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ObjectiveTo evaluate the provision of bystander interventions and rates of survival after exercise-related sudden cardiac arrest (SCA).DesignSystematic review.Data sourcesMEDLINE, EMBASE, PubMed, CINAHL, SPORTDiscus, Cochrane Library and grey literature sources were searched from inception to November/December 2020.Study eligibility criteriaObservational studies assessing a population of exercise-related SCA (out-of-hospital cardiac arrests that occurred during exercise or within 1 hour of cessation of activity), where bystander cardiopulmonary resuscitation (CPR) and/or automated external defibrillator (AED) use were reported, and survival outcomes were ascertained.MethodsAmong all included studies, the median (IQR) proportions of bystander CPR and bystander AED use, as well as median (IQR) rate of survival to hospital discharge, were calculated.ResultsA total of 29 studies were included in this review, with a median study duration of 78.7 months and a median sample size of 91. Most exercise-related SCA patients were male (median: 92%, IQR: 86%–96%), middle-aged (median: 51, IQR: 39–56 years), and presented with a shockable arrest rhythm (median: 78%, IQR: 62%–86%). Bystander CPR was initiated in a median of 71% (IQR: 59%–87%) of arrests, whereas bystander AED use occurred in a median of 31% (IQR: 19%–42%) of arrests. Among the 19 studies that reported survival to hospital discharge, the median rate of survival was 32% (IQR: 24%–49%). Studies which evaluated the relationship between bystander interventions and survival outcomes reported that both bystander CPR and AED use were associated with survival after exercise-related SCA.ConclusionExercise-related SCA occurs predominantly in males and presents with a shockable ventricular arrhythmia in most cases, emphasising the importance of rapid access to defibrillation. Further efforts are needed to promote early recognition and a rapid bystander response to exercise-related SCA.

Due to the intricate relationship between the small non-coding ribonucleic acid (miRNA) sequences, the classification of miRNA species, namely Human, Gorilla, Rat, and Mouse is challenging. Previous methods are not robust and accurate. In this study, we present AtheroPoint’s GeneAI 3.0, a powerful, novel, and generalized method for extracting features from the fixed patterns of purines and pyrimidines in each miRNA sequence in ensemble paradigms in machine learning (EML) and convolutional neural network (CNN)-based deep learning (EDL) frameworks. GeneAI 3.0 utilized five conventional (Entropy, Dissimilarity, Energy, Homogeneity, and Contrast), and three contemporary (Shannon entropy, Hurst exponent, Fractal dimension) features, to generate a composite feature set from given miRNA sequences which were then passed into our ML and DL classification framework. A set of 11 new classifiers was designed consisting of 5 EML and 6 EDL for binary/multiclass classification. It was benchmarked against 9 solo ML (SML), 6 solo DL (SDL), 12 hybrid DL (HDL) models, resulting in a total of 11 + 27 = 38 models were designed. Four hypotheses were formulated and validated using explainable AI (XAI) as well as reliability/statistical tests. The order of the mean performance using accuracy (ACC)/area-under-the-curve (AUC) of the 24 DL classifiers was: EDL > HDL > SDL. The mean performance of EDL models with CNN layers was superior to that without CNN layers by 0.73%/0.92%. Mean performance of EML models was superior to SML models with improvements of ACC/AUC by 6.24%/6.46%. EDL models performed significantly better than EML models, with a mean increase in ACC/AUC of 7.09%/6.96%. The GeneAI 3.0 tool produced expected XAI feature plots, and the statistical tests showed significant p -values. Ensemble models with composite features are highly effective and generalized models for effectively classifying miRNA sequences.

Sudden cardiac death (SCD) is the leading cause of death among athletes during sports and exercise [1]. Given the unexpected nature of these medical emergencies, the sudden cardiac arrest (SCA) of an athlete often garners widespread community shock and media attention, particularly during professional sporting events [2].During a televised National Football League game between the Buffalo Bills and Cincinnati Bengals on January 2nd, 2023, 24-year-old safety Damar Hamlin sustained a major hit to his chest and helmet while involved in a defensive tackle. Despite rising quickly to his feet after the play, Hamlin suddenly collapsed. After his arms displayed brief seizure-like activity, he laid motionless on the field. Within seconds, team staff and emergency personnel rushed to his side to administer cardiopulmonary resuscitation (CPR) and use an automated external defibrillator (AED), while an ambulance later entered the field [3]. The cause of SCA has not been confirmed, although experts have speculated that commotio cordis caused from blunt trauma to the chest is a possible explanation if other underlying cardiac conditions, such as hypertrophic cardiomyopathy and coronary abnormalities, are ruled out [3].Although it is accepted that the SCA of an athlete is a tragic event, there is limited evidence to quantify the emotions and opinions experienced by the general public, which may help to guide education and awareness campaigns. The aim of this study was to evaluate the public reaction following SCA witnessed during a nationally televised sporting event.

Background and Purpose: Only 1–2% of the internal carotid artery asymptomatic plaques are unstable as a result of >80% stenosis. Thus, unnecessary efforts can be saved if these plaques can be characterized and classified into symptomatic and asymptomatic using non-invasive B-mode ultrasound. Earlier plaque tissue characterization (PTC) methods were machine learning (ML)-based, which used hand-crafted features that yielded lower accuracy and unreliability. The proposed study shows the role of transfer learning (TL)-based deep learning models for PTC. Methods: As pertained weights were used in the supercomputer framework, we hypothesize that transfer learning (TL) provides improved performance compared with deep learning. We applied 11 kinds of artificial intelligence (AI) models, 10 of them were augmented and optimized using TL approaches—a class of Atheromatic™ 2.0 TL (AtheroPoint™, Roseville, CA, USA) that consisted of (i–ii) Visual Geometric Group-16, 19 (VGG16, 19); (iii) Inception V3 (IV3); (iv–v) DenseNet121, 169; (vi) XceptionNet; (vii) ResNet50; (viii) MobileNet; (ix) AlexNet; (x) SqueezeNet; and one DL-based (xi) SuriNet-derived from UNet. We benchmark 11 AI models against our earlier deep convolutional neural network (DCNN) model. Results: The best performing TL was MobileNet, with accuracy and area-under-the-curve (AUC) pairs of 96.10 ± 3% and 0.961 (p < 0.0001), respectively. In DL, DCNN was comparable to SuriNet, with an accuracy of 95.66% and 92.7 ± 5.66%, and an AUC of 0.956 (p < 0.0001) and 0.927 (p < 0.0001), respectively. We validated the performance of the AI architectures with established biomarkers such as greyscale median (GSM), fractal dimension (FD), higher-order spectra (HOS), and visual heatmaps. We benchmarked against previously developed Atheromatic™ 1.0 ML and showed an improvement of 12.9%. Conclusions: TL is a powerful AI tool for PTC into symptomatic and asymptomatic plaques.

Background: The previous COVID-19 lung diagnosis system lacks both scientific validation and the role of explainable artificial intelligence (AI) for understanding lesion localization. This study presents a cloud-based explainable AI, the “COVLIAS 2.0-cXAI” system using four kinds of class activation maps (CAM) models. Methodology: Our cohort consisted of ~6000 CT slices from two sources (Croatia, 80 COVID-19 patients and Italy, 15 control patients). COVLIAS 2.0-cXAI design consisted of three stages: (i) automated lung segmentation using hybrid deep learning ResNet-UNet model by automatic adjustment of Hounsfield units, hyperparameter optimization, and parallel and distributed training, (ii) classification using three kinds of DenseNet (DN) models (DN-121, DN-169, DN-201), and (iii) validation using four kinds of CAM visualization techniques: gradient-weighted class activation mapping (Grad-CAM), Grad-CAM++, score-weighted CAM (Score-CAM), and FasterScore-CAM. The COVLIAS 2.0-cXAI was validated by three trained senior radiologists for its stability and reliability. The Friedman test was also performed on the scores of the three radiologists. Results: The ResNet-UNet segmentation model resulted in dice similarity of 0.96, Jaccard index of 0.93, a correlation coefficient of 0.99, with a figure-of-merit of 95.99%, while the classifier accuracies for the three DN nets (DN-121, DN-169, and DN-201) were 98%, 98%, and 99% with a loss of ~0.003, ~0.0025, and ~0.002 using 50 epochs, respectively. The mean AUC for all three DN models was 0.99 (p < 0.0001). The COVLIAS 2.0-cXAI showed 80% scans for mean alignment index (MAI) between heatmaps and gold standard, a score of four out of five, establishing the system for clinical settings. Conclusions: The COVLIAS 2.0-cXAI successfully showed a cloud-based explainable AI system for lesion localization in lung CT scans.

Background and Motivation: Cardiovascular disease (CVD) causes the highest mortality globally. With escalating healthcare costs, early non-invasive CVD risk assessment is vital. Conventional methods have shown poor performance compared to more recent and fast-evolving Artificial Intelligence (AI) methods. The proposed study reviews the three most recent paradigms for CVD risk assessment, namely multiclass, multi-label, and ensemble-based methods in (i) office-based and (ii) stress-test laboratories. Methods: A total of 265 CVD-based studies were selected using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) model. Due to its popularity and recent development, the study analyzed the above three paradigms using machine learning (ML) frameworks. We review comprehensively these three methods using attributes, such as architecture, applications, pro-and-cons, scientific validation, clinical evaluation, and AI risk-of-bias (RoB) in the CVD framework. These ML techniques were then extended under mobile and cloud-based infrastructure. Findings: Most popular biomarkers used were office-based, laboratory-based, image-based phenotypes, and medication usage. Surrogate carotid scanning for coronary artery risk prediction had shown promising results. Ground truth (GT) selection for AI-based training along with scientific and clinical validation is very important for CVD stratification to avoid RoB. It was observed that the most popular classification paradigm is multiclass followed by the ensemble, and multi-label. The use of deep learning techniques in CVD risk stratification is in a very early stage of development. Mobile and cloud-based AI technologies are more likely to be the future. Conclusions: AI-based methods for CVD risk assessment are most promising and successful. Choice of GT is most vital in AI-based models to prevent the RoB. The amalgamation of image-based strategies with conventional risk factors provides the highest stability when using the three CVD paradigms in non-cloud and cloud-based frameworks.

The presence of carotid arterial plaque by ultrasound enhances cardiovascular risk stratification beyond traditional risk factors. However, plaque quantification techniques require further outcomes-based investigation. The purpose of this study was to evaluate the utility of a focused carotid ultrasound protocol and novel plaque grading system developed by the American Society of Echocardiography (ASE). A retrospective analysis of 514 outpatients who were referred for coronary angiography between 2011 and 2014 was performed using a province-sponsored health database. All participants prospectively received a focused carotid ultrasound. Maximum plaque height (MPH) of arterial carotid plaque was quantified, using the Grade II–III plaque definition of MPH ≥ 1.5 mm for stratification, according to recent ASE recommendations. Participants were followed for 1.33–5.11 years (average follow-up = 3.60 ± 1.65 years) to identify the occurrence of cardiovascular events. Major events (death, myocardial infarction [MI], stroke, and transient ischemic attack [TIA]) were correlated to MPH. Participants with MPH ≥ 1.5 mm were more likely to experience stable angina, coronary artery bypass grafting, and stress testing at both 1-year and total follow-up. After adjusting for cardiac risk factors, increased MPH was shown to be predictive for TIA (odds ratio [OR] = 1.33, 95% confidence interval (CI) = 1.01–1.75); p = 0.04), whereas the odds of non-ST-elevation MI (OR = 1.55, 95% CI = 0.99–2.43; p = 0.06) approached significance. Using Kaplan–Meier survival analysis, MPH ≥ 1.5 mm demonstrated good separation for the composite outcome of death, MI, stroke, and TIA over total follow-up (p = 0.02). This rapid, office-based quantification of MPH in carotid ultrasound may serve as a stratification tool for predicting major cardiovascular events.

Background L-carnitine (L-C), a ubiquitous nutritional supplement, has been investigated as a potential therapy for cardiovascular disease, but its effects on human atherosclerosis are unknown. Clinical studies suggest improvement of some cardiovascular risk factors, whereas others show increased plasma levels of pro-atherogenic trimethylamine N-oxide. The primary aim was to determine whether L-C therapy led to progression or regression of carotid total plaque volume (TPV) in participants with metabolic syndrome (MetS). Methods This was a phase 2, prospective, double blinded, randomized, placebo-controlled, two-center trial. MetS was defined as ≥ 3/5 cardiac risk factors: elevated waist circumference; elevated triglycerides; reduced HDL-cholesterol; elevated blood pressure; elevated glucose or HbA1c; or on treatment. Participants with a baseline TPV ≥ 50 mm 3 were randomized to placebo or 2 g L-C daily for 6 months. Results The primary outcome was the percent change in TPV over 6 months. In 157 participants (L-C N = 76, placebo N = 81), no difference in TPV change between arms was found. The L-C group had a greater increase in carotid atherosclerotic stenosis of 9.3% ( p  = 0.02) than the placebo group. There was a greater increase in total cholesterol and LDL-C levels in the L-C arm. Conclusions Though total carotid plaque volume did not change in MetS participants taking L-C over 6-months, there was a concerning progression of carotid plaque stenosis. The potential harm of L-C in MetS and its association with pro-atherogenic metabolites raises concerns for its further use as a potential therapy and its widespread availability as a nutritional supplement. Trial registration : ClinicalTrials.gov, NCT02117661, Registered April 21, 2014, https://clinicaltrials.gov/ct2/show/NCT02117661 .

Background: COVID-19 is a disease with multiple variants, and is quickly spreading throughout the world. It is crucial to identify patients who are suspected of having COVID-19 early, because the vaccine is not readily available in certain parts of the world. Methodology: Lung computed tomography (CT) imaging can be used to diagnose COVID-19 as an alternative to the RT-PCR test in some cases. The occurrence of ground-glass opacities in the lung region is a characteristic of COVID-19 in chest CT scans, and these are daunting to locate and segment manually. The proposed study consists of a combination of solo deep learning (DL) and hybrid DL (HDL) models to tackle the lesion location and segmentation more quickly. One DL and four HDL models—namely, PSPNet, VGG-SegNet, ResNet-SegNet, VGG-UNet, and ResNet-UNet—were trained by an expert radiologist. The training scheme adopted a fivefold cross-validation strategy on a cohort of 3000 images selected from a set of 40 COVID-19-positive individuals. Results: The proposed variability study uses tracings from two trained radiologists as part of the validation. Five artificial intelligence (AI) models were benchmarked against MedSeg. The best AI model, ResNet-UNet, was superior to MedSeg by 9% and 15% for Dice and Jaccard, respectively, when compared against MD 1, and by 4% and 8%, respectively, when compared against MD 2. Statistical tests—namely, the Mann–Whitney test, paired t-test, and Wilcoxon test—demonstrated its stability and reliability, with p < 0.0001. The online system for each slice was <1 s. Conclusions: The AI models reliably located and segmented COVID-19 lesions in CT scans. The COVLIAS 1.0Lesion lesion locator passed the intervariability test.

Background: For COVID-19 lung severity, segmentation of lungs on computed tomography (CT) is the first crucial step. Current deep learning (DL)-based Artificial Intelligence (AI) models have a bias in the training stage of segmentation because only one set of ground truth (GT) annotations are evaluated. We propose a robust and stable inter-variability analysis of CT lung segmentation in COVID-19 to avoid the effect of bias. Methodology: The proposed inter-variability study consists of two GT tracers for lung segmentation on chest CT. Three AI models, PSP Net, VGG-SegNet, and ResNet-SegNet, were trained using GT annotations. We hypothesized that if AI models are trained on the GT tracings from multiple experience levels, and if the AI performance on the test data between these AI models is within the 5% range, one can consider such an AI model robust and unbiased. The K5 protocol (training to testing: 80%:20%) was adapted. Ten kinds of metrics were used for performance evaluation. Results: The database consisted of 5000 CT chest images from 72 COVID-19-infected patients. By computing the coefficient of correlations (CC) between the output of the two AI models trained corresponding to the two GT tracers, computing their differences in their CC, and repeating the process for all three AI-models, we show the differences as 0%, 0.51%, and 2.04% (all < 5%), thereby validating the hypothesis. The performance was comparable; however, it had the following order: ResNet-SegNet > PSP Net > VGG-SegNet. Conclusions: The AI models were clinically robust and stable during the inter-variability analysis on the CT lung segmentation on COVID-19 patients.