Long-COVID diagnosis: From diagnostic to advanced AI-driven models

Heterogeneity of diagnosis and documentation of post-COVID conditions in primary care: A machine learning analysis

BACKGROUND

Post-COVID conditions (PCC) have proven difficult to diagnose. In this retrospective observational study, we aimed to characterize the level of variation in PCC diagnoses observed across clinicians from a number of methodological angles and to determine whether natural language classifiers trained on clinical notes can reconcile differences in diagnostic definitions.

METHODS

We used data from 519 primary care clinics around the United States who were in the American Family Cohort registry between October 1, 2021 (when the ICD-10 code for PCC was activated) and November 1, 2023. There were 6,116 patients with a diagnostic code for PCC (U09.9), and 5,020 with diagnostic codes for both PCC and COVID-19. We explored these data using 4 different outcomes: 1) Time between COVID-19 and PCC diagnostic codes; 2) Count of patients with PCC diagnostic codes per clinician; 3) Patient-specific probability of PCC diagnostic code based on patient and clinician characteristics; and 4) Performance of a natural language classifier trained on notes from 5,000 patients annotated by two physicians to indicate probable PCC.

RESULTS

Of patients with diagnostic codes for PCC and COVID-19, 61.3% were diagnosed with PCC less than 12 weeks after initial recorded COVID-19. Clinicians in the top 1% of diagnostic propensity accounted for more than a third of all PCC diagnoses (35.8%). Comparing LASSO logistic regressions predicting documentation of PCC diagnosis, a log-likelihood test showed significantly better fit when clinician and practice site indicators were included (p < 0.0001). Inter-rater agreement between physician annotators on PCC diagnosis was moderate (Cohen's kappa: 0.60), and performance of the natural language classifiers was marginal (best AUC: 0.724, 95% credible interval: 0.555-0.878).

CONCLUSION

We found evidence of substantial disagreement between clinicians on diagnostic criteria for PCC. The variation in diagnostic rates across clinicians points to the possibilities of under- and over-diagnosis for patients.

Current and future applications of photon-counting computed tomography in cardiovascular medicine

Photon-counting CT (PCCT) represents a transformative advancement in cardiac imaging, addressing key limitations of conventional CT. This review synthesises current evidence to demonstrate how PCCT's superior spatial resolution, enhanced tissue characterisation and multienergy capabilities broaden the diagnostic potential of cardiac CT. Applications include the precise detection and quantification of coronary artery calcifications, evaluation of coronary plaque burden and composition, improved assessment of coronary stents, and comprehensive myocardial tissue characterisation and perfusion analysis. By offering high-quality spectral information and detailed tissue characterisation, PCCT provides a non-invasive alternative for assessing coronary artery disease and myocardial pathology, reducing the need for invasive coronary angiography and cardiac MRI. Despite ongoing challenges in technology and clinical implementation, PCCT has the potential to revolutionise cardiovascular diagnostics, optimise diagnostic workflows and enhance patient care through more accurate, streamlined and comprehensive assessments.

A multidisciplinary review of long COVID to address the challenges in diagnosis and updated management guidelines

Long COVID has emerged as a significant challenge since the COVID-19 pandemic, which was declared as an outbreak in March 2020, marked by diverse symptoms and prolonged duration of disease. Defined by the WHO as symptoms persisting or emerging for at least two months post-SARS-CoV-2 infection without an alternative cause, its prevalence varies globally, with estimates of 10-20% in Europe, 7.3% in the USA, and 3.0% in the UK. The condition's etiology remains unclear, involving factors, such as renin-angiotensin system overactivation, persistent viral reservoirs, immune dysregulation, and autoantibodies. Reactivated viruses, like EBV and HSV-6, alongside epigenetic alterations, exacerbate mitochondrial dysfunction and energy imbalance. Emerging evidence links SARS-CoV-2 to chromatin and gut microbiome changes, further influencing long-term health impacts. Diagnosis of long COVID requires detailed systemic evaluation through medical history and physical examination. Management is highly individualized, focusing mainly on the patient's symptoms and affected systems. A multidisciplinary approach is essential, integrating diverse perspectives to address systemic manifestations, underlying mechanisms, and therapeutic strategies. Enhanced understanding of long COVID's pathophysiology and clinical features is critical to improving patient outcomes and quality of life. With a growing number of cases expected globally, advancing research and disseminating knowledge on long COVID remain vital for developing effective diagnostic and management frameworks, ultimately supporting better care for affected individuals.

A Review on Long COVID Screening: Challenges and Perspectives Focusing on Exhaled Breath Gas Sensing

Long COVID (LC) is a great global health concern, affecting individuals recovering from SARS-CoV-2 infection. The persistent and varied symptoms across multiple organs complicate diagnosis and management, and an incomplete understanding of the condition hinders advancements in therapeutics. Current diagnostic methods face challenges related to standardization and completeness. To overcome this, new technologies such as sensor-based electronic noses are being explored for LC assessment, offering a noninvasive screening approach via volatile organic compounds (VOC) sensing in exhaled breath. Although specific LC-associated VOCs have not been fully characterized, insights from COVID-19 research suggest their potential as biomarkers. Additionally, AI-driven chemometrics are promising in identifying and predicting outcomes; despite challenges, AI-driven technologies hold the potential to enhance LC evaluation, providing rapid and accurate diagnostics for improved patient care and outcomes. This review underscores the importance of emerging and sensing technologies and comprehensive diagnostic strategies to address screening and treatment challenges in the face of LC.

The Untapped Potential of Dimension Reduction in Neuroimaging: Artificial Intelligence-Driven Multimodal Analysis of Long COVID Fatigue

This perspective paper explores the untapped potential of artificial intelligence (AI), particularly machine learning-based dimension reduction techniques in multimodal neuroimaging analysis of Long COVID fatigue. The complexity and high dimensionality of neuroimaging data from modalities such as positron emission tomography (PET) and magnetic resonance imaging (MRI) pose significant analytical challenges. Deep neural networks and other machine learning approaches offer powerful tools for managing this complexity and extracting meaningful patterns. The paper discusses current challenges in neuroimaging data analysis, reviews state-of-the-art AI approaches for dimension reduction and multimodal integration, and examines their potential applications in Long COVID research. Key areas of focus include the development of AI-based biomarkers, AI-informed treatment strategies, and personalized medicine approaches. The authors argue that AI-driven multimodal neuroimaging analysis represents a paradigm shift in studying complex brain disorders like Long COVID. While acknowledging technical and ethical challenges, the paper emphasizes the potential of these advanced techniques to uncover new insights into the condition, which might lead to improved diagnostic and therapeutic strategies for those affected by Long COVID fatigue. The broader implications for understanding and treating other complex neurological and psychiatric conditions are also discussed.

Machine learning predicts pulmonary Long Covid sequelae using clinical data

Long COVID is a multi-systemic disease characterized by the persistence or occurrence of many symptoms that in many cases affect the pulmonary system. These, in turn, may deteriorate the patient's quality of life making it easier to develop severe complications. Being able to predict this syndrome is therefore important as this enables early treatment. In this work, we investigated three machine learning approaches that use clinical data collected at the time of hospitalization to this goal. The first works with all the descriptors feeding a traditional shallow learner, the second exploits the benefits of an ensemble of classifiers, and the third is driven by the intrinsic multimodality of the data so that different models learn complementary information. The experiments on a new cohort of data from 152 patients show that it is possible to predict pulmonary Long Covid sequelae with an accuracy of up to 94 % . As a further contribution, this work also publicly discloses the related data repository to foster research in this field.

Health care for patients with long COVID: a scoping review

OBJECTIVE

To map the scientific evidence on the care offered to health service users with Long Covid-19.

METHOD

This is a scoping review based on the methods of the Joanna Briggs Institute. Primary studies were included, in Portuguese, English and Spanish, published between December 2019 and June 2023, in the Virtual Health Library, Web of Science, Scopus, PUBMED, SciELO and LITCovid LongCovid databases.

RESULTS

Of the ١٣ articles analyzed, it stands out that the care provided to patients with Long Covid is associated with drug prescription, indication of physical exercises, telerehabilitation and physiotherapy.

CONCLUSION

A fragmentation was identified in the care provided to users of health services with Long Covid, with care directed only at isolated symptoms, without addressing the biopsychosocial care that people with this condition need.

Text mining method to unravel long COVID's clinical condition in hospitalized patients

Long COVID is characterized by persistent that extends symptoms beyond established timeframes. Its varied presentation across different populations and healthcare systems poses significant challenges in understanding its clinical manifestations and implications. In this study, we present a novel application of text mining technique to automatically extract unstructured data from a long COVID survey conducted at a prominent university hospital in São Paulo, Brazil. Our phonetic text clustering (PTC) method enables the exploration of unstructured Electronic Healthcare Records (EHR) data to unify different written forms of similar terms into a single phonemic representation. We used n-gram text analysis to detect compound words and negated terms in Portuguese-BR, focusing on medical conditions and symptoms related to long COVID. By leveraging text mining, we aim to contribute to a deeper understanding of this chronic condition and its implications for healthcare systems globally. The model developed in this study has the potential for scalability and applicability in other healthcare settings, thereby supporting broader research efforts and informing clinical decision-making for long COVID patients.

129Xe MRI Ventilation Textures and Longitudinal Quality-of-Life Improvements in Long-COVID

RATIONALE AND OBJECTIVES

It remains difficult to predict longitudinal outcomes in long-COVID, even with chest CT and functional MRI. 129Xe MRI reflects airway dysfunction, measured using ventilation defect percent (VDP) and in long-COVID patients, MRI VDP was abnormal, suggestive of airways disease. While MRI VDP and quality-of-life improved 15-month post-COVID infection, both remained abnormal. To better understand the relationship of airways disease and quality-of-life improvements in patients with long-COVID, we extracted 129Xe ventilation MRI textures and generated machine-learning models in an effort to predict improved quality-of-life, 15-month post-infection.

MATERIALS AND METHODS

Long-COVID patients provided written-informed consent to 3-month and 15-month post-infection visits. Pyradiomics was used to extract 129Xe ventilation MRI texture features, which were ranked using a Random-Forest classifier. Top-ranking features were used in classification models to dichotomize patients based on St. George's Respiratory Questionnaire (SGRQ) score improvement greater than the minimal-clinically-important-difference (MCID). Classification performance was evaluated using the area under the receiver-operator-characteristic-curve (AUC), sensitivity, and specificity.

RESULTS

120 texture features were extracted from 129Xe ventilation MRI in 44 long-COVID participants (54 ± 14 years), including 30 (52 ± 12 years) with ΔSGRQ≥MCID and 14 (58 ± 18 years) with ΔSGRQ

CONCLUSION

A machine learning model exclusively trained on 129Xe MRI ventilation textures explained improved SGRQ-scores 12 months later, and outperformed clinical models. Their unique spatial-intensity information helps build our understanding about long-COVID airway dysfunction.

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MESH Headings

• Humans
• COVID-19/diagnostic imaging
• COVID-19/complications
• Magnetic Resonance Imaging/methods
• Male
• Female
• Middle Aged
• Quality of Life
• Xenon Isotopes
• Aged
• Machine Learning
• Longitudinal Studies
• SARS-CoV-2
• Lung/diagnostic imaging

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Affiliation(s)

Harkiran K Kooner Robarts Research Institute, Western University, London, Canada; Department of Medical Biophysics, Western University, London, Canada
Maksym Sharma Robarts Research Institute, Western University, London, Canada; Department of Medical Biophysics, Western University, London, Canada
Marrissa J McIntosh Robarts Research Institute, Western University, London, Canada; Department of Medical Biophysics, Western University, London, Canada
Inderdeep Dhaliwal Division of Respirology, Department of Medicine, Western University, London, Canada
J Michael Nicholson Division of Respirology, Department of Medicine, Western University, London, Canada
Miranda Kirby Department of Physics, Toronto Metropolitan University, Toronto, Canada
Sarah Svenningsen Division of Respirology, Department of Medicine, McMaster University and Firestone Institute for Respiratory Health, St. Joseph's Health Care, Hamilton, Canada
Grace Parraga Robarts Research Institute, Western University, London, Canada; Department of Medical Biophysics, Western University, London, Canada; Division of Respirology, Department of Medicine, Western University, London, Canada.

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Technical principles, benefits, challenges, and applications of photon counting computed tomography in coronary imaging: a narrative review

Background and Objective

The introduction of photon-counting computed tomography (PCCT) represents the most recent groundbreaking advancement in clinical computed tomography (CT). PCCT has the potential to overcome the limitations of traditional CT and to provide new quantitative imaging information. This narrative review aims to summarize the technical principles, benefits, and challenges of PCCT and to provide a concise yet comprehensive summary of the applications of PCCT in the domain of coronary imaging.

Methods

A review of PubMed, Scopus, and Google Scholar was performed until October 2023 by using relevant keywords. Articles in English were considered.

Key Content and Findings

The main advantages of PCCT over traditional CT are enhanced spatial resolution, improved signal and contrast characteristics, diminished electronic noise and image artifacts, lower radiation exposure, and multi-energy capability with enhanced material discrimination. These key characteristics have made room for improved assessment of plaque volume and severity of stenosis, more precise assessment of coronary artery calcifications, also preserved in the case of a reduced radiation dose, improved assessment of plaque composition, possibility to provide details regarding the biological processes occurring within the plaque, enhanced quality and accuracy of coronary stent imaging, and improved radiomic analyses.

Conclusions

PCCT can significantly impact diagnostic and clinical pathways and improve the management of patients with coronary artery diseases (CADs).

Significance of Artificial Intelligence in the Study of Virus-Host Cell Interactions

A highly critical event in a virus's life cycle is successfully entering a given host. This process begins when a viral glycoprotein interacts with a target cell receptor, which provides the molecular basis for target virus-host cell interactions for novel drug discovery. Over the years, extensive research has been carried out in the field of virus-host cell interaction, generating a massive number of genetic and molecular data sources. These datasets are an asset for predicting virus-host interactions at the molecular level using machine learning (ML), a subset of artificial intelligence (AI). In this direction, ML tools are now being applied to recognize patterns in these massive datasets to predict critical interactions between virus and host cells at the protein-protein and protein-sugar levels, as well as to perform transcriptional and translational analysis. On the other end, deep learning (DL) algorithms-a subfield of ML-can extract high-level features from very large datasets to recognize the hidden patterns within genomic sequences and images to develop models for rapid drug discovery predictions that address pathogenic viruses displaying heightened affinity for receptor docking and enhanced cell entry. ML and DL are pivotal forces, driving innovation with their ability to perform analysis of enormous datasets in a highly efficient, cost-effective, accurate, and high-throughput manner. This review focuses on the complexity of virus-host cell interactions at the molecular level in light of the current advances of ML and AI in viral pathogenesis to improve new treatments and prevention strategies.

Four Transformer-Based Deep Learning Classifiers Embedded with an Attention U-Net-Based Lung Segmenter and Layer-Wise Relevance Propagation-Based Heatmaps for COVID-19 X-ray Scans

Background: Diagnosing lung diseases accurately is crucial for proper treatment. Convolutional neural networks (CNNs) have advanced medical image processing, but challenges remain in their accurate explainability and reliability. This study combines U-Net with attention and Vision Transformers (ViTs) to enhance lung disease segmentation and classification. We hypothesize that Attention U-Net will enhance segmentation accuracy and that ViTs will improve classification performance. The explainability methodologies will shed light on model decision-making processes, aiding in clinical acceptance. Methodology: A comparative approach was used to evaluate deep learning models for segmenting and classifying lung illnesses using chest X-rays. The Attention U-Net model is used for segmentation, and architectures consisting of four CNNs and four ViTs were investigated for classification. Methods like Gradient-weighted Class Activation Mapping plus plus (Grad-CAM++) and Layer-wise Relevance Propagation (LRP) provide explainability by identifying crucial areas influencing model decisions. Results: The results support the conclusion that ViTs are outstanding in identifying lung disorders. Attention U-Net obtained a Dice Coefficient of 98.54% and a Jaccard Index of 97.12%. ViTs outperformed CNNs in classification tasks by 9.26%, reaching an accuracy of 98.52% with MobileViT. An 8.3% increase in accuracy was seen while moving from raw data classification to segmented image classification. Techniques like Grad-CAM++ and LRP provided insights into the decision-making processes of the models. Conclusions: This study highlights the benefits of integrating Attention U-Net and ViTs for analyzing lung diseases, demonstrating their importance in clinical settings. Emphasizing explainability clarifies deep learning processes, enhancing confidence in AI solutions and perhaps enhancing clinical acceptance for improved healthcare results.

Spectral Photon-Counting Computed Tomography: Technical Principles and Applications in the Assessment of Cardiovascular Diseases

Spectral Photon-Counting Computed Tomography (SPCCT) represents a groundbreaking advancement in X-ray imaging technology. The core innovation of SPCCT lies in its photon-counting detectors, which can count the exact number of incoming x-ray photons and individually measure their energy. The first part of this review summarizes the key elements of SPCCT technology, such as energy binning, energy weighting, and material decomposition. Its energy-discriminating ability represents the key to the increase in the contrast between different tissues, the elimination of the electronic noise, and the correction of beam-hardening artifacts. Material decomposition provides valuable insights into specific elements' composition, concentration, and distribution. The capability of SPCCT to operate in three or more energy regimes allows for the differentiation of several contrast agents, facilitating quantitative assessments of elements with specific energy thresholds within the diagnostic energy range. The second part of this review provides a brief overview of the applications of SPCCT in the assessment of various cardiovascular disease processes. SPCCT can support the study of myocardial blood perfusion and enable enhanced tissue characterization and the identification of contrast agents, in a manner that was previously unattainable.

Long COVID-19 and primary care: Challenges, management and recommendations

Long COVID-19, also known as post-acute sequelae of SARS-CoV-2 infection (PASC), is characterized by persistent symptoms after COVID-19 onset. This article explores the challenges, management strategies, and recommendations for addressing long COVID-19 in primary care settings. The epidemiology of long COVID-19 reveals significant variability, with a substantial portion of COVID-19 survivors experiencing post-acute symptoms. Pathophysiological mechanisms include viral persistence, endothelial dysfunction, autoimmunity, neurological dysregulation, and gastrointestinal dysbiosis. Multiple risk factors, including age, sex, pre-existing comorbidities, smoking, BMI, and acute COVID-19 severity, influence the development of long COVID-19. Effective management requires proactive measures such as vaccination, identification of high-risk populations, public awareness, and post-infection vaccination. Collaboration of primary care physicians with specialists is essential for holistic and individualized patient care. This article underscores the role of primary care physicians in diagnosing, managing, and mitigating the long-term effects of COVID-19.

Possible Role of Cannabis in the Management of Neuroinflammation in Patients with Post-COVID Condition

The post-COVID condition (PCC) is a pathology stemming from COVID-19, and studying its pathophysiology, diagnosis, and treatment is crucial. Neuroinflammation causes the most common manifestations of this disease including headaches, fatigue, insomnia, depression, anxiety, among others. Currently, there are no specific management proposals; however, given that the inflammatory component involves cytokines and free radicals, these conditions must be treated to reduce the current symptoms and provide neuroprotection to reduce the risk of a long-term neurodegenerative disease. It has been shown that cannabis has compounds with immunomodulatory and antioxidant functions in other pathologies. Therefore, exploring this approach could provide a viable therapeutic option for PCC, which is the purpose of this review. This review involved an exhaustive search in specialized databases including PubMed, PubChem, ProQuest, EBSCO, Scopus, Science Direct, Web of Science, and Clinical Trials. Phytocannabinoids, including cannabidiol (CBD), cannabigerol (CBG), and Delta-9-tetrahydrocannabinol (THC), exhibit significant antioxidative and anti-inflammatory properties and have been shown to be an effective treatment for neuroinflammatory conditions. These compounds could be promising adjuvants for PCC alone or in combination with other antioxidants or therapies. PCC presents significant challenges to neurological health, and neuroinflammation and oxidative stress play central roles in its pathogenesis. Antioxidant therapy and cannabinoid-based approaches represent promising areas of research and treatment for mitigating adverse effects, but further studies are needed.

Detection of COVID-19 features in lung ultrasound images using deep neural networks

BACKGROUND

Deep neural networks (DNNs) to detect COVID-19 features in lung ultrasound B-mode images have primarily relied on either in vivo or simulated images as training data. However, in vivo images suffer from limited access to required manual labeling of thousands of training image examples, and simulated images can suffer from poor generalizability to in vivo images due to domain differences. We address these limitations and identify the best training strategy.

METHODS

We investigated in vivo COVID-19 feature detection with DNNs trained on our carefully simulated datasets (40,000 images), publicly available in vivo datasets (174 images), in vivo datasets curated by our team (958 images), and a combination of simulated and internal or external in vivo datasets. Seven DNN training strategies were tested on in vivo B-mode images from COVID-19 patients.

RESULTS

Here, we show that Dice similarity coefficients (DSCs) between ground truth and DNN predictions are maximized when simulated data are mixed with external in vivo data and tested on internal in vivo data (i.e., 0.482 ± 0.211), compared with using only simulated B-mode image training data (i.e., 0.464 ± 0.230) or only external in vivo B-mode training data (i.e., 0.407 ± 0.177). Additional maximization is achieved when a separate subset of the internal in vivo B-mode images are included in the training dataset, with the greatest maximization of DSC (and minimization of required training time, or epochs) obtained after mixing simulated data with internal and external in vivo data during training, then testing on the held-out subset of the internal in vivo dataset (i.e., 0.735 ± 0.187).

CONCLUSIONS

DNNs trained with simulated and in vivo data are promising alternatives to training with only real or only simulated data when segmenting in vivo COVID-19 lung ultrasound features.

A survey on the role of artificial intelligence in managing Long COVID

In the last years, several techniques of artificial intelligence have been applied to data from COVID-19. In addition to the symptoms related to COVID-19, many individuals with SARS-CoV-2 infection have described various long-lasting symptoms, now termed Long COVID. In this context, artificial intelligence techniques have been utilized to analyze data from Long COVID patients in order to assist doctors and alleviate the considerable strain on care and rehabilitation facilities. In this paper, we explore the impact of the machine learning methodologies that have been applied to analyze the many aspects of Long COVID syndrome, from clinical presentation through diagnosis. We also include the text mining techniques used to extract insights and trends from large amounts of text data related to Long COVID. Finally, we critically compare the various approaches and outline the work that has to be done to create a robust artificial intelligence approach for efficient diagnosis and treatment of Long COVID.

Artificial Intelligence in the Differential Diagnosis of Cardiomyopathy Phenotypes

Artificial intelligence (AI) is rapidly being applied to the medical field, especially in the cardiovascular domain. AI approaches have demonstrated their applicability in the detection, diagnosis, and management of several cardiovascular diseases, enhancing disease stratification and typing. Cardiomyopathies are a leading cause of heart failure and life-threatening ventricular arrhythmias. Identifying the etiologies is fundamental for the management and diagnostic pathway of these heart muscle diseases, requiring the integration of various data, including personal and family history, clinical examination, electrocardiography, and laboratory investigations, as well as multimodality imaging, making the clinical diagnosis challenging. In this scenario, AI has demonstrated its capability to capture subtle connections from a multitude of multiparametric datasets, enabling the discovery of hidden relationships in data and handling more complex tasks than traditional methods. This review aims to present a comprehensive overview of the main concepts related to AI and its subset. Additionally, we review the existing literature on AI-based models in the differential diagnosis of cardiomyopathy phenotypes, and we finally examine the advantages and limitations of these AI approaches.

Unveiling the future of COVID-19 patient care: groundbreaking prediction models for severe outcomes or mortality in hospitalized cases

Background

Previous studies have identified COVID-19 risk factors, such as age and chronic health conditions, linked to severe outcomes and mortality. However, accurately predicting severe illness in COVID-19 patients remains challenging, lacking precise methods.

Objective

This study aimed to leverage clinical real-world data and multiple machine-learning algorithms to formulate innovative predictive models for assessing the risk of severe outcomes or mortality in hospitalized patients with COVID-19.

Methods

Data were obtained from the Taipei Medical University Clinical Research Database (TMUCRD) including electronic health records from three Taiwanese hospitals in Taiwan. This study included patients admitted to the hospitals who received an initial diagnosis of COVID-19 between January 1, 2021, and May 31, 2022. The primary outcome was defined as the composite of severe infection, including ventilator use, intubation, ICU admission, and mortality. Secondary outcomes consisted of individual indicators. The dataset encompassed demographic data, health status, COVID-19 specifics, comorbidities, medications, and laboratory results. Two modes (full mode and simplified mode) are used; the former includes all features, and the latter only includes the 30 most important features selected based on the algorithm used by the best model in full mode. Seven machine learning was employed algorithms the performance of the models was evaluated using metrics such as the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, and specificity.

Results

The study encompassed 22,192 eligible in-patients diagnosed with COVID-19. In the full mode, the model using the light gradient boosting machine algorithm achieved the highest AUROC value (0.939), with an accuracy of 85.5%, a sensitivity of 0.897, and a specificity of 0.853. Age, vaccination status, neutrophil count, sodium levels, and platelet count were significant features. In the simplified mode, the extreme gradient boosting algorithm yielded an AUROC of 0.935, an accuracy of 89.9%, a sensitivity of 0.843, and a specificity of 0.902.

Conclusion

This study illustrates the feasibility of constructing precise predictive models for severe outcomes or mortality in COVID-19 patients by leveraging significant predictors and advanced machine learning. These findings can aid healthcare practitioners in proactively predicting and monitoring severe outcomes or mortality among hospitalized COVID-19 patients, improving treatment and resource allocation.

Body composition and cardiorespiratory fitness of overweight COVID-19 survivors in different severity degrees: a cohort study

COVID-19 sequelae are varied, and whether they are temporary or permanent is still unknown. Identifying these sequelae may guide therapeutic strategies to improve these individuals' recovery. This prospective cohort aimed to assess body composition, cardiopulmonary fitness, and long-term symptoms of overweight individuals affected by COVID-19. Participants (n = 90) were divided into three groups according to the severity of acute COVID-19: mild (no hospitalization), moderate (hospitalization, without oxygen support), and severe/critical cases (hospitalized in Intensive Care Unit). We assessed body composition with a tetrapolar multifrequency bioimpedance, hemodynamic variables (heart rate, blood pressure, and peripheral oxygen saturation-SpO2) at rest, and the Bruce test with direct gas exchange. Two assessments with a one-year interval were performed. The most prevalent long-term symptoms were memory deficit (66.7%), lack of concentration (51.7%), fatigue (65.6%), and dyspnea (40%). Bruce test presented a time effect with an increase in the distance walked after 1 year just for severe/critical group (p < 0.05). SpO2 was significantly lower in the severe/critical group up to 5 min after the Bruce test when compared to the mild group, and diastolic blood pressure at the end of the Bruce test was significantly higher in the severe/critical group when compared to mild group (p < 0.05; for all comparisons). A time effect was observed for body composition, with increased lean mass, skeletal muscle mass, fat-free mass, and lean mass just for the severe/critical group after 1 year (p < 0.05). Cardiopulmonary fitness parameters did not differ among the groups, except for respiratory quotient with higher values for the severe/critical group when compared to itself after 1 year. All COVID-19 patients might present long-term sequelae, regardless of the acute disease severity. Reassessing and identifying the most prevalent long-term sequelae are essential to perform more precise health promotion interventions.

Benzoindolizidine Alkaloids Tylophorine and Lycorine and Their Analogues with Antiviral, Anti-Inflammatory, and Anticancer Properties: Promises and Challenges

Ongoing viral research, essential for public health due to evolving viruses, gains significance owing to emerging viral infections such as the SARS-CoV-2 pandemic. Marine and plant alkaloids show promise as novel potential pharmacological strategies. In this narrative review, we elucidated the potential of tylophorine and lycorine, two naturally occurring plant-derived alkaloids with a shared benzoindolizidine scaffold, as antiviral agents to be potentially harnessed against respiratory viral infections. Possible structure-activity relationships have also been highlighted. The substances and their derivatives were found to be endowed with powerful and broad-spectrum antiviral properties; moreover, they were able to counteract inflammation, which often underpins the complications of viral diseases. At last, their anticancer properties hold promise not only for advancing cancer research but also for mitigating the oncogenic effects of viruses. This evidence suggests that tylophorine and lycorine could effectively counteract the pathogenesis of respiratory viral disease and its harmful effects. Although common issues about the pharmacologic development of natural substances remain to be addressed, the collected evidence highlights a possible interest in tylophorine and lycorine as antiviral and/or adjuvant strategies and encourages future more in-depth pre-clinical and clinical investigations to overcome their drawbacks and harness their power for therapeutic purposes.

Epstein-Barr virus-acquired immunodeficiency in myalgic encephalomyelitis-Is it present in long COVID?

Both myalgic encephalomyelitis or chronic fatigue syndrome (ME/CFS) and long COVID (LC) are characterized by similar immunological alterations, persistence of chronic viral infection, autoimmunity, chronic inflammatory state, viral reactivation, hypocortisolism, and microclot formation. They also present with similar symptoms such as asthenia, exercise intolerance, sleep disorders, cognitive dysfunction, and neurological and gastrointestinal complaints. In addition, both pathologies present Epstein-Barr virus (EBV) reactivation, indicating the possibility of this virus being the link between both pathologies. Therefore, we propose that latency and recurrent EBV reactivation could generate an acquired immunodeficiency syndrome in three steps: first, an acquired EBV immunodeficiency develops in individuals with "weak" EBV HLA-II haplotypes, which prevents the control of latency I cells. Second, ectopic lymphoid structures with EBV latency form in different tissues (including the CNS), promoting inflammatory responses and further impairment of cell-mediated immunity. Finally, immune exhaustion occurs due to chronic exposure to viral antigens, with consolidation of the disease. In the case of LC, prior to the first step, there is the possibility of previous SARS-CoV-2 infection in individuals with "weak" HLA-II haplotypes against this virus and/or EBV.

Applications of artificial intelligence-based models in vulnerable carotid plaque

Carotid atherosclerotic disease is a widely acknowledged risk factor for ischemic stroke, making it a major concern on a global scale. To alleviate the socio-economic impact of carotid atherosclerotic disease, crucial objectives include prioritizing prevention efforts and early detection. So far, the degree of carotid stenosis has been regarded as the primary parameter for risk assessment and determining appropriate therapeutic interventions. Histopathological and imaging-based studies demonstrated important differences in the risk of cardiovascular events given a similar degree of luminal stenosis, identifying plaque structure and composition as key determinants of either plaque vulnerability or stability. The application of Artificial Intelligence (AI)-based techniques to carotid imaging can offer several solutions for tissue characterization and classification. This review aims to present a comprehensive overview of the main concepts related to AI. Additionally, we review the existing literature on AI-based models in ultrasound (US), computed tomography (CT), and Magnetic Resonance Imaging (MRI) for vulnerable plaque detection, and we finally examine the advantages and limitations of these AI approaches.

Cardiovascular Applications of Photon-Counting CT Technology: A Revolutionary New Diagnostic Step

Photon-counting computed tomography (PCCT) is an emerging technology that can potentially transform clinical CT imaging. After a brief description of the PCCT technology, this review summarizes its main advantages over conventional CT: improved spatial resolution, improved signal and contrast behavior, reduced electronic noise and artifacts, decreased radiation dose, and multi-energy capability with improved material discrimination. Moreover, by providing an overview of the existing literature, this review highlights how the PCCT benefits have been harnessed to enhance and broaden the diagnostic capabilities of CT for cardiovascular applications, including the detection of coronary artery calcifications, evaluation of coronary plaque extent and composition, evaluation of coronary stents, and assessment of myocardial tissue characteristics and perfusion.

Artificial Intelligence Applications in Cardiovascular Magnetic Resonance Imaging: Are We on the Path to Avoiding the Administration of Contrast Media?

In recent years, cardiovascular imaging examinations have experienced exponential growth due to technological innovation, and this trend is consistent with the most recent chest pain guidelines. Contrast media have a crucial role in cardiovascular magnetic resonance (CMR) imaging, allowing for more precise characterization of different cardiovascular diseases. However, contrast media have contraindications and side effects that limit their clinical application in determinant patients. The application of artificial intelligence (AI)-based techniques to CMR imaging has led to the development of non-contrast models. These AI models utilize non-contrast imaging data, either independently or in combination with clinical and demographic data, as input to generate diagnostic or prognostic algorithms. In this review, we provide an overview of the main concepts pertaining to AI, review the existing literature on non-contrast AI models in CMR, and finally, discuss the strengths and limitations of these AI models and their possible future development.

Dual-Source Photon-Counting Computed Tomography-Part I: Clinical Overview of Cardiac CT and Coronary CT Angiography Applications

The photon-counting detector (PCD) is a new computed tomography detector technology (photon-counting computed tomography, PCCT) that provides substantial benefits for cardiac and coronary artery imaging. Compared with conventional CT, PCCT has multi-energy capability, increased spatial resolution and soft tissue contrast with near-null electronic noise, reduced radiation exposure, and optimization of the use of contrast agents. This new technology promises to overcome several limitations of traditional cardiac and coronary CT angiography (CCT/CCTA) including reduction in blooming artifacts in heavy calcified coronary plaques or beam-hardening artifacts in patients with coronary stents, and a more precise assessment of the degree of stenosis and plaque characteristic thanks to its better spatial resolution. Another potential application of PCCT is the use of a double-contrast agent to characterize myocardial tissue. In this current overview of the existing PCCT literature, we describe the strengths, limitations, recent applications, and promising developments of employing PCCT technology in CCT.

Use of machine learning to assess the prognostic utility of radiomic features for in-hospital COVID-19 mortality

As portable chest X-rays are an efficient means of triaging emergent cases, their use has raised the question as to whether imaging carries additional prognostic utility for survival among patients with COVID-19. This study assessed the importance of known risk factors on in-hospital mortality and investigated the predictive utility of radiomic texture features using various machine learning approaches. We detected incremental improvements in survival prognostication utilizing texture features derived from emergent chest X-rays, particularly among older patients or those with a higher comorbidity burden. Important features included age, oxygen saturation, blood pressure, and certain comorbid conditions, as well as image features related to the intensity and variability of pixel distribution. Thus, widely available chest X-rays, in conjunction with clinical information, may be predictive of survival outcomes of patients with COVID-19, especially older, sicker patients, and can aid in disease management by providing additional information.

COVID-19 detection based on self-supervised transfer learning using chest X-ray images

PURPOSE

Considering several patients screened due to COVID-19 pandemic, computer-aided detection has strong potential in assisting clinical workflow efficiency and reducing the incidence of infections among radiologists and healthcare providers. Since many confirmed COVID-19 cases present radiological findings of pneumonia, radiologic examinations can be useful for fast detection. Therefore, chest radiography can be used to fast screen COVID-19 during the patient triage, thereby determining the priority of patient's care to help saturated medical facilities in a pandemic situation.

METHODS

In this paper, we propose a new learning scheme called self-supervised transfer learning for detecting COVID-19 from chest X-ray (CXR) images. We compared six self-supervised learning (SSL) methods (Cross, BYOL, SimSiam, SimCLR, PIRL-jigsaw, and PIRL-rotation) with the proposed method. Additionally, we compared six pretrained DCNNs (ResNet18, ResNet50, ResNet101, CheXNet, DenseNet201, and InceptionV3) with the proposed method. We provide quantitative evaluation on the largest open COVID-19 CXR dataset and qualitative results for visual inspection.

RESULTS

Our method achieved a harmonic mean (HM) score of 0.985, AUC of 0.999, and four-class accuracy of 0.953. We also used the visualization technique Grad-CAM++ to generate visual explanations of different classes of CXR images with the proposed method to increase the interpretability.

CONCLUSIONS

Our method shows that the knowledge learned from natural images using transfer learning is beneficial for SSL of the CXR images and boosts the performance of representation learning for COVID-19 detection. Our method promises to reduce the incidence of infections among radiologists and healthcare providers.

Long COVID and the cardiovascular system-elucidating causes and cellular mechanisms in order to develop targeted diagnostic and therapeutic strategies: a joint Scientific Statement of the ESC Working Groups on Cellular Biology of the Heart and Myocardial and Pericardial Diseases

Long COVID has become a world-wide, non-communicable epidemic, caused by long-lasting multiorgan symptoms that endure for weeks or months after SARS-CoV-2 infection has already subsided. This scientific document aims to provide insight into the possible causes and therapeutic options available for the cardiovascular manifestations of long COVID. In addition to chronic fatigue, which is a common symptom of long COVID, patients may present with chest pain, ECG abnormalities, postural orthostatic tachycardia, or newly developed supraventricular or ventricular arrhythmias. Imaging of the heart and vessels has provided evidence of chronic, post-infectious perimyocarditis with consequent left or right ventricular failure, arterial wall inflammation, or microthrombosis in certain patient populations. Better understanding of the underlying cellular and molecular mechanisms of long COVID will aid in the development of effective treatment strategies for its cardiovascular manifestations. A number of mechanisms have been proposed, including those involving direct effects on the myocardium, microthrombotic damage to vessels or endothelium, or persistent inflammation. Unfortunately, existing circulating biomarkers, coagulation, and inflammatory markers, are not highly predictive for either the presence or outcome of long COVID when measured 3 months after SARS-CoV-2 infection. Further studies are needed to understand underlying mechanisms, identify specific biomarkers, and guide future preventive strategies or treatments to address long COVID and its cardiovascular sequelae.

COVID-19 and its long-term impact on the cardiovascular system

INTRODUCTION

TheSARS-CoV-2 virus caused a pandemic affecting healthcare deliveryglobally. Despite the presentation of COVID-19 infection beingfrequently dominated by respiratory symptoms; it is now notorious tohave potentially serious cardiovascular sequelae. This articleexplores current data to provide a comprehensive overview of thepathophysiology, cardiovascular risk factors, and implications ofCOVID-19.

AREAS COVERED

Inherentstructure of SARS-CoV-2, and its interaction with both ACE-2 andnon-ACE-2 mediated pathways have been implicated in the developmentof cardiovascular manifestations, progressively resulting in acuterespiratory distress syndrome, multiorgan failure, cytokine releasesyndrome, and subsequent myocardial damage. The interplay betweenexisting and de novo cardiac complications must be noted. Forindividuals taking cardiovascular medications, pharmacologicinteractions are a crucial component. Short-term cardiovascularimpacts include arrhythmia, myocarditis, pericarditis, heart failure,and thromboembolism, whereas long-term impacts include diabetes andhypertension. To identify suitable studies, a PubMed literaturesearch was performed including key words such as 'Covid 19,''Cardiovascular disease,' 'Long covid,' etc.

EXPERT OPINION

Moresophisticated planning and effective management for cardiologyhealthcare provision is crucial, especially for accommodatingchallenges associated with Long-COVID. With the potential applicationof AI and automated data, there are many avenues and sequelae thatcan be approached for investigation.

Post-COVID-19 interstitial lung disease: Insights from a machine learning radiographic model

Introduction

Post-acute sequelae of COVID-19 seem to be an emerging global crisis. Machine learning radiographic models have great potential for meticulous evaluation of post-COVID-19 interstitial lung disease (ILD).

Methods

In this multicenter, retrospective study, we included consecutive patients that had been evaluated 3 months following severe acute respiratory syndrome coronavirus 2 infection between 01/02/2021 and 12/5/2022. High-resolution computed tomography was evaluated through Imbio Lung Texture Analysis 2.1.

Results

Two hundred thirty-two (n = 232) patients were analyzed. FVC% predicted was ≥80, between 60 and 79 and <60 in 74.2% (n = 172), 21.1% (n = 49), and 4.7% (n = 11) of the cohort, respectively. DLCO% predicted was ≥80, between 60 and 79 and <60 in 69.4% (n = 161), 15.5% (n = 36), and 15.1% (n = 35), respectively. Extent of ground glass opacities was ≥30% in 4.3% of patients (n = 10), between 5 and 29% in 48.7% of patients (n = 113) and <5% in 47.0% of patients (n = 109). The extent of reticulation was ≥30%, 5-29% and <5% in 1.3% (n = 3), 24.1% (n = 56), and 74.6% (n = 173) of the cohort, respectively. Patients (n = 13, 5.6%) with fibrotic lung disease and persistent functional impairment at the 6-month follow-up received antifibrotics and presented with an absolute change of +10.3 (p = 0.01) and +14.6 (p = 0.01) in FVC% predicted at 3 and 6 months after the initiation of antifibrotic.

Conclusion

Post-COVID-19-ILD represents an emerging entity. A substantial minority of patients presents with fibrotic lung disease and might experience benefit from antifibrotic initiation at the time point that fibrotic-like changes are "immature." Machine learning radiographic models could be of major significance for accurate radiographic evaluation and subsequently for the guidance of therapeutic approaches.

Clustered Regularly Interspaced short palindromic repeats-Based Microfluidic System in Infectious Diseases Diagnosis: Current Status, Challenges, and Perspectives

Mitigating the spread of global infectious diseases requires rapid and accurate diagnostic tools. Conventional diagnostic techniques for infectious diseases typically require sophisticated equipment and are time consuming. Emerging clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated proteins (Cas) detection systems have shown remarkable potential as next-generation diagnostic tools to achieve rapid, sensitive, specific, and field-deployable diagnoses of infectious diseases, based on state-of-the-art microfluidic platforms. Therefore, a review of recent advances in CRISPR-based microfluidic systems for infectious diseases diagnosis is urgently required. This review highlights the mechanisms of CRISPR/Cas biosensing and cutting-edge microfluidic devices including paper, digital, and integrated wearable platforms. Strategies to simplify sample pretreatment, improve diagnostic performance, and achieve integrated detection are discussed. Current challenges and future perspectives contributing to the development of more effective CRISPR-based microfluidic diagnostic systems are also proposed.

Recommender System for the Efficient Treatment of COVID-19 Using a Convolutional Neural Network Model and Image Similarity

Background: Hospitals face a significant problem meeting patients' medical needs during epidemics, especially when the number of patients increases rapidly, as seen during the recent COVID-19 pandemic. This study designs a treatment recommender system (RS) for the efficient management of human capital and resources such as doctors, medicines, and resources in hospitals. We hypothesize that a deep learning framework, when combined with search paradigms in an image framework, can make the RS very efficient. Methodology: This study uses a Convolutional neural network (CNN) model for the feature extraction of the images and discovers the most similar patients. The input queries patients from the hospital database with similar chest X-ray images. It uses a similarity metric for the similarity computation of the images. Results: This methodology recommends the doctors, medicines, and resources associated with similar patients to a COVID-19 patients being admitted to the hospital. The performance of the proposed RS is verified with five different feature extraction CNN models and four similarity measures. The proposed RS with a ResNet-50 CNN feature extraction model and Maxwell-Boltzmann similarity is found to be a proper framework for treatment recommendation with a mean average precision of more than 0.90 for threshold similarities in the range of 0.7 to 0.9 and an average highest cosine similarity of more than 0.95. Conclusions: Overall, an RS with a CNN model and image similarity is proven as an efficient tool for the proper management of resources during the peak period of pandemics and can be adopted in clinical settings.

COVID-19 pneumonia: lessons learned, challenges, and preparing for the future

Coronavirus disease 2019 (COVID-19) is a viral disease that causes life-threatening health problems during acute illness, causing a pandemic and millions of deaths. Although computed tomography (CT) was used as a diagnostic tool for COVID-19 in the early period of the pan demic due to the inaccessibility or long duration of the polymerase chain reaction tests, cur rent studies have revealed that CT scan should not be used to diagnose COVID-19. However, radiologic findings are vital in assessing pneumonia severity and investigating complications in patients with COVID-19. Long-term symptoms, also known as long COVID, in people recovering from COVID-19 affect patients' quality of life and cause global health problems. Herein, we aimed to summarize the lessons learned in COVID-19 pneumonia, the challenges in diagnosing the disease and complications, and the prospects for future studies.

Post-acute COVID syndrome (long COVID): What should radiographers know and the potential impact for imaging services

OBJECTIVES

The COVID-19 pandemic caused an unprecedented health crisis resulting in over 6 million deaths worldwide, a figure, which continues to grow. In addition to the excess mortality, there are individuals who recovered from the acute stages, but suffered long-term changes in their health post COVID-19, commonly referred to as long COVID. It is estimated there are currently 1.8 million long COVID sufferers by May 2022 in the UK alone. The aim of this narrative literature review is to explore the signs, symptoms and diagnosis of long COVID and the potential impact on imaging services.

KEY FINDINGS

Long COVID is estimated to occur in 9.5% of those with two doses of vaccination and 14.6% if those with a single dose or no vaccination. Long COVID is defined by ongoing symptoms lasting for 12 or more weeks post acute infection. Symptoms are associated with reductions in the quality of daily life and may involve multisystem manifestations or present as a single symptom.

CONCLUSION

The full impact of long COVID on imaging services is yet to be realised, but there is likely to be significant increased demand for imaging, particularly in CT for the assessment of lung disease. Educators will need to include aspects related to long COVID pathophysiology and imaging presentations in curricula, underpinned by the rapidly evolving evidence base.

IMPLICATIONS FOR PRACTICE

Symptoms relating to long COVID are likely to become a common reason for imaging, with a particular burden on Computed Tomography services. Planning, education and updating protocols in line with a rapidly emerging evidence base is going to be essential.

Segmentation-Based Classification Deep Learning Model Embedded with Explainable AI for COVID-19 Detection in Chest X-ray Scans

Background and Motivation: COVID-19 has resulted in a massive loss of life during the last two years. The current imaging-based diagnostic methods for COVID-19 detection in multiclass pneumonia-type chest X-rays are not so successful in clinical practice due to high error rates. Our hypothesis states that if we can have a segmentation-based classification error rate <5%, typically adopted for 510 (K) regulatory purposes, the diagnostic system can be adapted in clinical settings. Method: This study proposes 16 types of segmentation-based classification deep learning-based systems for automatic, rapid, and precise detection of COVID-19. The two deep learning-based segmentation networks, namely UNet and UNet+, along with eight classification models, namely VGG16, VGG19, Xception, InceptionV3, Densenet201, NASNetMobile, Resnet50, and MobileNet, were applied to select the best-suited combination of networks. Using the cross-entropy loss function, the system performance was evaluated by Dice, Jaccard, area-under-the-curve (AUC), and receiver operating characteristics (ROC) and validated using Grad-CAM in explainable AI framework. Results: The best performing segmentation model was UNet, which exhibited the accuracy, loss, Dice, Jaccard, and AUC of 96.35%, 0.15%, 94.88%, 90.38%, and 0.99 (p-value <0.0001), respectively. The best performing segmentation-based classification model was UNet+Xception, which exhibited the accuracy, precision, recall, F1-score, and AUC of 97.45%, 97.46%, 97.45%, 97.43%, and 0.998 (p-value <0.0001), respectively. Our system outperformed existing methods for segmentation-based classification models. The mean improvement of the UNet+Xception system over all the remaining studies was 8.27%. Conclusion: The segmentation-based classification is a viable option as the hypothesis (error rate <5%) holds true and is thus adaptable in clinical practice.

Role of cardiac MRI in the diagnosis of immune checkpoint inhibitor-associated myocarditis

Immune Checkpoint Inhibitor (ICI)-induced cardiotoxicity is a rare immune-related adverse event (irAE) characterized by a high mortality rate. From a pathological point of view, this condition can result from a series of causes, including binding of ICIs to target molecules on non-lymphocytic cells, cross-reaction of T lymphocytes against tumor antigens with off-target tissues, generation of autoantibodies, and production of pro-inflammatory cytokines. The diagnosis of ICI-induced cardiotoxicity can be challenging, and cardiac magnetic resonance (CMR) represents the diagnostic tool of choice in clinically stable patients with suspected myocarditis. CMR is gaining a central role in diagnosis and monitoring of cardiovascular damage in cancer patients, and it is entering international cardiology and oncology guidelines. In this narrative review, we summarized the clinical aspects of ICI-associated myocarditis, highlighting its radiological aspects and proposing a novel algorithm for the use of CMR.

COVLIAS 2.0-cXAI: Cloud-Based Explainable Deep Learning System for COVID-19 Lesion Localization in Computed Tomography Scans

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.

The effects of acute respiratory illness on exercise and sports performance outcomes in athletes - a systematic review by a subgroup of the IOC consensus group on "Acute respiratory illness in the athlete"

Acute respiratory infections (ARinf) are common in athletes, but their effects on exercise and sports performance remain unclear. This systematic review aimed to determine the acute (short-term) and longer-term effects of ARinf, including SARS-CoV-2 infection, on exercise and sports performance outcomes in athletes. Data sources searched included PubMed, Web of Science, and EBSCOhost, from January 1990-31 December 2021. Eligibility criteria included original research studies published in English, measuring exercise and/or sports performance outcomes in athletes/physically active/military aged 15-65years with ARinf. Information regarding the study cohort, diagnostic criteria, illness classification, and quantitative data on the effect on exercise/sports performance were extracted. Database searches identified 1707 studies. After full text screening, 17 studies were included (n = 7793). Outcomes were acute or longer-term effects on exercise (cardiovascular or pulmonary responses), or sports performance (training modifications, change in standardised point scoring systems, running biomechanics, match performance or ability to start/finish an event). There was substantial methodological heterogeneity between studies. ARinf was associated with acute decrements in sports performance outcomes (4 studies) and pulmonary function (3 studies), but minimal effects on cardiorespiratory endurance (7 studies in mild ARinf). Longer-term detrimental effects of ARinf on sports performance (6 studies) were divided. Training mileage, overall training load, standardised sports performance-dependent points and match play can be affected over time. Despite few studies, there is a trend towards impairment in acute and longer-term exercise and sports outcomes after ARinf in athletes. Future research should consider a uniform approach to explore relationships between ARinf and exercise/sports performance.

Cardiovascular Risk Stratification in Diabetic Retinopathy via Atherosclerotic Pathway in COVID-19/Non-COVID-19 Frameworks Using Artificial Intelligence Paradigm: A Narrative Review

Diabetes is one of the main causes of the rising cases of blindness in adults. This microvascular complication of diabetes is termed diabetic retinopathy (DR) and is associated with an expanding risk of cardiovascular events in diabetes patients. DR, in its various forms, is seen to be a powerful indicator of atherosclerosis. Further, the macrovascular complication of diabetes leads to coronary artery disease (CAD). Thus, the timely identification of cardiovascular disease (CVD) complications in DR patients is of utmost importance. Since CAD risk assessment is expensive for low-income countries, it is important to look for surrogate biomarkers for risk stratification of CVD in DR patients. Due to the common genetic makeup between the coronary and carotid arteries, low-cost, high-resolution imaging such as carotid B-mode ultrasound (US) can be used for arterial tissue characterization and risk stratification in DR patients. The advent of artificial intelligence (AI) techniques has facilitated the handling of large cohorts in a big data framework to identify atherosclerotic plaque features in arterial ultrasound. This enables timely CVD risk assessment and risk stratification of patients with DR. Thus, this review focuses on understanding the pathophysiology of DR, retinal and CAD imaging, the role of surrogate markers for CVD, and finally, the CVD risk stratification of DR patients. The review shows a step-by-step cyclic activity of how diabetes and atherosclerotic disease cause DR, leading to the worsening of CVD. We propose a solution to how AI can help in the identification of CVD risk. Lastly, we analyze the role of DR/CVD in the COVID-19 framework.

COVID-19 Diagnosis: A Review of Rapid Antigen, RT-PCR and Artificial Intelligence Methods

As of 27 December 2021, SARS-CoV-2 has infected over 278 million persons and caused 5.3 million deaths. Since the outbreak of COVID-19, different methods, from medical to artificial intelligence, have been used for its detection, diagnosis, and surveillance. Meanwhile, fast and efficient point-of-care (POC) testing and self-testing kits have become necessary in the fight against COVID-19 and to assist healthcare personnel and governments curb the spread of the virus. This paper presents a review of the various types of COVID-19 detection methods, diagnostic technologies, and surveillance approaches that have been used or proposed. The review provided in this article should be beneficial to researchers in this field and health policymakers at large.

Clinical prediction system of complications among patients with COVID-19: A development and validation retrospective multicentre study during first wave of the pandemic

Clinical evidence suggests that some patients diagnosed with coronavirus disease 2019 (COVID-19) experience a variety of complications associated with significant morbidity, especially in severe cases during the initial spread of the pandemic. To support early interventions, we propose a machine learning system that predicts the risk of developing multiple complications. We processed data collected from 3,352 patient encounters admitted to 18 facilities between April 1 and April 30, 2020, in Abu Dhabi (AD), United Arab Emirates. Using data collected during the first 24 h of admission, we trained machine learning models to predict the risk of developing any of three complications after 24 h of admission. The complications include Secondary Bacterial Infection (SBI), Acute Kidney Injury (AKI), and Acute Respiratory Distress Syndrome (ARDS). The hospitals were grouped based on geographical proximity to assess the proposed system's learning generalizability, AD Middle region and AD Western & Eastern regions, A and B, respectively. The overall system includes a data filtering criterion, hyperparameter tuning, and model selection. In test set A, consisting of 587 patient encounters (mean age: 45.5), the system achieved a good area under the receiver operating curve (AUROC) for the prediction of SBI (0.902 AUROC), AKI (0.906 AUROC), and ARDS (0.854 AUROC). Similarly, in test set B, consisting of 225 patient encounters (mean age: 42.7), the system performed well for the prediction of SBI (0.859 AUROC), AKI (0.891 AUROC), and ARDS (0.827 AUROC). The performance results and feature importance analysis highlight the system's generalizability and interpretability. The findings illustrate how machine learning models can achieve a strong performance even when using a limited set of routine input variables. Since our proposed system is data-driven, we believe it can be easily repurposed for different outcomes considering the changes in COVID-19 variants over time.