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Medhi D, Kamidi SR, Mamatha Sree KP, Shaikh S, Rasheed S, Thengu Murichathil AH, Nazir Z. Artificial Intelligence and Its Role in Diagnosing Heart Failure: A Narrative Review. Cureus 2024; 16:e59661. [PMID: 38836155 PMCID: PMC11148729 DOI: 10.7759/cureus.59661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/04/2024] [Indexed: 06/06/2024] Open
Abstract
Heart failure (HF) is prevalent globally. It is a dynamic disease with varying definitions and classifications due to multiple pathophysiologies and etiologies. The diagnosis, clinical staging, and treatment of HF become complex and subjective, impacting patient prognosis and mortality. Technological advancements, like artificial intelligence (AI), have been significant roleplays in medicine and are increasingly used in cardiovascular medicine to transform drug discovery, clinical care, risk prediction, diagnosis, and treatment. Medical and surgical interventions specific to HF patients rely significantly on early identification of HF. Hospitalization and treatment costs for HF are high, with readmissions increasing the burden. AI can help improve diagnostic accuracy by recognizing patterns and using them in multiple areas of HF management. AI has shown promise in offering early detection and precise diagnoses with the help of ECG analysis, advanced cardiac imaging, leveraging biomarkers, and cardiopulmonary stress testing. However, its challenges include data access, model interpretability, ethical concerns, and generalizability across diverse populations. Despite these ongoing efforts to refine AI models, it suggests a promising future for HF diagnosis. After applying exclusion and inclusion criteria, we searched for data available on PubMed, Google Scholar, and the Cochrane Library and found 150 relevant papers. This review focuses on AI's significant contribution to HF diagnosis in recent years, drastically altering HF treatment and outcomes.
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Affiliation(s)
- Diptiman Medhi
- Internal Medicine, Gauhati Medical College and Hospital, Guwahati, Guwahati, IND
| | | | | | - Shifa Shaikh
- Cardiology, SMBT Institute of Medical Sciences and Research Centre, Igatpuri, IND
| | - Shanida Rasheed
- Emergency Medicine, East Sussex Healthcare NHS Trust, Eastbourne, GBR
| | | | - Zahra Nazir
- Internal Medicine, Combined Military Hospital, Quetta, Quetta, PAK
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Kotanidis CP, Xie C, Alexander D, Rodrigues JCL, Burnham K, Mentzer A, O'Connor D, Knight J, Siddique M, Lockstone H, Thomas S, Kotronias R, Oikonomou EK, Badi I, Lyasheva M, Shirodaria C, Lumley SF, Constantinides B, Sanderson N, Rodger G, Chau KK, Lodge A, Tsakok M, Gleeson F, Adlam D, Rao P, Indrajeet D, Deshpande A, Bajaj A, Hudson BJ, Srivastava V, Farid S, Krasopoulos G, Sayeed R, Ho LP, Neubauer S, Newby DE, Channon KM, Deanfield J, Antoniades C. Constructing custom-made radiotranscriptomic signatures of vascular inflammation from routine CT angiograms: a prospective outcomes validation study in COVID-19. Lancet Digit Health 2022; 4:e705-e716. [PMID: 36038496 PMCID: PMC9417284 DOI: 10.1016/s2589-7500(22)00132-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2022] [Revised: 06/16/2022] [Accepted: 07/05/2022] [Indexed: 12/15/2022]
Abstract
BACKGROUND Direct evaluation of vascular inflammation in patients with COVID-19 would facilitate more efficient trials of new treatments and identify patients at risk of long-term complications who might respond to treatment. We aimed to develop a novel artificial intelligence (AI)-assisted image analysis platform that quantifies cytokine-driven vascular inflammation from routine CT angiograms, and sought to validate its prognostic value in COVID-19. METHODS For this prospective outcomes validation study, we developed a radiotranscriptomic platform that uses RNA sequencing data from human internal mammary artery biopsies to develop novel radiomic signatures of vascular inflammation from CT angiography images. We then used this platform to train a radiotranscriptomic signature (C19-RS), derived from the perivascular space around the aorta and the internal mammary artery, to best describe cytokine-driven vascular inflammation. The prognostic value of C19-RS was validated externally in 435 patients (331 from study arm 3 and 104 from study arm 4) admitted to hospital with or without COVID-19, undergoing clinically indicated pulmonary CT angiography, in three UK National Health Service (NHS) trusts (Oxford, Leicester, and Bath). We evaluated the diagnostic and prognostic value of C19-RS for death in hospital due to COVID-19, did sensitivity analyses based on dexamethasone treatment, and investigated the correlation of C19-RS with systemic transcriptomic changes. FINDINGS Patients with COVID-19 had higher C19-RS than those without (adjusted odds ratio [OR] 2·97 [95% CI 1·43-6·27], p=0·0038), and those infected with the B.1.1.7 (alpha) SARS-CoV-2 variant had higher C19-RS values than those infected with the wild-type SARS-CoV-2 variant (adjusted OR 1·89 [95% CI 1·17-3·20] per SD, p=0·012). C19-RS had prognostic value for in-hospital mortality in COVID-19 in two testing cohorts (high [≥6·99] vs low [<6·99] C19-RS; hazard ratio [HR] 3·31 [95% CI 1·49-7·33], p=0·0033; and 2·58 [1·10-6·05], p=0·028), adjusted for clinical factors, biochemical biomarkers of inflammation and myocardial injury, and technical parameters. The adjusted HR for in-hospital mortality was 8·24 (95% CI 2·16-31·36, p=0·0019) in patients who received no dexamethasone treatment, but 2·27 (0·69-7·55, p=0·18) in those who received dexamethasone after the scan, suggesting that vascular inflammation might have been a therapeutic target of dexamethasone in COVID-19. Finally, C19-RS was strongly associated (r=0·61, p=0·00031) with a whole blood transcriptional module representing dysregulation of coagulation and platelet aggregation pathways. INTERPRETATION Radiotranscriptomic analysis of CT angiography scans introduces a potentially powerful new platform for the development of non-invasive imaging biomarkers. Application of this platform in routine CT pulmonary angiography scans done in patients with COVID-19 produced the radiotranscriptomic signature C19-RS, a marker of cytokine-driven inflammation driving systemic activation of coagulation and responsible for adverse clinical outcomes, which predicts in-hospital mortality and might allow targeted therapy. FUNDING Engineering and Physical Sciences Research Council, British Heart Foundation, Oxford BHF Centre of Research Excellence, Innovate UK, NIHR Oxford Biomedical Research Centre, Wellcome Trust, Onassis Foundation.
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Affiliation(s)
- Christos P Kotanidis
- Acute Multidisciplinary Imaging & Interventional Centre, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Cheng Xie
- Acute Multidisciplinary Imaging & Interventional Centre, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Donna Alexander
- Department of Cardiovascular Sciences and NIHR Leicester Biomedical Research Centre, University of Leicester, Leicester, UK
| | | | | | | | - Daniel O'Connor
- Oxford Vaccine Group, Department of Paediatrics, University of Oxford and NIHR Oxford Biomedical Research Centre, Oxford, UK
| | - Julian Knight
- MRC Human Immunology Unit, Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Muhammad Siddique
- Acute Multidisciplinary Imaging & Interventional Centre, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK; Caristo Diagnostics Ltd, Oxford, UK
| | - Helen Lockstone
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Sheena Thomas
- Acute Multidisciplinary Imaging & Interventional Centre, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Rafail Kotronias
- Acute Multidisciplinary Imaging & Interventional Centre, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Evangelos K Oikonomou
- Acute Multidisciplinary Imaging & Interventional Centre, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK; Department of Internal Medicine, Yale-New Haven Hospital, Yale School of Medicine, New Haven, CT, USA
| | - Ileana Badi
- Acute Multidisciplinary Imaging & Interventional Centre, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Maria Lyasheva
- Acute Multidisciplinary Imaging & Interventional Centre, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | | | - Sheila F Lumley
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | | | | | - Gillian Rodger
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Kevin K Chau
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Archie Lodge
- Medical Sciences Division, University of Oxford, Oxford, UK
| | - Maria Tsakok
- Department of Radiology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Fergus Gleeson
- Department of Radiology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - David Adlam
- Department of Cardiovascular Sciences and NIHR Leicester Biomedical Research Centre, University of Leicester, Leicester, UK
| | - Praveen Rao
- Department of Cardiovascular Sciences and NIHR Leicester Biomedical Research Centre, University of Leicester, Leicester, UK
| | - Das Indrajeet
- Department of Cardiovascular Sciences and NIHR Leicester Biomedical Research Centre, University of Leicester, Leicester, UK
| | - Aparna Deshpande
- Department of Cardiovascular Sciences and NIHR Leicester Biomedical Research Centre, University of Leicester, Leicester, UK
| | - Amrita Bajaj
- Department of Cardiovascular Sciences and NIHR Leicester Biomedical Research Centre, University of Leicester, Leicester, UK
| | - Benjamin J Hudson
- Department of Radiology, Royal United Hospitals Bath NHS Foundation Trust, Bath, UK
| | | | - Shakil Farid
- Department of Cardiothoracic Surgery, Oxford, UK
| | | | - Rana Sayeed
- Department of Cardiothoracic Surgery, Oxford, UK
| | - Ling-Pei Ho
- MRC Human Immunology Unit, Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Stefan Neubauer
- Acute Multidisciplinary Imaging & Interventional Centre, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - David E Newby
- British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | - Keith M Channon
- Acute Multidisciplinary Imaging & Interventional Centre, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK; British Heart Foundation-National Institute of Health Research Cardiovascular Partnership, Oxford NIHR Biomedical Research Centre, Oxford, UK
| | - John Deanfield
- Institute of Cardiovascular Sciences, University College London, London, UK
| | - Charalambos Antoniades
- Acute Multidisciplinary Imaging & Interventional Centre, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK.
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