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Ghobrial M, Haley HA, Gosling R, Rammohan V, Lawford PV, Hose DR, Gunn JP, Morris PD. The new role of diagnostic angiography in coronary physiological assessment. Heart 2021; 107:783-789. [PMID: 33419878 PMCID: PMC8077221 DOI: 10.1136/heartjnl-2020-318289] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 12/05/2020] [Accepted: 12/08/2020] [Indexed: 11/28/2022] Open
Abstract
The role of 'stand-alone' coronary angiography (CAG) in the management of patients with chronic coronary syndromes is the subject of debate, with arguments for its replacement with CT angiography on the one hand and its confinement to the interventional cardiac catheter laboratory on the other. Nevertheless, it remains the standard of care in most centres. Recently, computational methods have been developed in which the laws of fluid dynamics can be applied to angiographic images to yield 'virtual' (computed) measures of blood flow, such as fractional flow reserve. Together with the CAG itself, this technology can provide an 'all-in-one' anatomical and functional investigation, which is particularly useful in the case of borderline lesions. It can add to the diagnostic value of CAG by providing increased precision and reduce the need for further non-invasive and functional tests of ischaemia, at minimal cost. In this paper, we place this technology in context, with emphasis on its potential to become established in the diagnostic workup of patients with suspected coronary artery disease, particularly in the non-interventional setting. We discuss the derivation and reliability of angiographically derived fractional flow reserve (CAG-FFR) as well as its limitations and how CAG-FFR could be integrated within existing national guidance. The assessment of coronary physiology may no longer be the preserve of the interventional cardiologist.
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Affiliation(s)
- Mina Ghobrial
- Mathematical Modellling in Medicine, Department of Infection Immunity and Cardiovascular Disease, The Medical School, University of Sheffield, Sheffield, UK
| | - Hazel Arfah Haley
- Mathematical Modellling in Medicine, Department of Infection Immunity and Cardiovascular Disease, The Medical School, University of Sheffield, Sheffield, UK
| | - Rebecca Gosling
- Mathematical Modellling in Medicine, Department of Infection Immunity and Cardiovascular Disease, The Medical School, University of Sheffield, Sheffield, UK
- Department of Cardiology, Sheffield Teaching Hospitals, Sheffield, UK
| | - Vignesh Rammohan
- Mathematical Modellling in Medicine, Department of Infection Immunity and Cardiovascular Disease, The Medical School, University of Sheffield, Sheffield, UK
- Insigneo, In Silico Medicine, University of Sheffield, Sheffield, UK
| | - Patricia V Lawford
- Mathematical Modellling in Medicine, Department of Infection Immunity and Cardiovascular Disease, The Medical School, University of Sheffield, Sheffield, UK
- Insigneo, In Silico Medicine, University of Sheffield, Sheffield, UK
| | - D Rod Hose
- Mathematical Modellling in Medicine, Department of Infection Immunity and Cardiovascular Disease, The Medical School, University of Sheffield, Sheffield, UK
- Insigneo, In Silico Medicine, University of Sheffield, Sheffield, UK
| | - Julian P Gunn
- Mathematical Modellling in Medicine, Department of Infection Immunity and Cardiovascular Disease, The Medical School, University of Sheffield, Sheffield, UK
- Department of Cardiology, Sheffield Teaching Hospitals, Sheffield, UK
- Insigneo, In Silico Medicine, University of Sheffield, Sheffield, UK
| | - Paul D Morris
- Mathematical Modellling in Medicine, Department of Infection Immunity and Cardiovascular Disease, The Medical School, University of Sheffield, Sheffield, UK
- Department of Cardiology, Sheffield Teaching Hospitals, Sheffield, UK
- Insigneo, In Silico Medicine, University of Sheffield, Sheffield, UK
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Applying Deep Neural Networks over Homomorphic Encrypted Medical Data. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2020; 2020:3910250. [PMID: 32351612 PMCID: PMC7171620 DOI: 10.1155/2020/3910250] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Revised: 10/24/2019] [Accepted: 03/09/2020] [Indexed: 12/27/2022]
Abstract
In recent years, powered by state-of-the-art achievements in a broad range of areas, machine learning has received considerable attention from the healthcare sector. Despite their ability to provide solutions within personalized medicine, strict regulations on the confidentiality of patient health information have in many cases hindered the adoption of deep learning-based solutions in clinical workflows. To allow for the processing of sensitive health information without disclosing the underlying data, we propose a solution based on fully homomorphic encryption (FHE). The considered encryption scheme, MORE (Matrix Operation for Randomization or Encryption), enables the computations within a neural network model to be directly performed on floating point data with a relatively small computational overhead. We consider the well-known MNIST digit recognition problem to evaluate the feasibility of the proposed method and show that performance does not decrease when deep learning is applied on MORE homomorphic data. To further evaluate the suitability of the method for healthcare applications, we first train a model on encrypted data to estimate the outputs of a whole-body circulation (WBC) hemodynamic model and then provide a solution for classifying encrypted X-ray coronary angiography medical images. The findings highlight the potential of the proposed privacy-preserving deep learning methods to outperform existing approaches by providing, within a reasonable amount of time, results equivalent to those achieved by unencrypted models. Lastly, we discuss the security implications of the encryption scheme and show that while the considered cryptosystem promotes efficiency and utility at a lower security level, it is still applicable in certain practical use cases.
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Song YB, Arbab-Zadeh A, Matheson MB, Ostovaneh MR, Vavere AL, Dewey M, Rochitte C, Niinuma H, Laham R, Schuijf JD, Cox C, Brinker J, di Carli M, Lima JA, Miller JM. Contemporary Discrepancies of Stenosis Assessment by Computed Tomography and Invasive Coronary Angiography. Circ Cardiovasc Imaging 2019; 12:e007720. [DOI: 10.1161/circimaging.118.007720] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Affiliation(s)
- Young Bin Song
- Division of Cardiology, Department of Medicine, Heart Vascular Stroke Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea (Y.B.S.)
- Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD (Y.B.S., A.A.-Z., M.R.O., A.L.V., J.B., J.A.C.L., J.M.M.)
| | - Armin Arbab-Zadeh
- Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD (Y.B.S., A.A.-Z., M.R.O., A.L.V., J.B., J.A.C.L., J.M.M.)
| | - Matthew B. Matheson
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD (M.B.M., C.C.)
| | - Mohammad R. Ostovaneh
- Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD (Y.B.S., A.A.-Z., M.R.O., A.L.V., J.B., J.A.C.L., J.M.M.)
| | - Andrea L. Vavere
- Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD (Y.B.S., A.A.-Z., M.R.O., A.L.V., J.B., J.A.C.L., J.M.M.)
| | - Marc Dewey
- Department of Radiology, Charité Medical School, Humboldt, Berlin, Germany (M.D.)
| | - Carlos Rochitte
- Department of Medicine, InCor Heart Institute, University of Sao Paulo Medical School, Brazil (C.R.)
| | - Hiroyuki Niinuma
- Department of Medicine, Division of Cardiology, Memorial Heart Center, Iwate Medical University, Morioka, Japan (H.N.)
| | - Roger Laham
- Department of Radiology, Beth Israel Deaconess Medical Center, Harvard University, Boston, MA (R.L.)
| | | | - Christopher Cox
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD (M.B.M., C.C.)
| | - Jeffrey Brinker
- Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD (Y.B.S., A.A.-Z., M.R.O., A.L.V., J.B., J.A.C.L., J.M.M.)
| | - Marcelo di Carli
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA (M.d.C.)
| | - João A.C. Lima
- Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD (Y.B.S., A.A.-Z., M.R.O., A.L.V., J.B., J.A.C.L., J.M.M.)
| | - Julie M. Miller
- Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD (Y.B.S., A.A.-Z., M.R.O., A.L.V., J.B., J.A.C.L., J.M.M.)
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