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Danieli MG, Brunetto S, Gammeri L, Palmeri D, Claudi I, Shoenfeld Y, Gangemi S. Machine learning application in autoimmune diseases: State of art and future prospectives. Autoimmun Rev 2024; 23:103496. [PMID: 38081493 DOI: 10.1016/j.autrev.2023.103496] [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: 11/12/2023] [Accepted: 11/29/2023] [Indexed: 04/30/2024]
Abstract
Autoimmune diseases are a group of disorders resulting from an alteration of immune tolerance, characterized by the formation of autoantibodies and the consequent development of heterogeneous clinical manifestations. Diagnosing autoimmune diseases is often complicated, and the available prognostic tools are limited. Machine learning allows us to analyze large amounts of data and carry out complex calculations quickly and with minimal effort. In this work, we examine the literature focusing on the use of machine learning in the field of the main systemic (systemic lupus erythematosus and rheumatoid arthritis) and organ-specific autoimmune diseases (type 1 diabetes mellitus, autoimmune thyroid, gastrointestinal, and skin diseases). From our analysis, interesting applications of machine learning emerged for developing algorithms useful in the early diagnosis of disease or prognostic models (risk of complications, therapeutic response). Subsequent studies and the creation of increasingly rich databases to be supplied to the algorithms will eventually guide the clinician in the diagnosis, allowing intervention when the pathology is still in an early stage and immediately directing towards a correct therapeutic approach.
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Affiliation(s)
- Maria Giovanna Danieli
- SOS Immunologia delle Malattie Rare e dei Trapianti. AOU delle Marche & Dipartimento di Scienze Cliniche e Molecolari, Università Politecnica delle Marche, via Tronto 10/A, 60126 Torrette di Ancona, Italy; Postgraduate School of Allergy and Clinical Immunology, Università Politecnica delle Marche, via Tronto 10/A, 60126 Ancona, Italy.
| | - Silvia Brunetto
- Operative Unit of Allergy and Clinical Immunology, Department of Clinical and Experimental Medicine, University of Messina, Via Consolare Valeria 1, 98125 Messina, Italy
| | - Luca Gammeri
- Operative Unit of Allergy and Clinical Immunology, Department of Clinical and Experimental Medicine, University of Messina, Via Consolare Valeria 1, 98125 Messina, Italy
| | - Davide Palmeri
- Postgraduate School of Allergy and Clinical Immunology, Università Politecnica delle Marche, via Tronto 10/A, 60126 Ancona, Italy
| | - Ilaria Claudi
- Postgraduate School of Allergy and Clinical Immunology, Università Politecnica delle Marche, via Tronto 10/A, 60126 Ancona, Italy
| | - Yehuda Shoenfeld
- Zabludowicz Center for Autoimmune Diseases, Sheba Medical Center, and Reichman University Herzliya, Israel.
| | - Sebastiano Gangemi
- Operative Unit of Allergy and Clinical Immunology, Department of Clinical and Experimental Medicine, University of Messina, Via Consolare Valeria 1, 98125 Messina, Italy.
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2
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Al-Maini M, Maindarkar M, Kitas GD, Khanna NN, Misra DP, Johri AM, Mantella L, Agarwal V, Sharma A, Singh IM, Tsoulfas G, Laird JR, Faa G, Teji J, Turk M, Viskovic K, Ruzsa Z, Mavrogeni S, Rathore V, Miner M, Kalra MK, Isenovic ER, Saba L, Fouda MM, Suri JS. Artificial intelligence-based preventive, personalized and precision medicine for cardiovascular disease/stroke risk assessment in rheumatoid arthritis patients: a narrative review. Rheumatol Int 2023; 43:1965-1982. [PMID: 37648884 DOI: 10.1007/s00296-023-05415-1] [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: 07/10/2023] [Accepted: 07/31/2023] [Indexed: 09/01/2023]
Abstract
The challenges associated with diagnosing and treating cardiovascular disease (CVD)/Stroke in Rheumatoid arthritis (RA) arise from the delayed onset of symptoms. Existing clinical risk scores are inadequate in predicting cardiac events, and conventional risk factors alone do not accurately classify many individuals at risk. Several CVD biomarkers consider the multiple pathways involved in the development of atherosclerosis, which is the primary cause of CVD/Stroke in RA. To enhance the accuracy of CVD/Stroke risk assessment in the RA framework, a proposed approach involves combining genomic-based biomarkers (GBBM) derived from plasma and/or serum samples with innovative non-invasive radiomic-based biomarkers (RBBM), such as measurements of synovial fluid, plaque area, and plaque burden. This review presents two hypotheses: (i) RBBM and GBBM biomarkers exhibit a significant correlation and can precisely detect the severity of CVD/Stroke in RA patients. (ii) Artificial Intelligence (AI)-based preventive, precision, and personalized (aiP3) CVD/Stroke risk AtheroEdge™ model (AtheroPoint™, CA, USA) that utilizes deep learning (DL) to accurately classify the risk of CVD/stroke in RA framework. The authors conducted a comprehensive search using the PRISMA technique, identifying 153 studies that assessed the features/biomarkers of RBBM and GBBM for CVD/Stroke. The study demonstrates how DL models can be integrated into the AtheroEdge™-aiP3 framework to determine the risk of CVD/Stroke in RA patients. The findings of this review suggest that the combination of RBBM with GBBM introduces a new dimension to the assessment of CVD/Stroke risk in the RA framework. Synovial fluid levels that are higher than normal lead to an increase in the plaque burden. Additionally, the review provides recommendations for novel, unbiased, and pruned DL algorithms that can predict CVD/Stroke risk within a RA framework that is preventive, precise, and personalized.
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Affiliation(s)
- Mustafa Al-Maini
- Allergy, Clinical Immunology and Rheumatology Institute, Toronto, ON, L4Z 4C4, Canada
| | - Mahesh Maindarkar
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, 95661, USA
- Asia Pacific Vascular Society, New Delhi, 110001, India
| | - George D Kitas
- Academic Affairs, Dudley Group NHS Foundation Trust, Dudley, DY1 2HQ, UK
- Arthritis Research UK Epidemiology Unit, Manchester University, Manchester, M13 9PL, UK
| | - Narendra N Khanna
- Asia Pacific Vascular Society, New Delhi, 110001, India
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, 110001, India
| | | | - Amer M Johri
- Division of Cardiology, Department of Medicine, Queen's University, Kingston, Canada
| | - Laura Mantella
- Division of Cardiology, Department of Medicine, University of Toronto, Toronto, Canada
| | - Vikas Agarwal
- Department of Immunology, SGPIMS, Lucknow, 226014, India
| | - Aman Sharma
- Department of Immunology, SGPIMS, Lucknow, 226014, India
| | - Inder M Singh
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, 95661, USA
| | - George Tsoulfas
- Department of Surgery, Aristoteleion University of Thessaloniki, 54124, Thessaloniki, Greece
| | - John R Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA, 94574, USA
| | - Gavino Faa
- Department of Pathology, Azienda Ospedaliero Universitaria, 09124, Cagliari, Italy
| | - Jagjit Teji
- Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, 60611, USA
| | - Monika Turk
- The Hanse-Wissenschaftskolleg Institute for Advanced Study, 27753, Delmenhorst, Germany
| | - Klaudija Viskovic
- Department of Radiology and Ultrasound, UHID, 10 000, Zagreb, Croatia
| | - Zoltan Ruzsa
- Invasive Cardiology Division, University of Szeged, Szeged, Hungary
| | - Sophie Mavrogeni
- Cardiology Clinic, Onassis Cardiac Surgery Centre, Athens, Greece
| | - Vijay Rathore
- Nephrology Department, Kaiser Permanente, Sacramento, CA, 95823, USA
| | - Martin Miner
- Men's Health Centre, Miriam Hospital Providence, Providence, RI, 02906, USA
| | - Manudeep K Kalra
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Esma R Isenovic
- Department of Radiobiology and Molecular Genetics, National Institute of the Republic of Serbia, University of Belgrade, 11000, Belgrade, Serbia
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria, 40138, Cagliari, Italy
| | - Mostafa M Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID, 83209, USA
| | - Jasjit S Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, 95661, USA.
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Ayoub M, Liao Z, Li L, Wong KKL. HViT: Hybrid vision inspired transformer for the assessment of carotid artery plaque by addressing the cross-modality domain adaptation problem in MRI. Comput Med Imaging Graph 2023; 109:102295. [PMID: 37717365 DOI: 10.1016/j.compmedimag.2023.102295] [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: 04/12/2023] [Revised: 08/24/2023] [Accepted: 08/24/2023] [Indexed: 09/19/2023]
Abstract
BACKGROUND Medical image classification is crucial for accurate and efficient diagnosis, and deep learning frameworks have shown significant potential in this area. When a general learning deep model is directly deployed to a new dataset with heterogeneous features, the effect of domain shifts is usually ignored, which degrades the performance of deep learning models and leads to inaccurate predictions. PURPOSE This study aims to propose a framework that utilized the cross-modality domain adaptation and accurately diagnose and classify MRI scans and domain knowledge into stable and vulnerable plaque categories by a modified Vision Transformer (ViT) model for the classification of MRI scans and transformer model for domain knowledge classification. METHODS This study proposes a Hybrid Vision Inspired Transformer (HViT) framework that employs a convolutional layer for image pre-processing and normalization and a 3D convolutional layer to enable ViT to classify 3D images. Our proposed HViT framework introduces a slim design with a multi-branch network and channel attention, improving patch embedding extraction and information learning. Auxiliary losses target shallow features, linking them with deeper ones, enhancing information gain, and model generalization. Furthermore, replacing the MLP Head with RNN enables better backpropagation for improved performance. Moreover, we utilized a modified transformer model with LSTM positional encoding and Golve word vector to classify domain knowledge. By using ensemble learning techniques, specifically stacking ensemble learning with hard and soft prediction, we combine the predictive power of both models to address the cross-modality domain adaptation problem and improve overall performance. RESULTS The proposed framework achieved an accuracy of 94.32% for carotid artery plaque classification into stable and vulnerable plaque by addressing the cross-modality domain adaptation problem and improving overall performance. CONCLUSION The model was further evaluated using an independent dataset acquired from different hardware protocols. The results demonstrate that the proposed deep learning model significantly improves the generalization ability across different MRI scans acquired from different hardware protocols without requiring additional calibration data.
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Affiliation(s)
- Muhammad Ayoub
- School of Computer Science and Engineering, Central South University, Changsha 410017, Hunan, China
| | - Zhifang Liao
- School of Computer Science and Engineering, Central South University, Changsha 410017, Hunan, China.
| | - Lifeng Li
- Department of Radiology, The Affiliated Changsha Central Hospital, Hengyang Medical school, University of South China, Changsha 410017, China
| | - Kelvin K L Wong
- Department of Mechanical Engineering, College of Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada.
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Borra SR, Panjiyar BK, Panicker SS, Danduboyina A. Role of Cardiac Biomarkers in the Evaluation of Rheumatoid Arthritis: A Systematic Review. Cureus 2023; 15:e47416. [PMID: 38021518 PMCID: PMC10658213 DOI: 10.7759/cureus.47416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/21/2023] [Indexed: 12/01/2023] Open
Abstract
Rheumatoid arthritis (RA) is a chronic inflammatory disease that can cause permanent joint damage and premature death. Cardiovascular disease (CVD) has recently been known to have become a significant cause of death in rheumatoid arthritis patients, and cardiovascular (CV) deaths have risen by 20-50% in rheumatoid arthritis patients. Early detection methods are necessary to improve the outcome for such patients. Cardiac biomarkers have been proven to be an effective tool for evaluating the heart's activity. In this study, we have used a systematic literature review approach in order to establish an overview of the current literature, highlight the advantages of using cardiac biomarkers in early detection and diagnosis, and improve the prognosis of patients with rheumatoid arthritis. We reviewed 269 articles from January 1, 2012, to August 6, 2023, from reputed journals, out of which we focused on seven papers for in-depth analysis. This analysis considered certain factors, including the age factor, sex factor, clinical risk score, and comparison of the benefits of using this method amongst clinicians for diagnosis purposes. The systematic review has revealed that cardiac biomarkers have a good ability to act as predictors of subsequent cardiovascular events. Cardiac biomarkers include high-sensitivity troponin T (hsTropT) and B-type natriuretic peptide (BNP). We learned that the cardiac biomarkers indicate inflammation, extracellular matrix remodeling, congestion, and myocardial injury, which are linked with elementary changes in cardiac structure and function. Biomarkers could be used for the purpose of screening cardiac variations in patients with rheumatoid arthritis. However, this method tends to have its own challenges to implement, considering other factors such as age and NSAID use. Nonetheless, further research and intervention about the use of cardiac biomarkers are important in order to earn the potential to make this method available to be used worldwide to improve outcomes for patients with rheumatoid arthritis.
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Affiliation(s)
- Saatvika R Borra
- Internal Medicine, Jawaharlal Nehru Medical College, Belagavi, IND
| | - Binay K Panjiyar
- Global Clinical Scholars Research Training (GCSRT) Post Graduate Medical Education (PGMEE), Harvard Medical School, Boston, USA
- Internal Medicine, California Institute of Behavioral Neurosciences and Psychology, Fairfield, USA
| | - Sourav S Panicker
- Internal Medicine, Dr. D. Y. Patil Medical College, Hospital and Research Centre, Pune, IND
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Madrid-García A, Merino-Barbancho B, Rodríguez-González A, Fernández-Gutiérrez B, Rodríguez-Rodríguez L, Menasalvas-Ruiz E. Understanding the role and adoption of artificial intelligence techniques in rheumatology research: An in-depth review of the literature. Semin Arthritis Rheum 2023; 61:152213. [PMID: 37315379 DOI: 10.1016/j.semarthrit.2023.152213] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 04/28/2023] [Accepted: 05/02/2023] [Indexed: 06/16/2023]
Abstract
The major and upward trend in the number of published research related to rheumatic and musculoskeletal diseases, in which artificial intelligence plays a key role, has exhibited the interest of rheumatology researchers in using these techniques to answer their research questions. In this review, we analyse the original research articles that combine both worlds in a five- year period (2017-2021). In contrast to other published papers on the same topic, we first studied the review and recommendation articles that were published during that period, including up to October 2022, as well as the publication trends. Secondly, we review the published research articles and classify them into one of the following categories: disease identification and prediction, disease classification, patient stratification and disease subtype identification, disease progression and activity, treatment response, and predictors of outcomes. Thirdly, we provide a table with illustrative studies in which artificial intelligence techniques have played a central role in more than twenty rheumatic and musculoskeletal diseases. Finally, the findings of the research articles, in terms of disease and/or data science techniques employed, are highlighted in a discussion. Therefore, the present review aims to characterise how researchers are applying data science techniques in the rheumatology medical field. The most immediate conclusions that can be drawn from this work are: multiple and novel data science techniques have been used in a wide range of rheumatic and musculoskeletal diseases including rare diseases; the sample size and the data type used are heterogeneous, and new technical approaches are expected to arrive in the short-middle term.
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Affiliation(s)
- Alfredo Madrid-García
- Grupo de Patología Musculoesquelética. Hospital Clínico San Carlos, Prof. Martin Lagos s/n, Madrid, 28040, Spain; Escuela Técnica Superior de Ingenieros de Telecomunicación. Universidad Politécnica de Madrid, Avenida Complutense, 30, Madrid, 28040, Spain.
| | - Beatriz Merino-Barbancho
- Escuela Técnica Superior de Ingenieros de Telecomunicación. Universidad Politécnica de Madrid, Avenida Complutense, 30, Madrid, 28040, Spain
| | | | - Benjamín Fernández-Gutiérrez
- Grupo de Patología Musculoesquelética. Hospital Clínico San Carlos, Prof. Martin Lagos s/n, Madrid, 28040, Spain
| | - Luis Rodríguez-Rodríguez
- Grupo de Patología Musculoesquelética. Hospital Clínico San Carlos, Prof. Martin Lagos s/n, Madrid, 28040, Spain
| | - Ernestina Menasalvas-Ruiz
- Centro de Tecnología Biomédica. Universidad Politécnica de Madrid, Pozuelo de Alarcón, Madrid, 28223, Spain
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6
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Tchuente Foguem G, Teguede Keleko A. Artificial intelligence applied in pulmonary hypertension: a bibliometric analysis. AI AND ETHICS 2023:1-31. [PMID: 37360147 PMCID: PMC9989999 DOI: 10.1007/s43681-023-00267-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 02/07/2023] [Indexed: 03/09/2023]
Abstract
Introduction Advances in Artificial Intelligence (AI) offer new Information Technology (IT) opportunities in various applications and fields (industry, health, etc.). The medical informatics scientific community expends tremendous effort on the management of diseases affecting vital organs making it a complex disease (lungs, heart, brain, kidneys, pancreas, and liver). Scientific research becomes more complex when several organs are simultaneously affected, as is the case with Pulmonary Hypertension (PH), which affects both the lungs and the heart. Therefore, early detection and diagnosis of PH are essential to monitor the disease's progression and prevent associated mortality. Method The issue addressed relates to knowledge of recent developments in AI approaches applied to PH. The aim is to provide a systematic review through a quantitative analysis of the scientific production concerning PH and the analysis of the networks of this production. This bibliometric approach is based on various statistical, data mining, and data visualization methods to assess research performance using scientific publications and various indicators (e.g., direct indicators of scientific production and scientific impact). Results The main sources used to obtain citation data are the Web of Science Core Collection and Google Scholar. The results indicate a diversity of journals (e.g., IEEE Access, Computers in Biology and Medicine, Biology Signal Processing and Control, Frontiers in Cardiovascular Medicine, Sensors) at the top of publications. The most relevant affiliations are universities from United States of America (Boston Univ, Harvard Med Sch, Univ Oxford, Stanford Univ) and United Kingdom (Imperial Coll London). The most cited keywords are "Classification", "Diagnosis", "Disease", "Prediction", and "Risk". Conclusion This bibliometric study is a crucial part of the review of the scientific literature on PH. It can be viewed as a guideline or tool that helps researchers and practitioners to understand the main scientific issues and challenges of AI modeling applied to PH. On the one hand, it makes it possible to increase the visibility of the progress made or the limits observed. Consequently, it promotes their wide dissemination. Furthermore, it offers valuable assistance in understanding the evolution of scientific AI activities applied to managing the diagnosis, treatment, and prognosis of PH. Finally, ethical considerations are described in each activity of data collection, treatment, and exploitation to preserve patients' legitimate rights.
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Affiliation(s)
| | - Aurelien Teguede Keleko
- Ecole Nationale d’Ingénieurs de Tarbes (ENIT), 47 Avenue Azereix, BP 1629, 65016 Tarbes, France
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Misra DP, Hauge EM, Crowson CS, Kitas GD, Ormseth SR, Karpouzas GA. Atherosclerotic Cardiovascular Risk Stratification in the Rheumatic Diseases:: An Integrative, Multiparametric Approach. Rheum Dis Clin North Am 2023; 49:19-43. [PMID: 36424025 DOI: 10.1016/j.rdc.2022.07.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Cardiovascular disease (CVD) risk is increased in most inflammatory rheumatic diseases (IRDs), reiterating the role of inflammation in the initiation and progression of atherosclerosis. An inverse association of CVD risk with body weight and lipid levels has been described in IRDs. Coronary artery calcium scores, plaque burden and characteristics, and carotid plaques on ultrasound optimize CVD risk estimate in IRDs. Biomarkers of cardiac injury, autoantibodies, lipid biomarkers, and cytokines also improve risk assessment in IRDs. Machine learning and deep learning algorithms for phenotype and image analysis hold promise to improve CVD risk stratification in IRDs.
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Affiliation(s)
- Durga Prasanna Misra
- Department of Clinical Immunology and Rheumatology, Sanjay Gandhi Postgraduate Institute of Medical Sciences (SGPGIMS), Rae Bareli Road, Lucknow 226014, India
| | - Ellen M Hauge
- Division of Rheumatology, Aarhus University Hospital, Palle Juul-Jensens Boulevard 99 DK-8200, Aarhus, Denmark
| | - Cynthia S Crowson
- Department of Quantitative Health Sciences and Division of Rheumatology, Mayo Clinic, 200 first St SW, Rochester, MN 55905, USA
| | | | - Sarah R Ormseth
- The Lundquist Institute and Harbor-UCLA Medical Center, 1124 West Carson Street, Building E4-R17, Torrance, CA 90502, USA
| | - George A Karpouzas
- The Lundquist Institute and Harbor-UCLA Medical Center, 1124 West Carson Street, Building E4-R17, Torrance, CA 90502, USA.
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Lv P, Yang J, Wang J, Guo Y, Tang Q, Magnier B, Lin J, Zhou J. Ischemic stroke prediction of patients with carotid atherosclerotic stenosis via multi-modality fused network. Front Neurosci 2023; 17:1118376. [PMID: 36908778 PMCID: PMC9998529 DOI: 10.3389/fnins.2023.1118376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 02/06/2023] [Indexed: 03/14/2023] Open
Abstract
Carotid atherosclerotic stenosis of the carotid artery is an important cause of ischemic cerebrovascular disease. The aim of this study was to predict the presence or absence of clinical symptoms in unknown patients by studying the existence or lack of symptoms of patients with carotid atherosclerotic stenosis. First, a deep neural network prediction model based on brain MRI imaging data of patients with multiple modalities is constructed; it uses the multi-modality features extracted from the neural network as inputs and the incidence of diagnosis as output to train the model. Then, a machine learning-based classification algorithm is developed to utilize the clinical features for comparison and evaluation. The experimental results showed that the deep learning model using imaging data could better predict the clinical symptom classification of patients. As part of preventive medicine, this study could help patients with carotid atherosclerosis narrowing to prepare for stroke prevention based on the prediction results.
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Affiliation(s)
- Peng Lv
- Department of Radiology, Zhongshan Hospital, Fudan University and Shanghai Institute of Medical Imaging, Shanghai, China
| | - Jing Yang
- School of Medicine, Xiamen University, Xiamen, China
| | - Jiacheng Wang
- Department of Computer Science at School of Informatics, Xiamen University, Xiamen, China
| | - Yi Guo
- Department of Radiology, Zhongshan Hospital Xiamen, Fudan University, Xiamen, China.,Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China
| | - Qiying Tang
- Department of Radiology, Zhongshan Hospital Xiamen, Fudan University, Xiamen, China.,Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China
| | - Baptiste Magnier
- Euromov Digital Health in Motion, Univ Montpellier, IMT Mines Ales, Ales, France
| | - Jiang Lin
- Department of Radiology, Zhongshan Hospital, Fudan University and Shanghai Institute of Medical Imaging, Shanghai, China
| | - Jianjun Zhou
- Department of Radiology, Zhongshan Hospital Xiamen, Fudan University, Xiamen, China.,Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China
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Johri AM, Singh KV, Mantella LE, Saba L, Sharma A, Laird JR, Utkarsh K, Singh IM, Gupta S, Kalra MS, Suri JS. Deep learning artificial intelligence framework for multiclass coronary artery disease prediction using combination of conventional risk factors, carotid ultrasound, and intraplaque neovascularization. Comput Biol Med 2022; 150:106018. [PMID: 36174330 DOI: 10.1016/j.compbiomed.2022.106018] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 08/06/2022] [Accepted: 08/20/2022] [Indexed: 11/23/2022]
Abstract
OBJECTIVE Cardiovascular disease (CVD) is a major healthcare challenge and therefore early risk assessment is vital. Previous assessment techniques use either "conventional CVD risk calculators (CCVRC)" or machine learning (ML) paradigms. These techniques are ad-hoc, unreliable, not fully automated, and have variabilities. We, therefore, introduce AtheroEdge-MCDLAI (AE3.0DL) windows-based platform using multiclass Deep Learning (DL) system. METHODS Data was collected on 500 patients having both carotid ultrasound and corresponding coronary angiography scores (CAS), measured as stenosis in coronary arteries and considered as the gold standard. A total of 39 covariates were used, clubbed into three clusters, namely (i) Office-based: age, gender, body mass index, smoker, hypertension, systolic blood pressure, and diastolic blood pressure; (ii) Laboratory-based: Hyperlipidemia, hemoglobin A1c, and estimated glomerular filtration rate; and (iii) Carotid ultrasound image phenotypes: maximum plaque height, total plaque area, and intra-plaque neovascularization. Baseline characteristics for four classes (target labels) having significant (p < 0.0001) values were calculated using Chi-square and ANOVA. For handling the cohort's imbalance in the risk classes, AE3.0DL used the synthetic minority over-sampling technique (SMOTE). AE3.0DL used Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) DL models and the performance (accuracy and area-under-the-curve) was computed using 10-fold cross-validation (90% training, 10% testing) frameworks. AE3.0DL was validated and benchmarked. RESULTS The AE3.0DL using RNN and LSTM showed an accuracy and AUC (p < 0.0001) pairs as (95.00% and 0.98), and (95.34% and 0.99), respectively, and showed an improvement of 32.93% and 9.94% against CCVRC and ML, respectively. AE3.0DL runs in <1 s. CONCLUSION DL algorithms are a powerful paradigm for coronary artery disease (CAD) risk prediction and CVD risk stratification.
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Affiliation(s)
- Amer M Johri
- Department of Medicine, Division of Cardiology, Queen's University, Kingston, ON, Canada
| | | | - Laura E Mantella
- Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Cagliari, Italy
| | - Aditya Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA, USA
| | - John R Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA, USA
| | | | | | - Suneet Gupta
- Department of Computer Science, Bennett University, Gr. Noida, India
| | - Manudeep S Kalra
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA
| | - Jasjit S Suri
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA, USA; Knowledge Engineering Center, Global Biomedical Technologies, Inc., Roseville, CA, USA.
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10
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Momtazmanesh S, Nowroozi A, Rezaei N. Artificial Intelligence in Rheumatoid Arthritis: Current Status and Future Perspectives: A State-of-the-Art Review. Rheumatol Ther 2022; 9:1249-1304. [PMID: 35849321 PMCID: PMC9510088 DOI: 10.1007/s40744-022-00475-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 06/24/2022] [Indexed: 11/23/2022] Open
Abstract
Investigation of the potential applications of artificial intelligence (AI), including machine learning (ML) and deep learning (DL) techniques, is an exponentially growing field in medicine and healthcare. These methods can be critical in providing high-quality care to patients with chronic rheumatological diseases lacking an optimal treatment, like rheumatoid arthritis (RA), which is the second most prevalent autoimmune disease. Herein, following reviewing the basic concepts of AI, we summarize the advances in its applications in RA clinical practice and research. We provide directions for future investigations in this field after reviewing the current knowledge gaps and technical and ethical challenges in applying AI. Automated models have been largely used to improve RA diagnosis since the early 2000s, and they have used a wide variety of techniques, e.g., support vector machine, random forest, and artificial neural networks. AI algorithms can facilitate screening and identification of susceptible groups, diagnosis using omics, imaging, clinical, and sensor data, patient detection within electronic health record (EHR), i.e., phenotyping, treatment response assessment, monitoring disease course, determining prognosis, novel drug discovery, and enhancing basic science research. They can also aid in risk assessment for incidence of comorbidities, e.g., cardiovascular diseases, in patients with RA. However, the proposed models may vary significantly in their performance and reliability. Despite the promising results achieved by AI models in enhancing early diagnosis and management of patients with RA, they are not fully ready to be incorporated into clinical practice. Future investigations are required to ensure development of reliable and generalizable algorithms while they carefully look for any potential source of bias or misconduct. We showed that a growing body of evidence supports the potential role of AI in revolutionizing screening, diagnosis, and management of patients with RA. However, multiple obstacles hinder clinical applications of AI models. Incorporating the machine and/or deep learning algorithms into real-world settings would be a key step in the progress of AI in medicine.
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Affiliation(s)
- Sara Momtazmanesh
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
- Network of Immunity in Infection, Malignancy and Autoimmunity (NIIMA), Universal Scientific Education and Research Network (USERN), Tehran, Iran
- Research Center for Immunodeficiencies, Pediatrics Center of Excellence, Children's Medical Center, Tehran University of Medical Sciences, Dr. Gharib St, Keshavarz Blvd, Tehran, Iran
| | - Ali Nowroozi
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
- Network of Immunity in Infection, Malignancy and Autoimmunity (NIIMA), Universal Scientific Education and Research Network (USERN), Tehran, Iran
| | - Nima Rezaei
- Network of Immunity in Infection, Malignancy and Autoimmunity (NIIMA), Universal Scientific Education and Research Network (USERN), Tehran, Iran.
- Research Center for Immunodeficiencies, Pediatrics Center of Excellence, Children's Medical Center, Tehran University of Medical Sciences, Dr. Gharib St, Keshavarz Blvd, Tehran, Iran.
- Department of Immunology, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.
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11
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Jain PK, Dubey A, Saba L, Khanna NN, Laird JR, Nicolaides A, Fouda MM, Suri JS, Sharma N. Attention-Based UNet Deep Learning Model for Plaque Segmentation in Carotid Ultrasound for Stroke Risk Stratification: An Artificial Intelligence Paradigm. J Cardiovasc Dev Dis 2022; 9:326. [PMID: 36286278 PMCID: PMC9604424 DOI: 10.3390/jcdd9100326] [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: 08/21/2022] [Revised: 09/06/2022] [Accepted: 09/14/2022] [Indexed: 11/17/2022] Open
Abstract
Stroke and cardiovascular diseases (CVD) significantly affect the world population. The early detection of such events may prevent the burden of death and costly surgery. Conventional methods are neither automated nor clinically accurate. Artificial Intelligence-based methods of automatically detecting and predicting the severity of CVD and stroke in their early stages are of prime importance. This study proposes an attention-channel-based UNet deep learning (DL) model that identifies the carotid plaques in the internal carotid artery (ICA) and common carotid artery (CCA) images. Our experiments consist of 970 ICA images from the UK, 379 CCA images from diabetic Japanese patients, and 300 CCA images from post-menopausal women from Hong Kong. We combined both CCA images to form an integrated database of 679 images. A rotation transformation technique was applied to 679 CCA images, doubling the database for the experiments. The cross-validation K5 (80% training: 20% testing) protocol was applied for accuracy determination. The results of the Attention-UNet model are benchmarked against UNet, UNet++, and UNet3P models. Visual plaque segmentation showed improvement in the Attention-UNet results compared to the other three models. The correlation coefficient (CC) value for Attention-UNet is 0.96, compared to 0.93, 0.96, and 0.92 for UNet, UNet++, and UNet3P models. Similarly, the AUC value for Attention-UNet is 0.97, compared to 0.964, 0.966, and 0.965 for other models. Conclusively, the Attention-UNet model is beneficial in segmenting very bright and fuzzy plaque images that are hard to diagnose using other methods. Further, we present a multi-ethnic, multi-center, racial bias-free study of stroke risk assessment.
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Affiliation(s)
- Pankaj K. Jain
- School of Biomedical Engineering, Indian Institute of Technology (BHU), Varanasi 221005, India
| | - Abhishek Dubey
- School of Biomedical Engineering, Indian Institute of Technology (BHU), Varanasi 221005, India
- Department of Electronics and Communication, Shree Mata Vaishno Devi University, Jammu 182301, India
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09100 Cagliari, Italy
| | - Narender N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospital, New Delhi 110076, India
| | - John R. Laird
- Heart and Vascular Institute, Adventist Heath St. Helena, St. Helena, CA 94574, USA
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre and University of Nicosia Medical School, Nicosia 2409, Cyprus
| | - Mostafa M. Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USA
| | - Jasjit S. Suri
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA
| | - Neeraj Sharma
- Department of Electronics and Communication, Shree Mata Vaishno Devi University, Jammu 182301, India
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12
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Bai L, Zhang Y, Wang P, Zhu X, Xiong JW, Cui L. Improved diagnosis of rheumatoid arthritis using an artificial neural network. Sci Rep 2022; 12:9810. [PMID: 35697754 PMCID: PMC9192742 DOI: 10.1038/s41598-022-13750-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Accepted: 05/27/2022] [Indexed: 11/29/2022] Open
Abstract
Rheumatoid arthritis (RA) is chronic systemic disease that can cause joint damage, disability and destructive polyarthritis. Current diagnosis of RA is based on a combination of clinical and laboratory features. However, RA diagnosis can be difficult at its disease onset on account of overlapping symptoms with other arthritis, so early recognition and diagnosis of RA permit the better management of patients. In order to improve the medical diagnosis of RA and evaluate the effects of different clinical features on RA diagnosis, we applied an artificial neural network (ANN) as the training algorithm, and used fivefold cross-validation to evaluate its performance. From each sample, we obtained data on 6 features: age, sex, rheumatoid factor, anti-citrullinated peptide antibody (CCP), 14-3-3η, and anti-carbamylated protein (CarP) antibodies. After training, this ANN model assigned each sample a probability for being either an RA patient or a non-RA patient. On the validation dataset, the F1 for all samples by this ANN model was 0.916, which was higher than the 0.906 we previously reported using an optimal threshold algorithm. Therefore, this ANN algorithm not only improved the accuracy of RA diagnosis, but also revealed that anti-CCP had the greatest effect while age and anti-CarP had a weaker on RA diagnosis.
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Affiliation(s)
- Linlu Bai
- Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Institute of Molecular Medicine, College of Future Technology, Academy for Advanced Interdisciplinary Studies, and State Key Laboratory of Natural and Biomimetic Drugs, Peking University, No. 5 Yiheyuan Road, Haidian District, Beijing, 100871, China
| | - Yuan Zhang
- Department of Laboratory Medicine, Peking University Third Hospital, No. 49 North Garden Road, Haidian District, Beijing, 100191, China
| | - Pan Wang
- Department of Laboratory Medicine, Peking University Third Hospital, No. 49 North Garden Road, Haidian District, Beijing, 100191, China
| | - Xiaojun Zhu
- Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Institute of Molecular Medicine, College of Future Technology, Academy for Advanced Interdisciplinary Studies, and State Key Laboratory of Natural and Biomimetic Drugs, Peking University, No. 5 Yiheyuan Road, Haidian District, Beijing, 100871, China
| | - Jing-Wei Xiong
- Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Institute of Molecular Medicine, College of Future Technology, Academy for Advanced Interdisciplinary Studies, and State Key Laboratory of Natural and Biomimetic Drugs, Peking University, No. 5 Yiheyuan Road, Haidian District, Beijing, 100871, China.
| | - Liyan Cui
- Department of Laboratory Medicine, Peking University Third Hospital, No. 49 North Garden Road, Haidian District, Beijing, 100191, China.
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Role of Artificial Intelligence in Radiogenomics for Cancers in the Era of Precision Medicine. Cancers (Basel) 2022; 14:cancers14122860. [PMID: 35740526 PMCID: PMC9220825 DOI: 10.3390/cancers14122860] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Revised: 06/03/2022] [Accepted: 06/07/2022] [Indexed: 12/12/2022] Open
Abstract
Simple Summary Recently, radiogenomics has played a significant role and offered a new understanding of cancer’s biology and behavior in response to standard therapy. It also provides a more precise prognosis, investigation, and analysis of the patient’s cancer. Over the years, Artificial Intelligence (AI) has provided a significant strength in radiogenomics. In this paper, we offer computational and oncological prospects of the role of AI in radiogenomics, as well as its offers, achievements, opportunities, and limitations in the current clinical practices. Abstract Radiogenomics, a combination of “Radiomics” and “Genomics,” using Artificial Intelligence (AI) has recently emerged as the state-of-the-art science in precision medicine, especially in oncology care. Radiogenomics syndicates large-scale quantifiable data extracted from radiological medical images enveloped with personalized genomic phenotypes. It fabricates a prediction model through various AI methods to stratify the risk of patients, monitor therapeutic approaches, and assess clinical outcomes. It has recently shown tremendous achievements in prognosis, treatment planning, survival prediction, heterogeneity analysis, reoccurrence, and progression-free survival for human cancer study. Although AI has shown immense performance in oncology care in various clinical aspects, it has several challenges and limitations. The proposed review provides an overview of radiogenomics with the viewpoints on the role of AI in terms of its promises for computational as well as oncological aspects and offers achievements and opportunities in the era of precision medicine. The review also presents various recommendations to diminish these obstacles.
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Abstract
Machine learning (ML) methods are pervading an increasing number of fields of application because of their capacity to effectively solve a wide variety of challenging problems. The employment of ML techniques in ultrasound imaging applications started several years ago but the scientific interest in this issue has increased exponentially in the last few years. The present work reviews the most recent (2019 onwards) implementations of machine learning techniques for two of the most popular ultrasound imaging fields, medical diagnostics and non-destructive evaluation. The former, which covers the major part of the review, was analyzed by classifying studies according to the human organ investigated and the methodology (e.g., detection, segmentation, and/or classification) adopted, while for the latter, some solutions to the detection/classification of material defects or particular patterns are reported. Finally, the main merits of machine learning that emerged from the study analysis are summarized and discussed.
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15
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Rhodes CJ, Sweatt AJ, Maron BA. Harnessing Big Data to Advance Treatment and Understanding of Pulmonary Hypertension. Circ Res 2022; 130:1423-1444. [PMID: 35482840 PMCID: PMC9070103 DOI: 10.1161/circresaha.121.319969] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Pulmonary hypertension is a complex disease with multiple causes, corresponding to phenotypic heterogeneity and variable therapeutic responses. Advancing understanding of pulmonary hypertension pathogenesis is likely to hinge on integrated methods that leverage data from health records, imaging, novel molecular -omics profiling, and other modalities. In this review, we summarize key data sets generated thus far in the field and describe analytical methods that hold promise for deciphering the molecular mechanisms that underpin pulmonary vascular remodeling, including machine learning, network medicine, and functional genetics. We also detail how genetic and subphenotyping approaches enable earlier diagnosis, refined prognostication, and optimized treatment prediction. We propose strategies that identify functionally important molecular pathways, bolstered by findings across multi-omics platforms, which are well-positioned to individualize drug therapy selection and advance precision medicine in this highly morbid disease.
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Affiliation(s)
- Christopher J Rhodes
- Department of Medicine, National Heart and Lung Institute, Imperial College London, United Kingdom (C.J.R.)
| | - Andrew J Sweatt
- Department of Medicine, National Heart and Lung Institute, Imperial College London, United Kingdom (C.J.R.)
| | - Bradley A Maron
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA (B.A.M.).,Division of Cardiology, VA Boston Healthcare System, West Roxbury, MA (B.A.M.)
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16
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Suri JS, Bhagawati M, Paul S, Protogeron A, Sfikakis PP, Kitas GD, Khanna NN, Ruzsa Z, Sharma AM, Saxena S, Faa G, Paraskevas KI, Laird JR, Johri AM, Saba L, Kalra M. Understanding the bias in machine learning systems for cardiovascular disease risk assessment: The first of its kind review. Comput Biol Med 2022; 142:105204. [DOI: 10.1016/j.compbiomed.2021.105204] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Revised: 12/29/2021] [Accepted: 12/29/2021] [Indexed: 02/09/2023]
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17
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Wu M, Wu H, Wu L, Cui C, Shi S, Xu J, Liu Y, Dong F. A deep learning classification of metacarpophalangeal joints synovial proliferation in rheumatoid arthritis by ultrasound images. JOURNAL OF CLINICAL ULTRASOUND : JCU 2022; 50:296-301. [PMID: 35038176 DOI: 10.1002/jcu.23143] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 12/23/2021] [Accepted: 12/25/2021] [Indexed: 06/14/2023]
Abstract
OBJECTIVE To evaluate if an automatic classification of rheumatoid arthritis (RA) metacarpophalangeal joint conditions in ultrasound images is feasible by deep learning (DL) method, to provide a more objective, automated, and fast way of RA diagnosis in clinical setting. MATERIALS AND METHODS DenseNet-based DL model was used and both training and testing are implemented in TensorFlow 1.13.1 with Keras DL libraries. The area under curve (AUC), accuracy, sensitivity, and specificity values with 95% CIs were reported. The statistical analysis was performed by using scikit-learn libraries in Python 3.7. RESULTS A total of 1337 RA ultrasound images were acquired from 208 patients, the number of images is 313, 657, 178, and 189 in OESS Grade L0, L1, L2, and L3, respectively. In Classification Scenario 1 SP-no versus SP-yes, three experiments with region of interest of size 192 × 448 (Group 1), 96 × 224 (Group 2), and 96 × 224 stacked with pre-segmented annotated mask of SP area (Group 3) as input achieve an AUC of 0.863 (95% CI: 0.809, 0.917), 0.861 (95% CI: 0.805, 0.916), and 0.886 (95% CI: 0.836, 0.936), respectively. In Classification Scenario 2 Healthy versus Diseased, experiments in Group 1, Group 2 and Group 3 achieve an AUC of 0.848 (95% CI: 0.799, 0.896), 0.864 (95% CI: 0.819, 0.909), and 0.916 (95% CI: 0.883, 0.952), respectively. CONCLUSION We combined DenseNet model with ultrasound images for RA condition assessment. The feasibility of using DL to create an automatic RA condition classification system was also demonstrated. The proposed method can be an alternative to the initial screening of RA patients.
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Affiliation(s)
- Min Wu
- Physical Examination, Xintai People's Hospital, Tai'an, Shandong, China
| | - Huaiuy Wu
- Department of Ultrasound, The Second Clinical Medical College,Jinan University, Guangdong, China
- The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen People's Hospital, Shenzhen, Guangdong, China
| | - Lili Wu
- Department of Ultrasound, Xintai Maternity and Child Health Hospital, Tai'an, Shandong, China
| | - Chen Cui
- Department of Ultrasound, The Second Clinical Medical College,Jinan University, Guangdong, China
- The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen People's Hospital, Shenzhen, Guangdong, China
| | - Siyuan Shi
- Department of Ultrasound, The Second Clinical Medical College,Jinan University, Guangdong, China
- The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen People's Hospital, Shenzhen, Guangdong, China
| | - Jinfeng Xu
- Department of Ultrasound, The Second Clinical Medical College,Jinan University, Guangdong, China
- The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen People's Hospital, Shenzhen, Guangdong, China
| | - Yan Liu
- The Key Laboratory of Cardiovascular Remodeling and Function Research, Chinese Ministry of Education and Chinese Ministry of Health, and The State and Shandong Province Joint Key Laboratory of Translational Cardiovascular Medicine, Qilu Hospital of Shandong University, Jinan, Puerto Rico, China
| | - Fajin Dong
- Department of Ultrasound, The Second Clinical Medical College,Jinan University, Guangdong, China
- The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen People's Hospital, Shenzhen, Guangdong, China
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Konstantonis G, Singh KV, Sfikakis PP, Jamthikar AD, Kitas GD, Gupta SK, Saba L, Verrou K, Khanna NN, Ruzsa Z, Sharma AM, Laird JR, Johri AM, Kalra M, Protogerou A, Suri JS. Cardiovascular disease detection using machine learning and carotid/femoral arterial imaging frameworks in rheumatoid arthritis patients. Rheumatol Int 2022; 42:215-239. [PMID: 35013839 DOI: 10.1007/s00296-021-05062-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 11/29/2021] [Indexed: 12/31/2022]
Abstract
The study proposes a novel machine learning (ML) paradigm for cardiovascular disease (CVD) detection in individuals at medium to high cardiovascular risk using data from a Greek cohort of 542 individuals with rheumatoid arthritis, or diabetes mellitus, and/or arterial hypertension, using conventional or office-based, laboratory-based blood biomarkers and carotid/femoral ultrasound image-based phenotypes. Two kinds of data (CVD risk factors and presence of CVD-defined as stroke, or myocardial infarction, or coronary artery syndrome, or peripheral artery disease, or coronary heart disease) as ground truth, were collected at two-time points: (i) at visit 1 and (ii) at visit 2 after 3 years. The CVD risk factors were divided into three clusters (conventional or office-based, laboratory-based blood biomarkers, carotid ultrasound image-based phenotypes) to study their effect on the ML classifiers. Three kinds of ML classifiers (Random Forest, Support Vector Machine, and Linear Discriminant Analysis) were applied in a two-fold cross-validation framework using the data augmented by synthetic minority over-sampling technique (SMOTE) strategy. The performance of the ML classifiers was recorded. In this cohort with overall 46 CVD risk factors (covariates) implemented in an online cardiovascular framework, that requires calculation time less than 1 s per patient, a mean accuracy and area-under-the-curve (AUC) of 98.40% and 0.98 (p < 0.0001) for CVD presence detection at visit 1, and 98.39% and 0.98 (p < 0.0001) at visit 2, respectively. The performance of the cardiovascular framework was significantly better than the classical CVD risk score. The ML paradigm proved to be powerful for CVD prediction in individuals at medium to high cardiovascular risk.
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Affiliation(s)
- George Konstantonis
- Rheumatology Unit, National Kapodistrian University of Athens, Athens, Greece
| | | | - Petros P Sfikakis
- Rheumatology Unit, National Kapodistrian University of Athens, Athens, Greece
| | - Ankush D Jamthikar
- Research Scientist, AtheroPoint™, USA, Roseville, CA, USA.,Visvesvaraya National Institute of Technology, Nagpur, India
| | - George D Kitas
- Academic Affairs, Dudley Group NHS Foundation Trust, Dudley, UK.,Arthritis Research UK Epidemiology Unit, Manchester University, Manchester, M13, UK
| | - Suneet K Gupta
- Department of Computer Science, Bennett University, Gr. Noida, India
| | - Luca Saba
- Department of Radiology, University of Cagliari, Cagliari, Italy
| | - Kleio Verrou
- Department of Medicine, National and Kapodistrian University of Athens, Athens, Greece
| | - Narendra N Khanna
- Department of Cardiology, Indraprastha Apollo Hospitals, New Delhi, India
| | - Zoltan Ruzsa
- Department of Internal Medicines, Invasive Cardiology Division, University of Szeged, Szeged, Hungary
| | - Aditya M Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA, USA
| | - John R Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA, USA
| | - Amer M Johri
- Department of Medicine, Division of Cardiology, Queen's University, Kingston, ON, Canada
| | - Manudeep Kalra
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, USA
| | - Athanasios Protogerou
- Cardiovascular Prevention Unit, Department of Pathophysiology, National Kapodistrian University of Athens, Athens, Greece
| | - Jasjit S Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, 95661, USA.
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Kedra J, Davergne T, Braithwaite B, Servy H, Gossec L. Machine learning approaches to improve disease management of patients with rheumatoid arthritis: review and future directions. Expert Rev Clin Immunol 2021; 17:1311-1321. [PMID: 34890271 DOI: 10.1080/1744666x.2022.2017773] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
INTRODUCTION Although the management of rheumatoid arthritis (RA) has improved in major way over the last decades, this disease still leads to an important burden for patients and society, and there is a need to develop more personalized approaches. Machine learning (ML) methods are more and more used in health-related studies and can be applied to different sorts of data (clinical, radiological, or 'omics' data). Such approaches may improve the management of patients with RA. AREAS COVERED In this paper, we propose a review regarding ML approaches applied to RA. A scoping literature search was performed in PubMed, in September 2021 using the following MeSH terms: 'arthritis, rheumatoid' and 'machine learning'. Based on this search, the usefulness of ML methods for RA diagnosis, monitoring, and prediction of response to treatment and RA outcomes, is discussed. EXPERT OPINION ML methods have the potential to revolutionize RA-related research and improve disease management and patient care. Nevertheless, these models are not yet ready to contribute fully to rheumatologists' daily practice. Indeed, these methods raise technical, methodological, and ethical issues, which should be addressed properly to allow their implementation. Collaboration between data scientists, clinical researchers, and physicians is therefore required to move this field forward.
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Affiliation(s)
- Joanna Kedra
- Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique, Paris, France.,Rheumatology Department, Pitié-Salpêtrière Hospital, AP-HP, Paris, France
| | - Thomas Davergne
- Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique, Paris, France
| | | | | | - Laure Gossec
- Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique, Paris, France.,Rheumatology Department, Pitié-Salpêtrière Hospital, AP-HP, Paris, France
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20
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Walsh AP, Gordon HN, Peter K, Wang X. Ultrasonic particles: An approach for targeted gene delivery. Adv Drug Deliv Rev 2021; 179:113998. [PMID: 34662671 PMCID: PMC8518240 DOI: 10.1016/j.addr.2021.113998] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 09/24/2021] [Accepted: 10/05/2021] [Indexed: 02/07/2023]
Abstract
Gene therapy has been widely investigated for the treatment of genetic, acquired, and infectious diseases. Pioneering work utilized viral vectors; however, these are suspected of causing serious adverse events, resulting in the termination of several clinical trials. Non-viral vectors, such as lipid nanoparticles, have attracted significant interest, mainly due to their successful use in vaccines in the current COVID-19 pandemic. Although they allow safe delivery, they come with the disadvantage of off-target delivery. The application of ultrasound to ultrasound-sensitive particles allows for a direct, site-specific transfer of genetic materials into the organ/site of interest. This process, termed ultrasound-targeted gene delivery (UTGD), also increases cell membrane permeability and enhances gene uptake. This review focuses on the advances in ultrasound and the development of ultrasonic particles for UTGD across a range of diseases. Furthermore, we discuss the limitations and future perspectives of UTGD.
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Affiliation(s)
- Aidan P.G. Walsh
- Molecular Imaging and Theranostics Laboratory, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia,Atherothrombosis and Vascular Biology Laboratory, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia,Department of Medicine, Monash University, Melbourne, VIC, Australia
| | - Henry N. Gordon
- Molecular Imaging and Theranostics Laboratory, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia,Atherothrombosis and Vascular Biology Laboratory, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia,Department of Biochemistry and Pharmacology, University of Melbourne, VIC, Australia
| | - Karlheinz Peter
- Atherothrombosis and Vascular Biology Laboratory, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia,Department of Medicine, Monash University, Melbourne, VIC, Australia,Department of Cardiometabolic Health, University of Melbourne, VIC, Australia,La Trobe Institute for Molecular Science, La Trobe University, Melbourne, VIC, Australia
| | - Xiaowei Wang
- Molecular Imaging and Theranostics Laboratory, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia,Atherothrombosis and Vascular Biology Laboratory, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia,Department of Medicine, Monash University, Melbourne, VIC, Australia,Department of Cardiometabolic Health, University of Melbourne, VIC, Australia,La Trobe Institute for Molecular Science, La Trobe University, Melbourne, VIC, Australia,Corresponding author at: Baker Heart and Diabetes Institute, 75 Commercial Road, Melbourne, VIC 3004, Australia
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21
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Cheng IT, Wong KT, Li EK, Wong PCH, Lai BT, Yim IC, Ying SK, Kwok KY, Li M, Li TK, Lee JJ, Lee AP, Tam LS. Comparison of carotid artery ultrasound and Framingham risk score for discriminating coronary artery disease in patients with psoriatic arthritis. RMD Open 2021; 6:rmdopen-2020-001364. [PMID: 32973102 PMCID: PMC7539857 DOI: 10.1136/rmdopen-2020-001364] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Revised: 08/13/2020] [Accepted: 09/05/2020] [Indexed: 12/11/2022] Open
Abstract
Objectives This study aimed to assess the performance of carotid ultrasound (US) parameters alone or in combination with Framingham Risk Score (FRS) in discriminating patients with psoriatic arthritis (PsA) with and without coronary artery disease (CAD). Methods Ninety-one patients with PsA (56 males; age: 50±11 years, disease duration: 9.4±9.2 years) without overt cardiovascular (CV) diseases were recruited. Carotid intima-media thickness (cIMT), the presence of plaque and total plaque area (TPA) was determined by high-resolution US. CAD was defined as the presence of any coronary plaque on coronary CT angiography (CCTA). Obstructive-CAD (O-CAD) was defined as >50% stenosis of the lumen. Results Thirty-five (38%) patients had carotid plaque. Fifty-four (59%) patients had CAD (CAD+) and 9 (10%) patients had O-CAD (O-CAD+). No significant associations between the presence of carotid plaque and CAD were found. However, cIMT and TPA were higher in both the CAD+ and O-CAD+ group compared with the CAD− or O-CAD− groups, respectively. Multivariate logistic regression analysis revealed that mean cIMT was an independent explanatory variable associated with CAD and O-CAD, while maximum cIMT and TPA were independent explanatory variables associated with O-CAD after adjusting for covariates. The optimal cut-offs for detecting the presence of CAD were FRS >5% and mean cIMT at 0.62 mm (AUC: 0.71; sensitivity: 67%; specificity: 76%), while the optimal cut-offs for detecting the presence of O-CAD were FRS >10% in combination with mean cIMT at 0.73 mm (AUC: 0.71; sensitivity: 56%; specificity: 85%). Conclusion US parameters including cIMT and TPA may be considered in addition to FRS for CV risk stratification in patients with PsA.
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Affiliation(s)
- Isaac T Cheng
- Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong
| | - Ka Tak Wong
- Diagnostic and Interventional Radiology, Prince of Wales Hospital, Hong Kong
| | - Edmund K Li
- Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong
| | | | | | | | - Shirley K Ying
- Medicine and Geriatrics, Princess Margaret Hospital, Hong Kong
| | | | - Martin Li
- Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong
| | - Tena K Li
- Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong
| | - Jack J Lee
- School of Public Health Division of Biostatistics, The Chinese University of Hong Kong Faculty of Medicine, Hong Kong
| | - Alex P Lee
- Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong
| | - Lai-Shan Tam
- Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong
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22
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Baldini C, Moriconi FR, Galimberti S, Libby P, De Caterina R. The JAK-STAT pathway: an emerging target for cardiovascular disease in rheumatoid arthritis and myeloproliferative neoplasms. Eur Heart J 2021; 42:4389-4400. [PMID: 34343257 DOI: 10.1093/eurheartj/ehab447] [Citation(s) in RCA: 67] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/10/2021] [Revised: 05/21/2021] [Accepted: 07/31/2021] [Indexed: 01/07/2023] Open
Abstract
Inflammation contributes centrally to cardiovascular diseases, and anti-inflammatory treatments can reduce cardiovascular events. The JAK-STAT pathway is an emerging target in inflammation, mainly in rheumatoid arthritis (RA) and chronic myeloproliferative neoplasms (MPNs), disorders that heighten cardiovascular risk. The aim of this study was to review the international literature on the relationship between dysregulation of the JAK-STAT pathway in RA/MPNs and cardiovascular risk and on the potential cardiovascular effects of JAK-STAT inhibitors. The JAK-STAT pathway sustains inflammatory and thrombotic events in autoimmune disorders such as RA and MPNs. Here, an imbalance exists between pro- and anti-inflammatory cytokines [increased levels of interleukin (IL)-6, IL-1-β, tumour necrosis factor-α, decreased levels of IL-10] and the over-expression of some prothrombotic proteins, such as protein kinase Cε, on the surface of activated platelets. This pathway also operates in atherosclerotic cardiovascular disease. JAK-STAT inhibitors may reduce cardiovascular events and related deaths in such conditions, but the potential of these agents requires more studies, especially with regard to cardiovascular safety, and particularly for potential prothrombotic effects. JAK-STAT inhibitors merit consideration to curb heightened cardiovascular risk in patients with RA and MPNs, with rigorous assessment of the potential benefits and risks.
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Affiliation(s)
- Chiara Baldini
- Division of Rheumatology, University of Pisa and Pisa University Hospital, Via Paradisa, 2, Pisa 56124, Italy
| | - Francesca Romana Moriconi
- Division of Rheumatology, University of Pisa and Pisa University Hospital, Via Paradisa, 2, Pisa 56124, Italy.,Division of Cardiology, University of Pisa and Pisa University Hospital, Via Paradisa, 2, Pisa 56124, Italy
| | - Sara Galimberti
- Division of Hematology, University of Pisa and Pisa University Hospital, Via Paradisa, 2, Pisa 56124, Italy
| | - Peter Libby
- Cardiovascular Division, Brigham and Women's Hospital-Harvard Medical School, 75 Francis Street, Boston, MA 02115, USA
| | - Raffaele De Caterina
- Division of Cardiology, University of Pisa and Pisa University Hospital, Via Paradisa, 2, Pisa 56124, Italy
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23
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Biswas M, Saba L, Omerzu T, Johri AM, Khanna NN, Viskovic K, Mavrogeni S, Laird JR, Pareek G, Miner M, Balestrieri A, Sfikakis PP, Protogerou A, Misra DP, Agarwal V, Kitas GD, Kolluri R, Sharma A, Viswanathan V, Ruzsa Z, Nicolaides A, Suri JS. A Review on Joint Carotid Intima-Media Thickness and Plaque Area Measurement in Ultrasound for Cardiovascular/Stroke Risk Monitoring: Artificial Intelligence Framework. J Digit Imaging 2021; 34:581-604. [PMID: 34080104 PMCID: PMC8329154 DOI: 10.1007/s10278-021-00461-2] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 03/19/2021] [Accepted: 05/04/2021] [Indexed: 02/06/2023] Open
Abstract
Cardiovascular diseases (CVDs) are the top ten leading causes of death worldwide. Atherosclerosis disease in the arteries is the main cause of the CVD, leading to myocardial infarction and stroke. The two primary image-based phenotypes used for monitoring the atherosclerosis burden is carotid intima-media thickness (cIMT) and plaque area (PA). Earlier segmentation and measurement methods were based on ad hoc conventional and semi-automated digital imaging solutions, which are unreliable, tedious, slow, and not robust. This study reviews the modern and automated methods such as artificial intelligence (AI)-based. Machine learning (ML) and deep learning (DL) can provide automated techniques in the detection and measurement of cIMT and PA from carotid vascular images. Both ML and DL techniques are examples of supervised learning, i.e., learn from "ground truth" images and transformation of test images that are not part of the training. This review summarizes (1) the evolution and impact of the fast-changing AI technology on cIMT/PA measurement, (2) the mathematical representations of ML/DL methods, and (3) segmentation approaches for cIMT/PA regions in carotid scans based for (a) region-of-interest detection and (b) lumen-intima and media-adventitia interface detection using ML/DL frameworks. AI-based methods for cIMT/PA segmentation have emerged for CVD/stroke risk monitoring and may expand to the recommended parameters for atherosclerosis assessment by carotid ultrasound.
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Affiliation(s)
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Cagliari, Italy
| | - Tomaž Omerzu
- Department of Neurology, University Medical Centre Maribor, Maribor, Slovenia
| | - Amer M Johri
- Department of Medicine, Division of Cardiology, Queen's University, Kingston, ON, Canada
| | - Narendra N Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, India
| | | | - Sophie Mavrogeni
- Cardiology Clinic, Onassis Cardiac Surgery Center, Athens, Greece
| | - John R Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA, USA
| | - Gyan Pareek
- Minimally Invasive Urology Institute, Brown University, Providence, Rhode Island, USA
| | - Martin Miner
- Men's Health Center, Miriam Hospital Providence, Rhode Island, USA
| | - Antonella Balestrieri
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Cagliari, Italy
| | - Petros P Sfikakis
- Rheumatology Unit, National Kapodistrian University of Athens, Athens, Greece
| | | | | | - Vikas Agarwal
- Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, UP, India
| | - George D Kitas
- Academic Affairs, Dudley Group NHS Foundation Trust, Dudley, UK
- Arthritis Research UK Epidemiology Unit, Manchester University, Manchester, UK
| | | | - Aditya Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA, USA
| | - Vijay Viswanathan
- MV Hospital for Diabetes and Professor M Viswanathan Diabetes Research Centre, Chennai, India
| | - Zoltan Ruzsa
- Invasive Cardiology Division, University of Szeged, Budapest, Hungary
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre, University of Nicosia Medical School, Nicosia, Cyprus
| | - Jasjit S Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, 95661, USA.
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24
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JAMTHIKAR AD, PUVVULA A, GUPTA D, JOHRI AM, NAMBI V, KHANNA NN, SABA L, MAVROGENI S, LAIRD JR, PAREEK G, MINER M, SFIKAKIS PP, PROTOGEROU A, KITAS GD, NICOLAIDES A, SHARMA AM, VISWANATHAN V, RATHORE VS, KOLLURI R, BHATT DL, SURI JS. Cardiovascular disease and stroke risk assessment in patients with chronic kidney disease using integration of estimated glomerular filtration rate, ultrasonic image phenotypes, and artificial intelligence: a narrative review. INT ANGIOL 2021; 40:150-164. [DOI: 10.23736/s0392-9590.20.04538-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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25
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Figus FA, Piga M, Azzolin I, McConnell R, Iagnocco A. Rheumatoid arthritis: Extra-articular manifestations and comorbidities. Autoimmun Rev 2021; 20:102776. [PMID: 33609792 DOI: 10.1016/j.autrev.2021.102776] [Citation(s) in RCA: 166] [Impact Index Per Article: 55.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 12/27/2020] [Indexed: 12/20/2022]
Abstract
Although synovitis is the pathological hallmark of rheumatoid arthritis (RA), many extra-articular manifestations (EMs) and comorbidities likely occur due to the complex, chronic, inflammatory, and autoimmune features of RA. Cardiovascular (CV) disease is the most common cause of death in patients with RA. Compared to the general population, patients with RA have twice the risk of myocardial infarction and up to 50% increased CV mortality risk. Severe and prolonged disease activity, genetics, and inflammation (e.g. CRP, ACPA, cytokines, matrix-degrading enzymes) play important roles in CV disease and atheroscleroticdamage. The second major cause of death in patients with RA is respiratory disease, which occurs in 30-40% of patients. RA may affect the lung interstitium, airways, and pleurae, while pulmonary vascular involvement is less frequent. Central and peripheral nervous system involvement is usually due to small vessel vasculitis, joint damage, or drug toxicity. There is also evidence that microvascular cerebral damage caused by systemic inflammation is associated with the development of Alzheimer's disease and vascular dementia. Some observational studies have hinted how Disease Modified Anti-Rheumatic Drugs and biologics could reduce the incidence of dementia. Primary gastrointestinal and renal involvements are rare and often relate to drug therapy. To minimize morbidity and mortality, physicians must manage RA disease activity (treat-to-target) and monitor risk factors and concomitant conditions (e.g. smoking cessation; weight regulation; monitoring blood pressure, lipids, thyroid hormone, folic acid and homocysteine; screening for depression, anxiety, atlantoaxial instability, and atherosclerosis). This article aims to provide an overview of the most prevalent and important EMs and comorbidities associated with RA.
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Affiliation(s)
- Fabiana Assunta Figus
- Academic Rheumatology Centre, MFRU and Dipartimento Scienze Cliniche e Biologiche, Università degli Studi di Torino, Torino, Italy
| | - Matteo Piga
- Rheumatology Unit, University Clinic and AOU of Cagliari, Monserrato, Italy
| | - Irene Azzolin
- Academic Rheumatology Centre, MFRU and Dipartimento Scienze Cliniche e Biologiche, Università degli Studi di Torino, Torino, Italy
| | | | - Annamaria Iagnocco
- Academic Rheumatology Centre, MFRU and Dipartimento Scienze Cliniche e Biologiche, Università degli Studi di Torino, Torino, Italy.
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26
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Agarwal M, Saba L, Gupta SK, Johri AM, Khanna NN, Mavrogeni S, Laird JR, Pareek G, Miner M, Sfikakis PP, Protogerou A, Sharma AM, Viswanathan V, Kitas GD, Nicolaides A, Suri JS. Wilson disease tissue classification and characterization using seven artificial intelligence models embedded with 3D optimization paradigm on a weak training brain magnetic resonance imaging datasets: a supercomputer application. Med Biol Eng Comput 2021; 59:511-533. [PMID: 33547549 DOI: 10.1007/s11517-021-02322-0] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 01/18/2021] [Indexed: 01/16/2023]
Abstract
Wilson's disease (WD) is caused by copper accumulation in the brain and liver, and if not treated early, can lead to severe disability and death. WD has shown white matter hyperintensity (WMH) in the brain magnetic resonance scans (MRI) scans, but the diagnosis is challenging due to (i) subtle intensity changes and (ii) weak training MRI when using artificial intelligence (AI). Design and validate seven types of high-performing AI-based computer-aided design (CADx) systems consisting of 3D optimized classification, and characterization of WD against controls. We propose a "conventional deep convolution neural network" (cDCNN) and an "improved DCNN" (iDCNN) where rectified linear unit (ReLU) activation function was modified ensuring "differentiable at zero." Three-dimensional optimization was achieved by recording accuracy while changing the CNN layers and augmentation by several folds. WD was characterized using (i) CNN-based feature map strength and (ii) Bispectrum strengths of pixels having higher probabilities of WD. We further computed the (a) area under the curve (AUC), (b) diagnostic odds ratio (DOR), (c) reliability, and (d) stability and (e) benchmarking. Optimal results were achieved using 9 layers of CNN, with 4-fold augmentation. iDCNN yields superior performance compared to cDCNN with accuracy and AUC of 98.28 ± 1.55, 0.99 (p < 0.0001), and 97.19 ± 2.53%, 0.984 (p < 0.0001), respectively. DOR of iDCNN outperformed cDCNN fourfold. iDCNN also outperformed (a) transfer learning-based "Inception V3" paradigm by 11.92% and (b) four types of "conventional machine learning-based systems": k-NN, decision tree, support vector machine, and random forest by 55.13%, 28.36%, 15.35%, and 14.11%, respectively. The AI-based systems can potentially be useful in the early WD diagnosis. Graphical Abstract.
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Affiliation(s)
- Mohit Agarwal
- CSE Department, Bennett University, Greater Noida, UP, India
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Cagliari, Italy
| | - Suneet K Gupta
- CSE Department, Bennett University, Greater Noida, UP, India
| | - Amer M Johri
- Department of Medicine, Division of Cardiology, Queen's University, Ontario, Kingston, Canada
| | - Narendra N Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, India
| | - Sophie Mavrogeni
- Cardiology Clinic, Onassis Cardiac Surgery Center, Athens, Greece
| | - John R Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St. Helena, CA, USA
| | - Gyan Pareek
- Minimally Invasive Urology Institute, Brown University, Providence, RI, USA
| | - Martin Miner
- Men's Health Center, Miriam Hospital Providence, Providence, RI, USA
| | - Petros P Sfikakis
- Rheumatology Unit, National Kapodistrian University of Athens, Athens, Greece
| | - Athanasios Protogerou
- Department of Cardiovascular Prevention, National and Kapodistrian Univ. of Athens, Athens, Greece
| | - Aditya M Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA, USA
| | - Vijay Viswanathan
- MV Hospital for Diabetes & Professor M Viswanathan Diabetes Research Centre, Chennai, India
| | - George D Kitas
- R & D Academic Affairs, Dudley Group NHS Foundation Trust, Dudley, UK
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre, University of Nicosia, Nicosia, Cyprus
| | - Jasjit S Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, 95661, USA.
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27
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Ultrasound-based internal carotid artery plaque characterization using deep learning paradigm on a supercomputer: a cardiovascular disease/stroke risk assessment system. Int J Cardiovasc Imaging 2021; 37:1511-1528. [DOI: 10.1007/s10554-020-02124-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Accepted: 11/28/2020] [Indexed: 12/17/2022]
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28
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Melissaropoulos K, Bogdanos D, Dimitroulas T, Sakkas LI, Kitas GD, Daoussis D. Primary Sjögren's Syndrome and Cardiovascular Disease. Curr Vasc Pharmacol 2020; 18:447-454. [PMID: 31995009 DOI: 10.2174/1570161118666200129125320] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2019] [Revised: 11/19/2019] [Accepted: 12/03/2019] [Indexed: 02/07/2023]
Abstract
Sjögren's syndrome is a rheumatic autoimmune disease that primarily affects middle-aged women and runs a slowly progressing course with sicca symptoms being the prevalent manifestation. Premature atherosclerosis and increased cardiovascular (CV) morbidity and mortality are frequently encountered in rheumatic diseases characterized by significant systemic inflammation, such as the inflammatory arthritides, systemic vasculitides and systemic lupus erythematosus. In the same context, chronic inflammation and immune aberrations underlying Sjögren's syndrome are also reported to be associated with augmented risk of atherosclerosis. Increased CV disease (CVD) frequency has been found in recent meta-analyses. The involvement of the CV system is not a common feature of Sjögren's syndrome; however, specific manifestations, such as autoantibody-mediated heart block, pericarditis, pulmonary arterial hypertension and dysautonomia, have been described. This review focuses on studies addressing CV morbidity in Sjögren's syndrome and presents current data regarding distinct CV features of the disease.
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Affiliation(s)
| | - Dimitrios Bogdanos
- Department of Rheumatology, Faculty of Medicine, School of Health Sciences, University of Thessaly, Larissa 41 110, Greece
| | - Theodoros Dimitroulas
- 4th Department of Internal Medicine Hippokration Hospital, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Lazaros I Sakkas
- Department of Rheumatology, Faculty of Medicine, School of Health Sciences, University of Thessaly, Larissa 41 110, Greece
| | - George D Kitas
- Department of Rheumatology, Dudley Group NHS Foundation Trust, Russells Hall Hospital, Dudley, West Midlands, United Kingdom
| | - Dimitrios Daoussis
- Department of Rheumatology, Patras University Hospital, University of Patras Medical School, Patras, Greece
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29
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Multiclass machine learning vs. conventional calculators for stroke/CVD risk assessment using carotid plaque predictors with coronary angiography scores as gold standard: a 500 participants study. Int J Cardiovasc Imaging 2020; 37:1171-1187. [DOI: 10.1007/s10554-020-02099-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 11/03/2020] [Indexed: 02/07/2023]
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30
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Low-Cost Office-Based Cardiovascular Risk Stratification Using Machine Learning and Focused Carotid Ultrasound in an Asian-Indian Cohort. J Med Syst 2020; 44:208. [DOI: 10.1007/s10916-020-01675-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Accepted: 11/09/2020] [Indexed: 12/13/2022]
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31
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Jamthikar AD, Gupta D, Saba L, Khanna NN, Viskovic K, Mavrogeni S, Laird JR, Sattar N, Johri AM, Pareek G, Miner M, Sfikakis PP, Protogerou A, Viswanathan V, Sharma A, Kitas GD, Nicolaides A, Kolluri R, Suri JS. Artificial intelligence framework for predictive cardiovascular and stroke risk assessment models: A narrative review of integrated approaches using carotid ultrasound. Comput Biol Med 2020; 126:104043. [PMID: 33065389 DOI: 10.1016/j.compbiomed.2020.104043] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 09/10/2020] [Accepted: 10/04/2020] [Indexed: 12/12/2022]
Abstract
RECENT FINDINGS Cardiovascular disease (CVD) is the leading cause of mortality and poses challenges for healthcare providers globally. Risk-based approaches for the management of CVD are becoming popular for recommending treatment plans for asymptomatic individuals. Several conventional predictive CVD risk models based do not provide an accurate CVD risk assessment for patients with different baseline risk profiles. Artificial intelligence (AI) algorithms have changed the landscape of CVD risk assessment and demonstrated a better performance when compared against conventional models, mainly due to its ability to handle the input nonlinear variations. Further, it has the flexibility to add risk factors derived from medical imaging modalities that image the morphology of the plaque. The integration of noninvasive carotid ultrasound image-based phenotypes with conventional risk factors in the AI framework has further provided stronger power for CVD risk prediction, so-called "integrated predictive CVD risk models." PURPOSE of the review: The objective of this review is (i) to understand several aspects in the development of predictive CVD risk models, (ii) to explore current conventional predictive risk models and their successes and challenges, and (iii) to refine the search for predictive CVD risk models using noninvasive carotid ultrasound as an exemplar in the artificial intelligence-based framework. CONCLUSION Conventional predictive CVD risk models are suboptimal and could be improved. This review examines the potential to include more noninvasive image-based phenotypes in the CVD risk assessment using powerful AI-based strategies.
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Affiliation(s)
- Ankush D Jamthikar
- Department of Electronics and Communication Engineering, Visvesvaraya National Institute of Technology, Nagpur, Maharashtra, India
| | - Deep Gupta
- Department of Electronics and Communication Engineering, Visvesvaraya National Institute of Technology, Nagpur, Maharashtra, India
| | - Luca Saba
- Department of Radiology, University of Cagliari, Italy
| | - Narendra N Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, India
| | - Klaudija Viskovic
- Department of Radiology and Ultrasound, University Hospital for Infectious Diseases, Croatia
| | - Sophie Mavrogeni
- Cardiology Clinic, Onassis Cardiac Surgery Center, Athens, Greece
| | - John R Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA, USA
| | - Naveed Sattar
- Institute of Cardiovascular & Medical Sciences, University of Glasgow, Scotland, UK
| | - Amer M Johri
- Department of Medicine, Division of Cardiology, Queen's University, Kingston, Ontario, Canada
| | - Gyan Pareek
- Minimally Invasive Urology Institute, Brown University, Providence, RI, USA
| | - Martin Miner
- Men's Health Center, Miriam Hospital Providence, Rhode Island, USA
| | - Petros P Sfikakis
- Rheumatology Unit, National Kapodistrian University of Athens, Greece
| | - Athanasios Protogerou
- Department of Cardiovascular Prevention & Research Unit Clinic & Laboratory of Pathophysiology, National and Kapodistrian Univ. of Athens, Greece
| | - Vijay Viswanathan
- MV Hospital for Diabetes and Professor M Viswanathan Diabetes Research Centre, Chennai, India
| | - Aditya Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA, USA
| | - George D Kitas
- R & D Academic Affairs, Dudley Group NHS Foundation Trust, Dudley, United Kingdom
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre and University of Nicosia Medical School, Nicosia, Cyprus
| | | | - Jasjit S Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA.
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32
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Suri JS, Puvvula A, Biswas M, Majhail M, Saba L, Faa G, Singh IM, Oberleitner R, Turk M, Chadha PS, Johri AM, Sanches JM, Khanna NN, Viskovic K, Mavrogeni S, Laird JR, Pareek G, Miner M, Sobel DW, Balestrieri A, Sfikakis PP, Tsoulfas G, Protogerou A, Misra DP, Agarwal V, Kitas GD, Ahluwalia P, Kolluri R, Teji J, Maini MA, Agbakoba A, Dhanjil SK, Sockalingam M, Saxena A, Nicolaides A, Sharma A, Rathore V, Ajuluchukwu JNA, Fatemi M, Alizad A, Viswanathan V, Krishnan PR, Naidu S. COVID-19 pathways for brain and heart injury in comorbidity patients: A role of medical imaging and artificial intelligence-based COVID severity classification: A review. Comput Biol Med 2020; 124:103960. [PMID: 32919186 PMCID: PMC7426723 DOI: 10.1016/j.compbiomed.2020.103960] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 08/06/2020] [Accepted: 08/07/2020] [Indexed: 02/05/2023]
Abstract
Artificial intelligence (AI) has penetrated the field of medicine, particularly the field of radiology. Since its emergence, the highly virulent coronavirus disease 2019 (COVID-19) has infected over 10 million people, leading to over 500,000 deaths as of July 1st, 2020. Since the outbreak began, almost 28,000 articles about COVID-19 have been published (https://pubmed.ncbi.nlm.nih.gov); however, few have explored the role of imaging and artificial intelligence in COVID-19 patients-specifically, those with comorbidities. This paper begins by presenting the four pathways that can lead to heart and brain injuries following a COVID-19 infection. Our survey also offers insights into the role that imaging can play in the treatment of comorbid patients, based on probabilities derived from COVID-19 symptom statistics. Such symptoms include myocardial injury, hypoxia, plaque rupture, arrhythmias, venous thromboembolism, coronary thrombosis, encephalitis, ischemia, inflammation, and lung injury. At its core, this study considers the role of image-based AI, which can be used to characterize the tissues of a COVID-19 patient and classify the severity of their infection. Image-based AI is more important than ever as the pandemic surges and countries worldwide grapple with limited medical resources for detection and diagnosis.
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Affiliation(s)
- Jasjit S Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA.
| | - Anudeep Puvvula
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA; Annu's Hospitals for Skin and Diabetes, Nellore, AP, India
| | | | - Misha Majhail
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA; Oakmont High School and AtheroPoint™, Roseville, CA, USA
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria, Cagliari, Italy
| | - Gavino Faa
- Department of Pathology - AOU of Cagliari, Italy
| | - Inder M Singh
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
| | | | - Monika Turk
- The Hanse-Wissenschaftskolleg Institute for Advanced Study, Delmenhorst, Germany
| | - Paramjit S Chadha
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
| | - Amer M Johri
- Department of Medicine, Division of Cardiology,Queen's University, Kingston, Ontario, Canada
| | - J Miguel Sanches
- Institute of Systems and Robotics, Instituto Superior Tecnico, Lisboa, Portugal
| | - Narendra N Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, India
| | | | - Sophie Mavrogeni
- Cardiology Clinic, Onassis Cardiac Surgery Center, Athens, Greece
| | - John R Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA, USA
| | - Gyan Pareek
- Minimally Invasive Urology Institute, Brown University, Providence, RI, USA
| | - Martin Miner
- Men's Health Center, Miriam Hospital Providence, Rhode Island, USA
| | - David W Sobel
- Minimally Invasive Urology Institute, Brown University, Providence, RI, USA
| | | | - Petros P Sfikakis
- Rheumatology Unit, National Kapodistrian University of Athens, Greece
| | - George Tsoulfas
- Aristoteleion University of Thessaloniki, Thessaloniki, Greece
| | | | | | - Vikas Agarwal
- Academic Affairs, Dudley Group NHS Foundation Trust, Dudley, UK
| | - George D Kitas
- Academic Affairs, Dudley Group NHS Foundation Trust, Dudley, UK; Arthritis Research UK Epidemiology Unit, Manchester University, Manchester, UK
| | - Puneet Ahluwalia
- Max Institute of Cancer Care, Max Superspeciality Hospital, New Delhi, India
| | | | - Jagjit Teji
- Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, USA
| | - Mustafa Al Maini
- Allergy, Clinical Immunology and Rheumatology Institute, Toronto, Canada
| | | | | | | | - Ajit Saxena
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, India
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre and University of Nicosia Medical School, Cyprus
| | - Aditya Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA, USA
| | - Vijay Rathore
- Nephrology Department, Kaiser Permanente, Sacramento, CA, USA
| | | | - Mostafa Fatemi
- Dept. of Physiology & Biomedical Engg., Mayo Clinic College of Medicine and Science, MN, USA
| | - Azra Alizad
- Dept. of Radiology, Mayo Clinic College of Medicine and Science, MN, USA
| | - Vijay Viswanathan
- MV Hospital for Diabetes and Professor M Viswanathan Diabetes Research Centre, Chennai, India
| | | | - Subbaram Naidu
- Electrical Engineering Department, University of Minnesota, Duluth, MN, USA
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Jamthikar AD, Gupta D, Puvvula A, Johri AM, Khanna NN, Saba L, Mavrogeni S, Laird JR, Pareek G, Miner M, Sfikakis PP, Protogerou A, Kitas GD, Kolluri R, Sharma AM, Viswanathan V, Rathore VS, Suri JS. Cardiovascular risk assessment in patients with rheumatoid arthritis using carotid ultrasound B-mode imaging. Rheumatol Int 2020; 40:1921-1939. [PMID: 32857281 PMCID: PMC7453675 DOI: 10.1007/s00296-020-04691-5] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Accepted: 08/18/2020] [Indexed: 12/18/2022]
Abstract
Rheumatoid arthritis (RA) is a systemic chronic inflammatory disease that affects synovial joints and has various extra-articular manifestations, including atherosclerotic cardiovascular disease (CVD). Patients with RA experience a higher risk of CVD, leading to increased morbidity and mortality. Inflammation is a common phenomenon in RA and CVD. The pathophysiological association between these diseases is still not clear, and, thus, the risk assessment and detection of CVD in such patients is of clinical importance. Recently, artificial intelligence (AI) has gained prominence in advancing healthcare and, therefore, may further help to investigate the RA-CVD association. There are three aims of this review: (1) to summarize the three pathophysiological pathways that link RA to CVD; (2) to identify several traditional and carotid ultrasound image-based CVD risk calculators useful for RA patients, and (3) to understand the role of artificial intelligence in CVD risk assessment in RA patients. Our search strategy involves extensively searches in PubMed and Web of Science databases using search terms associated with CVD risk assessment in RA patients. A total of 120 peer-reviewed articles were screened for this review. We conclude that (a) two of the three pathways directly affect the atherosclerotic process, leading to heart injury, (b) carotid ultrasound image-based calculators have shown superior performance compared with conventional calculators, and (c) AI-based technologies in CVD risk assessment in RA patients are aggressively being adapted for routine practice of RA patients.
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Affiliation(s)
- Ankush D Jamthikar
- Department of Electronics and Communications Engineering, Visvesvaraya National Institute of Technology, Nagpur, MH, India
| | - Deep Gupta
- Department of Electronics and Communications Engineering, Visvesvaraya National Institute of Technology, Nagpur, MH, India
| | | | - Amer M Johri
- Department of Medicine, Division of Cardiology, Queen's University, Kingston, ON, Canada
| | - Narendra N Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, India
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Cagliari, Italy
| | - Sophie Mavrogeni
- Cardiology Clinic, Onassis Cardiac Surgery Center, Athens, Greece
| | - John R Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA, USA
| | - Gyan Pareek
- Minimally Invasive Urology Institute, Brown University, Providence, RI, USA
| | - Martin Miner
- Men's Health Center, Miriam Hospital, Providence, RI, USA
| | - Petros P Sfikakis
- Rheumatology Unit, National Kapodistrian University of Athens, Athens, Greece
| | - Athanasios Protogerou
- Department of Cardiovascular Prevention, National and Kapodistrian University of Athens, Athens, Greece
| | - George D Kitas
- Department of Rheumatology, Dudley Group NHS Foundation Trust, Dudley, UK
| | | | - Aditya M Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA, USA
| | - Vijay Viswanathan
- MV Hospital for Diabetes and Professor M Viswanathan Diabetes Research Centre, Chennai, India
| | - Vijay S Rathore
- Nephrology Department, Kaiser Permanente, Sacramento, CA, USA
| | - Jasjit S Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, 95661, USA.
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Skandha SS, Gupta SK, Saba L, Koppula VK, Johri AM, Khanna NN, Mavrogeni S, Laird JR, Pareek G, Miner M, Sfikakis PP, Protogerou A, Misra DP, Agarwal V, Sharma AM, Viswanathan V, Rathore VS, Turk M, Kolluri R, Viskovic K, Cuadrado-Godia E, Kitas GD, Nicolaides A, Suri JS. 3-D optimized classification and characterization artificial intelligence paradigm for cardiovascular/stroke risk stratification using carotid ultrasound-based delineated plaque: Atheromatic™ 2.0. Comput Biol Med 2020; 125:103958. [PMID: 32927257 DOI: 10.1016/j.compbiomed.2020.103958] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 08/02/2020] [Accepted: 08/03/2020] [Indexed: 12/18/2022]
Abstract
BACKGROUND AND PURPOSE Atherosclerotic plaque tissue rupture is one of the leading causes of strokes. Early carotid plaque monitoring can help reduce cardiovascular morbidity and mortality. Manual ultrasound plaque classification and characterization methods are time-consuming and can be imprecise due to significant variations in tissue characteristics. We report a novel artificial intelligence (AI)-based plaque tissue classification and characterization system. METHODS We hypothesize that symptomatic plaque is hypoechoic due to its large lipid core and minimal collagen, as well as its heterogeneous makeup. Meanwhile, asymptomatic plaque is hyperechoic due to its small lipid core, abundant collagen, and the fact that it is often calcified. We designed a computer-aided diagnosis (CADx) system consisting of three kinds of deep learning (DL) classification paradigms: Deep Convolutional Neural Network (DCNN), Visual Geometric Group-16 (VGG16), and transfer learning, (tCNN). DCNN was 3-D optimized by varying the number of CNN layers and data augmentation frameworks. The DL systems were benchmarked against four types of machine learning (ML) classification systems, and the CADx system was characterized using two novel strategies consisting of DL mean feature strength (MFS) and a bispectrum model using higher-order spectra. RESULTS After balancing symptomatic and asymptomatic plaque classes, a five-fold augmentation process was applied, yielding 1000 carotid scans in each class. Then, using a K10 protocol (trained to test the ratio of 90%-10%), tCNN and DCNN yielded accuracy (area under the curve (AUC)) pairs of 83.33%, 0.833 (p < 0.0001) and 95.66%, 0.956 (p < 0.0001), respectively. DCNN was superior to ML by 7.01%. As part of the characterization process, the MFS of the symptomatic plaque was found to be higher compared to the asymptomatic plaque by 17.5% (p < 0.0001). A similar pattern was seen in the bispectrum, which was higher for symptomatic plaque by 5.4% (p < 0.0001). It took <2 s to perform the online CADx process on a supercomputer. CONCLUSIONS The performance order of the three AI systems was DCNN > tCNN > ML. Bispectrum-based on higher-order spectra proved a powerful paradigm for plaque tissue characterization. Overall, the AI-based systems offer a powerful solution for plaque tissue classification and characterization.
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Affiliation(s)
- Sanagala S Skandha
- CSE Department, CMR College of Engineering & Technology, Hyderabad, India; CSE Department, Bennett University, Greater Noida, UP, India
| | - Suneet K Gupta
- CSE Department, Bennett University, Greater Noida, UP, India
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Cagliari, Italy
| | - Vijaya K Koppula
- CSE Department, CMR College of Engineering & Technology, Hyderabad, India
| | - Amer M Johri
- Department of Medicine, Division of Cardiology, Queen's University, Kingston, Ontario, Canada
| | - Narendra N Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, India
| | - Sophie Mavrogeni
- Cardiology Clinic, Onassis Cardiac Surgery Center, Athens, Greece
| | - John R Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA, USA
| | - Gyan Pareek
- Minimally Invasive Urology Institute, Brown University, Providence, RI, USA
| | - Martin Miner
- Men's Health Center, Miriam Hospital Providence, RI, USA
| | - Petros P Sfikakis
- Rheumatology Unit, National Kapodistrian University of Athens, Greece
| | - Athanasios Protogerou
- Department of Cardiovascular Prevention, National and Kapodistrian Univ. of Athens, Greece
| | - Durga P Misra
- Dept. of Clinical Immunology and Rheumatology, SGPGIMS, Lucknow, India
| | - Vikas Agarwal
- Dept. of Clinical Immunology and Rheumatology, SGPGIMS, Lucknow, India
| | - Aditya M Sharma
- Division of Cardiovascular Medicine, University of Virginia, VA, USA
| | - Vijay Viswanathan
- MV Hospital for Diabetes & Professor M Viswanathan Diabetes Research Centre, Chennai, India
| | - Vijay S Rathore
- Nephrology Department, Kaiser Permanente, Sacramento, CA, USA
| | - Monika Turk
- The Hanse-Wissenschaftskolleg Institute for Advanced Study, Delmenhorst, Germany
| | | | | | | | - George D Kitas
- R & D Academic Affairs, Dudley Group NHS Foundation Trust, Dudley, UK
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre, University of Nicosia, Nicosia, Cyprus
| | - Jasjit S Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA.
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Jamthikar A, Gupta D, Khanna NN, Saba L, Laird JR, Suri JS. Cardiovascular/stroke risk prevention: A new machine learning framework integrating carotid ultrasound image-based phenotypes and its harmonics with conventional risk factors. Indian Heart J 2020; 72:258-264. [PMID: 32861380 PMCID: PMC7474133 DOI: 10.1016/j.ihj.2020.06.004] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Revised: 05/29/2020] [Accepted: 06/10/2020] [Indexed: 12/11/2022] Open
Abstract
MOTIVATION Machine learning (ML)-based stroke risk stratification systems have typically focused on conventional risk factors (CRF) (AtheroRisk-conventional). Besides CRF, carotid ultrasound image phenotypes (CUSIP) have shown to be powerful phenotypes risk stratification. This is the first ML study of its kind that integrates CUSIP and CRF for risk stratification (AtheroRisk-integrated) and compares against AtheroRisk-conventional. METHODS Two types of ML-based setups called (i) AtheroRisk-integrated and (ii) AtheroRisk-conventional were developed using random forest (RF) classifiers. AtheroRisk-conventional uses a feature set of 13 CRF such as age, gender, hemoglobin A1c, fasting blood sugar, low-density lipoprotein, and high-density lipoprotein (HDL) cholesterol, total cholesterol (TC), a ratio of TC and HDL, hypertension, smoking, family history, triglyceride, and ultrasound-based carotid plaque score. AtheroRisk-integrated system uses the feature set of 38 features with a combination of 13 CRF and 25 CUSIP features (6 types of current CUSIP, 6 types of 10-year CUSIP, 12 types of quadratic CUSIP (harmonics), and age-adjusted grayscale median). Logistic regression approach was used to select the significant features on which the RF classifier was trained. The performance of both ML systems was evaluated by area-under-the-curve (AUC) statistics computed using a leave-one-out cross-validation protocol. RESULTS Left and right common carotid arteries of 202 Japanese patients were retrospectively examined to obtain 404 ultrasound scans. RF classifier showed higher improvement in AUC (~57%) for leave-one-out cross-validation protocol. Using RF classifier, AUC statistics for AtheroRisk-integrated system was higher (AUC = 0.99,p-value<0.001) compared to AtheroRisk-conventional (AUC = 0.63,p-value<0.001). CONCLUSION The AtheroRisk-integrated ML system outperforms the AtheroRisk-conventional ML system using RF classifier.
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Affiliation(s)
- Ankush Jamthikar
- Department of Electronics and Communication Engineering, Visvesvaraya National Institute of Technology, Nagpur, Maharashtra, India
| | - Deep Gupta
- Department of Electronics and Communication Engineering, Visvesvaraya National Institute of Technology, Nagpur, Maharashtra, India
| | - Narendra N Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, India
| | - Luca Saba
- Department of Radiology, University of Cagliari, Italy
| | - John R Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA, USA
| | - Jasjit S Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA.
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The basics of data, big data, and machine learning in clinical practice. Clin Rheumatol 2020; 40:11-23. [PMID: 32504192 DOI: 10.1007/s10067-020-05196-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 05/05/2020] [Accepted: 05/20/2020] [Indexed: 12/29/2022]
Abstract
Health informatics and biomedical computing have introduced the use of computer methods to analyze clinical information and provide tools to assist clinicians during the diagnosis and treatment of diverse clinical conditions. With the amount of information that can be obtained in the healthcare setting, new methods to acquire, organize, and analyze the data are being developed each day, including new applications in the world of big data and machine learning. In this review, first we present the most basic concepts in data science, including the structural hierarchy of information and how it is managed. A section is dedicated to discussing topics relevant to the acquisition of data, importantly the availability and use of online resources such as survey software and cloud computing services. Along with digital datasets, these tools make it possible to create more diverse models and facilitate collaboration. After, we describe concepts and techniques in machine learning used to process and analyze health data, especially those most widely applied in rheumatology. Overall, the objective of this review is to aid in the comprehension of how data science is used in health, with a special emphasis on the relevance to the field of rheumatology. It provides clinicians with basic tools on how to approach and understand new trends in health informatics analysis currently being used in rheumatology practice. If clinicians understand the potential use and limitations of health informatics, this will facilitate interdisciplinary conversations and continued projects relating to data, big data, and machine learning.
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Atherosclerotic cardiovascular disease prevention in rheumatoid arthritis. Nat Rev Rheumatol 2020; 16:361-379. [PMID: 32494054 DOI: 10.1038/s41584-020-0428-y] [Citation(s) in RCA: 104] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/27/2020] [Indexed: 12/18/2022]
Abstract
Patients with rheumatoid arthritis (RA) are at high risk of developing cardiovascular disease (CVD). Inflammation has a pivotal role in the pathogenesis of CVD. RA is an inflammatory joint disease and, compared with the general population, patients with RA have approximately double the risk of atherosclerotic CVD, stroke, heart failure and atrial fibrillation. Although this high risk of CVD has been known for decades, patients with RA receive poorer primary and secondary CVD preventive care than other high-risk patients, and an unmet need exists for improved CVD preventive measures for patients with RA. This Review summarizes the evidence for atherosclerotic CVD in patients with RA and provides a contemporary analysis of what is known and what needs to be further clarified about recommendations for CVD prevention in patients with RA compared with the general population. The management of traditional CVD risk factors, including blood pressure, lipids, diabetes mellitus and lifestyle-related risk factors, as well as the effects of inflammation and the use of antirheumatic medication on CVD risk and risk management in patients with RA are discussed. The main aim is to provide a roadmap of atherosclerotic CVD risk management and prevention for patients with RA.
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Antin-Ozerkis D, Hinchcliff M. Connective Tissue Disease-Associated Interstitial Lung Disease: Evaluation and Management. Clin Chest Med 2020; 40:617-636. [PMID: 31376896 DOI: 10.1016/j.ccm.2019.05.008] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Interstitial lung disease is common among patients with connective tissue disease and is an important contributor to morbidity and mortality. Infection and drug toxicity must always be excluded as the cause of radiographic findings. Immunosuppression remains a mainstay of therapy despite few controlled trials supporting its use. When a decision regarding therapy initiation is made, considerations include an assessment of disease severity as well as a determination of the rate of progression. Because patients may have extrathoracic disease activity, a multidisciplinary approach is crucial and should include supportive and nonpharmacologic management strategies.
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Affiliation(s)
- Danielle Antin-Ozerkis
- Section of Pulmonary and Critical Care Medicine, Yale School of Medicine, PO Box 208057, New Haven, CT 06520-8057, USA.
| | - Monique Hinchcliff
- Section of Rheumatology, Allergy and Immunology, Yale School of Medicine, PO Box 208031, New Haven, CT 06520-8031, USA
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Moustafa Ahmed Y, Shehata Messiha BA, El-Sayed El-Daly M, Abo-Saif AA. Effects of ticagrelor, empagliflozin and tamoxifen against experimentally-induced vascular reactivity defects in rats in vivo and in vitro. Pharmacol Rep 2019; 71:1034-1043. [PMID: 31600634 DOI: 10.1016/j.pharep.2019.06.004] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2019] [Revised: 05/19/2019] [Accepted: 06/05/2019] [Indexed: 02/07/2023]
Abstract
BACKGROUND In the current investigation, the effects of the P2Y12 blocker ticagrelor, the sodium/glucose transporter-2-inhibitor empagliflozin, and the selective estrogen receptor modulator tamoxifen were examined against rheumatoid arthritis (RA)/diabetes mellitus (DM)-co-morbidity-induced defects in vascular reactivity. METHODS After model setting, rats were allocated into a normal control, an RA/DM-co-morbidity, and three treatment groups receiving ticagrelor, empagliflozin and tamoxifen. Aorta tissue was isolated for enzyme-linked immunosorbent assay (ELISA) and western blot estimation of the pro-inflammatory molecules angiotensin-II (Ang-II) and endothelin-1 (ET-1), the adhesion molecules P-selectin and vascular cell adhesion molecule-1 (VCAM-1), the energy preserving molecule adenosine-5'-monophosphate-activated protein kinase (AMPK), and the anti-inflammatory molecule vasoactive intestinal peptide (VIP). Estimations of endothelial nitric oxide synthase (eNOS) and matrix metalloproteinase (MMP)-2 were performed immunohistochemically, together with histopathological examination using hematoxylin and eosin and Masson trichrome staining. An in vitro study on rat aortic strips was conducted to assess aorta vasorelaxation. RESULTS Ticagrelor, empagliflozin and tamoxifen significantly increased aorta tissue AMPK and eNOS and decreased Ang-II, ET-1, P-selectin, VCAM-1 and VIP levels compared with RA/DM-co-morbidity, coupled with improved acetylcholine vasorelaxation in vitro. CONCLUSION Ticagrelor, empagliflozin and tamoxifen may correct vascular reactivity defects, where modulation of vascular AMPK, eNOS, Ang-II, ET-1, P-selectin, VCAM-1 and MMP-2 underline their protective effects.
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Affiliation(s)
- Yasmin Moustafa Ahmed
- Department of Pharmacology and Toxicology, Faculty of Pharmacy, Nahda University, Beni-Suef, Egypt
| | | | | | - Ali Ahmed Abo-Saif
- Department of Pharmacology and Toxicology, Faculty of Pharmacy, Nahda University, Beni-Suef, Egypt
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40
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Sayyid Z, Vendra V, Meister KD, Krawczeski CD, Speiser NJ, Sidell DR. Application-Based Translaryngeal Ultrasound for the Assessment of Vocal Fold Mobility in Children. Otolaryngol Head Neck Surg 2019; 161:1031-1035. [DOI: 10.1177/0194599819877650] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Objective To compare the evaluation of vocal fold mobility between flexible nasal laryngoscopy (FNL) and a handheld application-based translaryngeal ultrasound (TLUS) platform. Study Design Prospective analysis included patients with unknown vocal fold mobility status who underwent FNL and TLUS. Setting Tertiary referral center. Subjects and Methods TLUS was performed on 23 consecutive children (<18 years old) presenting for laryngoscopy due to unknown vocal fold mobility status. After the recording of three 10-second TLUS videos as well as FNL, the study was divided into 2 parts: parental assessment of laryngeal ultrasound at the time of patient evaluation and random practitioner assessment of ultrasound videos. Results We describe 23 patients who underwent TLUS and FNL. Ten patients (43.5%) had normal vocal fold function bilaterally, and 13 (56.5%) had either left or right vocal fold immobility. Family members and physicians correctly identified the presence and laterality of impaired vocal fold mobility in 22 of 23 cases (κ = 0.96). The sensitivity, specificity, positive predictive value, and negative predictive value of FLUS in diagnosing vocal fold immobility were 92.3%, 100%, 100%, and 90.9%, respectively. Random practitioners accurately identified the presence and laterality of vocal fold immobility under all circumstances. Conclusion A handheld application-based ultrasound platform is both sensitive and specific in its ability to identify vocal fold motion impairment. Portable handheld TLUS has the potential to serve as a validated screening examination, even by inexperienced providers, and in specific cases may obviate the need for an invasive transnasal laryngoscopy.
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Affiliation(s)
- Zahra Sayyid
- School of Medicine, Stanford University, Stanford, California, USA
| | - Varun Vendra
- Department of Otolaryngology–Head and Neck Surgery, Stanford University, Stanford, California, USA
| | - Kara D. Meister
- Department of Otolaryngology–Head and Neck Surgery, Stanford University, Stanford, California, USA
- Aerodigestive and Airway Reconstruction Center, Lucile Packard Children’s Hospital Stanford, Stanford, California, USA
| | - Catherine D. Krawczeski
- Division of Pediatric Cardiology, Department of Pediatrics, Lucile Packard Children’s Hospital Stanford, Stanford, California, USA
| | - Noah J. Speiser
- Research Internship Program, School of Medicine, Stanford University, Stanford, California, USA
- University of California Berkeley, Berkeley, California, USA
| | - Douglas R. Sidell
- Department of Otolaryngology–Head and Neck Surgery, Stanford University, Stanford, California, USA
- Aerodigestive and Airway Reconstruction Center, Lucile Packard Children’s Hospital Stanford, Stanford, California, USA
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Foulquier N, Redou P, Saraux A. How Health Information Technologies and Artificial Intelligence May Help Rheumatologists in Routine Practice. Rheumatol Ther 2019; 6:135-138. [PMID: 31028546 PMCID: PMC6513911 DOI: 10.1007/s40744-019-0154-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Indexed: 01/16/2023] Open
Affiliation(s)
- Nathan Foulquier
- Laboratoire de Traitement de l'Information Médicale (LATIM), UMR 1101, Brest Institute of Biological Research (IBRBS), Inserm, Université de Brest-Centre Hospitalier Universitaire, Brest, France
| | - Pascal Redou
- Laboratoire de Traitement de l'Information Médicale (LATIM), UMR 1101, Brest Institute of Biological Research (IBRBS), Inserm, Université de Brest-Centre Hospitalier Universitaire, Brest, France
| | - Alain Saraux
- Rheumatology Unit, Centre National de Référence des Maladies Auto-Immunes Rares (CERAINO), Université de Brest-Centre Hospitalier Universitaire, Brest, France.
- UMR 1227, Lymphocytes B et Autoimmunité, Inserm, Université de Brest-Centre Hospitalier Universitaire, Brest, France.
- LabEx IGO, Brest, France.
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Jamthikar A, Gupta D, Khanna NN, Araki T, Saba L, Nicolaides A, Sharma A, Omerzu T, Suri HS, Gupta A, Mavrogeni S, Turk M, Laird JR, Protogerou A, Sfikakis PP, Kitas GD, Viswanathan V, Pareek G, Miner M, Suri JS. A Special Report on Changing Trends in Preventive Stroke/Cardiovascular Risk Assessment Via B-Mode Ultrasonography. Curr Atheroscler Rep 2019; 21:25. [PMID: 31041615 DOI: 10.1007/s11883-019-0788-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
PURPOSE OF REVIEW Cardiovascular disease (CVD) and stroke risk assessment have been largely based on the success of traditional statistically derived risk calculators such as Pooled Cohort Risk Score or Framingham Risk Score. However, over the last decade, automated computational paradigms such as machine learning (ML) and deep learning (DL) techniques have penetrated into a variety of medical domains including CVD/stroke risk assessment. This review is mainly focused on the changing trends in CVD/stroke risk assessment and its stratification from statistical-based models to ML-based paradigms using non-invasive carotid ultrasonography. RECENT FINDINGS In this review, ML-based strategies are categorized into two types: non-image (or conventional ML-based) and image-based (or integrated ML-based). The success of conventional (non-image-based) ML-based algorithms lies in the different data-driven patterns or features which are used to train the ML systems. Typically these features are the patients' demographics, serum biomarkers, and multiple clinical parameters. The integrated (image-based) ML-based algorithms integrate the features derived from the ultrasound scans of the arterial walls (such as morphological measurements) with conventional risk factors in ML frameworks. Even though the review covers ML-based system designs for carotid and coronary ultrasonography, the main focus of the review is on CVD/stroke risk scores based on carotid ultrasound. There are two key conclusions from this review: (i) fusion of image-based features with conventional cardiovascular risk factors can lead to more accurate CVD/stroke risk stratification; (ii) the ability to handle multiple sources of information in big data framework using artificial intelligence-based paradigms (such as ML and DL) is likely to be the future in preventive CVD/stroke risk assessment.
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Affiliation(s)
- Ankush Jamthikar
- Department of ECE, Visvesvaraya National Institute of Technology, Nagpur, Maharashtra, India
| | - Deep Gupta
- Department of ECE, Visvesvaraya National Institute of Technology, Nagpur, Maharashtra, India
| | - Narendra N Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, India
| | - Tadashi Araki
- Division of Cardiovascular Medicine, Toho University, Tokyo, Japan
| | - Luca Saba
- Department of Radiology, University of Cagliari, Cagliari, Italy
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre, University of Cyprus, Nicosia, Cyprus
| | - Aditya Sharma
- Cardiovascular Medicine, University of Virginia, Charlottesville, VA, USA
| | - Tomaz Omerzu
- Department of Neurology, University Medical Centre Maribor, Maribor, Slovenia
| | | | - Ajay Gupta
- Department of Radiology, Cornell Medical Center, New York, NY, USA
| | - Sophie Mavrogeni
- Cardiology Clinic, Onassis Cardiac Surgery Center, Athens, Greece
| | - Monika Turk
- Department of Neurology, University Medical Centre Maribor, Maribor, Slovenia
| | - John R Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St. Helena, CA, USA
| | - Athanasios Protogerou
- Department of Cardiovascular Prevention & Research Unit Clinic & Laboratory of Pathophysiology
- , National and Kapodistrian University of Athens, Athens, Greece
| | - Petros P Sfikakis
- Rheumatology Unit, National Kapodistrian University of Athens, Athens, Greece
| | - George D Kitas
- R&D Academic Affairs, Dudley Group NHS Foundation Trust, Dudley, UK
| | - Vijay Viswanathan
- MV Hospital for Diabetes and Professor M Viswanathan Diabetes Research Centre, Chennai, India
| | - Gyan Pareek
- Minimally Invasive Urology Institute, Brown University, Providence, RI, USA
| | - Martin Miner
- Men's Health Center, Miriam Hospital Providence, Providence, RI, USA
| | - Jasjit S Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, 95661, USA.
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43
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Abstract
See Article by Al'Aref et al
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Affiliation(s)
- Kirk N Garratt
- 1 Center for Heart and Vascular Health Christiana Care Health System Wilmington DE
| | - Mark A Schneider
- 2 Applied Innovation Group Christiana Care Health System Wilmington DE
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