Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application

Cardiovascular disease (CVD) is one of the leading causes of morbidity and mortality in the United States of America and globally. Carotid arterial plaque, a cause and also a marker of such CVD, can be detected by various non-invasive imaging modalities such as magnetic resonance imaging (MRI), computer tomography (CT), and ultrasound (US). Characterization and classification of carotid plaque-type in these imaging modalities, especially into symptomatic and asymptomatic plaque, helps in the planning of carotid endarterectomy or stenting. It can be challenging to characterize plaque components due to (I) partial volume effect in magnetic resonance imaging (MRI) or (II) varying Hausdorff values in plaque regions in CT, and (III) attenuation of echoes reflected by the plaque during US causing acoustic shadowing. Artificial intelligence (AI) methods have become an indispensable part of healthcare and their applications to the non-invasive imaging technologies such as MRI, CT, and the US. In this narrative review, three main types of AI models (machine learning, deep learning, and transfer learning) are analyzed when applied to MRI, CT, and the US. A link between carotid plaque characteristics and the risk of coronary artery disease is presented. With regard to characterization, we review tools and techniques that use AI models to distinguish carotid plaque types based on signal processing and feature strengths. We conclude that AI-based solutions offer an accurate and robust path for tissue characterization and classification for carotid artery plaque imaging in all three imaging modalities. Due to cost, user-friendliness, and clinical effectiveness, AI in the US has dominated the most

Integration of cardiovascular risk assessment with COVID-19 using artificial intelligence

Artificial Intelligence (AI), in general, refers to the machines (or computers) that mimic "cognitive" functions that we associate with our mind, such as "learning" and "solving problem". New biomarkers derived from medical imaging are being discovered and are then fused with non-imaging biomarkers (such as office, laboratory, physiological, genetic, epidemiological, and clinical-based biomarkers) in a big data framework, to develop AI systems. These systems can support risk prediction and monitoring. This perspective narrative shows the powerful methods of AI for tracking cardiovascular risks. We conclude that AI could potentially become an integral part of the COVID-19 disease management system. Countries, large and small, should join hands with the WHO in building biobanks for scientists around the world to build AI-based platforms for tracking the cardiovascular risk assessment during COVID-19 times and long-term follow-up of the survivors

Juvenile Reactive Arthritis and other Spondyloarthritides of Childhood:A 28-year Experience from India

OBJECTIVES: Understanding of Juvenile reactive arthritis (jReA) and other spondyloarthritides of childhood (jSpA) is limited to small case series. Since most of them have speculated pathogenic origins in the gut, we compared and contrasted jReA with other jSpA -Enthesitis-related arthritis (ERA) and undifferentiated SpA (jUSpA). METHODS: A record-based medical data review of jReA, and jUSpA was compared with cohort data of ERA collected for other studies. Data are presented as median (interquartile range) and non-parametric tests used for analysis. RESULTS: Of 179 juvenile SpA (61 jReA; 101 ERA; and 17 jUSpA), 61 had jReA [M:F-52:9; 15.5 (12–18) years] with a disease duration of 2.75(1–36) months. Inflammatory backache IBP (32%), dactylitis (21%) and enthesitis (29%) were common. A significant proportion (14 of 17, 82.3% at >6 months follow-up) had a chronic course. 101 ERA [M:F-93:7; age-16(14–20) years] had a longer disease duration (45 vs 2.75 months, p<0.001), as compared with jReA. Enthesitis and IBP was more common in ERA (OR-2.3 and 3.4 respectively). jUSpA (n=17) had a similar clinico-laboratory profile and exhibited significant (7 of 17, 58.3%) chronicity over 9.5(4.8–37) months follow-up. CONCLUSION: jReA and jSpA exhibit similar features apart from varying disease duration, suggesting that jspA may form a continuum with similar clinico-laboratory profiles plausibly due to shared pathogenesis

Serum Interleukin-6, Interleukin-17A, and transforming growth factor beta are raised in systemic sclerosis with interstitial lung disease

Background: Dysregulation in cytokines like interleukin-6 (IL-6), interleukin-17A (IL-17A) and transforming growth factor-beta (TGF β1) has been pathogenically implicated in systemic sclerosis (SSc). The present study aimed to assess their serum levels in patients with SSc and correlate with clinical manifestations. Methods: This cross-sectional, observational study included 93 patients fulfilling the 2013 revised ACR/EULAR SSc classification criteria and 33 age-and sex-matched healthy controls. Antinuclear antibody (ANA), extractable nuclear antigen (ENA) profile, chest radiograph, pulmonary function tests and electrocardiography were done. HRCT of thorax and echocardiography were done wherever indicated. Modified Rodnan skin score (MRSS) was calculated. Serum IL-6, IL-17A and TGF β1 levels were assayed using ELISA kit and compared among disease subtypes and clinical parameters. Spearman coefficient was used to test correlation between continuous variables. P value of <0.05 was considered significant. Results: The mean age of patients was 37.8+10.3 years (Female:Male: 30:1) with median duration of disease of 3 years. Serum IL-6, IL-17A and TGF β1 levels were significantly higher in patients as compared to controls (IL-6: 19.4±11 vs 6.7±3.9 pg/ml (P < 0.0001); IL-17A: 39.1±14.8 vs 16.4±2.1 pg/ml (P < 0.0001); TGF β1: 862.2±443 vs 377.2±208.8 pg/ml (P < 0.0001). Higher levels of these cytokines were also observed in patients of diffuse cutaneous SSc, those with lung fibrosis and anti-topoisomerase positivity. Conclusion: Serum IL-6, IL-17A and TGF β1 levels were significantly higher in SSc patients and higher levels were associated with ILD and skin fibrosis