V., O.C., R.Z., M.R.O., J.A.C.L.), and Department of Radiology (B.A.V.),
Johns Hopkins University, 600 N Wolfe St, Baltimore, MD 21287-0409; Office of
Biostatistics Research, National Heart, Lung, and Blood Institute, National
Institutes of Health, Bethesda, Md (C.O.W.); Department of Epidemiology and
Prevention, Wake Forest School of Medicine, Winston-Salem, NC (A.G.B.); Division
of Cardiology, Department of Medicine, Northwestern University Feinberg School
of Medicine, Chicago, Ill (S.J.S.); and Department of Radiology, University of
Wisconsin School of Medicine and Public Health, Madison, Wis (D.A.B.)
| | - Mohammad R. Ostovaneh
- From the Division of Cardiology, Department of Medicine (A.A., Y.K.,
H.B., V.V., O.C., R.Z., M.R.O., J.A.C.L.), and Department of Radiology (B.A.V.),
Johns Hopkins University, 600 N Wolfe St, Baltimore, MD 21287-0409; Office of
Biostatistics Research, National Heart, Lung, and Blood Institute, National
Institutes of Health, Bethesda, Md (C.O.W.); Department of Epidemiology and
Prevention, Wake Forest School of Medicine, Winston-Salem, NC (A.G.B.); Division
of Cardiology, Department of Medicine, Northwestern University Feinberg School
of Medicine, Chicago, Ill (S.J.S.); and Department of Radiology, University of
Wisconsin School of Medicine and Public Health, Madison, Wis (D.A.B.)
| | - Colin O. Wu
- From the Division of Cardiology, Department of Medicine (A.A., Y.K.,
H.B., V.V., O.C., R.Z., M.R.O., J.A.C.L.), and Department of Radiology (B.A.V.),
Johns Hopkins University, 600 N Wolfe St, Baltimore, MD 21287-0409; Office of
Biostatistics Research, National Heart, Lung, and Blood Institute, National
Institutes of Health, Bethesda, Md (C.O.W.); Department of Epidemiology and
Prevention, Wake Forest School of Medicine, Winston-Salem, NC (A.G.B.); Division
of Cardiology, Department of Medicine, Northwestern University Feinberg School
of Medicine, Chicago, Ill (S.J.S.); and Department of Radiology, University of
Wisconsin School of Medicine and Public Health, Madison, Wis (D.A.B.)
| | - Alain G. Bertoni
- From the Division of Cardiology, Department of Medicine (A.A., Y.K.,
H.B., V.V., O.C., R.Z., M.R.O., J.A.C.L.), and Department of Radiology (B.A.V.),
Johns Hopkins University, 600 N Wolfe St, Baltimore, MD 21287-0409; Office of
Biostatistics Research, National Heart, Lung, and Blood Institute, National
Institutes of Health, Bethesda, Md (C.O.W.); Department of Epidemiology and
Prevention, Wake Forest School of Medicine, Winston-Salem, NC (A.G.B.); Division
of Cardiology, Department of Medicine, Northwestern University Feinberg School
of Medicine, Chicago, Ill (S.J.S.); and Department of Radiology, University of
Wisconsin School of Medicine and Public Health, Madison, Wis (D.A.B.)
| | - Sanjiv J. Shah
- From the Division of Cardiology, Department of Medicine (A.A., Y.K.,
H.B., V.V., O.C., R.Z., M.R.O., J.A.C.L.), and Department of Radiology (B.A.V.),
Johns Hopkins University, 600 N Wolfe St, Baltimore, MD 21287-0409; Office of
Biostatistics Research, National Heart, Lung, and Blood Institute, National
Institutes of Health, Bethesda, Md (C.O.W.); Department of Epidemiology and
Prevention, Wake Forest School of Medicine, Winston-Salem, NC (A.G.B.); Division
of Cardiology, Department of Medicine, Northwestern University Feinberg School
of Medicine, Chicago, Ill (S.J.S.); and Department of Radiology, University of
Wisconsin School of Medicine and Public Health, Madison, Wis (D.A.B.)
| | - Bharath Ambale-Venkatesh
- From the Division of Cardiology, Department of Medicine (A.A., Y.K.,
H.B., V.V., O.C., R.Z., M.R.O., J.A.C.L.), and Department of Radiology (B.A.V.),
Johns Hopkins University, 600 N Wolfe St, Baltimore, MD 21287-0409; Office of
Biostatistics Research, National Heart, Lung, and Blood Institute, National
Institutes of Health, Bethesda, Md (C.O.W.); Department of Epidemiology and
Prevention, Wake Forest School of Medicine, Winston-Salem, NC (A.G.B.); Division
of Cardiology, Department of Medicine, Northwestern University Feinberg School
of Medicine, Chicago, Ill (S.J.S.); and Department of Radiology, University of
Wisconsin School of Medicine and Public Health, Madison, Wis (D.A.B.)
| | - David A. Bluemke
- From the Division of Cardiology, Department of Medicine (A.A., Y.K.,
H.B., V.V., O.C., R.Z., M.R.O., J.A.C.L.), and Department of Radiology (B.A.V.),
Johns Hopkins University, 600 N Wolfe St, Baltimore, MD 21287-0409; Office of
Biostatistics Research, National Heart, Lung, and Blood Institute, National
Institutes of Health, Bethesda, Md (C.O.W.); Department of Epidemiology and
Prevention, Wake Forest School of Medicine, Winston-Salem, NC (A.G.B.); Division
of Cardiology, Department of Medicine, Northwestern University Feinberg School
of Medicine, Chicago, Ill (S.J.S.); and Department of Radiology, University of
Wisconsin School of Medicine and Public Health, Madison, Wis (D.A.B.)
| | - João A. C. Lima
- From the Division of Cardiology, Department of Medicine (A.A., Y.K.,
H.B., V.V., O.C., R.Z., M.R.O., J.A.C.L.), and Department of Radiology (B.A.V.),
Johns Hopkins University, 600 N Wolfe St, Baltimore, MD 21287-0409; Office of
Biostatistics Research, National Heart, Lung, and Blood Institute, National
Institutes of Health, Bethesda, Md (C.O.W.); Department of Epidemiology and
Prevention, Wake Forest School of Medicine, Winston-Salem, NC (A.G.B.); Division
of Cardiology, Department of Medicine, Northwestern University Feinberg School
of Medicine, Chicago, Ill (S.J.S.); and Department of Radiology, University of
Wisconsin School of Medicine and Public Health, Madison, Wis (D.A.B.)
| | - Ariane Panzer
- From the Division of Cardiology, Department of Medicine (A.A., Y.K.,
H.B., V.V., O.C., R.Z., M.R.O., J.A.C.L.), and Department of Radiology (B.A.V.),
Johns Hopkins University, 600 N Wolfe St, Baltimore, MD 21287-0409; Office of
Biostatistics Research, National Heart, Lung, and Blood Institute, National
Institutes of Health, Bethesda, Md (C.O.W.); Department of Epidemiology and
Prevention, Wake Forest School of Medicine, Winston-Salem, NC (A.G.B.); Division
of Cardiology, Department of Medicine, Northwestern University Feinberg School
of Medicine, Chicago, Ill (S.J.S.); and Department of Radiology, University of
Wisconsin School of Medicine and Public Health, Madison, Wis (D.A.B.)
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彭 威, 张 泽, 肖 云. [Research progress on bioinformatics in pulmonary arterial hypertension]. ZHONGGUO DANG DAI ER KE ZA ZHI = CHINESE JOURNAL OF CONTEMPORARY PEDIATRICS 2024; 26:425-431. [PMID: 38660909 PMCID: PMC11057300 DOI: 10.7499/j.issn.1008-8830.2310076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 02/26/2024] [Indexed: 04/26/2024]
Abstract
Pulmonary arterial hypertension (PAH) is a severe disease characterized by abnormal pulmonary vascular remodeling and increased right ventricular pressure load, posing a significant threat to patient health. While some pathological mechanisms of PAH have been revealed, the deeper mechanisms of pathogenesis remain to be elucidated. In recent years, bioinformatics has provided a powerful tool for a deeper understanding of the complex mechanisms of PAH through the integration of techniques such as multi-omics analysis, artificial intelligence, and Mendelian randomization. This review focuses on the bioinformatics methods and technologies used in PAH research, summarizing their current applications in the study of disease mechanisms, diagnosis, and prognosis assessment. Additionally, it analyzes the existing challenges faced by bioinformatics and its potential applications in the clinical and basic research fields of PAH in the future.
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Affiliation(s)
| | - 泽盈 张
- 中南大学湘雅二医院心血管内科，湖南长沙410007
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Aquino GJ, Mastrodicasa D, Alabed S, Abohashem S, Wen L, Gill RR, Bardo DME, Abbara S, Hanneman K. Radiology: Cardiothoracic Imaging Highlights 2023. Radiol Cardiothorac Imaging 2024; 6:e240020. [PMID: 38602468 PMCID: PMC11056755 DOI: 10.1148/ryct.240020] [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: 01/17/2024] [Revised: 02/15/2024] [Accepted: 02/28/2024] [Indexed: 04/12/2024]
Abstract
Radiology: Cardiothoracic Imaging publishes novel research and technical developments in cardiac, thoracic, and vascular imaging. The journal published many innovative studies during 2023 and achieved an impact factor for the first time since its inaugural issue in 2019, with an impact factor of 7.0. The current review article, led by the Radiology: Cardiothoracic Imaging trainee editorial board, highlights the most impactful articles published in the journal between November 2022 and October 2023. The review encompasses various aspects of coronary CT, photon-counting detector CT, PET/MRI, cardiac MRI, congenital heart disease, vascular imaging, thoracic imaging, artificial intelligence, and health services research. Key highlights include the potential for photon-counting detector CT to reduce contrast media volumes, utility of combined PET/MRI in the evaluation of cardiac sarcoidosis, the prognostic value of left atrial late gadolinium enhancement at MRI in predicting incident atrial fibrillation, the utility of an artificial intelligence tool to optimize detection of incidental pulmonary embolism, and standardization of medical terminology for cardiac CT. Ongoing research and future directions include evaluation of novel PET tracers for assessment of myocardial fibrosis, deployment of AI tools in clinical cardiovascular imaging workflows, and growing awareness of the need to improve environmental sustainability in imaging. Keywords: Coronary CT, Photon-counting Detector CT, PET/MRI, Cardiac MRI, Congenital Heart Disease, Vascular Imaging, Thoracic Imaging, Artificial Intelligence, Health Services Research © RSNA, 2024.
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Affiliation(s)
| | | | - Samer Alabed
- From the Department of Radiology, SUNY Upstate Medical University,
750 E Adams St, Syracuse, NY, 13210 (G.J.A); Department of Radiology, University
of Washington School of Medicine, UW Medical Center Montlake, Seattle, Wash
(D.M.); Department of Radiology, OncoRad/Tumor Imaging Metrics Core (TIMC),
University of Washington School of Medicine, Seattle, Wash (D.M.); Division of
Clinical Medicine, School of Medicine and Population Health, University of
Sheffield, Sheffield, United Kingdom (S. Alabed); National Institute for Health
and Care Research, Sheffield Biomedical Research Centre, Sheffield, United
Kingdom (S. Alabed); Department of Radiology, Cardiovascular Imaging Research
Center, Massachusetts General Hospital and Harvard Medical School, Boston, Mass
(S. Abohashem); Department of Radiology, Key Laboratory of Birth Defects and
Related Diseases of Women and Children, Ministry of Education, West China Second
University Hospital, Sichuan University, Sichuan, China (L.W.); Department of
Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston,
Mass (R.R.G.); Department of Medical Imaging, Ann & Robert H. Lurie
Children’s Hospital of Chicago, Chicago, Ill (D.M.E.B.); Department of
Radiology, UT Southwestern Medical Center, Dallas, Tex (S. Abbara); Department
of Medical Imaging, University Medical Imaging Toronto, University of Toronto,
Toronto, Ontario, Canada (K.H.); and Peter Munk Cardiac Centre, Toronto General
Hospital, University of Toronto, Toronto, Ontario, Canada (K.H.)
| | - Shady Abohashem
- From the Department of Radiology, SUNY Upstate Medical University,
750 E Adams St, Syracuse, NY, 13210 (G.J.A); Department of Radiology, University
of Washington School of Medicine, UW Medical Center Montlake, Seattle, Wash
(D.M.); Department of Radiology, OncoRad/Tumor Imaging Metrics Core (TIMC),
University of Washington School of Medicine, Seattle, Wash (D.M.); Division of
Clinical Medicine, School of Medicine and Population Health, University of
Sheffield, Sheffield, United Kingdom (S. Alabed); National Institute for Health
and Care Research, Sheffield Biomedical Research Centre, Sheffield, United
Kingdom (S. Alabed); Department of Radiology, Cardiovascular Imaging Research
Center, Massachusetts General Hospital and Harvard Medical School, Boston, Mass
(S. Abohashem); Department of Radiology, Key Laboratory of Birth Defects and
Related Diseases of Women and Children, Ministry of Education, West China Second
University Hospital, Sichuan University, Sichuan, China (L.W.); Department of
Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston,
Mass (R.R.G.); Department of Medical Imaging, Ann & Robert H. Lurie
Children’s Hospital of Chicago, Chicago, Ill (D.M.E.B.); Department of
Radiology, UT Southwestern Medical Center, Dallas, Tex (S. Abbara); Department
of Medical Imaging, University Medical Imaging Toronto, University of Toronto,
Toronto, Ontario, Canada (K.H.); and Peter Munk Cardiac Centre, Toronto General
Hospital, University of Toronto, Toronto, Ontario, Canada (K.H.)
| | - Lingyi Wen
- From the Department of Radiology, SUNY Upstate Medical University,
750 E Adams St, Syracuse, NY, 13210 (G.J.A); Department of Radiology, University
of Washington School of Medicine, UW Medical Center Montlake, Seattle, Wash
(D.M.); Department of Radiology, OncoRad/Tumor Imaging Metrics Core (TIMC),
University of Washington School of Medicine, Seattle, Wash (D.M.); Division of
Clinical Medicine, School of Medicine and Population Health, University of
Sheffield, Sheffield, United Kingdom (S. Alabed); National Institute for Health
and Care Research, Sheffield Biomedical Research Centre, Sheffield, United
Kingdom (S. Alabed); Department of Radiology, Cardiovascular Imaging Research
Center, Massachusetts General Hospital and Harvard Medical School, Boston, Mass
(S. Abohashem); Department of Radiology, Key Laboratory of Birth Defects and
Related Diseases of Women and Children, Ministry of Education, West China Second
University Hospital, Sichuan University, Sichuan, China (L.W.); Department of
Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston,
Mass (R.R.G.); Department of Medical Imaging, Ann & Robert H. Lurie
Children’s Hospital of Chicago, Chicago, Ill (D.M.E.B.); Department of
Radiology, UT Southwestern Medical Center, Dallas, Tex (S. Abbara); Department
of Medical Imaging, University Medical Imaging Toronto, University of Toronto,
Toronto, Ontario, Canada (K.H.); and Peter Munk Cardiac Centre, Toronto General
Hospital, University of Toronto, Toronto, Ontario, Canada (K.H.)
| | - Ritu R. Gill
- From the Department of Radiology, SUNY Upstate Medical University,
750 E Adams St, Syracuse, NY, 13210 (G.J.A); Department of Radiology, University
of Washington School of Medicine, UW Medical Center Montlake, Seattle, Wash
(D.M.); Department of Radiology, OncoRad/Tumor Imaging Metrics Core (TIMC),
University of Washington School of Medicine, Seattle, Wash (D.M.); Division of
Clinical Medicine, School of Medicine and Population Health, University of
Sheffield, Sheffield, United Kingdom (S. Alabed); National Institute for Health
and Care Research, Sheffield Biomedical Research Centre, Sheffield, United
Kingdom (S. Alabed); Department of Radiology, Cardiovascular Imaging Research
Center, Massachusetts General Hospital and Harvard Medical School, Boston, Mass
(S. Abohashem); Department of Radiology, Key Laboratory of Birth Defects and
Related Diseases of Women and Children, Ministry of Education, West China Second
University Hospital, Sichuan University, Sichuan, China (L.W.); Department of
Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston,
Mass (R.R.G.); Department of Medical Imaging, Ann & Robert H. Lurie
Children’s Hospital of Chicago, Chicago, Ill (D.M.E.B.); Department of
Radiology, UT Southwestern Medical Center, Dallas, Tex (S. Abbara); Department
of Medical Imaging, University Medical Imaging Toronto, University of Toronto,
Toronto, Ontario, Canada (K.H.); and Peter Munk Cardiac Centre, Toronto General
Hospital, University of Toronto, Toronto, Ontario, Canada (K.H.)
| | - Dianna M. E. Bardo
- From the Department of Radiology, SUNY Upstate Medical University,
750 E Adams St, Syracuse, NY, 13210 (G.J.A); Department of Radiology, University
of Washington School of Medicine, UW Medical Center Montlake, Seattle, Wash
(D.M.); Department of Radiology, OncoRad/Tumor Imaging Metrics Core (TIMC),
University of Washington School of Medicine, Seattle, Wash (D.M.); Division of
Clinical Medicine, School of Medicine and Population Health, University of
Sheffield, Sheffield, United Kingdom (S. Alabed); National Institute for Health
and Care Research, Sheffield Biomedical Research Centre, Sheffield, United
Kingdom (S. Alabed); Department of Radiology, Cardiovascular Imaging Research
Center, Massachusetts General Hospital and Harvard Medical School, Boston, Mass
(S. Abohashem); Department of Radiology, Key Laboratory of Birth Defects and
Related Diseases of Women and Children, Ministry of Education, West China Second
University Hospital, Sichuan University, Sichuan, China (L.W.); Department of
Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston,
Mass (R.R.G.); Department of Medical Imaging, Ann & Robert H. Lurie
Children’s Hospital of Chicago, Chicago, Ill (D.M.E.B.); Department of
Radiology, UT Southwestern Medical Center, Dallas, Tex (S. Abbara); Department
of Medical Imaging, University Medical Imaging Toronto, University of Toronto,
Toronto, Ontario, Canada (K.H.); and Peter Munk Cardiac Centre, Toronto General
Hospital, University of Toronto, Toronto, Ontario, Canada (K.H.)
| | - Suhny Abbara
- From the Department of Radiology, SUNY Upstate Medical University,
750 E Adams St, Syracuse, NY, 13210 (G.J.A); Department of Radiology, University
of Washington School of Medicine, UW Medical Center Montlake, Seattle, Wash
(D.M.); Department of Radiology, OncoRad/Tumor Imaging Metrics Core (TIMC),
University of Washington School of Medicine, Seattle, Wash (D.M.); Division of
Clinical Medicine, School of Medicine and Population Health, University of
Sheffield, Sheffield, United Kingdom (S. Alabed); National Institute for Health
and Care Research, Sheffield Biomedical Research Centre, Sheffield, United
Kingdom (S. Alabed); Department of Radiology, Cardiovascular Imaging Research
Center, Massachusetts General Hospital and Harvard Medical School, Boston, Mass
(S. Abohashem); Department of Radiology, Key Laboratory of Birth Defects and
Related Diseases of Women and Children, Ministry of Education, West China Second
University Hospital, Sichuan University, Sichuan, China (L.W.); Department of
Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston,
Mass (R.R.G.); Department of Medical Imaging, Ann & Robert H. Lurie
Children’s Hospital of Chicago, Chicago, Ill (D.M.E.B.); Department of
Radiology, UT Southwestern Medical Center, Dallas, Tex (S. Abbara); Department
of Medical Imaging, University Medical Imaging Toronto, University of Toronto,
Toronto, Ontario, Canada (K.H.); and Peter Munk Cardiac Centre, Toronto General
Hospital, University of Toronto, Toronto, Ontario, Canada (K.H.)
| | - Kate Hanneman
- From the Department of Radiology, SUNY Upstate Medical University,
750 E Adams St, Syracuse, NY, 13210 (G.J.A); Department of Radiology, University
of Washington School of Medicine, UW Medical Center Montlake, Seattle, Wash
(D.M.); Department of Radiology, OncoRad/Tumor Imaging Metrics Core (TIMC),
University of Washington School of Medicine, Seattle, Wash (D.M.); Division of
Clinical Medicine, School of Medicine and Population Health, University of
Sheffield, Sheffield, United Kingdom (S. Alabed); National Institute for Health
and Care Research, Sheffield Biomedical Research Centre, Sheffield, United
Kingdom (S. Alabed); Department of Radiology, Cardiovascular Imaging Research
Center, Massachusetts General Hospital and Harvard Medical School, Boston, Mass
(S. Abohashem); Department of Radiology, Key Laboratory of Birth Defects and
Related Diseases of Women and Children, Ministry of Education, West China Second
University Hospital, Sichuan University, Sichuan, China (L.W.); Department of
Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston,
Mass (R.R.G.); Department of Medical Imaging, Ann & Robert H. Lurie
Children’s Hospital of Chicago, Chicago, Ill (D.M.E.B.); Department of
Radiology, UT Southwestern Medical Center, Dallas, Tex (S. Abbara); Department
of Medical Imaging, University Medical Imaging Toronto, University of Toronto,
Toronto, Ontario, Canada (K.H.); and Peter Munk Cardiac Centre, Toronto General
Hospital, University of Toronto, Toronto, Ontario, Canada (K.H.)
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Salehi M, Maiter A, Strickland S, Aldabbagh Z, Karunasaagarar K, Thomas R, Lopez-Dee T, Capener D, Dwivedi K, Sharkey M, Metherall P, van der Geest R, Alabed S, Swift AJ. Clinical assessment of an AI tool for measuring biventricular parameters on cardiac MR. Front Cardiovasc Med 2024; 11:1279298. [PMID: 38374997 PMCID: PMC10875016 DOI: 10.3389/fcvm.2024.1279298] [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: 08/17/2023] [Accepted: 01/22/2024] [Indexed: 02/21/2024] Open
Abstract
Introduction Cardiac magnetic resonance (CMR) is of diagnostic and prognostic value in a range of cardiopulmonary conditions. Current methods for evaluating CMR studies are laborious and time-consuming, contributing to delays for patients. As the demand for CMR increases, there is a growing need to automate this process. The application of artificial intelligence (AI) to CMR is promising, but the evaluation of these tools in clinical practice has been limited. This study assessed the clinical viability of an automatic tool for measuring cardiac volumes on CMR. Methods Consecutive patients who underwent CMR for any indication between January 2022 and October 2022 at a single tertiary centre were included prospectively. For each case, short-axis CMR images were segmented by the AI tool and manually to yield volume, mass and ejection fraction measurements for both ventricles. Automated and manual measurements were compared for agreement and the quality of the automated contours was assessed visually by cardiac radiologists. Results 462 CMR studies were included. No statistically significant difference was demonstrated between any automated and manual measurements (p > 0.05; independent T-test). Intraclass correlation coefficient and Bland-Altman analysis showed excellent agreement across all metrics (ICC > 0.85). The automated contours were evaluated visually in 251 cases, with agreement or minor disagreement in 229 cases (91.2%) and failed segmentation in only a single case (0.4%). The AI tool was able to provide automated contours in under 90 s. Conclusions Automated segmentation of both ventricles on CMR by an automatic tool shows excellent agreement with manual segmentation performed by CMR experts in a retrospective real-world clinical cohort. Implementation of the tool could improve the efficiency of CMR reporting and reduce delays between imaging and diagnosis.
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Affiliation(s)
- Mahan Salehi
- Department of Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom
| | - Ahmed Maiter
- Department of Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom
- Department of Cardiovascular Disease, NIHR Sheffield Biomedical Research Centre, Sheffield, United Kingdom
| | - Scarlett Strickland
- Department of Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom
| | - Ziad Aldabbagh
- Department of Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom
| | - Kavita Karunasaagarar
- Department of Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom
| | - Richard Thomas
- Department of Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom
| | - Tristan Lopez-Dee
- Department of Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom
| | - Dave Capener
- Department of Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom
| | - Krit Dwivedi
- Department of Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom
| | - Michael Sharkey
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom
| | - Pete Metherall
- Department of Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom
| | - Rob van der Geest
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, Netherlands
| | - Samer Alabed
- Department of Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom
- Department of Cardiovascular Disease, NIHR Sheffield Biomedical Research Centre, Sheffield, United Kingdom
| | - Andrew J. Swift
- Department of Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom
- Department of Cardiovascular Disease, NIHR Sheffield Biomedical Research Centre, Sheffield, United Kingdom
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6
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Celant LR, Wessels JN, Marcus JT, Meijboom LJ, Bogaard HJ, de Man FS, Vonk Noordegraaf A. Toward the Implementation of Optimal Cardiac Magnetic Resonance Risk Stratification in Pulmonary Arterial Hypertension. Chest 2024; 165:181-191. [PMID: 37527773 DOI: 10.1016/j.chest.2023.07.028] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 07/06/2023] [Accepted: 07/24/2023] [Indexed: 08/03/2023] Open
Abstract
BACKGROUND The 2022 European Society of Cardiology/European Respiratory Society pulmonary hypertension (PH) guidelines incorporate cardiac magnetic resonance (CMR) imaging metrics in the risk stratification of patients with pulmonary arterial hypertension (PAH). Thresholds to identify patients at estimated 1-year mortality risks of < 5%, 5% to 20%, and > 20% are introduced. However, these cutoff values are mostly single center-based and require external validation. RESEARCH QUESTION What are the discriminative prognostic properties of the current CMR risk thresholds stratifying patients with PAH? STUDY DESIGN AND METHODS We analyzed data from incident, treatment-naïve patients with PAH from the Amsterdam University Medical Centres, Vrije Universiteit, The Netherlands. The discriminative properties of the proposed CMR three risk strata were tested at baseline and first reassessment, using the following PH guideline variables: right ventricular ejection fraction, indexed right ventricular end-systolic volume, and indexed left ventricular stroke volume. RESULTS A total of 258 patients with PAH diagnosed between 2001 and 2022 fulfilled the study criteria and were included in this study. Of these, 172 had follow-up CMR imaging after 3 months to 1.5 years. According to the CMR three risk strata, most patients were classified at intermediate risk (n = 115 [45%]) upon diagnosis. Only 29 (11%) of patients with PAH were classified at low risk, and 114 (44%) were classified at high risk. Poor survival discrimination was seen between risk groups. Appropriate survival discrimination was seen at first reassessment. INTERPRETATION Risk stratifying patients with PAH with the recent proposed CMR cutoffs from the European Society of Cardiology/European Respiratory Society 2022 PH guidelines requires adjustment because post-processing consensus is lacking and general applicability is limited. Risk assessment at follow-up yielded better survival discrimination, emphasizing the importance of the individual treatment response.
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Affiliation(s)
- Lucas R Celant
- Department of Pulmonary Medicine, Amsterdam UMC location Vrije Universiteit Amsterdam, The Netherlands; Pulmonary Hypertension and Thrombosis, Amsterdam Cardiovascular Sciences, Amsterdam UMC location Vrije Universiteit Amsterdam, The Netherlands
| | - Jeroen N Wessels
- Department of Pulmonary Medicine, Amsterdam UMC location Vrije Universiteit Amsterdam, The Netherlands; Pulmonary Hypertension and Thrombosis, Amsterdam Cardiovascular Sciences, Amsterdam UMC location Vrije Universiteit Amsterdam, The Netherlands
| | - J Tim Marcus
- Pulmonary Hypertension and Thrombosis, Amsterdam Cardiovascular Sciences, Amsterdam UMC location Vrije Universiteit Amsterdam, The Netherlands; Department of Radiology and Nuclear Medicine, Amsterdam UMC location Vrije Universiteit Amsterdam, The Netherlands
| | - Lilian J Meijboom
- Pulmonary Hypertension and Thrombosis, Amsterdam Cardiovascular Sciences, Amsterdam UMC location Vrije Universiteit Amsterdam, The Netherlands; Department of Radiology and Nuclear Medicine, Amsterdam UMC location Vrije Universiteit Amsterdam, The Netherlands
| | - Harm Jan Bogaard
- Department of Pulmonary Medicine, Amsterdam UMC location Vrije Universiteit Amsterdam, The Netherlands; Pulmonary Hypertension and Thrombosis, Amsterdam Cardiovascular Sciences, Amsterdam UMC location Vrije Universiteit Amsterdam, The Netherlands
| | - Frances S de Man
- Department of Pulmonary Medicine, Amsterdam UMC location Vrije Universiteit Amsterdam, The Netherlands; Pulmonary Hypertension and Thrombosis, Amsterdam Cardiovascular Sciences, Amsterdam UMC location Vrije Universiteit Amsterdam, The Netherlands
| | - Anton Vonk Noordegraaf
- Department of Pulmonary Medicine, Amsterdam UMC location Vrije Universiteit Amsterdam, The Netherlands; Pulmonary Hypertension and Thrombosis, Amsterdam Cardiovascular Sciences, Amsterdam UMC location Vrije Universiteit Amsterdam, The Netherlands.
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Morales MA, Manning WJ, Nezafat R. Present and Future Innovations in AI and Cardiac MRI. Radiology 2024; 310:e231269. [PMID: 38193835 PMCID: PMC10831479 DOI: 10.1148/radiol.231269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 10/21/2023] [Accepted: 10/26/2023] [Indexed: 01/10/2024]
Abstract
Cardiac MRI is used to diagnose and treat patients with a multitude of cardiovascular diseases. Despite the growth of clinical cardiac MRI, complicated image prescriptions and long acquisition protocols limit the specialty and restrain its impact on the practice of medicine. Artificial intelligence (AI)-the ability to mimic human intelligence in learning and performing tasks-will impact nearly all aspects of MRI. Deep learning (DL) primarily uses an artificial neural network to learn a specific task from example data sets. Self-driving scanners are increasingly available, where AI automatically controls cardiac image prescriptions. These scanners offer faster image collection with higher spatial and temporal resolution, eliminating the need for cardiac triggering or breath holding. In the future, fully automated inline image analysis will most likely provide all contour drawings and initial measurements to the reader. Advanced analysis using radiomic or DL features may provide new insights and information not typically extracted in the current analysis workflow. AI may further help integrate these features with clinical, genetic, wearable-device, and "omics" data to improve patient outcomes. This article presents an overview of AI and its application in cardiac MRI, including in image acquisition, reconstruction, and processing, and opportunities for more personalized cardiovascular care through extraction of novel imaging markers.
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Affiliation(s)
- Manuel A. Morales
- From the Department of Medicine, Cardiovascular Division (M.A.M.,
W.J.M., R.N.), and Department of Radiology (W.J.M.), Beth Israel Deaconess
Medical Center and Harvard Medical School, 330 Brookline Ave, Boston, MA
02215
| | - Warren J. Manning
- From the Department of Medicine, Cardiovascular Division (M.A.M.,
W.J.M., R.N.), and Department of Radiology (W.J.M.), Beth Israel Deaconess
Medical Center and Harvard Medical School, 330 Brookline Ave, Boston, MA
02215
| | - Reza Nezafat
- From the Department of Medicine, Cardiovascular Division (M.A.M.,
W.J.M., R.N.), and Department of Radiology (W.J.M.), Beth Israel Deaconess
Medical Center and Harvard Medical School, 330 Brookline Ave, Boston, MA
02215
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8
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Assadi H, Matthews G, Zhao X, Li R, Alabed S, Grafton-Clarke C, Mehmood Z, Kasmai B, Limbachia V, Gosling R, Yashoda GK, Halliday I, Swoboda P, Ripley DP, Zhong L, Vassiliou VS, Swift AJ, Geest RJVD, Garg P. Cardiac MR modelling of systolic and diastolic blood pressure. Open Heart 2023; 10:e002484. [PMID: 38114194 DOI: 10.1136/openhrt-2023-002484] [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: 09/12/2023] [Accepted: 11/01/2023] [Indexed: 12/21/2023] Open
Abstract
AIMS Blood pressure (BP) is a crucial factor in cardiovascular health and can affect cardiac imaging assessments. However, standard outpatient cardiovascular MR (CMR) imaging procedures do not typically include BP measurements prior to image acquisition. This study proposes that brachial systolic BP (SBP) and diastolic BP (DBP) can be modelled using patient characteristics and CMR data. METHODS In this multicentre study, 57 patients from the PREFER-CMR registry and 163 patients from other registries were used as the derivation cohort. All subjects had their brachial SBP and DBP measured using a sphygmomanometer. Multivariate linear regression analysis was applied to predict brachial BP. The model was subsequently validated in a cohort of 169 healthy individuals. RESULTS Age and left ventricular ejection fraction were associated with SBP. Aortic forward flow, body surface area and left ventricular mass index were associated with DBP. When applied to the validation cohort, the correlation coefficient between CMR-derived SBP and brachial SBP was (r=0.16, 95% CI 0.011 to 0.305, p=0.03), and CMR-derived DBP and brachial DBP was (r=0.27, 95% CI 0.122 to 0.403, p=0.0004). The area under the curve (AUC) for CMR-derived SBP to predict SBP>120 mmHg was 0.59, p=0.038. Moreover, CMR-derived DBP to predict DBP>80 mmHg had an AUC of 0.64, p=0.002. CONCLUSION CMR-derived SBP and DBP models can estimate brachial SBP and DBP. Such models may allow efficient prospective collection, as well as retrospective estimation of BP, which should be incorporated into assessments due to its critical effect on load-dependent parameters.
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Affiliation(s)
- Hosamadin Assadi
- Department of Cardiovascular and Metabolic Health, University of East Anglia, Norwich, UK
- Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, UK
| | - Gareth Matthews
- Department of Cardiovascular and Metabolic Health, University of East Anglia, Norwich, UK
- Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, UK
| | - Xiaodan Zhao
- National Heart Research Institute, National Heart Centre, Singapore
| | - Rui Li
- Department of Cardiovascular and Metabolic Health, University of East Anglia, Norwich, UK
- Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, UK
| | - Samer Alabed
- Department of Infection, Immunity & Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Ciaran Grafton-Clarke
- Department of Cardiovascular and Metabolic Health, University of East Anglia, Norwich, UK
- Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, UK
| | - Zia Mehmood
- Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, UK
| | - Bahman Kasmai
- Department of Cardiovascular and Metabolic Health, University of East Anglia, Norwich, UK
- Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, UK
| | - Vaishali Limbachia
- Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, UK
| | - Rebecca Gosling
- Department of Infection, Immunity & Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | | | - Ian Halliday
- Department of Infection, Immunity & Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | | | - David Paul Ripley
- Department of Cardiology, Northumbria Specialist Emergency Care Hospital, Cramlington, UK
| | - Liang Zhong
- National Heart Research Institute, National Heart Centre, Singapore
- Cardiovascular Science Academic Program, Duke-NUS Medical School, Singapore
| | - Vassilios S Vassiliou
- Department of Cardiovascular and Metabolic Health, University of East Anglia, Norwich, UK
- Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, UK
| | - Andrew J Swift
- Department of Infection, Immunity & Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Rob J van der Geest
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Pankaj Garg
- Department of Cardiovascular and Metabolic Health, University of East Anglia, Norwich, UK
- Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, UK
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9
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Alabed S, Garg P, Alandejani F, Dwivedi K, Maiter A, Karunasaagarar K, Rajaram S, Hill C, Thomas S, Gossling R, Sharkey MJ, Salehi M, Wild JM, Watson L, Hameed A, Charalampopoulos A, Lu H, Rothman AMK, Thompson AAR, Elliot CA, Hamilton N, Johns CS, Armstrong I, Condliffe R, van der Geest RJ, Swift AJ, Kiely DG. Establishing minimally important differences for cardiac MRI end-points in pulmonary arterial hypertension. Eur Respir J 2023; 62:2202225. [PMID: 37414419 PMCID: PMC10397469 DOI: 10.1183/13993003.02225-2022] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Accepted: 05/23/2023] [Indexed: 07/08/2023]
Abstract
BACKGROUND Cardiac magnetic resonance (CMR) is the gold standard technique to assess biventricular volumes and function, and is increasingly being considered as an end-point in clinical studies. Currently, with the exception of right ventricular (RV) stroke volume and RV end-diastolic volume, there is only limited data on minimally important differences (MIDs) reported for CMR metrics. Our study aimed to identify MIDs for CMR metrics based on US Food and Drug Administration recommendations for a clinical outcome measure that should reflect how a patient "feels, functions or survives". METHODS Consecutive treatment-naïve patients with pulmonary arterial hypertension (PAH) between 2010 and 2022 who had two CMR scans (at baseline prior to treatment and 12 months following treatment) were identified from the ASPIRE registry. All patients were followed up for 1 additional year after the second scan. For both scans, cardiac measurements were obtained from a validated fully automated segmentation tool. The MID in CMR metrics was determined using two distribution-based (0.5sd and minimal detectable change) and two anchor-based (change difference and generalised linear model regression) methods benchmarked to how a patient "feels" (emPHasis-10 quality of life questionnaire), "functions" (incremental shuttle walk test) or "survives" for 1-year mortality to changes in CMR measurements. RESULTS 254 patients with PAH were included (mean±sd age 53±16 years, 79% female and 66% categorised as intermediate risk based on the 2022 European Society of Cardiology/European Respiratory Society risk score). We identified a 5% absolute increase in RV ejection fraction and a 17 mL decrease in RV end-diastolic or end-systolic volumes as the MIDs for improvement. Conversely, a 5% decrease in RV ejection fraction and a 10 mL increase in RV volumes were associated with worsening. CONCLUSIONS This study establishes clinically relevant CMR MIDs for how a patient "feels, functions or survives" in response to PAH treatment. These findings provide further support for the use of CMR as a clinically relevant clinical outcome measure and will aid trial size calculations for studies using CMR.
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Affiliation(s)
- Samer Alabed
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
- Department of Clinical Radiology, Sheffield Teaching Hospitals, Sheffield, UK
- INSIGNEO, Institute for in silico Medicine, University of Sheffield, Sheffield, UK
| | - Pankaj Garg
- Norwich Medical School, University of East Anglia, Norwich, UK
| | - Faisal Alandejani
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Krit Dwivedi
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
- Department of Clinical Radiology, Sheffield Teaching Hospitals, Sheffield, UK
| | - Ahmed Maiter
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
- Department of Clinical Radiology, Sheffield Teaching Hospitals, Sheffield, UK
| | - Kavita Karunasaagarar
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
- Department of Clinical Radiology, Sheffield Teaching Hospitals, Sheffield, UK
| | - Smitha Rajaram
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
- Department of Clinical Radiology, Sheffield Teaching Hospitals, Sheffield, UK
| | - Catherine Hill
- Department of Clinical Radiology, Sheffield Teaching Hospitals, Sheffield, UK
| | - Steven Thomas
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
- Department of Clinical Radiology, Sheffield Teaching Hospitals, Sheffield, UK
| | - Rebecca Gossling
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Michael J Sharkey
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Mahan Salehi
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Jim M Wild
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
- INSIGNEO, Institute for in silico Medicine, University of Sheffield, Sheffield, UK
| | - Lisa Watson
- Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield, UK
| | - Abdul Hameed
- Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield, UK
| | | | - Haiping Lu
- INSIGNEO, Institute for in silico Medicine, University of Sheffield, Sheffield, UK
- Department of Computer Science, University of Sheffield, Sheffield, UK
| | - Alex M K Rothman
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - A A Roger Thompson
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
- Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield, UK
| | - Charlie A Elliot
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
- Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield, UK
| | - Neil Hamilton
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
- Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield, UK
| | - Christopher S Johns
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
- Department of Clinical Radiology, Sheffield Teaching Hospitals, Sheffield, UK
| | - Iain Armstrong
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
- Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield, UK
| | - Robin Condliffe
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
- Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield, UK
| | | | - Andrew J Swift
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
- Department of Clinical Radiology, Sheffield Teaching Hospitals, Sheffield, UK
- INSIGNEO, Institute for in silico Medicine, University of Sheffield, Sheffield, UK
- National Institute for Health and Care Research, Sheffield Biomedical Research Centre, Sheffield, UK
- Joint senior authors
| | - David G Kiely
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
- INSIGNEO, Institute for in silico Medicine, University of Sheffield, Sheffield, UK
- Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield, UK
- National Institute for Health and Care Research, Sheffield Biomedical Research Centre, Sheffield, UK
- Joint senior authors
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10
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Maiter A, Salehi M, Swift AJ, Alabed S. How should studies using AI be reported? lessons from a systematic review in cardiac MRI. FRONTIERS IN RADIOLOGY 2023; 3:1112841. [PMID: 37492379 PMCID: PMC10364997 DOI: 10.3389/fradi.2023.1112841] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 01/11/2023] [Indexed: 07/27/2023]
Abstract
Recent years have seen a dramatic increase in studies presenting artificial intelligence (AI) tools for cardiac imaging. Amongst these are AI tools that undertake segmentation of structures on cardiac MRI (CMR), an essential step in obtaining clinically relevant functional information. The quality of reporting of these studies carries significant implications for advancement of the field and the translation of AI tools to clinical practice. We recently undertook a systematic review to evaluate the quality of reporting of studies presenting automated approaches to segmentation in cardiac MRI (Alabed et al. 2022 Quality of reporting in AI cardiac MRI segmentation studies-a systematic review and recommendations for future studies. Frontiers in Cardiovascular Medicine 9:956811). 209 studies were assessed for compliance with the Checklist for AI in Medical Imaging (CLAIM), a framework for reporting. We found variable-and sometimes poor-quality of reporting and identified significant and frequently missing information in publications. Compliance with CLAIM was high for descriptions of models (100%, IQR 80%-100%), but lower than expected for descriptions of study design (71%, IQR 63-86%), datasets used in training and testing (63%, IQR 50%-67%) and model performance (60%, IQR 50%-70%). Here, we present a summary of our key findings, aimed at general readers who may not be experts in AI, and use them as a framework to discuss the factors determining quality of reporting, making recommendations for improving the reporting of research in this field. We aim to assist researchers in presenting their work and readers in their appraisal of evidence. Finally, we emphasise the need for close scrutiny of studies presenting AI tools, even in the face of the excitement surrounding AI in cardiac imaging.
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Affiliation(s)
- Ahmed Maiter
- Department of Infection, Immunity & Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom
- Department of Radiology, Sheffield Teaching Hospitals, Sheffield, United Kingdom
| | - Mahan Salehi
- Department of Infection, Immunity & Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom
| | - Andrew J. Swift
- Department of Infection, Immunity & Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom
- Department of Radiology, Sheffield Teaching Hospitals, Sheffield, United Kingdom
| | - Samer Alabed
- Department of Infection, Immunity & Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom
- Department of Radiology, Sheffield Teaching Hospitals, Sheffield, United Kingdom
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11
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Garg P, Swift AJ. Importance of cardiac magnetic resonance imaging assessment of left ventricular filling pressure at resting state. Eur Heart J 2022; 43:3495. [PMID: 35929602 DOI: 10.1093/eurheartj/ehac420] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- Pankaj Garg
- Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, UK.,Cardiovascular and Metabolic Health, Norwich Medical School, University of East Anglia, Norwich, UK.,Cardiology Department, Norfolk and Norwich University Teaching Hospitals, Norwich, UK
| | - Andrew J Swift
- Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, UK
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12
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Alabed S, Maiter A, Salehi M, Mahmood A, Daniel S, Jenkins S, Goodlad M, Sharkey M, Mamalakis M, Rakocevic V, Dwivedi K, Assadi H, Wild JM, Lu H, O’Regan DP, van der Geest RJ, Garg P, Swift AJ. Quality of reporting in AI cardiac MRI segmentation studies - A systematic review and recommendations for future studies. Front Cardiovasc Med 2022; 9:956811. [PMID: 35911553 PMCID: PMC9334661 DOI: 10.3389/fcvm.2022.956811] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 06/30/2022] [Indexed: 11/29/2022] Open
Abstract
Background There has been a rapid increase in the number of Artificial Intelligence (AI) studies of cardiac MRI (CMR) segmentation aiming to automate image analysis. However, advancement and clinical translation in this field depend on researchers presenting their work in a transparent and reproducible manner. This systematic review aimed to evaluate the quality of reporting in AI studies involving CMR segmentation. Methods MEDLINE and EMBASE were searched for AI CMR segmentation studies in April 2022. Any fully automated AI method for segmentation of cardiac chambers, myocardium or scar on CMR was considered for inclusion. For each study, compliance with the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) was assessed. The CLAIM criteria were grouped into study, dataset, model and performance description domains. Results 209 studies published between 2012 and 2022 were included in the analysis. Studies were mainly published in technical journals (58%), with the majority (57%) published since 2019. Studies were from 37 different countries, with most from China (26%), the United States (18%) and the United Kingdom (11%). Short axis CMR images were most frequently used (70%), with the left ventricle the most commonly segmented cardiac structure (49%). Median compliance of studies with CLAIM was 67% (IQR 59-73%). Median compliance was highest for the model description domain (100%, IQR 80-100%) and lower for the study (71%, IQR 63-86%), dataset (63%, IQR 50-67%) and performance (60%, IQR 50-70%) description domains. Conclusion This systematic review highlights important gaps in the literature of CMR studies using AI. We identified key items missing-most strikingly poor description of patients included in the training and validation of AI models and inadequate model failure analysis-that limit the transparency, reproducibility and hence validity of published AI studies. This review may support closer adherence to established frameworks for reporting standards and presents recommendations for improving the quality of reporting in this field. Systematic Review Registration [www.crd.york.ac.uk/prospero/], identifier [CRD42022279214].
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Affiliation(s)
- Samer Alabed
- Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom
- Department of Clinical Radiology, Sheffield Teaching Hospitals, Sheffield, United Kingdom
- INSIGNEO, Institute for in silico Medicine, The University of Sheffield, Sheffield, United Kingdom
| | - Ahmed Maiter
- Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom
- Department of Clinical Radiology, Sheffield Teaching Hospitals, Sheffield, United Kingdom
| | - Mahan Salehi
- Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom
| | - Aqeeb Mahmood
- Medical School, The University of Sheffield, Sheffield, United Kingdom
| | - Sonali Daniel
- Medical School, The University of Sheffield, Sheffield, United Kingdom
| | - Sam Jenkins
- Medical School, The University of Sheffield, Sheffield, United Kingdom
| | - Marcus Goodlad
- Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom
| | - Michael Sharkey
- Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom
| | - Michail Mamalakis
- Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom
- INSIGNEO, Institute for in silico Medicine, The University of Sheffield, Sheffield, United Kingdom
| | - Vera Rakocevic
- Medical School, The University of Sheffield, Sheffield, United Kingdom
| | - Krit Dwivedi
- Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom
- Department of Clinical Radiology, Sheffield Teaching Hospitals, Sheffield, United Kingdom
| | - Hosamadin Assadi
- University of East Anglia, Norwich Medical School, Norwich, United Kingdom
| | - Jim M. Wild
- Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom
- INSIGNEO, Institute for in silico Medicine, The University of Sheffield, Sheffield, United Kingdom
| | - Haiping Lu
- INSIGNEO, Institute for in silico Medicine, The University of Sheffield, Sheffield, United Kingdom
- Department of Computer Science, The University of Sheffield, Sheffield, United Kingdom
| | - Declan P. O’Regan
- MRC London Institute of Medical Sciences, Imperial College London, London, United Kingdom
| | | | - Pankaj Garg
- University of East Anglia, Norwich Medical School, Norwich, United Kingdom
| | - Andrew J. Swift
- Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom
- INSIGNEO, Institute for in silico Medicine, The University of Sheffield, Sheffield, United Kingdom
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Ambale-Venkatesh B, Lima JAC. Human-in-the-Loop Artificial Intelligence in Cardiac MRI. Radiology 2022; 305:80-81. [PMID: 35699584 DOI: 10.1148/radiol.221132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Bharath Ambale-Venkatesh
- From the Department of Radiology (B.A.V.) and School of Medicine (J.A.C.L.), Johns Hopkins University, 600 N Wolfe St, MR 110, Baltimore, MD 21287
| | - João A C Lima
- From the Department of Radiology (B.A.V.) and School of Medicine (J.A.C.L.), Johns Hopkins University, 600 N Wolfe St, MR 110, Baltimore, MD 21287
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