Kinetics of Spinodal Phase Separation in Unstable Thin Liquid Films

We study universality in the kinetics of spinodal phase separation in unstable thin liquid films, via simulations of the thin film equation. It is shown that in addition to morphology and free energy,the number density of local maxima in the film profile can also be used to identify the early, intermediate and late stages of spinodal phase separation. A universal curve between the number density of local maxima and rescaled time describes the kinetics of early stage in d = 2, 3. The Lifshitz-Slyozov exponent of -1/3 describes the kinetics of the late stage in d = 2 even in the absence of coexisting equilibrium phases.Comment: 5 figure

Successful transcatheter closure of very large ruptured sinus of Valsalva aneurysm

Sinus of Valsalva aneurysm, usually a congenital anomaly, almost always ruptures into the right side of the heart causing a left-to-right shunt with profound hemodynamic consequences. With the availability of devices and hardware, transcatheter closure is gradually replacing surgical one. Till now, most of closures have been performed by Amplatzer duct occluder. To the best of our knowledge, the present case is first to be reported with this rare defect undergoing successful transcatheter closure of largest ruptured sinus of Valsalva aneurysm arising from right coronary sinus by using 20/18 mm Cocoon Duct Occluder (Vascular Innovations, Nonthaburi, Thailand)

Aorto-renal Bifurcation Stenting in a Juvenile Non-specific Aorto-Arteritis: case report

Takayasu Arteritis (TA) is a granulomatous inflammation of unknown aetiology affecting the aorta and its major branches with usual affliction among patients younger than 50 years and rarely among children. We present a 7-years old boy referred for evaluation of hypertension. He had a significant blood pressure difference between right arm, left arm and lower limbs. Computed tomography imaging of thorax and abdomen showed stenosis of left subclavian artery, left renal artery and juxtareanl aorta which was subsequently confirmed on aortogram. He underwent percutaneous endovascular therapy with aorto-renal bifurcation stenting with reduction of blood pressure and gradient. Renal angioplasty with stenting remains a challenging procedure in patients with tight ostial lesion, and juxtarenal aortic involvement in lieu of precise stent placement and avoiding side branch occlusion

Socio-Demographic and Clinical Profile of Health Care Workers Diagnosed for COVID-19 by Truenat at a Tertiary Care COVID Hospital

Background: In December 2019, in Wuhan, China; a new coronavirus emerged that had not been previously identified in humans. Hence is crucial to characterize the infection risk among infected health care workers (HCWs), being responsible for secondary transmission to patients, and others. Objectives: The current study aimed to assess the disease burden among the front-line warriors and efficiently planned the preventive and management strategies for such infections. Methods: HCWs with clinical suspicion of COVID-19 infection, who reported to Fever Clinic for possible diagnosis by Truenat testing, were enrolled through a self-reporting Risk Assessment form. An oropharyngeal swab was subjected to Truenat testing based on the principle of Real time reverse transcription polymerase chain reaction (RT-PCR). Results: Doctors comprised 60% of our HCWs. Eighty-three percent of the HCWs under study reported either the presence of BCG scar or gave a history of BCG immunization at birth. The maximum number of HCWs (29.16%) took Hydroxychloroquine prophylaxis for four weeks. Seventy-four percent of the HCWs affirmed the use of personal protective equipment (PPE) at the time of exposure. The most common mode of infection reported was the exposure to COVID-19 patients. Fever was the most common reported symptom. Truenat was positive in 9 of 100 HCWs who were tested, giving an infection rate of 9%. Conclusion: The study provides insights into the burden of COVID-19 infection among HCWs, and guides us to evaluate and plan our preventive measures and management strategies for such infections

IMPACT OF THREE DIFFERENT MATCHING METHODS ON PATIENT SET-UP ERROR IN X-RAY VOLUMETRIC IMAGING FOR HEAD AND NECK CANCER

Impact of three different matching methods for delivery of Volumetric Modulated Arc Therapy (VMAT) in Cone-beam computed tomography (CBCT) on patient set-up error. As per institutional imaging protocol, 300 CBCT scans of 20 VMAT head and neck cancer patients treated with 60 Gy/30 fractions were chosen for the present study. Approved CT images of the plan were registered as a reference with the CBCT images on board. Grey-scale matching (GM), manual matching (MM), and bone matching (BM) between on-board CBCT and reference CT images were used to assess patient translation errors. Patient positioning verification was evaluated using the Clip-box registration in all three matching methods. Using the GM approach as a reference point, two additional matchings were rendered in offline mode using BM and MM. For analysis, random error (σ), systematic error (∑), maximum error (E) mean set-up error (M), mean displacement vector (R), matching time (Mt), and multiple comparisons using Post hoc Tukey's HSD test were performed. In MM, less random and systematic errors were found than in GM and BM with an insignificant difference (p > 0.05) Compared to BM and GM, the maximum error, mean set-up error, and displacement vector were marginally less in MM (p > 0.05). In MM, an increased Mt relative to BM and GM was observed (p > 0.05). Furthermore, an insignificant difference in set-up error was revealed in a multiple comparison test (p > 0.05). Any of the three matching methods can be used during CBCT to check patient translation errors for the delivery of the VMAT head and neck patients

Deep Learning Paradigm and Its Bias for Coronary Artery Wall Segmentation in Intravascular Ultrasound Scans: A Closer Look

Background and motivation: Coronary artery disease (CAD) has the highest mortality rate; therefore, its diagnosis is vital. Intravascular ultrasound (IVUS) is a high-resolution imaging solution that can image coronary arteries, but the diagnosis software via wall segmentation and quantification has been evolving. In this study, a deep learning (DL) paradigm was explored along with its bias. Methods: Using a PRISMA model, 145 best UNet-based and non-UNet-based methods for wall segmentation were selected and analyzed for their characteristics and scientific and clinical validation. This study computed the coronary wall thickness by estimating the inner and outer borders of the coronary artery IVUS cross-sectional scans. Further, the review explored the bias in the DL system for the first time when it comes to wall segmentation in IVUS scans. Three bias methods, namely (i) ranking, (ii) radial, and (iii) regional area, were applied and compared using a Venn diagram. Finally, the study presented explainable AI (XAI) paradigms in the DL framework. Findings and conclusions: UNet provides a powerful paradigm for the segmentation of coronary walls in IVUS scans due to its ability to extract automated features at different scales in encoders, reconstruct the segmented image using decoders, and embed the variants in skip connections. Most of the research was hampered by a lack of motivation for XAI and pruned AI (PAI) models. None of the UNet models met the criteria for bias-free design. For clinical assessment and settings, it is necessary to move from a paper-to-practice approach

Vascular Implications of COVID-19: Role of Radiological Imaging, Artificial Intelligence, and Tissue Characterization: A Special Report

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

Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson’s Disease Affected by COVID‐19: A Narrative Review

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 be-come 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. © 2022 by the authors. Licensee MDPI, Basel, Switzerland

Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment

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. © 2022 by the authors