Citations

Background and motivation: Lung computed tomography (CT) techniques are high-resolution and are well adopted in the intensive care unit (ICU) for COVID-19 disease control classification. Most artificial intelligence (AI) systems do not undergo generalization and are typically overfitted. Such trained AI systems are not practical for clinical settings and therefore do not give accurate results when executed on unseen data sets. We hypothesize that ensemble deep learning (EDL) is superior to deep transfer learning (TL) in both non-augmented and augmented frameworks. Methodology: The system consists of a cascade of quality control, ResNet–UNet-based hybrid deep learning for lung segmentation, and seven models using TL-based classification followed by five types of EDL’s. To prove our hypothesis, five different kinds of data combinations (DC) were designed using a combination of two multicenter cohorts—Croatia (80 COVID) and Italy (72 COVID and 30 controls)—leading to 12,000 CT slices. As part of generalization, the system was tested on unseen data and statistically tested for reliability/stability. Results: Using the K5 (80:20) cross-validation protocol on the balanced and augmented dataset, the five DC datasets improved TL mean accuracy by 3.32%, 6.56%, 12.96%, 47.1%, and 2.78%, respectively. The five EDL systems showed improvements in accuracy of 2.12%, 5.78%, 6.72%, 32.05%, and 2.40%, thus validating our hypothesis. All statistical tests proved positive for reliability and stability. Conclusion: EDL showed superior performance to TL systems for both (a) unbalanced and unaugmented and (b) balanced and augmented datasets for both (i) seen and (ii) unseen paradigms, validating both our hypotheses.

Abstract Background Biomarkers for predicting the development of Ulcerative Colitis (UC) and disease-related complications are lacking. Anti-integrin αvβ6 IgG autoantibodies (anti-αvβ6) have been recently described in patients diagnosed with UC. Here, we sought to determine if anti-αvβ6 autoantibodies predate UC development and to study associations between anti-αvβ6 and disease outcomes in newly diagnosed UC patients. Methods In a unique pre-clinical IBD cohort from the PRoteomic Evaluation and Discovery in an IBD Cohort of Tri-service Subjects (PREDICTS), anti-αvβ6 were measured in longitudinal pre-diagnosis serum samples from 82 subjects who later developed UC as well as in 82 matched subjects that did not develop IBD (HC). Sample A was at the time of UC diagnosis, Sample B was at a median of 2 years before Sample A, Sample C was at a median of 4 years before Sample A and Sample D was at a median of 10 years before Sample A. In a distinct, external validation cohort (the Crohn’s and Colitis Canada Genetics Environment Microbial (GEM) Project), we tested samples from 12 subjects who later developed UC and compared those with 49 matched subjects who remained asymptomatic. Further, anti-αvβ6 were measured in 2 incident IBD cohorts (COMPASS, n=55 and OSCCAR, n=104 UC subjects) and associations between anti-αvβ6 and adverse UC-outcomes were defined using Cox proportional-hazards model. Results Anti-αvβ6 were significantly higher in the PREDICTS cohort among individuals who developed UC compared to HC up to 10 years prior to diagnosis (Figure 1A). The anti-αvβ6 seropositivity was 12.2% 10 years before diagnosis and increased to 52.4% at the time of diagnosis in subjects who developed UC compared with 2.7% in controls across the 4 timepoints. Additionally, the predictive performance of anti-αvβ6 autoantibodies as assessed via area under the receiver operator curves (AUROC) with 10-fold cross validation was at least 0.8 up to 10 years before diagnosis (Figure 1B). Elevated levels of anti-αvβ6 in pre-clinical UC samples were further validated in the GEM cohort (Figure 1C). Finally, high anti-αvβ6 were associated with a composite of adverse UC-outcomes including hospitalization, disease extension, colectomy, systemic steroid use and/or escalation to biologic therapy in both incident cohorts (Figure 1D). Conclusion Anti-integrin αvβ6 autoantibodies precede the clinical diagnosis of UC by up to 10 years and are associated with adverse UC-related outcomes.

Motivation: The price of medical treatment continues to rise due to (i) an increasing population; (ii) an aging human growth; (iii) disease prevalence; (iv) a rise in the frequency of patients that utilize health care services; and (v) increase in the price. Objective: Artificial Intelligence (AI) is already well-known for its superiority in various healthcare applications, including the segmentation of lesions in images, speech recognition, smartphone personal assistants, navigation, ride-sharing apps, and many more. Our study is based on two hypotheses: (i) AI offers more economic solutions compared to conventional methods; (ii) AI treatment offers stronger economics compared to AI diagnosis. This novel study aims to evaluate AI technology in the context of healthcare costs, namely in the areas of diagnosis and treatment, and then compare it to the traditional or non-AI-based approaches. Methodology: PRISMA was used to select the best 200 studies for AI in healthcare with a primary focus on cost reduction, especially towards diagnosis and treatment. We defined the diagnosis and treatment architectures, investigated their characteristics, and categorized the roles that AI plays in the diagnostic and therapeutic paradigms. We experimented with various combinations of different assumptions by integrating AI and then comparing it against conventional costs. Lastly, we dwell on three powerful future concepts of AI, namely, pruning, bias, explainability, and regulatory approvals of AI systems. Conclusions: The model shows tremendous cost savings using AI tools in diagnosis and treatment. The economics of AI can be improved by incorporating pruning, reduction in AI bias, explainability, and regulatory approvals.

This study is the first to examine the applicability of BCL-2 inhibition in the setting of active HIV infectionin vivo. Furthermore, this study demonstrates that venetoclax significantly enhances target cell killing induced by Fas ligand, TRAIL, and perforin/granzyme B and synergistically enhances autologous NK and CD8 cells’ killing of target cells.

A diabetic foot infection (DFI) is among the most serious, incurable, and costly to treat conditions. The presence of a DFI renders machine learning (ML) systems extremely nonlinear, posing difficulties in CVD/stroke risk stratification. In addition, there is a limited number of well-explained ML paradigms due to comorbidity, sample size limits, and weak scientific and clinical validation methodologies. Deep neural networks (DNN) are potent machines for learning that generalize nonlinear situations. The objective of this article is to propose a novel investigation of deep learning (DL) solutions for predicting CVD/stroke risk in DFI patients. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) search strategy was used for the selection of 207 studies. We hypothesize that a DFI is responsible for increased morbidity and mortality due to the worsening of atherosclerotic disease and affecting coronary artery disease (CAD). Since surrogate biomarkers for CAD, such as carotid artery disease, can be used for monitoring CVD, we can thus use a DL-based model, namely, Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) for CVD/stroke risk prediction in DFI patients, which combines covariates such as office and laboratory-based biomarkers, carotid ultrasound image phenotype (CUSIP) lesions, along with the DFI severity. We confirmed the viability of CVD/stroke risk stratification in the DFI patients. Strong designs were found in the research of the DL architectures for CVD/stroke risk stratification. Finally, we analyzed the AI bias and proposed strategies for the early diagnosis of CVD/stroke in DFI patients. Since DFI patients have an aggressive atherosclerotic disease, leading to prominent CVD/stroke risk, we, therefore, conclude that the DL paradigm is very effective for predicting the risk of CVD/stroke in DFI patients.

Abstract Bone marrow transplant (BMT) is a curative procedure for patients with hematological malignancies, hemoglobinopathies, and errors of inborn metabolism. Survivors are not without symptom burden. The purpose of this study was to assess the feasibility of a 12-week online yoga intervention compared to an educational control group in survivors of allogenic BMT. Participants were recruited nationally. Consenting participants were assigned to online yoga or a podcast control. Yoga and control group participants were instructed to complete 12 weeks of 60 minutes/week of online yoga and podcasts, respectively. Study participants were asked to complete online questionnaires at baseline (wk 0), midpoint (wk 6), postintervention (wk 12), and follow-up (wk 20). Feasibility benchmarks included > 70% satisfied with intervention and > 70% intending to continue participating in online yoga (acceptability); > 70% of participants achieving > 42 minutes/week of online yoga (demand); > 70% completing all four questionnaires (practicality); and at least small effect sizes of the intervention on Lee Symptom Score, physical function, fatigue, anxiety, sleep disturbance, social functioning, pain interference, depression, and quality of life as compared to control group (limited efficacy). Seventy-two participants consented and enrolled in the study (yoga group n = 33; podcast group n = 39). Of the yoga group participants, 54.5% (n = 19) completed the postintervention questionnaire, with 73.7% (n = 14) indicating they were satisfied/very satisfied and 15.8% (n = 3) likely/very likely to continue online yoga. Yoga participation averaged 31.98 minutes/week across 12 weeks. Both groups experienced a significant improvement in the Lee Symptom Score (6.2%–8.7% improvement from baseline) and depressive symptoms (5.3%–6.6% improvement from baseline). The yoga group experienced a significant reduction in pain (6.1% from baseline), and the podcast group experienced a significant reduction in fatigue (6.4% improvement from baseline). Online yoga was not found to be feasible to deliver to this population of survivors of BMT. However, there was satisfaction with online yoga among the majority of survivors of BMT, and beneficial effects were experienced in the yoga group on Lee Symptom Score, depressive symptoms, and pain. Future research is needed to enhance our understanding of barriers to online yoga participation for survivors of BMT and to determine its efficacy.

The SARS-CoV-2 virus has caused a pandemic, infecting nearly 80 million people worldwide, with mortality exceeding six million. The average survival span is just 14 days from the time the symptoms become aggressive. The present study delineates the deep-driven vascular damage in the pulmonary, renal, coronary, and carotid vessels due to SARS-CoV-2. This special report addresses an important gap in the literature in understanding (i) the pathophysiology of vascular damage and the role of medical imaging in the visualization of the damage caused by SARS-CoV-2, and (ii) further understanding the severity of COVID-19 using artificial intelligence (AI)-based tissue characterization (TC). PRISMA was used to select 296 studies for AI-based TC. Radiological imaging techniques such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound were selected for imaging of the vasculature infected by COVID-19. Four kinds of hypotheses are presented for showing the vascular damage in radiological images due to COVID-19. Three kinds of AI models, namely, machine learning, deep learning, and transfer learning, are used for TC. Further, the study presents recommendations for improving AI-based architectures for vascular studies. We conclude that the process of vascular damage due to COVID-19 has similarities across vessel types, even though it results in multi-organ dysfunction. Although the mortality rate is ~2% of those infected, the long-term effect of COVID-19 needs monitoring to avoid deaths. AI seems to be penetrating the health care industry at warp speed, and we expect to see an emerging role in patient care, reduce the mortality and morbidity rate.

Background and Motivation: Parkinson’s disease (PD) is one of the most serious, non-curable, and expensive to treat. Recently, machine learning (ML) has shown to be able to predict cardiovascular/stroke risk in PD patients. The presence of COVID-19 causes the ML systems to become severely non-linear and poses challenges in cardiovascular/stroke risk stratification. Further, due to comorbidity, sample size constraints, and poor scientific and clinical validation techniques, there have been no well-explained ML paradigms. Deep neural networks are powerful learning machines that generalize non-linear conditions. This study presents a novel investigation of deep learning (DL) solutions for CVD/stroke risk prediction in PD patients affected by the COVID-19 framework. Method: The PRISMA search strategy was used for the selection of 292 studies closely associated with the effect of PD on CVD risk in the COVID-19 framework. We study the hypothesis that PD in the presence of COVID-19 can cause more harm to the heart and brain than in non-COVID-19 conditions. COVID-19 lung damage severity can be used as a covariate during DL training model designs. We, therefore, propose a DL model for the estimation of, (i) COVID-19 lesions in computed tomography (CT) scans and (ii) combining the covariates of PD, COVID-19 lesions, office and laboratory arterial atherosclerotic image-based biomarkers, and medicine usage for the PD patients for the design of DL point-based models for CVD/stroke risk stratification. Results: We validated the feasibility of CVD/stroke risk stratification in PD patients in the presence of a COVID-19 environment and this was also verified. DL architectures like long short-term memory (LSTM), and recurrent neural network (RNN) were studied for CVD/stroke risk stratification showing powerful designs. Lastly, we examined the artificial intelligence bias and provided recommendations for early detection of CVD/stroke in PD patients in the presence of COVID-19. Conclusion: The DL is a very powerful tool for predicting CVD/stroke risk in PD patients affected by COVID-19.

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.