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Assadi H, Alabed S, Li R, Matthews G, Karunasaagarar K, Kasmai B, Nair S, Mehmood Z, Grafton-Clarke C, Swoboda PP, Swift AJ, Greenwood JP, Vassiliou VS, Plein S, van der Geest RJ, Garg P. Development and validation of AI-derived segmentation of four-chamber cine cardiac magnetic resonance. Eur Radiol Exp 2024; 8:77. [PMID: 38992116 PMCID: PMC11239622 DOI: 10.1186/s41747-024-00477-7] [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: 02/13/2024] [Accepted: 04/30/2024] [Indexed: 07/13/2024] Open
Abstract
BACKGROUND Cardiac magnetic resonance (CMR) in the four-chamber plane offers comprehensive insight into the volumetrics of the heart. We aimed to develop an artificial intelligence (AI) model of time-resolved segmentation using the four-chamber cine. METHODS A fully automated deep learning algorithm was trained using retrospective multicentre and multivendor data of 814 subjects. Validation, reproducibility, and mortality prediction were evaluated on an independent cohort of 101 subjects. RESULTS The mean age of the validation cohort was 54 years, and 66 (65%) were males. Left and right heart parameters demonstrated strong correlations between automated and manual analysis, with a ρ of 0.91-0.98 and 0.89-0.98, respectively, with minimal bias. All AI four-chamber volumetrics in repeatability analysis demonstrated high correlation (ρ = 0.99-1.00) and no bias. Automated four-chamber analysis underestimated both left ventricular (LV) and right ventricular (RV) volumes compared to ground-truth short-axis cine analysis. Two correction factors for LV and RV four-chamber analysis were proposed based on systematic bias. After applying the correction factors, a strong correlation and minimal bias for LV volumetrics were observed. During a mean follow-up period of 6.75 years, 16 patients died. On stepwise multivariable analysis, left atrial ejection fraction demonstrated an independent association with death in both manual (hazard ratio (HR) = 0.96, p = 0.003) and AI analyses (HR = 0.96, p < 0.001). CONCLUSION Fully automated four-chamber CMR is feasible, reproducible, and has the same real-world prognostic value as manual analysis. LV volumes by four-chamber segmentation were comparable to short-axis volumetric assessment. TRIALS REGISTRATION ClinicalTrials.gov: NCT05114785. RELEVANCE STATEMENT Integrating fully automated AI in CMR promises to revolutionise clinical cardiac assessment, offering efficient, accurate, and prognostically valuable insights for improved patient care and outcomes. KEY POINTS • Four-chamber cine sequences remain one of the most informative acquisitions in CMR examination. • This deep learning-based, time-resolved, fully automated four-chamber volumetric, functional, and deformation analysis solution. • LV and RV were underestimated by four-chamber analysis compared to ground truth short-axis segmentation. • Correction bias for both LV and RV volumes by four-chamber segmentation, minimises the systematic bias.
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Affiliation(s)
- Hosamadin Assadi
- Department of Cardiovascular and Metabolic Health, Norwich Medical School, University of East Anglia, Norwich, Norfolk, UK
- Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, Norfolk, UK
| | - Samer Alabed
- Department of Infection, Immunity & Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Rui Li
- Department of Cardiovascular and Metabolic Health, Norwich Medical School, University of East Anglia, Norwich, Norfolk, UK
- Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, Norfolk, UK
| | - Gareth Matthews
- Department of Cardiovascular and Metabolic Health, Norwich Medical School, University of East Anglia, Norwich, Norfolk, UK
- Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, Norfolk, UK
| | - Kavita Karunasaagarar
- Department of Infection, Immunity & Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Bahman Kasmai
- Department of Cardiovascular and Metabolic Health, Norwich Medical School, University of East Anglia, Norwich, Norfolk, UK
- Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, Norfolk, UK
| | - Sunil Nair
- Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, Norfolk, UK
| | - Zia Mehmood
- Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, Norfolk, UK
| | - Ciaran Grafton-Clarke
- Department of Cardiovascular and Metabolic Health, Norwich Medical School, University of East Anglia, Norwich, Norfolk, UK
- Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, Norfolk, UK
| | - Peter P Swoboda
- Division of Biomedical Imaging, Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK
| | - Andrew J Swift
- Department of Infection, Immunity & Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - John P Greenwood
- Division of Biomedical Imaging, Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK
| | - Vassilios S Vassiliou
- Department of Cardiovascular and Metabolic Health, Norwich Medical School, University of East Anglia, Norwich, Norfolk, UK
- Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, Norfolk, UK
| | - Sven Plein
- Division of Biomedical Imaging, Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK
| | - Rob J van der Geest
- Department of Radiology, Division of Image Processing, Leiden University Medical Center, Leiden, The Netherlands
| | - Pankaj Garg
- Department of Cardiovascular and Metabolic Health, Norwich Medical School, University of East Anglia, Norwich, Norfolk, UK.
- Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, Norfolk, UK.
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Assadi H, Sawh N, Bailey C, Matthews G, Li R, Grafton-Clarke C, Mehmood Z, Kasmai B, Swoboda PP, Swift AJ, van der Geest RJ, Garg P. Validation of Left Atrial Volume Correction for Single Plane Method on Four-Chamber Cine Cardiac MRI. Tomography 2024; 10:459-470. [PMID: 38668393 PMCID: PMC11054972 DOI: 10.3390/tomography10040035] [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: 02/19/2024] [Revised: 03/21/2024] [Accepted: 03/22/2024] [Indexed: 04/29/2024] Open
Abstract
BACKGROUND Left atrial (LA) assessment is an important marker of adverse cardiovascular outcomes. Cardiovascular magnetic resonance (CMR) accurately quantifies LA volume and function based on biplane long-axis imaging. We aimed to validate single-plane-derived LA indices against the biplane method to simplify the post-processing of cine CMR. METHODS In this study, 100 patients from Leeds Teaching Hospitals were used as the derivation cohort. Bias correction for the single plane method was applied and subsequently validated in 79 subjects. RESULTS There were significant differences between the biplane and single plane mean LA maximum and minimum volumes and LA ejection fraction (EF) (all p < 0.01). After correcting for biases in the validation cohort, significant correlations in all LA indices were observed (0.89 to 0.98). The area under the curve (AUC) for the single plane to predict biplane cutoffs of LA maximum volume ≥ 112 mL was 0.97, LA minimum volume ≥ 44 mL was 0.99, LA stroke volume (SV) ≤ 21 mL was 1, and LA EF ≤ 46% was 1, (all p < 0.001). CONCLUSIONS LA volumetric and functional assessment by the single plane method has a systematic bias compared to the biplane method. After bias correction, single plane LA volume and function are comparable to the biplane method.
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Affiliation(s)
- Hosamadin Assadi
- Department of Cardiovascular and Metabolic Health, Norwich Medical School, University of East Anglia, Norwich NR4 7TJ, UK
- Department of Cardiology, Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich NR4 7UY, UK
| | - Nicholas Sawh
- Faculty of Medicine, Medical University of Sofia, Blvd Akademik Ivan Evstratiev Geshov 15, 1431 Sofia, Bulgaria
| | - Ciara Bailey
- Department of Cardiovascular and Metabolic Health, Norwich Medical School, University of East Anglia, Norwich NR4 7TJ, UK
- Department of Cardiology, Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich NR4 7UY, UK
| | - Gareth Matthews
- Department of Cardiovascular and Metabolic Health, Norwich Medical School, University of East Anglia, Norwich NR4 7TJ, UK
- Department of Cardiology, Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich NR4 7UY, UK
| | - Rui Li
- Department of Cardiovascular and Metabolic Health, Norwich Medical School, University of East Anglia, Norwich NR4 7TJ, UK
- Department of Cardiology, Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich NR4 7UY, UK
| | - Ciaran Grafton-Clarke
- Department of Cardiovascular and Metabolic Health, Norwich Medical School, University of East Anglia, Norwich NR4 7TJ, UK
- Department of Cardiology, Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich NR4 7UY, UK
| | - Zia Mehmood
- Department of Cardiovascular and Metabolic Health, Norwich Medical School, University of East Anglia, Norwich NR4 7TJ, UK
- Department of Cardiology, Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich NR4 7UY, UK
| | - Bahman Kasmai
- Department of Cardiovascular and Metabolic Health, Norwich Medical School, University of East Anglia, Norwich NR4 7TJ, UK
- Department of Cardiology, Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich NR4 7UY, UK
| | - Peter P. Swoboda
- Division of Biomedical Imaging, Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds LS2 9JT, UK
| | - Andrew J. Swift
- Department of Infection, Immunity & Cardiovascular Disease, University of Sheffield, Sheffield S10 2RX, UK
| | - Rob J. van der Geest
- Department of Radiology, Division of Image Processing, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
| | - Pankaj Garg
- Department of Cardiovascular and Metabolic Health, Norwich Medical School, University of East Anglia, Norwich NR4 7TJ, UK
- Department of Cardiology, Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich NR4 7UY, UK
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Wessels JN, Celant LR, de Man FS, Vonk Noordegraaf A. The Right Ventricle in Pulmonary Hypertension. Semin Respir Crit Care Med 2023; 44:738-745. [PMID: 37487527 DOI: 10.1055/s-0043-1770117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/26/2023]
Abstract
The right ventricle plays a pivotal role in patients with pulmonary hypertension (PH). Its adaptation to pressure overload determines a patient's functional status as well as survival. In a healthy situation, the right ventricle is part of a low pressure, high compliance system. It is built to accommodate changes in preload, but not very well suited for dealing with pressure overload. In PH, right ventricular (RV) contractility must increase to maintain cardiac output. In other words, the balance between the degree of RV contractility and afterload determines stroke volume. Hypertrophy is one of the major hallmarks of RV adaptation, but it may cause stiffening of the ventricle in addition to intrinsic changes to the RV myocardium. Ventricular filling becomes more difficult for which the right atrium tries to compensate through increased stroke work. Interaction of RV diastolic stiffness and right atrial (RA) function determines RV filling, but also causes vena cava backflow. Assessment of RV and RA function is critical in the evaluation of patient status. In recent guidelines, this is acknowledged by incorporating additional RV parameters in the risk stratification in PH. Several conventional parameters of RV and RA function have been part of risk stratification for many years. Understanding the pathophysiology of RV failure and the interactions with the pulmonary circulation and right atrium requires consideration of the unique RV anatomy. This review will therefore describe normal RV structure and function and changes that occur during adaptation to increased afterload. Consequences of a failing right ventricle and its implications for RA function will be discussed. Subsequently, we will describe RV and RA assessment in clinical practice.
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Affiliation(s)
- Jeroen N Wessels
- PHEniX Laboratory, Department of Pulmonary Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Cardiovascular Sciences, Pulmonary Hypertension and Thrombosis, Amsterdam, The Netherlands
- European Reference Network on Rare Pulmonary Diseases (ERN-LUNG), Germany
| | - Lucas R Celant
- PHEniX Laboratory, Department of Pulmonary Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Cardiovascular Sciences, Pulmonary Hypertension and Thrombosis, Amsterdam, The Netherlands
- European Reference Network on Rare Pulmonary Diseases (ERN-LUNG), Germany
| | - Frances S de Man
- PHEniX Laboratory, Department of Pulmonary Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Cardiovascular Sciences, Pulmonary Hypertension and Thrombosis, Amsterdam, The Netherlands
- European Reference Network on Rare Pulmonary Diseases (ERN-LUNG), Germany
| | - Anton Vonk Noordegraaf
- PHEniX Laboratory, Department of Pulmonary Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Cardiovascular Sciences, Pulmonary Hypertension and Thrombosis, Amsterdam, The Netherlands
- European Reference Network on Rare Pulmonary Diseases (ERN-LUNG), Germany
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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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Hameed A, Condliffe R, Swift AJ, Alabed S, Kiely DG, Charalampopoulos A. Assessment of Right Ventricular Function-a State of the Art. Curr Heart Fail Rep 2023; 20:194-207. [PMID: 37271771 PMCID: PMC10256637 DOI: 10.1007/s11897-023-00600-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/17/2023] [Indexed: 06/06/2023]
Abstract
PURPOSE OF REVIEW The right ventricle (RV) has a complex geometry and physiology which is distinct from the left. RV dysfunction and failure can be the aftermath of volume- and/or pressure-loading conditions, as well as myocardial and pericardial diseases. RECENT FINDINGS Echocardiography, magnetic resonance imaging and right heart catheterisation can assess RV function by using several qualitative and quantitative parameters. In pulmonary hypertension (PH) in particular, RV function can be impaired and is related to survival. An accurate assessment of RV function is crucial for the early diagnosis and management of these patients. This review focuses on the different modalities and indices used for the evaluation of RV function with an emphasis on PH.
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Affiliation(s)
- Abdul Hameed
- Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield, UK
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Robin Condliffe
- Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield, UK
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Andrew J Swift
- Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield, UK
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
- INSIGNEO, Institute for in silico Medicine, University of Sheffield, Sheffield, UK
| | - Samer Alabed
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
- INSIGNEO, Institute for in silico Medicine, University of Sheffield, Sheffield, UK
| | - David G Kiely
- Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield, UK
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
- NIHR Sheffield Biomedical Research Centre, Sheffield, UK
| | - Athanasios Charalampopoulos
- Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield, UK.
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK.
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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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Macdonald A, Salehi M, Alabed S, Maiter A, Goh ZM, Dwivedi K, Johns C, Cogliano M, Alandejani F, Condliffe R, Wild JM, Kiely DG, Garg P, Swift AJ. Semi-automatic thresholding of RV trabeculation improves repeatability and diagnostic value in suspected pulmonary hypertension. Front Cardiovasc Med 2023; 9:1037385. [PMID: 36684562 PMCID: PMC9845927 DOI: 10.3389/fcvm.2022.1037385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 12/13/2022] [Indexed: 01/05/2023] Open
Abstract
Objectives Right ventricle (RV) mass is an imaging biomarker of mean pulmonary artery pressure (MPAP) and pulmonary vascular resistance (PVR). Some methods of RV mass measurement on cardiac MRI (CMR) exclude RV trabeculation. This study assessed the reproducibility of measurement methods and evaluated whether the inclusion of trabeculation in RV mass affects diagnostic accuracy in suspected pulmonary hypertension (PH). Materials and methods Two populations were enrolled prospectively. (i) A total of 144 patients with suspected PH who underwent CMR followed by right heart catheterization (RHC). Total RV mass (including trabeculation) and compacted RV mass (excluding trabeculation) were measured on the end-diastolic CMR images using both semi-automated pixel-intensity-based thresholding and manual contouring techniques. (ii) A total of 15 healthy volunteers and 15 patients with known PH. Interobserver agreement and scan-scan reproducibility were evaluated for RV mass measurements using the semi-automated thresholding and manual contouring techniques. Results Total RV mass correlated more strongly with MPAP and PVR (r = 0.59 and 0.63) than compacted RV mass (r = 0.25 and 0.38). Using a diagnostic threshold of MPAP ≥ 25 mmHg, ROC analysis showed better performance for total RV mass (AUC 0.77 and 0.81) compared to compacted RV mass (AUC 0.61 and 0.66) when both parameters were indexed for LV mass. Semi-automated thresholding was twice as fast as manual contouring (p < 0.001). Conclusion Using a semi-automated thresholding technique, inclusion of trabecular mass and indexing RV mass for LV mass (ventricular mass index), improves the diagnostic accuracy of CMR measurements in suspected PH.
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Affiliation(s)
- Alistair Macdonald
- 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
- Department of Clinical Radiology, Sheffield Teaching Hospitals, Sheffield, United Kingdom
| | - 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
| | - Ze Ming Goh
- Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom
- Department of Clinical Radiology, Sheffield Teaching Hospitals, 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
| | - Chris Johns
- Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom
| | - Marcella Cogliano
- Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom
| | - Faisal Alandejani
- Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom
| | - Robin Condliffe
- Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield, United Kingdom
| | - James M. Wild
- Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom
| | - David G. Kiely
- Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom
- Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield, United Kingdom
| | - Pankaj Garg
- Norwich Medical School, University of East Anglia, 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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8
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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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9
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Argentiero A, Muscogiuri G, Rabbat MG, Martini C, Soldato N, Basile P, Baggiano A, Mushtaq S, Fusini L, Mancini ME, Gaibazzi N, Santobuono VE, Sironi S, Pontone G, Guaricci AI. The Applications of Artificial Intelligence in Cardiovascular Magnetic Resonance-A Comprehensive Review. J Clin Med 2022; 11:jcm11102866. [PMID: 35628992 PMCID: PMC9147423 DOI: 10.3390/jcm11102866] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 05/13/2022] [Accepted: 05/16/2022] [Indexed: 12/11/2022] Open
Abstract
Cardiovascular disease remains an integral field on which new research in both the biomedical and technological fields is based, as it remains the leading cause of mortality and morbidity worldwide. However, despite the progress of cardiac imaging techniques, the heart remains a challenging organ to study. Artificial intelligence (AI) has emerged as one of the major innovations in the field of diagnostic imaging, with a dramatic impact on cardiovascular magnetic resonance imaging (CMR). AI will be increasingly present in the medical world, with strong potential for greater diagnostic efficiency and accuracy. Regarding the use of AI in image acquisition and reconstruction, the main role was to reduce the time of image acquisition and analysis, one of the biggest challenges concerning magnetic resonance; moreover, it has been seen to play a role in the automatic correction of artifacts. The use of these techniques in image segmentation has allowed automatic and accurate quantification of the volumes and masses of the left and right ventricles, with occasional need for manual correction. Furthermore, AI can be a useful tool to directly help the clinician in the diagnosis and derivation of prognostic information of cardiovascular diseases. This review addresses the applications and future prospects of AI in CMR imaging, from image acquisition and reconstruction to image segmentation, tissue characterization, diagnostic evaluation, and prognostication.
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Affiliation(s)
- Adriana Argentiero
- University Cardiology Unit, Cardio-Thoracic Department, Policlinic University Hospital, 70121 Bari, Italy; (A.A.); (N.S.); (P.B.); (V.E.S.)
| | - Giuseppe Muscogiuri
- School of Medicine and Surgery, University of Milano-Bicocca, 20126 Milan, Italy; (G.M.); (S.S.)
- Department of Radiology, IRCCS Istituto Auxologico Italiano, San Luca Hospital, 20149 Milan, Italy
| | - Mark G. Rabbat
- Division of Cardiology, Loyola University of Chicago, Chicago, IL 60660, USA;
| | - Chiara Martini
- Radiologic Sciences, Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy;
| | - Nicolò Soldato
- University Cardiology Unit, Cardio-Thoracic Department, Policlinic University Hospital, 70121 Bari, Italy; (A.A.); (N.S.); (P.B.); (V.E.S.)
| | - Paolo Basile
- University Cardiology Unit, Cardio-Thoracic Department, Policlinic University Hospital, 70121 Bari, Italy; (A.A.); (N.S.); (P.B.); (V.E.S.)
| | - Andrea Baggiano
- Perioperative and Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy; (A.B.); (S.M.); (L.F.); (M.E.M.); (G.P.)
| | - Saima Mushtaq
- Perioperative and Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy; (A.B.); (S.M.); (L.F.); (M.E.M.); (G.P.)
| | - Laura Fusini
- Perioperative and Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy; (A.B.); (S.M.); (L.F.); (M.E.M.); (G.P.)
| | - Maria Elisabetta Mancini
- Perioperative and Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy; (A.B.); (S.M.); (L.F.); (M.E.M.); (G.P.)
| | - Nicola Gaibazzi
- Department of Cardiology, Azienda Ospedaliero-Universitaria, 43126 Parma, Italy;
| | - Vincenzo Ezio Santobuono
- University Cardiology Unit, Cardio-Thoracic Department, Policlinic University Hospital, 70121 Bari, Italy; (A.A.); (N.S.); (P.B.); (V.E.S.)
| | - Sandro Sironi
- School of Medicine and Surgery, University of Milano-Bicocca, 20126 Milan, Italy; (G.M.); (S.S.)
- Department of Radiology, ASST Papa Giovanni XXIII Hospital, 24127 Bergamo, Italy
| | - Gianluca Pontone
- Perioperative and Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy; (A.B.); (S.M.); (L.F.); (M.E.M.); (G.P.)
| | - Andrea Igoren Guaricci
- University Cardiology Unit, Cardio-Thoracic Department, Policlinic University Hospital, 70121 Bari, Italy; (A.A.); (N.S.); (P.B.); (V.E.S.)
- Department of Emergency and Organ Transplantation, University of Bari, 70121 Bari, Italy
- Correspondence:
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10
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Sharkey MJ, Taylor JC, Alabed S, Dwivedi K, Karunasaagarar K, Johns CS, Rajaram S, Garg P, Alkhanfar D, Metherall P, O'Regan DP, van der Geest RJ, Condliffe R, Kiely DG, Mamalakis M, Swift AJ. Fully automatic cardiac four chamber and great vessel segmentation on CT pulmonary angiography using deep learning. Front Cardiovasc Med 2022; 9:983859. [PMID: 36225963 PMCID: PMC9549370 DOI: 10.3389/fcvm.2022.983859] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 09/02/2022] [Indexed: 11/13/2022] Open
Abstract
Introduction Computed tomography pulmonary angiography (CTPA) is an essential test in the work-up of suspected pulmonary vascular disease including pulmonary hypertension and pulmonary embolism. Cardiac and great vessel assessments on CTPA are based on visual assessment and manual measurements which are known to have poor reproducibility. The primary aim of this study was to develop an automated whole heart segmentation (four chamber and great vessels) model for CTPA. Methods A nine structure semantic segmentation model of the heart and great vessels was developed using 200 patients (80/20/100 training/validation/internal testing) with testing in 20 external patients. Ground truth segmentations were performed by consultant cardiothoracic radiologists. Failure analysis was conducted in 1,333 patients with mixed pulmonary vascular disease. Segmentation was achieved using deep learning via a convolutional neural network. Volumetric imaging biomarkers were correlated with invasive haemodynamics in the test cohort. Results Dice similarity coefficients (DSC) for segmented structures were in the range 0.58-0.93 for both the internal and external test cohorts. The left and right ventricle myocardium segmentations had lower DSC of 0.83 and 0.58 respectively while all other structures had DSC >0.89 in the internal test cohort and >0.87 in the external test cohort. Interobserver comparison found that the left and right ventricle myocardium segmentations showed the most variation between observers: mean DSC (range) of 0.795 (0.785-0.801) and 0.520 (0.482-0.542) respectively. Right ventricle myocardial volume had strong correlation with mean pulmonary artery pressure (Spearman's correlation coefficient = 0.7). The volume of segmented cardiac structures by deep learning had higher or equivalent correlation with invasive haemodynamics than by manual segmentations. The model demonstrated good generalisability to different vendors and hospitals with similar performance in the external test cohort. The failure rates in mixed pulmonary vascular disease were low (<3.9%) indicating good generalisability of the model to different diseases. Conclusion Fully automated segmentation of the four cardiac chambers and great vessels has been achieved in CTPA with high accuracy and low rates of failure. DL volumetric biomarkers can potentially improve CTPA cardiac assessment and invasive haemodynamic prediction.
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Affiliation(s)
- Michael J Sharkey
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom.,3D Imaging Lab, Sheffield Teaching Hospitals NHSFT, Sheffield, United Kingdom
| | - Jonathan C Taylor
- 3D Imaging Lab, Sheffield Teaching Hospitals NHSFT, Sheffield, United Kingdom
| | - Samer Alabed
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom
| | - Krit Dwivedi
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom.,Insigneo Institute for in Silico Medicine, University of Sheffield, Sheffield, United Kingdom
| | - Kavitasagary Karunasaagarar
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom.,Radiology Department, Sheffield Teaching Hospitals NHSFT, Sheffield, United Kingdom
| | - Christopher S Johns
- Radiology Department, Sheffield Teaching Hospitals NHSFT, Sheffield, United Kingdom
| | - Smitha Rajaram
- Radiology Department, Sheffield Teaching Hospitals NHSFT, Sheffield, United Kingdom
| | - Pankaj Garg
- Norwich Medical School, University of East Anglia, Norwich, United Kingdom
| | - Dheyaa Alkhanfar
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom
| | - Peter Metherall
- 3D Imaging Lab, Sheffield Teaching Hospitals NHSFT, Sheffield, United Kingdom
| | - Declan P O'Regan
- MRC London Institute of Medical Sciences, Imperial College London, London, United Kingdom
| | - Rob J van der Geest
- Department of Radiology, Leiden University Medical Center, Leiden, Netherlands
| | - Robin Condliffe
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom.,Sheffield Pulmonary Vascular Disease Unit, Sheffield Teaching Hospitals NHS Trust, Sheffield, United Kingdom
| | - David G Kiely
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom.,Insigneo Institute for in Silico Medicine, University of Sheffield, Sheffield, United Kingdom.,Sheffield Pulmonary Vascular Disease Unit, Sheffield Teaching Hospitals NHS Trust, Sheffield, United Kingdom
| | - Michail Mamalakis
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom.,Insigneo Institute for in Silico Medicine, University of Sheffield, Sheffield, United Kingdom.,Department of Computer Science, University of Sheffield, Sheffield, United Kingdom
| | - Andrew J Swift
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom.,Insigneo Institute for in Silico Medicine, University of Sheffield, Sheffield, United Kingdom
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