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Puyol-Antón E, Sidhu BS, Gould J, Porter B, Elliott MK, Mehta V, Rinaldi CA, King AP. A multimodal deep learning model for cardiac resynchronisation therapy response prediction. Med Image Anal 2022; 79:102465. [PMID: 35487111 PMCID: PMC7616169 DOI: 10.1016/j.media.2022.102465] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 01/03/2022] [Accepted: 04/15/2022] [Indexed: 01/03/2023]
Abstract
We present a novel multimodal deep learning framework for cardiac resynchronisation therapy (CRT) response prediction from 2D echocardiography and cardiac magnetic resonance (CMR) data. The proposed method first uses the 'nnU-Net' segmentation model to extract segmentations of the heart over the full cardiac cycle from the two modalities. Next, a multimodal deep learning classifier is used for CRT response prediction, which combines the latent spaces of the segmentation models of the two modalities. At test time, this framework can be used with 2D echocardiography data only, whilst taking advantage of the implicit relationship between CMR and echocardiography features learnt from the model. We evaluate our pipeline on a cohort of 50 CRT patients for whom paired echocardiography/CMR data were available, and results show that the proposed multimodal classifier results in a statistically significant improvement in accuracy compared to the baseline approach that uses only 2D echocardiography data. The combination of multimodal data enables CRT response to be predicted with 77.38% accuracy (83.33% sensitivity and 71.43% specificity), which is comparable with the current state-of-the-art in machine learning-based CRT response prediction. Our work represents the first multimodal deep learning approach for CRT response prediction.
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Affiliation(s)
- Esther Puyol-Antón
- School of Biomedical Engineering & Imaging Sciences, King's College London, UK.
| | - Baldeep S Sidhu
- School of Biomedical Engineering & Imaging Sciences, King's College London, UK; Guy's and St Thomas' Hospital, London, UK
| | - Justin Gould
- School of Biomedical Engineering & Imaging Sciences, King's College London, UK; Guy's and St Thomas' Hospital, London, UK
| | - Bradley Porter
- School of Biomedical Engineering & Imaging Sciences, King's College London, UK; Guy's and St Thomas' Hospital, London, UK
| | - Mark K Elliott
- School of Biomedical Engineering & Imaging Sciences, King's College London, UK; Guy's and St Thomas' Hospital, London, UK
| | - Vishal Mehta
- School of Biomedical Engineering & Imaging Sciences, King's College London, UK; Guy's and St Thomas' Hospital, London, UK
| | - Christopher A Rinaldi
- School of Biomedical Engineering & Imaging Sciences, King's College London, UK; Guy's and St Thomas' Hospital, London, UK
| | - Andrew P King
- School of Biomedical Engineering & Imaging Sciences, King's College London, UK
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Fotaki A, Puyol-Antón E, Chiribiri A, Botnar R, Pushparajah K, Prieto C. Artificial Intelligence in Cardiac MRI: Is Clinical Adoption Forthcoming? Front Cardiovasc Med 2022; 8:818765. [PMID: 35083303 PMCID: PMC8785419 DOI: 10.3389/fcvm.2021.818765] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 12/15/2021] [Indexed: 12/24/2022] Open
Abstract
Artificial intelligence (AI) refers to the area of knowledge that develops computerised models to perform tasks that typically require human intelligence. These algorithms are programmed to learn and identify patterns from "training data," that can be subsequently applied to new datasets, without being explicitly programmed to do so. AI is revolutionising the field of medical imaging and in particular of Cardiovascular Magnetic Resonance (CMR) by providing deep learning solutions for image acquisition, reconstruction and analysis, ultimately supporting the clinical decision making. Numerous methods have been developed over recent years to enhance and expedite CMR data acquisition, image reconstruction, post-processing and analysis; along with the development of promising AI-based biomarkers for a wide spectrum of cardiac conditions. The exponential rise in the availability and complexity of CMR data has fostered the development of different AI models. Integration in clinical routine in a meaningful way remains a challenge. Currently, innovations in this field are still mostly presented in proof-of-concept studies with emphasis on the engineering solutions; often recruiting small patient cohorts or relying on standardised databases such as Multi-ethnic Study on atherosclerosis (MESA), UK Biobank and others. The wider incorporation of clinically valid endpoints such as symptoms, survival, need and response to treatment remains to be seen. This review briefly summarises the current principles of AI employed in CMR and explores the relevant prospective observational studies in cardiology patient cohorts. It provides an overview of clinical studies employing undersampled reconstruction techniques to speed up the scan encompassing cine imaging, whole-heart imaging, multi-parametric mapping and magnetic resonance fingerprinting along with the clinical utility of AI applications in image post-processing, and analysis. Specific focus is given to studies that have incorporated CMR-derived prediction models for prognostication in cardiac disease. It also discusses current limitations and proposes potential developments to enable multi-disciplinary collaboration for improved evidence-based medicine. AI is an extremely promising field and the timely integration of clinician's input in the ingenious technical investigator's paradigm holds promise for a bright future in the medical field.
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Affiliation(s)
- Anastasia Fotaki
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Guy's and St. Thomas' NHS Foundation Trust, London, United Kingdom
| | - Esther Puyol-Antón
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Amedeo Chiribiri
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Guy's and St. Thomas' NHS Foundation Trust, London, United Kingdom
| | - René Botnar
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Kuberan Pushparajah
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Guy's and St. Thomas' NHS Foundation Trust, London, United Kingdom
| | - Claudia Prieto
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
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Philip C, Seifried R, Peterson PG, Liotta R, Steel K, Bittencourt MS, Hulten EA. Cardiac MRI for Patients with Increased Cardiometabolic Risk. Radiol Cardiothorac Imaging 2021; 3:e200575. [PMID: 33969314 DOI: 10.1148/ryct.2021200575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 02/07/2021] [Accepted: 02/12/2021] [Indexed: 11/11/2022]
Abstract
Cardiac MRI (CMR) has rich potential for future cardiovascular screening even though not approved clinically for routine screening for cardiovascular disease among patients with increased cardiometabolic risk. Patients with increased cardiometabolic risk include those with abnormal blood pressure, body mass, cholesterol level, or fasting glucose level, which may be related to dietary and exercise habits. However, CMR does accurately evaluate cardiac structure and function. CMR allows for effective tissue characterization with a variety of sequences that provide unique insights as to fibrosis, infiltration, inflammation, edema, presence of fat, strain, and other potential pathologic features that influence future cardiovascular risk. Ongoing epidemiologic and clinical research may demonstrate clinical benefit leading to increased future use. © RSNA, 2021.
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Affiliation(s)
- Cynthia Philip
- Department of Medicine, Cardiology Service (C.P., R.S., E.A.H.) and Department of Radiology (P.G.P., R.L.), Walter Reed National Military Medical Center, Bethesda, Md; Department of Radiology and Radiological Sciences, Uniformed Services University of the Health Sciences, Bethesda, Md (C.P., R.S., P.G.P., R.L., E.A.H.); PeaceHealth Medical Group, Bellingham, Wash (K.S.); University Hospital, University of São Paulo School of Medicine, São Paulo, Brazil (M.S.B.); Faculdade Israelita de Ciências da Saúde Albert Einstein, São Paulo, Brazil (M.S.B.); and DASA São Paulo, São Paulo, Brazil (M.S.B.)
| | - Rebecca Seifried
- Department of Medicine, Cardiology Service (C.P., R.S., E.A.H.) and Department of Radiology (P.G.P., R.L.), Walter Reed National Military Medical Center, Bethesda, Md; Department of Radiology and Radiological Sciences, Uniformed Services University of the Health Sciences, Bethesda, Md (C.P., R.S., P.G.P., R.L., E.A.H.); PeaceHealth Medical Group, Bellingham, Wash (K.S.); University Hospital, University of São Paulo School of Medicine, São Paulo, Brazil (M.S.B.); Faculdade Israelita de Ciências da Saúde Albert Einstein, São Paulo, Brazil (M.S.B.); and DASA São Paulo, São Paulo, Brazil (M.S.B.)
| | - P Gabriel Peterson
- Department of Medicine, Cardiology Service (C.P., R.S., E.A.H.) and Department of Radiology (P.G.P., R.L.), Walter Reed National Military Medical Center, Bethesda, Md; Department of Radiology and Radiological Sciences, Uniformed Services University of the Health Sciences, Bethesda, Md (C.P., R.S., P.G.P., R.L., E.A.H.); PeaceHealth Medical Group, Bellingham, Wash (K.S.); University Hospital, University of São Paulo School of Medicine, São Paulo, Brazil (M.S.B.); Faculdade Israelita de Ciências da Saúde Albert Einstein, São Paulo, Brazil (M.S.B.); and DASA São Paulo, São Paulo, Brazil (M.S.B.)
| | - Robert Liotta
- Department of Medicine, Cardiology Service (C.P., R.S., E.A.H.) and Department of Radiology (P.G.P., R.L.), Walter Reed National Military Medical Center, Bethesda, Md; Department of Radiology and Radiological Sciences, Uniformed Services University of the Health Sciences, Bethesda, Md (C.P., R.S., P.G.P., R.L., E.A.H.); PeaceHealth Medical Group, Bellingham, Wash (K.S.); University Hospital, University of São Paulo School of Medicine, São Paulo, Brazil (M.S.B.); Faculdade Israelita de Ciências da Saúde Albert Einstein, São Paulo, Brazil (M.S.B.); and DASA São Paulo, São Paulo, Brazil (M.S.B.)
| | - Kevin Steel
- Department of Medicine, Cardiology Service (C.P., R.S., E.A.H.) and Department of Radiology (P.G.P., R.L.), Walter Reed National Military Medical Center, Bethesda, Md; Department of Radiology and Radiological Sciences, Uniformed Services University of the Health Sciences, Bethesda, Md (C.P., R.S., P.G.P., R.L., E.A.H.); PeaceHealth Medical Group, Bellingham, Wash (K.S.); University Hospital, University of São Paulo School of Medicine, São Paulo, Brazil (M.S.B.); Faculdade Israelita de Ciências da Saúde Albert Einstein, São Paulo, Brazil (M.S.B.); and DASA São Paulo, São Paulo, Brazil (M.S.B.)
| | - Marcio S Bittencourt
- Department of Medicine, Cardiology Service (C.P., R.S., E.A.H.) and Department of Radiology (P.G.P., R.L.), Walter Reed National Military Medical Center, Bethesda, Md; Department of Radiology and Radiological Sciences, Uniformed Services University of the Health Sciences, Bethesda, Md (C.P., R.S., P.G.P., R.L., E.A.H.); PeaceHealth Medical Group, Bellingham, Wash (K.S.); University Hospital, University of São Paulo School of Medicine, São Paulo, Brazil (M.S.B.); Faculdade Israelita de Ciências da Saúde Albert Einstein, São Paulo, Brazil (M.S.B.); and DASA São Paulo, São Paulo, Brazil (M.S.B.)
| | - Edward A Hulten
- Department of Medicine, Cardiology Service (C.P., R.S., E.A.H.) and Department of Radiology (P.G.P., R.L.), Walter Reed National Military Medical Center, Bethesda, Md; Department of Radiology and Radiological Sciences, Uniformed Services University of the Health Sciences, Bethesda, Md (C.P., R.S., P.G.P., R.L., E.A.H.); PeaceHealth Medical Group, Bellingham, Wash (K.S.); University Hospital, University of São Paulo School of Medicine, São Paulo, Brazil (M.S.B.); Faculdade Israelita de Ciências da Saúde Albert Einstein, São Paulo, Brazil (M.S.B.); and DASA São Paulo, São Paulo, Brazil (M.S.B.)
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Leiner T, Rueckert D, Suinesiaputra A, Baeßler B, Nezafat R, Išgum I, Young AA. Machine learning in cardiovascular magnetic resonance: basic concepts and applications. J Cardiovasc Magn Reson 2019; 21:61. [PMID: 31590664 PMCID: PMC6778980 DOI: 10.1186/s12968-019-0575-y] [Citation(s) in RCA: 116] [Impact Index Per Article: 23.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2018] [Accepted: 09/02/2019] [Indexed: 12/18/2022] Open
Abstract
Machine learning (ML) is making a dramatic impact on cardiovascular magnetic resonance (CMR) in many ways. This review seeks to highlight the major areas in CMR where ML, and deep learning in particular, can assist clinicians and engineers in improving imaging efficiency, quality, image analysis and interpretation, as well as patient evaluation. We discuss recent developments in the field of ML relevant to CMR in the areas of image acquisition & reconstruction, image analysis, diagnostic evaluation and derivation of prognostic information. To date, the main impact of ML in CMR has been to significantly reduce the time required for image segmentation and analysis. Accurate and reproducible fully automated quantification of left and right ventricular mass and volume is now available in commercial products. Active research areas include reduction of image acquisition and reconstruction time, improving spatial and temporal resolution, and analysis of perfusion and myocardial mapping. Although large cohort studies are providing valuable data sets for ML training, care must be taken in extending applications to specific patient groups. Since ML algorithms can fail in unpredictable ways, it is important to mitigate this by open source publication of computational processes and datasets. Furthermore, controlled trials are needed to evaluate methods across multiple centers and patient groups.
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Affiliation(s)
- Tim Leiner
- Department of Radiology | E.01.132, Utrecht University Medical Center, Heidelberglaan 100, 3584CX Utrecht, The Netherlands
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College, London, UK
| | - Avan Suinesiaputra
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
| | - Bettina Baeßler
- Department of Radiology, University Hospital of Cologne, Cologne, Germany
- Institute of Clinical Radiology and Nuclear Medicine, University Medical Centre Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Reza Nezafat
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Centre, Harvard Medical School, Boston, MA USA
| | - Ivana Išgum
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands
| | - Alistair A. Young
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
- Department of Biomedical Engineering, King’s College London, London, UK
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