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Saba L, Cau R, Vergallo R, Kooi ME, Staub D, Faa G, Congiu T, Ntaios G, Wasserman BA, Benson J, Nardi V, Kawakami R, Lanzino G, Virmani R, Libby P. Carotid artery atherosclerosis: mechanisms of instability and clinical implications. Eur Heart J 2025; 46:904-921. [PMID: 39791527 DOI: 10.1093/eurheartj/ehae933] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/18/2024] [Revised: 08/25/2024] [Accepted: 12/19/2024] [Indexed: 01/12/2025] Open
Abstract
Cardiovascular disease remains a prominent cause of disability and premature death worldwide. Within this spectrum, carotid artery atherosclerosis is a complex and multifaceted condition, and a prominent precursor of acute ischaemic stroke and other cardiovascular events. The intricate interplay among inflammation, oxidative stress, endothelial dysfunction, lipid metabolism, and immune responses participates in the development of lesions, leading to luminal stenosis and potential plaque instability. Even non-stenotic plaques can precipitate a sudden cerebrovascular event, regardless of the degree of luminal encroachment. In this context, carotid imaging modalities have proved their efficacy in providing in vivo characterization of plaque features, contributing substantially to patient risk stratification and clinical management. This review emphasizes the importance of identifying high-risk individuals by use of current imaging modalities, biomarkers, and risk stratification tools. Such approaches inform early intervention and the implementation of personalized therapeutic strategies, ultimately enhancing patient outcomes in the realm of cardiovascular disease management.
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Affiliation(s)
- Luca Saba
- Department of Radiology, University of Cagliari, Via Università, 40, 09124 Cagliari, Italy
| | - Riccardo Cau
- Department of Radiology, University of Cagliari, Via Università, 40, 09124 Cagliari, Italy
| | - Rocco Vergallo
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Roma, Italy
| | - M Eline Kooi
- Department of Radiology and Nuclear Medicine, CARIM School for Cardiovascular Diseases, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Daniel Staub
- Vascular Medicine/Angiology, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Gavino Faa
- Department of Pathology, University of Cagliari, Cagliari, Italy
| | - Terenzio Congiu
- Department of Pathology, University of Cagliari, Cagliari, Italy
| | - George Ntaios
- Department of Internal Medicine, School of Health Sciences, University of Thessaly, Larissa University Hospital, Larissa 41132, Greece
| | - Bruce A Wasserman
- Department of Diagnostic Radiology & Nuclear Medicine, University of Maryland School of Medicine, MD, USA
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, MD, USA
| | - John Benson
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Valentina Nardi
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN, USA
| | - Rika Kawakami
- Department of Cardiovascular Pathology, CVPath Institute, Inc., Gaithersburg, MD, USA
| | | | - Renu Virmani
- Department of Cardiovascular Pathology, CVPath Institute, Gaithersburg, MD, USA
| | - Peter Libby
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
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Cau R, Pisu F, Suri JS, Saba L. Addressing hidden risks: Systematic review of artificial intelligence biases across racial and ethnic groups in cardiovascular diseases. Eur J Radiol 2025; 183:111867. [PMID: 39637580 DOI: 10.1016/j.ejrad.2024.111867] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2024] [Revised: 11/25/2024] [Accepted: 11/28/2024] [Indexed: 12/07/2024]
Abstract
BACKGROUND Artificial intelligence (AI)-based models are increasingly being integrated into cardiovascular medicine. Despite promising potential, racial and ethnic biases remain a key concern regarding the development and implementation of AI models in clinical settings. OBJECTIVE This systematic review offers an overview of the accuracy and clinical applicability of AI models for cardiovascular diagnosis and prognosis across diverse racial and ethnic groups. METHOD A comprehensive literature search was conducted across four medical and scientific databases: PubMed, MEDLINE via Ovid, Scopus, and the Cochrane Library, to evaluate racial and ethnic disparities in cardiovascular medicine. RESULTS A total of 1704 references were screened, of which 11 articles were included in the final analysis. Applications of AI-based algorithms across different race/ethnic groups were varied and involved diagnosis, prognosis, and imaging segmentation. Among the 11 studies, 9 (82%) concluded that racial/ethnic bias existed, while 2 (18%) found no differences in the outcomes of AI models across various ethnicities. CONCLUSION Our results suggest significant differences in how AI models perform in cardiovascular medicine across diverse racial and ethnic groups. CLINICAL RELEVANCE STATEMENT The increasing integration of AI into cardiovascular medicine highlights the importance of evaluating its performance across diverse populations. This systematic review underscores the critical need to address racial and ethnic disparities in AI-based models to ensure equitable healthcare delivery.
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Affiliation(s)
- Riccardo Cau
- Department of Radiology, Azienda Ospedaliero Universitaria, Monserrato, Cagliari, Italy
| | - Francesco Pisu
- Department of Radiology, Azienda Ospedaliero Universitaria, Monserrato, Cagliari, Italy
| | - Jasjit S Suri
- Department of Radiology, Azienda Ospedaliero Universitaria, Monserrato, Cagliari, Italy
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria, Monserrato, Cagliari, Italy.
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Cau R, Pinna A, Montisci R, d'Errico L, Suri JS, Francone M, Muscogiuri G, Saba L. Impact of papillary muscle infarction on atrial and ventricular myocardial deformation in non-anterior STEMI patients. THE INTERNATIONAL JOURNAL OF CARDIOVASCULAR IMAGING 2025; 41:359-368. [PMID: 39825068 DOI: 10.1007/s10554-024-03317-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2024] [Accepted: 12/20/2024] [Indexed: 01/20/2025]
Abstract
The purpose of this study was to explore the impact of papillary muscle (PPM) infarction on left atrial and ventricular strain parameters in patients with non-anterior ST-segment elevation myocardial infarction (NA-STEMI) using cardiovascular magnetic resonance (CMR). This retrospective study performed CMR scans on 88 consecutive patients with NA-STEMI (68 males, 65 ± 10.05 years). Among them, 30 demonstrated PPM infarction (25 males, 67.12 ± 9.49 years), defined as late gadolinium enhancement (LGE) in a papillary muscle head in two contiguous LGE CMR slices, and confirmed on the long-axis LGE CMR slices. Atrial and ventricular strain were analyzed by CMR feature tracking with dedicated post-processing software. Patients with PPM infarction were older (p = 0.001), with lower left ventricular ejection fraction (p = 0.040), higher indexed left ventricular end-diastolic volume (p = 0.020), and end-systolic volume (p = 0.044) compared to patients without LGE in the papillary muscle. Additionally, patients with PPM infarction showed impaired reservoir strain, booster strain, global longitudinal strain (GLS), and higher LGE extent compared to NA-STEMI patients without PPM involvement (p = 0.001, p = 0.004, p = 0.001, and p = 0.003, respectively). In multivariable analysis, GLS, global radial strain, reservoir strain, and booster strain parameters were the only independent determinants of PPM infarction (p = 0.001, p = 0.041, p = 0.002, and p = 0.027, respectively). The presence of PPM infarction assessed by CMR is independently linked to atrial and ventricular strain impairment in patients with NA-STEMI.
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Affiliation(s)
- Riccardo Cau
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari - Polo di Monserrato s.s. 554 Monserrato (Cagliari), Monserrato, 09045, Italy
| | - Alessandro Pinna
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari - Polo di Monserrato s.s. 554 Monserrato (Cagliari), Monserrato, 09045, Italy
| | - Roberta Montisci
- Department of Cardiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari - Polo di Monserrato s.s. 554 Monserrato (Cagliari), Monserrato, 09045, Italy
| | - Luigia d'Errico
- Department of Medical Imaging, University Health Network, University of Toronto, Toronto, ON, Canada
| | - Jasjit S Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, 95661, USA
| | - Marco Francone
- IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, Italy
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
| | | | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari - Polo di Monserrato s.s. 554 Monserrato (Cagliari), Monserrato, 09045, Italy.
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Cau R, Saba L. Interlinking pathways: a narrative review on the role of IL-6 in cancer and atherosclerosis. Cardiovasc Diagn Ther 2024; 14:1186-1201. [PMID: 39790197 PMCID: PMC11707487 DOI: 10.21037/cdt-24-344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2024] [Accepted: 10/18/2024] [Indexed: 01/12/2025]
Abstract
Background and Objective Interleukin-6 (IL-6) plays multifaceted roles in cancer and atherosclerosis. Initially recognized for its role in immune response and inflammation, IL-6 promotes tumor progression via the JAK-STAT and MAP kinase pathways and is associated with poor cancer prognoses. In atherosclerosis, IL-6 contributes to endothelial dysfunction and plaque formation. This review highlights the shared inflammatory mechanisms of IL-6 in both diseases and explores the regulatory dynamics of IL-6 signaling, including gene polymorphisms and epigenetic modifications. Methods Google Scholar, Scopus, and PubMed were searched for English-language articles on IL-6 and those reporting shared pathogenic mechanisms of IL-6 in cancer and atherosclerosis from their inception through June 2024. Key Content and Findings The investigation into IL-6's mechanisms in cancer and atherosclerosis reveals the intricate and interconnected nature of inflammatory processes in chronic diseases. The role of IL-6 in both conditions underscores its centrality in disease pathology, particularly through its involvement in inflammation, immune modulation, and cellular proliferation. This commonality highlights IL-6 as a key player linking these seemingly distinct diseases. Conclusions Given the shared pathogenic mechanism of IL-6 in cancer and atherosclerosis, this narrative review concludes by emphasizing the therapeutic potential of modulating IL-6 in treating both cancer and atherosclerosis. It advocates for personalized treatment strategies that combine targeted therapies with lifestyle modifications. This holistic approach is considered crucial for effective disease management, given the diverse and complex roles IL-6 plays in these widespread conditions.
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Affiliation(s)
- Riccardo Cau
- Department of Radiology, University of Cagliari, Cagliari, Italy
| | - Luca Saba
- Department of Radiology, University of Cagliari, Cagliari, Italy
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Meloni A, Saba L, Cademartiri F, Positano V, Pistoia L, Cau R. Cardiovascular magnetic resonance in β-thalassemia major: beyond T2. LA RADIOLOGIA MEDICA 2024; 129:1812-1822. [PMID: 39511065 DOI: 10.1007/s11547-024-01916-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2024] [Accepted: 10/29/2024] [Indexed: 11/15/2024]
Abstract
Β-thalassemia major (TM) patients underwent regular transfusions to prevent complications of chronic anemia. However, these regular transfusions result in progressive iron accumulation in vital organs, including the heart. Myocardial iron overload can lead to cardiac dysfunction and ultimately to heart failure. Diagnosis of cardiac dysfunction in β-TM patients is usually made through clinical examination, electrocardiogram, and echocardiography. Cardiac magnetic resonance (CMR), through the measurement of T2* relaxation time, represents the diagnostic modality of choice for assessing myocardial iron overload and guiding the iron chelation therapy. Despite a tailored chelation therapy reducing myocardial iron overload, heart failure remains the leading cause of morbidity and mortality even in well-treated β-TM patients. Advances in CMR, including myocardial strain, parametric mapping (T1, T2, and extracellular volume), and late gadolinium enhancement (LGE) measurements, have expanded its role in the diagnosis, prognosis, and follow-up of these patients. This review seeks to offer a thorough overview of the potential uses of CMR in β-TM, extending beyond the established role of T2* measurement in guiding chelation therapy. It delves into the emerging applications of new CMR imaging biomarkers that could improve the overall management of β-TM patients.
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Affiliation(s)
- Antonella Meloni
- Bioengineering Unit, Fondazione G. Monasterio CNR-Regione Toscana, Pisa, Italy
- Department of Radiology, Fondazione G. Monasterio CNR-Regione Toscana, Pisa, Italy
| | - Luca Saba
- Dipartimento Di Radiologia, Azienda Ospedaliero-Universitaria di Cagliari-Polo di Monserrato, S.S.554 Monserrato, 09045, Cagliari, Italy
| | - Filippo Cademartiri
- Department of Radiology, Fondazione G. Monasterio CNR-Regione Toscana, Pisa, Italy
| | - Vincenzo Positano
- Bioengineering Unit, Fondazione G. Monasterio CNR-Regione Toscana, Pisa, Italy
- Department of Radiology, Fondazione G. Monasterio CNR-Regione Toscana, Pisa, Italy
| | - Laura Pistoia
- Department of Radiology, Fondazione G. Monasterio CNR-Regione Toscana, Pisa, Italy
- U.O.C. Ricerca Clinica, Fondazione G. Monasterio CNR-Regione Toscana, Pisa, Italy
| | - Riccardo Cau
- Dipartimento Di Radiologia, Azienda Ospedaliero-Universitaria di Cagliari-Polo di Monserrato, S.S.554 Monserrato, 09045, Cagliari, Italy.
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Cau R, Pisu F, Montisci R, D'Angelo T, Mantini C, Salgado R, Saba L. Assessing Acute Pericarditis with T1 Mapping: A Supportive Contrast-Free CMR Marker. Tomography 2024; 10:1881-1894. [PMID: 39728899 DOI: 10.3390/tomography10120137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2024] [Revised: 11/14/2024] [Accepted: 11/25/2024] [Indexed: 12/28/2024] Open
Abstract
OBJECTIVE The purpose of this study was to explore the impact of pericardial T1 mapping as a potential supportive non-contrast cardiovascular magnetic resonance (CMR) parameter in the diagnosis of acute pericarditis. Additionally, we investigated the relationship between T1 mapping values in acute pericarditis patients and their demographic data, cardiovascular risk factors, clinical parameters, cardiac biomarkers, and cardiac function. METHOD This retrospective study included CMR scans in 35 consecutive patients with acute pericarditis (26 males, 45.54 ± 23.38 years). Moreover, we included 17 sex- and age-matched healthy controls (12 males, mean age 47.78 ±19.38 years). CMR-derived pericardial T1 mapping values, which included all pericardial structures within the pericardial layers-encompassing both pericardial effusion and pericardial layer thickness-were analyzed and compared between acute pericarditis patients and controls. RESULTS Compared to the matched control group, acute pericarditis patients demonstrated significantly lower pericardial T1 mapping values (2137 ms ± 519 vs. 3268 ms ± 362, p = 0.001). In the multivariable analysis, the pericardial T1 mapping value was independently associated with the severity of pericardial late gadolinium enhancement (LGE) (β coefficient = -3.271, p = 0.003). The receiver operating characteristic curve analysis showed that the diagnostic performance of pericardial T1 mapping in discriminating acute pericarditis patients was excellent, with an area under the curve of 0.97 (95% CI = 0.94-0.98), using a threshold of 2862.5 ms. CONCLUSIONS Pericardial T1 mapping values could serve as an additional non-contrast CMR parameter for identifying patients with acute pericarditis, demonstrating an independent association with the severity of pericardial LGE.
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Affiliation(s)
- Riccardo Cau
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari-Polo di Monserrato s.s. 554 Monserrato, 09045 Cagliari, Italy
| | - Francesco Pisu
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari-Polo di Monserrato s.s. 554 Monserrato, 09045 Cagliari, Italy
| | - Roberta Montisci
- Department of Cardiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari-Polo di Monserrato s.s. 554 Monserrato, 09045 Cagliari, Italy
| | - Tommaso D'Angelo
- Department of Biomedical Sciences and Morphological and Functional Imaging, G. Martino University Hospital, University of Messina, 98124 Messina, Italy
- Department of Radiology and Nuclear Medicine, Erasmus MC, Doctor Molewaterplein 40, 3015 GD Rotterdam, The Netherlands
| | - Cesare Mantini
- Department of Neuroscience, Imaging and Clinical Sciences, 'G. d'Annunzio' University, 66100 Chieti, Italy
| | - Rodrigo Salgado
- Department of Radiology, Universitair Ziekenhuis Antwerpen, 2650 Edegem, Belgium
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari-Polo di Monserrato s.s. 554 Monserrato, 09045 Cagliari, Italy
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M. Odat R, Marsool Marsool MD, Nguyen D, Idrees M, Hussein AM, Ghabally M, A. Yasin J, Hanifa H, Sabet CJ, Dinh NH, Harky A, Jain J, Jain H. Presurgery and postsurgery: advancements in artificial intelligence and machine learning models for enhancing patient management in infective endocarditis. Int J Surg 2024; 110:7202-7214. [PMID: 39051669 PMCID: PMC11573050 DOI: 10.1097/js9.0000000000002003] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2024] [Accepted: 07/15/2024] [Indexed: 07/27/2024]
Abstract
Infective endocarditis (IE) is a severe infection of the inner lining of the heart, known as the endocardium. It is characterized by a range of symptoms and has a complicated pattern of occurrence, leading to a significant number of deaths. IE poses significant diagnostic and treatment difficulties. This evaluation examines the utilization of artificial intelligence (AI) and machine learning (ML) models in addressing IE management. It focuses on the most recent advancements and possible applications. Through this paper, the authors observe that AI/ML can significantly enhance and outperform traditional diagnostic methods leading to more accurate risk stratification, personalized therapies, as well and real-time monitoring facilities. For example, early postsurgical mortality prediction models like SYSUPMIE achieved 'very good' area under the curve (AUROC) values exceeding 0.81. Additionally, AI/ML has improved diagnostic accuracy for prosthetic valve endocarditis, with PET-ML models increasing sensitivity from 59 to 72% when integrated into ESC criteria and reaching a high specificity of 83%. Furthermore, inflammatory biomarkers such as IL-15 and CCL4 have been identified as predictive markers, showing 91% accuracy in forecasting mortality, and identifying high-risk patients with specific CRP, IL-15, and CCL4 levels. Even simpler ML models, like Naïve Bayes, demonstrated an excellent accuracy of 92.30% in death rate prediction following valvular surgery for IE patients. Furthermore, this review provides a vital assessment of the advantages and disadvantages of such AI/ML models, such as better-quality decision support approaches like adaptive response systems on one hand, and data privacy threats or ethical concerns on the other hand. In conclusion, Al and ML must continue, through multicentric and validated research, to advance cardiovascular medicine, and overcome implementation challenges to boost patient outcomes and healthcare delivery.
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Affiliation(s)
- Ramez M. Odat
- Faculty of Medicine, Jordan University of Science and Technology, Irbid
| | | | - Dang Nguyen
- Massachusetts General Hospital, Corrigan Minehan Heart Center, Harvard Medical School, Boston, Massachusetts
| | | | | | - Mike Ghabally
- Division of Cardiology, Department of Internal Medicine, Faculty of Medicine, University of Aleppo, Aleppo
| | - Jehad A. Yasin
- School of Medicine, The University of Jordan, Amman, Jordan
| | - Hamdah Hanifa
- Faculty of Medicine, University of Kalamoon, Al-Nabk, Syria
| | | | - Nguyen H. Dinh
- Department of Cardiovascular and Thoracic Surgery, Faculty of Medicine, University of Medicine and Pharmacy at Ho Chi Minh City, Vietnam
| | - Amer Harky
- Department of Cardiothoracic Surgery, Liverpool Heart and Chest Hospital, Liverpool, UK
| | - Jyoti Jain
- Department of Internal Medicine, All India Institute of Medical Sciences (AIIMS), Jodhpur, India
| | - Hritvik Jain
- Department of Internal Medicine, All India Institute of Medical Sciences (AIIMS), Jodhpur, India
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Cau R, Masala S, Manelli L, Porcu M, Scaglione M, D'Angelo T, Salgado R, Saba L. Cardiovascular Magnetic Resonance Imaging of Takotsubo Syndrome: Evolving Diagnostic and Prognostic Perspectives. Echocardiography 2024; 41:e15949. [PMID: 39367775 DOI: 10.1111/echo.15949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2024] [Revised: 09/16/2024] [Accepted: 09/18/2024] [Indexed: 10/07/2024] Open
Abstract
Takotsubo syndrome (TS) is a temporary form of left ventricular (LV) dysfunction characterized by a distinct pattern of LV impairment, often triggered by a physical or emotional stressful event. Historically, TS was considered a benign condition due to its prompt restoration of myocardial function and generally excellent outcomes. However, recent studies have shown that complications similar to those seen after myocardial infarction can occur, necessitating careful monitoring of these patients. Among noninvasive imaging techniques, cardiovascular magnetic resonance (CMR) is becoming increasingly important in evaluating patients with TS. CMR offers a unique ability to noninvasively assess myocardial tissue characteristics, allowing for detecting the typical features of TS, such as specific wall motion abnormalities and myocardial edema. Beyond its well-established diagnostic utility in the clinical management of TS, CMR has also proven valuable in prognosis and risk stratification for these patients. Advances in CMR, including myocardial strain and parametric mapping have expanded its role in the diagnosis, prognosis, and follow-up of these patients. This review aims to provide a comprehensive overview of the potential applications of CMR in the diagnostic and prognostic evaluation of TS patients. It explores the emerging use of novel CMR imaging biomarkers that may enhance diagnosis, improve prognostic accuracy, and contribute to the overall management of these patients.
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Affiliation(s)
- Riccardo Cau
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Cagliari, Italy
| | - Salvatore Masala
- Department of Medicine, Surgery and Pharmacy, University of Sassari, Sassari, Italy
| | | | - Michele Porcu
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Cagliari, Italy
| | - Mariano Scaglione
- Department of Medicine, Surgery and Pharmacy, University of Sassari, Sassari, Italy
| | - Tommaso D'Angelo
- Department of Biomedical Sciences and Morphological and Functional Imaging, G. Martino University Hospital, University of Messina, Messina, Italy
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Rodrigo Salgado
- Department of Radiology, Universitair Ziekenhuis Antwerpen, Edegem, Belgium
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Cagliari, Italy
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Thamman R, Hosseini N, Dikou ML, Hassan IU, Marchenko O, Abiola O, Grapsa J. Imaging Advances in Heart Failure. Card Fail Rev 2024; 10:e12. [PMID: 39386081 PMCID: PMC11462517 DOI: 10.15420/cfr.2023.10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 10/17/2023] [Indexed: 10/12/2024] Open
Abstract
This paper delves into the significance of imaging in the diagnosis, aetiology and therapeutic guidance of heart failure, aiming to facilitate early referral and improve patient outcomes. Imaging plays a crucial role not only in assessing left ventricular ejection fraction, but also in characterising the underlying cardiac abnormalities and reaching a specific diagnosis. By providing valuable data on cardiac structure, function and haemodynamics, imaging helps diagnose the condition, evaluate haemodynamic status and, consequently, identify the underlying pathophysiological phenotype, as well as stratifying the risk for outcomes. In this article, we provide a comprehensive exploration of these aspects.
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Affiliation(s)
- Ritu Thamman
- Department of Cardiology, University of Pittsburgh School of MedicinePittsburgh, PA, US
| | | | | | | | | | - Olukayode Abiola
- Department of Cardiology, Lister General HospitalStevenage, Hertfordshire, UK
| | - Julia Grapsa
- Department of Cardiology, St Thomas’ HospitalLondon, UK
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Trimarchi G, Pizzino F, Paradossi U, Gueli IA, Palazzini M, Gentile P, Di Spigno F, Ammirati E, Garascia A, Tedeschi A, Aschieri D. Charting the Unseen: How Non-Invasive Imaging Could Redefine Cardiovascular Prevention. J Cardiovasc Dev Dis 2024; 11:245. [PMID: 39195153 DOI: 10.3390/jcdd11080245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2024] [Revised: 08/02/2024] [Accepted: 08/03/2024] [Indexed: 08/29/2024] Open
Abstract
Cardiovascular diseases (CVDs) remain a major global health challenge, leading to significant morbidity and mortality while straining healthcare systems. Despite progress in medical treatments for CVDs, their increasing prevalence calls for a shift towards more effective prevention strategies. Traditional preventive approaches have centered around lifestyle changes, risk factors management, and medication. However, the integration of imaging methods offers a novel dimension in early disease detection, risk assessment, and ongoing monitoring of at-risk individuals. Imaging techniques such as supra-aortic trunks ultrasound, echocardiography, cardiac magnetic resonance, and coronary computed tomography angiography have broadened our understanding of the anatomical and functional aspects of cardiovascular health. These techniques enable personalized prevention strategies by providing detailed insights into the cardiac and vascular states, significantly enhancing our ability to combat the progression of CVDs. This review focuses on amalgamating current findings, technological innovations, and the impact of integrating advanced imaging modalities into cardiovascular risk prevention, aiming to offer a comprehensive perspective on their potential to transform preventive cardiology.
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Affiliation(s)
- Giancarlo Trimarchi
- Department of Clinical and Experimental Medicine, Cardiology Unit, University of Messina, 98124 Messina, Italy
- Interdisciplinary Center for Health Sciences, Scuola Superiore Sant'Anna, 56127 Pisa, Italy
| | - Fausto Pizzino
- Cardiology Unit, Heart Centre, Fondazione Gabriele Monasterio-Regione Toscana, 54100 Massa, Italy
| | - Umberto Paradossi
- Cardiology Unit, Heart Centre, Fondazione Gabriele Monasterio-Regione Toscana, 54100 Massa, Italy
| | - Ignazio Alessio Gueli
- Cardiology Unit, Heart Centre, Fondazione Gabriele Monasterio-Regione Toscana, 54100 Massa, Italy
| | - Matteo Palazzini
- "De Gasperis" Cardio Center, Niguarda Hospital, ASST Grande Ospedale Metropolitano Niguarda, 20162 Milan, Italy
| | - Piero Gentile
- "De Gasperis" Cardio Center, Niguarda Hospital, ASST Grande Ospedale Metropolitano Niguarda, 20162 Milan, Italy
| | - Francesco Di Spigno
- Cardiology Unit of Emergency Department, Guglielmo da Saliceto Hospital, 29121 Piacenza, Italy
| | - Enrico Ammirati
- "De Gasperis" Cardio Center, Niguarda Hospital, ASST Grande Ospedale Metropolitano Niguarda, 20162 Milan, Italy
| | - Andrea Garascia
- "De Gasperis" Cardio Center, Niguarda Hospital, ASST Grande Ospedale Metropolitano Niguarda, 20162 Milan, Italy
| | - Andrea Tedeschi
- Cardiology Unit of Emergency Department, Guglielmo da Saliceto Hospital, 29121 Piacenza, Italy
| | - Daniela Aschieri
- Cardiology Unit of Emergency Department, Guglielmo da Saliceto Hospital, 29121 Piacenza, Italy
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Zhang J, Dawkins A. Artificial Intelligence in Ultrasound Imaging: Where Are We Now? Ultrasound Q 2024; 40:93-97. [PMID: 38842384 DOI: 10.1097/ruq.0000000000000680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2024]
Affiliation(s)
- Jie Zhang
- From the Department of Radiology, University of Kentucky, Lexington, KY
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12
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Magboo VPC, Magboo MSA. SPECT-MPI for Coronary Artery Disease: A Deep Learning Approach. ACTA MEDICA PHILIPPINA 2024; 58:67-75. [PMID: 38812768 PMCID: PMC11132284 DOI: 10.47895/amp.vi0.7582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/31/2024]
Abstract
Background Worldwide, coronary artery disease (CAD) is a leading cause of mortality and morbidity and remains to be a top health priority in many countries. A non-invasive imaging modality for diagnosis of CAD such as single photon emission computed tomography-myocardial perfusion imaging (SPECT-MPI) is usually requested by cardiologists as it displays radiotracer distribution in the heart reflecting myocardial perfusion. The interpretation of SPECT-MPI is done visually by a nuclear medicine physician and is largely dependent on his clinical experience and showing significant inter-observer variability. Objective The aim of the study is to apply a deep learning approach in the classification of SPECT-MPI for perfusion abnormalities using convolutional neural networks (CNN). Methods A publicly available anonymized SPECT-MPI from a machine learning repository (https://www.kaggle.com/selcankaplan/spect-mpi) was used in this study involving 192 patients who underwent stress-test-rest Tc99m MPI. An exploratory approach of CNN hyperparameter selection to search for optimum neural network model was utilized with particular focus on various dropouts (0.2, 0.5, 0.7), batch sizes (8, 16, 32, 64), and number of dense nodes (32, 64, 128, 256). The base CNN model was also compared with the commonly used pre-trained CNNs in medical images such as VGG16, InceptionV3, DenseNet121 and ResNet50. All simulations experiments were performed in Kaggle using TensorFlow 2.6.0., Keras 2.6.0, and Python language 3.7.10. Results The best performing base CNN model with parameters consisting of 0.7 dropout, batch size 8, and 32 dense nodes generated the highest normalized Matthews Correlation Coefficient at 0.909 and obtained 93.75% accuracy, 96.00% sensitivity, 96.00% precision, and 96.00% F1-score. It also obtained higher classification performance as compared to the pre-trained architectures. Conclusions The results suggest that deep learning approaches through the use of CNN models can be deployed by nuclear medicine physicians in their clinical practice to further augment their decision skills in the interpretation of SPECT-MPI tests. These CNN models can also be used as a dependable and valid second opinion that can aid physicians as a decision-support tool as well as serve as teaching or learning materials for the less-experienced physicians particularly those still in their training career. These highlights the clinical utility of deep learning approaches through CNN models in the practice of nuclear cardiology.
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Affiliation(s)
- Vincent Peter C Magboo
- Department of Physical Sciences and Mathematics, College of Arts and Sciences, University of the Philippines Manila
| | - Ma Sheila A Magboo
- Department of Physical Sciences and Mathematics, College of Arts and Sciences, University of the Philippines Manila
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13
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Gennari AG, Rossi A, De Cecco CN, van Assen M, Sartoretti T, Giannopoulos AA, Schwyzer M, Huellner MW, Messerli M. Artificial intelligence in coronary artery calcium score: rationale, different approaches, and outcomes. Int J Cardiovasc Imaging 2024; 40:951-966. [PMID: 38700819 PMCID: PMC11147943 DOI: 10.1007/s10554-024-03080-4] [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: 01/27/2024] [Accepted: 03/09/2024] [Indexed: 06/05/2024]
Abstract
Almost 35 years after its introduction, coronary artery calcium score (CACS) not only survived technological advances but became one of the cornerstones of contemporary cardiovascular imaging. Its simplicity and quantitative nature established it as one of the most robust approaches for atherosclerotic cardiovascular disease risk stratification in primary prevention and a powerful tool to guide therapeutic choices. Groundbreaking advances in computational models and computer power translated into a surge of artificial intelligence (AI)-based approaches directly or indirectly linked to CACS analysis. This review aims to provide essential knowledge on the AI-based techniques currently applied to CACS, setting the stage for a holistic analysis of the use of these techniques in coronary artery calcium imaging. While the focus of the review will be detailing the evidence, strengths, and limitations of end-to-end CACS algorithms in electrocardiography-gated and non-gated scans, the current role of deep-learning image reconstructions, segmentation techniques, and combined applications such as simultaneous coronary artery calcium and pulmonary nodule segmentation, will also be discussed.
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Affiliation(s)
- Antonio G Gennari
- Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, Zurich, 8091, Switzerland
- University of Zurich, Zurich, Switzerland
| | - Alexia Rossi
- Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, Zurich, 8091, Switzerland
- University of Zurich, Zurich, Switzerland
| | - Carlo N De Cecco
- Division of Cardiothoracic Imaging, Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, USA
- Translational Laboratory for Cardiothoracic Imaging and Artificial Intelligence, Emory University, Atlanta, GA, USA
| | - Marly van Assen
- Translational Laboratory for Cardiothoracic Imaging and Artificial Intelligence, Emory University, Atlanta, GA, USA
| | - Thomas Sartoretti
- Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, Zurich, 8091, Switzerland
- University of Zurich, Zurich, Switzerland
| | - Andreas A Giannopoulos
- Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, Zurich, 8091, Switzerland
| | - Moritz Schwyzer
- Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, Zurich, 8091, Switzerland
- University of Zurich, Zurich, Switzerland
| | - Martin W Huellner
- Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, Zurich, 8091, Switzerland
- University of Zurich, Zurich, Switzerland
| | - Michael Messerli
- Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, Zurich, 8091, Switzerland.
- University of Zurich, Zurich, Switzerland.
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14
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Barris B, Karp A, Jacobs M, Frishman WH. Harnessing the Power of AI: A Comprehensive Review of Left Ventricular Ejection Fraction Assessment With Echocardiography. Cardiol Rev 2024:00045415-990000000-00237. [PMID: 38520327 DOI: 10.1097/crd.0000000000000691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/25/2024]
Abstract
The quantification of left ventricular ejection fraction (LVEF) has important clinical utility in the assessment of cardiac function and is vital for the diagnosis of cardiovascular diseases. A transthoracic echocardiogram serves as the most commonly used tool for LVEF assessment for several reasons, including, its noninvasive nature, great safety profile, real-time image processing ability, portability, and cost-effectiveness. However, transthoracic echocardiogram is highly dependent on the clinical skill of the sonographer and interpreting physician. Moreover, even amongst well-trained clinicians, significant interobserver variability exists in the quantification of LVEF. In search of possible solutions, the usage of artificial intelligence (AI) has been increasingly tested in the clinical setting. While AI-derived ejection fraction is in the preliminary stages of development, it has shown promise in its ability to rapidly quantify LVEF, decrease variability, increase accuracy, and utilize higher-order processing capabilities. This review will delineate the latest advancements of AI in evaluating LVEF through echocardiography and explore the challenges and future trajectory of this emerging domain.
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Affiliation(s)
- Ben Barris
- From the Department of Medicine, Westchester Medical Center, Valhalla, NY
| | - Avrohom Karp
- From the Department of Medicine, Westchester Medical Center, Valhalla, NY
| | - Menachem Jacobs
- Department of Medicine, SUNY Downstate Medical Center, Brooklyn, NY
| | - William H Frishman
- From the Department of Medicine, Westchester Medical Center, Valhalla, NY
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15
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Khenkina N, Aimo A, Fabiani I, Masci PG, Sagris D, Williams SE, Mavraganis G, Chen HS, Wintermark M, Michel P, Ntaios G, Georgiopoulos G. Magnetic resonance imaging for diagnostic workup of embolic stroke of undetermined source: A systematic review. Int J Stroke 2024; 19:293-304. [PMID: 37435743 DOI: 10.1177/17474930231189946] [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] [Indexed: 07/13/2023]
Abstract
BACKGROUND Embolic stroke of undetermined source (ESUS) refers to ischemic stroke where the underlying cause of thromboembolism cannot be found despite the recommended diagnostic workup. Unidentified source of emboli hinders clinical decision-making and patient management with detrimental consequences on long-term prognosis. The rapid development and versatility of magnetic resonance imaging (MRI) make it an appealing addition to the diagnostic routine of patients with ESUS for the assessment of potential vascular and cardiac embolic sources. AIMS To review the use of MRI in the identification of cardiac and vascular embolic sources in ESUS and to assess the reclassification value of MRI examinations added to the conventional workup of ESUS. SUMMARY OF REVIEW We reviewed the use of cardiac and vascular MRI for the identification of a variety of embolic sources associated with ESUS, including atrial cardiomyopathy, left ventricular pathologies, and supracervical atherosclerosis in carotid and intracranial arteries and in distal thoracic aorta. The additional reclassification after MRI examinations added to the workup of patients with ESUS ranged from 6.1% to 82.3% and varied depending on the combination of imaging modalities. CONCLUSION MRI techniques allow us to identify additional cardiac and vascular embolic sources and may further decrease the prevalence of patients with the diagnosis of ESUS.
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Affiliation(s)
- Natallia Khenkina
- Postgraduate School of Diagnostic and Interventional Radiology, University of Milan, Milan, Italy
| | - Alberto Aimo
- Institute of Life Sciences, Scuola Superiore Sant'Anna, Pisa, Italy
- Cardiology Division, Fondazione Toscana Gabriele Monasterio, Pisa, Italy
| | - Iacopo Fabiani
- Cardiology Division, Fondazione Toscana Gabriele Monasterio, Pisa, Italy
| | - Pier Giorgio Masci
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Dimitrios Sagris
- Liverpool Centre of Cardiovascular Sciences, University of Liverpool, Liverpool, UK
| | | | - George Mavraganis
- Department of Clinical Therapeutics, National and Kapodistrian University of Athens, Athens, Greece
| | - Hui-Sheng Chen
- Department of Neurology, General Hospital of Northern Theater Command, Shenyang, China
| | - Max Wintermark
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Patrik Michel
- Stroke Center, Neurology Service, Department of Clinical Neurosciences, Lausanne University Hospital, Lausanne, Switzerland
| | - George Ntaios
- Department of Internal Medicine, Faculty of Medicine, School of Health Sciences, University of Thessaly, Larissa, Greece
| | - Georgios Georgiopoulos
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
- Department of Clinical Therapeutics, National and Kapodistrian University of Athens, Athens, Greece
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16
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Jakubiak GK. Cardiac Troponin Serum Concentration Measurement Is Useful Not Only in the Diagnosis of Acute Cardiovascular Events. J Pers Med 2024; 14:230. [PMID: 38540973 PMCID: PMC10971222 DOI: 10.3390/jpm14030230] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2024] [Revised: 02/14/2024] [Accepted: 02/19/2024] [Indexed: 08/01/2024] Open
Abstract
Cardiac troponin serum concentration is the primary marker used for the diagnosis of acute coronary syndrome. Moreover, the measurement of cardiac troponin concentration is important for risk stratification in patients with pulmonary embolism. The cardiac troponin level is also a general marker of myocardial damage, regardless of etiology. The purpose of this study is to conduct a literature review and present the most important information regarding the current state of knowledge on the cardiac troponin serum concentration in patients with chronic cardiovascular disease (CVD), as well as on the relationships between cardiac troponin serum concentration and features of subclinical cardiovascular dysfunction. According to research conducted to date, patients with CVDs, such as chronic coronary syndrome, chronic lower extremities' ischemia, and cerebrovascular disease, are characterized by higher cardiac troponin concentrations than people without a CVD. Moreover, the literature data indicate that the concentration of cardiac troponin is correlated with markers of subclinical dysfunction of the cardiovascular system, such as the intima-media thickness, pulse wave velocity, ankle-brachial index, coronary artery calcium index (the Agatston score), and flow-mediated dilation. However, further research is needed in various patient subpopulations and in different clinical contexts.
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Affiliation(s)
- Grzegorz K Jakubiak
- Department and Clinic of Internal Medicine, Angiology, and Physical Medicine, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Batorego 15 St., 41-902 Bytom, Poland
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17
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Cau R, Pisu F, Suri JS, Montisci R, Gatti M, Mannelli L, Gong X, Saba L. Artificial Intelligence in the Differential Diagnosis of Cardiomyopathy Phenotypes. Diagnostics (Basel) 2024; 14:156. [PMID: 38248033 PMCID: PMC11154548 DOI: 10.3390/diagnostics14020156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 01/03/2024] [Accepted: 01/08/2024] [Indexed: 01/23/2024] Open
Abstract
Artificial intelligence (AI) is rapidly being applied to the medical field, especially in the cardiovascular domain. AI approaches have demonstrated their applicability in the detection, diagnosis, and management of several cardiovascular diseases, enhancing disease stratification and typing. Cardiomyopathies are a leading cause of heart failure and life-threatening ventricular arrhythmias. Identifying the etiologies is fundamental for the management and diagnostic pathway of these heart muscle diseases, requiring the integration of various data, including personal and family history, clinical examination, electrocardiography, and laboratory investigations, as well as multimodality imaging, making the clinical diagnosis challenging. In this scenario, AI has demonstrated its capability to capture subtle connections from a multitude of multiparametric datasets, enabling the discovery of hidden relationships in data and handling more complex tasks than traditional methods. This review aims to present a comprehensive overview of the main concepts related to AI and its subset. Additionally, we review the existing literature on AI-based models in the differential diagnosis of cardiomyopathy phenotypes, and we finally examine the advantages and limitations of these AI approaches.
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Affiliation(s)
- Riccardo Cau
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari-Polo di Monserrato s.s. 554 Monserrato, 09045 Cagliari, Italy; (R.C.); (F.P.)
| | - Francesco Pisu
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari-Polo di Monserrato s.s. 554 Monserrato, 09045 Cagliari, Italy; (R.C.); (F.P.)
| | - Jasjit S. Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoin™, Roseville, CA 95661, USA;
| | - Roberta Montisci
- Department of Cardiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari-Polo di Monserrato s.s. 554 Monserrato, 09045 Cagliari, Italy;
| | - Marco Gatti
- Department of Radiology, Università degli Studi di Torino, 10129 Turin, Italy;
| | | | - Xiangyang Gong
- Radiology Department, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou 310014, China;
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari-Polo di Monserrato s.s. 554 Monserrato, 09045 Cagliari, Italy; (R.C.); (F.P.)
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18
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Saba L, Scicolone R, Johansson E, Nardi V, Lanzino G, Kakkos SK, Pontone G, Annoni AD, Paraskevas KI, Fox AJ. Quantifying Carotid Stenosis: History, Current Applications, Limitations, and Potential: How Imaging Is Changing the Scenario. Life (Basel) 2024; 14:73. [PMID: 38255688 PMCID: PMC10821425 DOI: 10.3390/life14010073] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 12/24/2023] [Accepted: 12/29/2023] [Indexed: 01/24/2024] Open
Abstract
Carotid artery stenosis is a major cause of morbidity and mortality. The journey to understanding carotid disease has developed over time and radiology has a pivotal role in diagnosis, risk stratification and therapeutic management. This paper reviews the history of diagnostic imaging in carotid disease, its evolution towards its current applications in the clinical and research fields, and the potential of new technologies to aid clinicians in identifying the disease and tailoring medical and surgical treatment.
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Affiliation(s)
- Luca Saba
- Department of Radiology, University of Cagliari, 09042 Cagliari, Italy;
| | - Roberta Scicolone
- Department of Radiology, University of Cagliari, 09042 Cagliari, Italy;
| | - Elias Johansson
- Neuroscience and Physiology, Sahlgrenska Academy, 41390 Gothenburg, Sweden;
| | - Valentina Nardi
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN 55905, USA;
| | - Giuseppe Lanzino
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN 55905, USA;
| | - Stavros K. Kakkos
- Department of Vascular Surgery, University of Patras, 26504 Patras, Greece;
| | - Gianluca Pontone
- Centro Cardiologico Monzino IRCCS, Via C. Parea 4, 20138 Milan, Italy; (G.P.); (A.D.A.)
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, 20122 Milan, Italy
| | - Andrea D. Annoni
- Centro Cardiologico Monzino IRCCS, Via C. Parea 4, 20138 Milan, Italy; (G.P.); (A.D.A.)
| | | | - Allan J. Fox
- Department of Medical Imaging, Neuroradiology Section, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON M4N 3M5, Canada;
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19
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Cau R, Pisu F, Muscogiuri G, Mannelli L, Suri JS, Saba L. Applications of artificial intelligence-based models in vulnerable carotid plaque. VESSEL PLUS 2023. [DOI: 10.20517/2574-1209.2023.78] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2025]
Abstract
Carotid atherosclerotic disease is a widely acknowledged risk factor for ischemic stroke, making it a major concern on a global scale. To alleviate the socio-economic impact of carotid atherosclerotic disease, crucial objectives include prioritizing prevention efforts and early detection. So far, the degree of carotid stenosis has been regarded as the primary parameter for risk assessment and determining appropriate therapeutic interventions. Histopathological and imaging-based studies demonstrated important differences in the risk of cardiovascular events given a similar degree of luminal stenosis, identifying plaque structure and composition as key determinants of either plaque vulnerability or stability. The application of Artificial Intelligence (AI)-based techniques to carotid imaging can offer several solutions for tissue characterization and classification. This review aims to present a comprehensive overview of the main concepts related to AI. Additionally, we review the existing literature on AI-based models in ultrasound (US), computed tomography (CT), and Magnetic Resonance Imaging (MRI) for vulnerable plaque detection, and we finally examine the advantages and limitations of these AI approaches.
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20
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Perone F, Bernardi M, Redheuil A, Mafrica D, Conte E, Spadafora L, Ecarnot F, Tokgozoglu L, Santos-Gallego CG, Kaiser SE, Fogacci F, Sabouret A, Bhatt DL, Paneni F, Banach M, Santos R, Biondi Zoccai G, Ray KK, Sabouret P. Role of Cardiovascular Imaging in Risk Assessment: Recent Advances, Gaps in Evidence, and Future Directions. J Clin Med 2023; 12:5563. [PMID: 37685628 PMCID: PMC10487991 DOI: 10.3390/jcm12175563] [Citation(s) in RCA: 46] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 08/14/2023] [Accepted: 08/23/2023] [Indexed: 09/10/2023] Open
Abstract
Optimal risk assessment for primary prevention remains highly challenging. Recent registries have highlighted major discrepancies between guidelines and daily practice. Although guidelines have improved over time and provide updated risk scores, they still fail to identify a significant proportion of at-risk individuals, who then miss out on effective prevention measures until their initial ischemic events. Cardiovascular imaging is progressively assuming an increasingly pivotal role, playing a crucial part in enhancing the meticulous categorization of individuals according to their risk profiles, thus enabling the customization of precise therapeutic strategies for patients with increased cardiovascular risks. For the most part, the current approach to patients with atherosclerotic cardiovascular disease (ASCVD) is homogeneous. However, data from registries (e.g., REACH, CORONOR) and randomized clinical trials (e.g., COMPASS, FOURIER, and ODYSSEY outcomes) highlight heterogeneity in the risks of recurrent ischemic events, which are especially higher in patients with poly-vascular disease and/or multivessel coronary disease. This indicates the need for a more individualized strategy and further research to improve definitions of individual residual risk, with a view of intensifying treatments in the subgroups with very high residual risk. In this narrative review, we discuss advances in cardiovascular imaging, its current place in the guidelines, the gaps in evidence, and perspectives for primary and secondary prevention to improve risk assessment and therapeutic strategies using cardiovascular imaging.
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Affiliation(s)
- Francesco Perone
- Cardiac Rehabilitation Unit, Rehabilitation Clinic “Villa delle Magnolie”, Castel Morrone, 81020 Caserta, Italy;
| | - Marco Bernardi
- Department of Clinical, Internal Medicine, Anesthesiology and Cardiovascular Sciences, Sapienza University of Rome, 00185 Rome, Italy; (M.B.); (D.M.); (L.S.)
| | - Alban Redheuil
- Laboratoire d’Imagerie Biomédicale, Sorbonne University, INSERM 1146, CNRS 7371, 75005 Paris, France;
| | - Dario Mafrica
- Department of Clinical, Internal Medicine, Anesthesiology and Cardiovascular Sciences, Sapienza University of Rome, 00185 Rome, Italy; (M.B.); (D.M.); (L.S.)
| | - Edoardo Conte
- Cardiology Department, Galeazzi-Sant’Ambrogio Hospital IRCCS, 20100 Milan, Italy;
| | - Luigi Spadafora
- Department of Clinical, Internal Medicine, Anesthesiology and Cardiovascular Sciences, Sapienza University of Rome, 00185 Rome, Italy; (M.B.); (D.M.); (L.S.)
| | - Fiona Ecarnot
- Department of Cardiology, University Hospital Besancon, University of Franche-Comté, 25000 Besancon, France;
| | - Lale Tokgozoglu
- Department of Cardiology, Medical Faculty, Hacettepe University, 06230 Ankara, Turkey;
| | - Carlos G. Santos-Gallego
- Atherothrombosis Research Unit, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA;
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai Health System, New York, NY 10029, USA;
| | - Sergio Emanuel Kaiser
- Discipline of Clinical and Experimental Pathophysiology, Rio de Janeiro State University, Rio de Janeiro 23070-200, Brazil;
| | - Federica Fogacci
- Hypertension and Cardiovascular Risk Research Group, Medical and Surgical Sciences Department, Alma Mater Studiorum University of Bologna, 40126 Bologna, Italy;
| | | | - Deepak L. Bhatt
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai Health System, New York, NY 10029, USA;
| | - Francesco Paneni
- Department of Cardiology, University Heart Center, University Hospital Zurich, 8091 Zurich, Switzerland;
- Center for Translational and Experimental Cardiology (CTEC), University Hospital Zurich and University of Zurich, 8091 Zurich, Switzerland
| | - Maciej Banach
- Department of Preventive Cardiology and Lipidology, Medical University of Lodz (MUL), Rzgowska 281/289, 93-338 Lodz, Poland;
- Cardiovascular Research Centre, University of Zielona Gora, 65-417 Zielona Gora, Poland
| | - Raul Santos
- Heart Institute, University of Sao Paulo Medical School, São Paulo 05403-903, Brazil;
| | - Giuseppe Biondi Zoccai
- Department of Medical-Surgical Sciences and Biotechnologies, Sapienza University of Rome, 00185 Roma, Italy;
- Mediterranea Cardiocentro, 80122 Napoli, Italy
| | - Kausik K. Ray
- Imperial Centre for Cardiovascular Disease Prevention and Imperial Clinical Trials Unit, Department of Public Health and Primary Care, Imperial College London, London SW7 2BX, UK;
| | - Pierre Sabouret
- Heart Institute, Cardiology Department, Paris and National College of French Cardiologists, Pitié-Salpétrière Hospital, Sorbonne University, 75013 Paris, France
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21
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Cau R, Pisu F, Suri JS, Mannelli L, Scaglione M, Masala S, Saba L. Artificial Intelligence Applications in Cardiovascular Magnetic Resonance Imaging: Are We on the Path to Avoiding the Administration of Contrast Media? Diagnostics (Basel) 2023; 13:2061. [PMID: 37370956 PMCID: PMC10297403 DOI: 10.3390/diagnostics13122061] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 06/10/2023] [Accepted: 06/12/2023] [Indexed: 06/29/2023] Open
Abstract
In recent years, cardiovascular imaging examinations have experienced exponential growth due to technological innovation, and this trend is consistent with the most recent chest pain guidelines. Contrast media have a crucial role in cardiovascular magnetic resonance (CMR) imaging, allowing for more precise characterization of different cardiovascular diseases. However, contrast media have contraindications and side effects that limit their clinical application in determinant patients. The application of artificial intelligence (AI)-based techniques to CMR imaging has led to the development of non-contrast models. These AI models utilize non-contrast imaging data, either independently or in combination with clinical and demographic data, as input to generate diagnostic or prognostic algorithms. In this review, we provide an overview of the main concepts pertaining to AI, review the existing literature on non-contrast AI models in CMR, and finally, discuss the strengths and limitations of these AI models and their possible future development.
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Affiliation(s)
- Riccardo Cau
- Department of Radiology, University Hospital of Cagliari, 09042 Monserrato, Italy; (R.C.); (F.P.)
| | - Francesco Pisu
- Department of Radiology, University Hospital of Cagliari, 09042 Monserrato, Italy; (R.C.); (F.P.)
| | - Jasjit S. Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA;
| | | | - Mariano Scaglione
- Department of Radiology, University Hospital of Sassari, 07100 Sassari, Italy; (M.S.); (S.M.)
| | - Salvatore Masala
- Department of Radiology, University Hospital of Sassari, 07100 Sassari, Italy; (M.S.); (S.M.)
| | - Luca Saba
- Department of Radiology, University Hospital of Cagliari, 09042 Monserrato, Italy; (R.C.); (F.P.)
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22
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Tore D, Faletti R, Gaetani C, Bozzo E, Biondo A, Carisio A, Menchini F, Miccolis M, Papa FP, Trovato M, Fonio P, Gatti M. Cardiac magnetic resonance of hypertrophic heart phenotype: A review. Heliyon 2023; 9:e17336. [PMID: 37441401 PMCID: PMC10333467 DOI: 10.1016/j.heliyon.2023.e17336] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Revised: 06/05/2023] [Accepted: 06/14/2023] [Indexed: 07/15/2023] Open
Abstract
Hypertrophic heart phenotype is characterized by an abnormal left ventricular (LV) thickening. A hypertrophic phenotype can develop as adaptive response in many different conditions such as aortic stenosis, hypertension, athletic training, infiltrative heart muscle diseases, storage disorders and metabolic disorders. Hypertrophic cardiomyopathy (HCM) is the most frequent primary cardiomyopathy (CMP) and a genetical cause of cardiac hypertrophy. It requires the exclusion of any other cause of LV hypertrophy. Cardiac magnetic resonance (CMR) is a comprehensive imaging technique that allows a detailed evaluation of myocardial diseases. It provides reproducible measurements and myocardial tissue characterization. In clinical practice CMR is increasingly used to confirm the presence of ventricular hypertrophy, to detect the underlying cause of the phenotype and more recently as an efficient prognostic tool. This article aims to provide a detailed overview of the applications of CMR in the setting of hypertrophic heart phenotype and its role in the diagnostic workflow of such condition.
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Affiliation(s)
- Davide Tore
- Radiology Unit, Department of Surgical Sciences, AOU Città della Salute e della Scienza di Torino, University of Turin, Turin, Italy
| | - Riccardo Faletti
- Radiology Unit, Department of Surgical Sciences, AOU Città della Salute e della Scienza di Torino, University of Turin, Turin, Italy
| | - Clara Gaetani
- Radiology Unit, Department of Surgical Sciences, AOU Città della Salute e della Scienza di Torino, University of Turin, Turin, Italy
| | - Elena Bozzo
- Radiology Unit, Department of Surgical Sciences, AOU Città della Salute e della Scienza di Torino, University of Turin, Turin, Italy
| | - Andrea Biondo
- Radiology Unit, Department of Surgical Sciences, AOU Città della Salute e della Scienza di Torino, University of Turin, Turin, Italy
| | - Andrea Carisio
- Radiology Unit, Department of Surgical Sciences, AOU Città della Salute e della Scienza di Torino, University of Turin, Turin, Italy
| | - Francesca Menchini
- Radiology Unit, Department of Surgical Sciences, AOU Città della Salute e della Scienza di Torino, University of Turin, Turin, Italy
| | - Maria Miccolis
- Radiology Unit, Department of Surgical Sciences, AOU Città della Salute e della Scienza di Torino, University of Turin, Turin, Italy
| | - Francesco Pio Papa
- Radiology Unit, Department of Surgical Sciences, AOU Città della Salute e della Scienza di Torino, University of Turin, Turin, Italy
| | - Martina Trovato
- Radiology Unit, Department of Surgical Sciences, AOU Città della Salute e della Scienza di Torino, University of Turin, Turin, Italy
| | - Paolo Fonio
- Radiology Unit, Department of Surgical Sciences, AOU Città della Salute e della Scienza di Torino, University of Turin, Turin, Italy
| | - Marco Gatti
- Radiology Unit, Department of Surgical Sciences, AOU Città della Salute e della Scienza di Torino, University of Turin, Turin, Italy
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Cau R, Pisu F, Porcu M, Cademartiri F, Montisci R, Bassareo P, Muscogiuri G, Amadu A, Sironi S, Esposito A, Suri JS, Saba L. Machine learning approach in diagnosing Takotsubo cardiomyopathy: The role of the combined evaluation of atrial and ventricular strain, and parametric mapping. Int J Cardiol 2023; 373:124-133. [PMID: 36410545 DOI: 10.1016/j.ijcard.2022.11.021] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 10/23/2022] [Accepted: 11/15/2022] [Indexed: 11/19/2022]
Abstract
BACKGROUND Cardiac magnetic resonance (CMR) with late gadolinium enhancement (LGE) is a key diagnostic tool in the differential diagnosis between non-ischemic cause of cardiac chest pain. Some patients are not eligible for a gadolinium contrast-enhanced CMR; in this scenario, the diagnosis remains challenging without invasive examination. Our purpose was to derive a machine learning model integrating some non-contrast CMR parameters and demographic factors to identify Takotsubo cardiomyopathy (TTC) in subjects with cardiac chest pain. MATERIAL AND METHODS Three groups of patients were retrospectively studied: TTC, acute myocarditis, and healthy controls. Global and regional left ventricular longitudinal, circumferential, and radial strain (RS) analysis included were assessed. Reservoir, conduit, and booster bi-atrial functions were evaluated by tissue-tracking. Parametric mapping values were also assessed in all the patients. Five different tree-based ensemble learning algorithms were tested concerning their ability in recognizing TTC in a fully cross-validated framework. RESULTS The CMR-based machine learning (ML) ensemble model, by using the Extremely Randomized Trees algorithm with Elastic Net feature selection, showed a sensitivity of 92% (95% CI 78-100), specificity of 86% (95% CI 80-92) and area under the ROC of 0.94 (95% CI 0.90-0.99) in diagnosing TTC. Among non-contrast CMR parameters, the Shapley additive explanations analysis revealed that left atrial (LA) strain and strain rate were the top imaging markers in identifying TTC patients. CONCLUSIONS Our study demonstrated that using a tree-based ensemble learning algorithm on non-contrast CMR parameters and demographic factors enables the identification of subjects with TTC with good diagnostic accuracy. TRANSLATIONAL OUTLOOK Our results suggest that non-contrast CMR features can be implemented in a ML model to accurately identify TTC subjects. This model could be a valuable tool for aiding in the diagnosis of subjects with a contraindication to the contrast media. Furthermore, the left atrial conduit strain and strain rate were imaging markers that had a strong impact on TTC identification. Further prospective and longitudinal studies are needed to validate these findings and assess predictive performance in different cohorts, such as those with different ethnicities, and social backgrounds and undergoing different treatments.
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Affiliation(s)
- Riccardo Cau
- Department of Radiology, Azienda Ospedaliero Universitaria, Monserrato (Cagliari), Italy
| | - Francesco Pisu
- Department of Radiology, Azienda Ospedaliero Universitaria, Monserrato (Cagliari), Italy
| | - Michele Porcu
- Department of Radiology, Azienda Ospedaliero Universitaria, Monserrato (Cagliari), Italy
| | | | - Roberta Montisci
- Department of Cardiology, Azienda Ospedaliero Universitaria, Monserrato (Cagliari), Italy
| | - Pierpaolo Bassareo
- University College of Dublin, Mater Misericordiae University Hospital and Our Lady's Children's Hospital, Crumlin, Dublin, Ireland
| | - Giuseppe Muscogiuri
- Department of Radiology, IRCCS Istituto Auxologico Italiano, San Luca Hospital, Milan, Italy; University Milano Bicocca, Italy
| | | | - Sandro Sironi
- Department of Radiology, University of Milan-Bicocca, Milan, Italy
| | | | - Jasjit S Suri
- Stroke Monitoring and Diagnosis Division, AtheroPoint(tm) Roseville, CA, USA
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria, Monserrato (Cagliari), Italy.
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