Deep Learning Approach for Advanced COVID-19 Analysis

Since the spread of the COVID-19 pandemic, the number of patients has increased dramatically, making it difficult for medical staff, including doctors, to cover hospitals and monitor patients. Therefore, this work depends on Computerized Tomography (CT) scan images to diagnose COVID-19. CT scan images are used to diagnose and determine the severity of the disease. On the other hand, Deep Learning (DL) is widely used in medical research, making great progress in medical technologies. For the diagnosis process, the Convolutional Neural Network (CNN) algorithm is used as a type of DL algorithm. Hence, this work focuses on detecting COVID-19 from CT scan images and determining the severity of the illness. The proposed model is as follows: first, classifying CT scan images into infected or not infected using one of the CNN structures, Residual Neural Networks (ResNet50); second, applying a segmentation process for the infected images to identify lungs and pneumonia using the SegNet algorithm (a CNN architecture for semantic pixel-wise segmentation) so that the disease's severity can be determined; finally, applying linear regression to predict the disease's severity for any new image. The proposed approach reached an accuracy of 95.7% in the classification process and lung and pneumonia segmentation of 98.6% and 96.2%, respectively. Furthermore, a regression process reached an accuracy of 98.29%.Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) © Copyright: All rights reserved

Consensus definitions of 14 severe acute toxic effects for childhood lymphoblastic leukaemia treatment: a Delphi consensus

Although there are high survival rates for children with acute lymphoblastic leukaemia, their outcome is often counterbalanced by the burden of toxic effects. This is because reported frequencies vary widely across studies, partly because of diverse definitions of toxic effects. Using the Delphi method, 15 international childhood acute lymphoblastic leukaemia study groups assessed acute lymphoblastic leukaemia protocols to address toxic effects that were to be considered by the Ponte di Legno working group. 14 acute toxic effects (hypersensitivity to asparaginase, hyperlipidaemia, osteonecrosis, asparaginase-associated pancreatitis, arterial hypertension, posterior reversible encephalopathy syndrome, seizures, depressed level of consciousness, methotrexate-related stroke-like syndrome, peripheral neuropathy, high-dose methotrexate-related nephrotoxicity, sinusoidal obstructive syndrome, thromboembolism, and Pneumocystis jirovecii pneumonia) that are serious but too rare to be addressed comprehensively within any single group, or are deemed to need consensus definitions for reliable incidence comparisons, were selected for assessment. Our results showed that none of the protocols addressed all 14 toxic effects, that no two protocols shared identical definitions of all toxic effects, and that no toxic effect definition was shared by all protocols. Using the Delphi method over three face-to-face plenary meetings, consensus definitions were obtained for all 14 toxic effects. In the overall assessment of outcome of acute lymphoblastic leukaemia treatment, these expert opinion-based definitions will allow reliable comparisons of frequencies and severities of acute toxic effects across treatment protocols, and facilitate international research on cause, guidelines for treatment adaptation, preventive strategies, and development of consensus algorithms for reporting on acute lymphoblastic leukaemia treatment

Gut microbiota in HIV-pneumonia patients is related to peripheral CD4 counts, lung microbiota, and in vitro macrophage dysfunction.

Pneumonia is common and frequently fatal in HIV-infected patients, due to rampant, systemic inflammation and failure to control microbial infection. While airway microbiota composition is related to local inflammatory response, gut microbiota has been shown to correlate with the degree of peripheral immune activation (IL6 and IP10 expression) in HIV-infected patients. We thus hypothesized that both airway and gut microbiota are perturbed in HIV-infected pneumonia patients, that the gut microbiota is related to peripheral CD4+ cell counts, and that its associated products differentially program immune cell populations necessary for controlling microbial infection in CD4-high and CD4-low patients. To assess these relationships, paired bronchoalveolar lavage and stool microbiota (bacterial and fungal) from a large cohort of Ugandan, HIV-infected patients with pneumonia were examined, and in vitro tests of the effect of gut microbiome products on macrophage effector phenotypes performed. While lower airway microbiota stratified into three compositionally distinct microbiota as previously described, these were not related to peripheral CD4 cell count. In contrast, variation in gut microbiota composition significantly related to CD4 cell count, lung microbiota composition, and patient mortality. Compared with patients with high CD4+ cell counts, those with low counts possessed more compositionally similar airway and gut microbiota, evidence of microbial translocation, and their associated gut microbiome products reduced macrophage activation and IL-10 expression and increased IL-1β expression in vitro. These findings suggest that the gut microbiome is related to CD4 status and plays a key role in modulating macrophage function, critical to microbial control in HIV-infected patients with pneumonia

Quantitative CT analysis in ILD and use of artificial intelligence on imaging of ILD

Advances in computer technology over the past decade, particularly in the field of medical image analysis, have permitted the identification, characterisation and quantitation of abnormalities that can be used to diagnose disease or determine disease severity. On CT imaging performed in patients with ILD, deep-learning computer algorithms now demonstrate comparable performance with trained observers in the identification of a UIP pattern, which is associated with a poor prognosis in several fibrosing ILDs. Computer tools that quantify individual voxel-level CT features have also come of age and can predict mortality with greater power than visual CT analysis scores. As these tools become more established, they have the potential to improve the sensitivity with which minor degrees of disease progression are identified. Currently, PFTs are the gold standard measure used to assess clinical deterioration. However, the variation associated with pulmonary function measurements may mask the presence of small but genuine functional decline, which in the future could be confirmed by computer tools. The current chapter will describe the latest advances in quantitative CT analysis and deep learning as related to ILDs and suggest potential future directions for this rapidly advancing field

Optimal Prediction and Disease Severity Classification of Proteomic Survival in Pre and Post-Covid-19 Using Hybrid Machine Learning Approach

Uncertainty surrounds the underlying mechanisms of the severe COVID-19 disease of 2019. The capability to detect COVID-19 through artificial intelligence techniques, particularly deep learning, will help to do so in the early stages, which will increase the likelihood that patients around the world will recover rapidly. The load on the healthcare system globally will be relieved as a result. Several thousand plasmas and serum proteins from COVID-19 patients and symptomatic controls are longitudinally analysed in this study to identify non-immune and immune proteins associated with COVID-19. The development of predictive models thus involves taking into account the topological variations across networks from different scenarios (survivors vs. non-survivors). As a result, the study's test subjects, who weren't included in the machine learning (ML) training, had high prediction accuracy. This study successfully predicted the existence of critically ill (CI) patients both before and after COVID-19 by using an MLM built on a synonymic network that incorporates measurements of several proteins. A rise in some acute phase and inflammatory proteins (IP) with time (e.g. ITIH3, SAA1; CRP, SAA2, LBP, SERPINA1, and LRG1) is related to the danger of death after COVID-19, while an upsurge of kallikrein (KLKB1), kallistatin (SERPINA4), thrombin (F2), Apo lipoprotein C3 (APOC3), GPLD1, and the protease inhibitor A2M, is associated with survival. The same clinical symptoms, such as dry cough, fever, squatness of breath, and others, are linked to both severe and critical patients. The lesion outlines are then retrieved from the COVID-19-contaminated regions after the entropy texture features have been extracted using a Gray-level co-occurrence Matrix (GLCM) to confirm the infected regions (IR). Further, the study implemented a variety of features using CT images with a CNN-based Inception V3 model for selection algorithms to filter significant features. Finally, construct a model of transfer learning (TL) using the VGGNet16 model which could capture and further classify the disease severity. Based on Matlab software, the suggested work is assessed. With a compassion of 96.7% and specificity of 98.2%, the results demonstrate that VGGNet16 is the most suitable TL model to identify COVID-19, nonetheless, it also exceeds the most advanced methods at the moment. The clotting system and accompaniment cataract are home to the bulk of proteins in the forecast model with high significance. This work shows that plasma proteomics (PP) can result in prognostic predictions that vastly outperform the present prognostic markers in critical care, respectively

DATA SCIENCE METHODS FOR STANDARDIZATION, SAFETY, AND QUALITY ASSURANCE IN RADIATION ONCOLOGY

Radiation oncology is the field of medicine that deals with treating cancer patients through ionizing radiation. The clinical modality or technique used to treat the cancer patients in the radiation oncology domain is referred to as radiation therapy. Radiation therapy aims to deliver precisely measured dose irradiation to a defined tumor volume (target) with as minimal damage as possible to surrounding healthy tissue (organs-at-risk), resulting in eradication of the tumor, high quality of life, and prolongation of survival. A typical radiotherapy process requires the use of different clinical systems at various stages of the workflow. The data generated in these different stages of workflow is stored in an unstructured and non-standard format, which hinders interoperability and interconnectivity of data, thereby making it difficult to translate all of these datasets into knowledge that supports decision-making in routine clinical practice. In this dissertation, we present an enterprise-level informatics platform that can automatically extract and efficiently store clinical, treatment, imaging, and genomics data from radiation oncology patients. Additionally, we propose data science methods for data standardization, safety, and treatment quality analysis in radiation oncology. We demonstrate that our data standardization methods using word embeddings and machine learning are robust and highly generalizable on real-word clinical datasets collected from the nationwide radiation therapy centers administered by the US Veterans\u27 Health Administration. We also present different heterogeneous data integration approaches to enhance the data standardization process. For patient safety, we analyze the radiation oncology incident reports and propose an integrated natural language processing and machine learning based pipeline to automate the incident triage and prioritization process. We demonstrate that a deep learning based transfer learning approach helps in the automated incident triage process. Finally, we address the issue of treatment quality in terms of automated treatment planning in clinical decision support systems. We show that supervised machine learning methods can efficiently generate clinical hypotheses from radiation oncology treatment plans and demonstrate our framework\u27s data analytics capability

A study on clinico immuno pathological correlation of skin and pulmonary involvement in systemic sclerosis

INTRODUCTION: Systemic sclerosis is a chronic autoimmune connective tissue disease of unknown etiology involving multiple systems. It is characterized by significant dysfunction of the microvasculature, immune system and connective tissue. The currently used classification of Systemic Sclerosis is by the extent of skin involvement1. The extent of skin disease correlates with the disease course. Though many internal organs are involved, lung involvement is the major cause of morbidity and mortality in SSc. While some studies regard skin involvement as a surrogate marker for pulmonary involvement, there are studies that have shown improvement of sclerosis occurring spontaneously or as a result of treatment and therefore it does not reflect the pulmonary fibrosis. In addition several cutaneous features have been found to be associated with clinical and serological manifestations in systemic sclerosis. In a recent study2 elevated serum level of MMP-12 correlated with the severity of skin fibrosis and activity of interstitial lung disease in systemic sclerosis, suggesting the common pathogenesis between them. So the skin can be useful marker for early diagnosis and to assess pulmonary involvement. AIMS AND OBJECTIVES: 1. To study the skin and pulmonary manifestations in Systemic Sclerosis. 2. To study the correlation of the clinical, pathological, immunological features of skin and pulmonary involvement in Systemic Sclerosis. MATERIALS AND METHODS: SUBJECTS: Patients attending the Rheumatology Care Centre (RCC) outpatient and inpatient of Rajiv Gandhi Government General Hospital, Chennai were recruited from the period of June 2011 to February 2013. 55 eligible cases who fulfilled the inclusion criteria were enrolled. All subjects gave a written informed consent to enroll in this study. The Ethical committee approval was obtained. INCLUSION CRITERIA: American College of Rheumatology preliminary classification criteria. Major criteria or two minor criteria for diagnosis. Major criteria: Scleroderma proximal to the metacarpophalangeal joints. Minor Criteria: 1. Sclerodactyly. 2. Digital pitting scars or loss of finger pad substance. 3. Bibasilar pulmonary fibrosis. EXCLUSION CRITERIA Overlap syndrome, mixed connective tissue disease, other scleroderma spectrum disorders. RESULTS: Total number of cases were 55. The mean age was 35.5 years. The range was from 20-56 years. Majority were females 49 (89.1%) while males were 6 (10.9%). The mean disease duration was 3.1 years with range 4 months to 10 years. CONCLUSION: In this study on Systemic sclerosis there was a female gender Predominance (8:1). • The limited cutaneous SSc were more than diffuse cutaneous type in this study. • There was positive correlation between disease duration and PHT. • 43.6% of the study group were in the 7-15 MRSS Range. • Presence of salt and pepper had significant association with MRSS in this study. • Dyspnea was the most common respiratory symptom and it correlated positively with MRSS. • The MRSS was significantly associated with presence of ILD in the study group. • ILD was more common in diffuse cutaneous type and the mean MRSS was significantly associated with ILD in diffuse cutaneous type. • There was no association between MRSS and PHT in this study. • There was significant association between MRSS and the Medsger disease severity of lung. • Digital pitted scars and Raynaud‟s Phenomenon positively correlated with ILD in this study group. • ANA positivity was seen in 80% of the cases