Visual Analysis of Social Robots in Twitter on China Agenda Setting

With the development and progress of intelligent algorithms, more and more social robots are used to interfere with the information transmission and direction of international public opinion. This paper takes the agenda of COVID-19 in Twitter as the breakthrough point, and through the methods of web crawler, Twitter robot detection, data processing and analysis, aims at the agenda setting of social robots for China issues, that is, to carry out data visualization analysis for the stigmatized China image. Through case analysis, concrete and operable countermeasures for building the international communication system of China image were provided. © 2022 IEEE.

lungEcho - Resource Constrained Lung Ultrasound Video Analysis Tool for Faster Triaging and Active Learning

Lung ultrasound (LUS) is possibly the only medical imaging modality which could be used for continuous and periodic monitoring of the lung. This is extremely useful in tracking the lung manifestations either during the onset of lung infection or to track the effect of vaccination on lung as in pandemics such as COVID-19. There have been many attempts in automating the classification of the severity of lung involvement into various classes or automatic segmentation of various LUS landmarks and manifestations. However, all these approaches are based on training static machine learning models which require a significantly large clinically annotated dataset and are computationally heavy and are most of the time non-real time. In this work, a real-time light weight active learning-based approach is presented for faster triaging in COVID-19 subjects in resource constrained settings. The tool, based on the you look only once (YOLO) network, has the capability of providing the quality of images based on the identification of various LUS landmarks, artefacts and manifestations. This tool also predict the severity of lung infection and make use of the possibility of active learning based on the feedback from clinicians or on the image quality. The capability of this tool to summarize the significant frames which are having high severity of infection and high image quality will be helpful for clinicians to discern things more easily. The results show that the proposed object detection tool has a mean average precision (mAP) of 66% at an Intersection over Union (IoU) threshold of 0.5 for the prediction of LUS landmarks with initial training on less than 1000 images. The 14MB lightweight YOLOv5s network achieves 123 FPS while running on a Quadro P4000 GPU. The tool is available for usage and analysis upon request from the authors and details can be found online.

Comparative Analysis of Linear Regression and Logistic Regression for the COVID-19 Detection from CT-Scans

The new emerging Coronavirus disease (COVID-19) is a pandemic disease due to its enormous infectious capability. Generally affecting the lungs, COVID-19 engenders fever, dry cough, and tiredness. However, some patients may not show symptoms. An imaging test, such as a chest X-ray or a chest CT scan, is therefore requested for reliable detection of this pneumonia type. Despite the decreasing trends both in the new and death reported cases, there is an extent need for quick, accurate, and inexpensive new methods for diagnosis. In this framework, we propose two machine learning (ML) algorithms: linear regression and logistic regression for effective COVID-19 detection in the abdominal Computed Tomography (CT) dataset. The ML methods proposed in this paper, effectively classify the data into COVID-19 and normal classes without recourse to image preprocessing or analysis. The effectiveness of these algorithms was shown through the use of the performance measures: accuracy, precision, recall, and F1-score. The best classification accuracy was obtained as 96% with logistic regression using the saga solver with no added penalty against 95.3% with linear regression. As for precision, recall, and F1-score the value of 0.89 was reached by logistic regression for all these metrics, as well as the value of 0.87 by linear regression. © 2022 IEEE.

Comparative Analysis of Laboratory and Radiation Methods of Studies with the Degree of Severity of Patients with COVID-19

The study is devoted to a comparative analysis and retrospective evaluation of laboratory and instrumental data with the severity of lung tissue damage in COVID-19 of patients with COVID-19. An improvement was made in the methodology for interpreting and analyzing dynamic changes associated with COVID-19 on CT images of the lungs. The technique includes the following steps: pre-processing, segmentation with color coding, calculation and evaluation of signs to highlight areas with probable pathology (including combined evaluation of signs). Analysis and interpretation is carried out on the emerging database of patients. At the same time the following indicators are distinguished: the results of the analysis of CT images of the lungs in dynamics;the results of the analysis of clinical and laboratory data (severity course of the disease, temperature, saturation, etc.). The results of laboratory studies are analyzed with an emphasis on the values of the main indicator - interleukin-6. This indicator is a marker of significant and serious changes characterizing the severity of the patient's condition. © 2023 IEEE.

A narrative review on radiation risk from imaging for COVID-19: Breaking the myths and the mithya

Radiotherapy-induced secondary malignancy is a well-known occurrence. During the COVID-19 pandemic, many people have undergone serial computed tomography (CT) imaging, and concerns have been raised regarding radiation-induced malignancies due to frequent scanning. Accordingly, various low and ultra-low-dose CT (LDCT) thorax protocols have been developed to reduce the dose of radiation. Major governing bodies worldwide have established guidelines regarding the indications for CT scans and chest X-rays during the pandemic. We, therefore, aimed to provide facts about the effects of radiation (both diagnostic and therapeutic). Through this article, we intend to break the myths and 'mithya' (misbeliefs) regarding diagnostic radiation and its association with cancer in this COVID-19 era. For this review, we performed a search in Google using specific keywords pertaining to imaging during COVID-19 and radiation risk. We also included the names of various global governing bodies in the Google search. We included only full text articles and guidelines from authentic websites. From this review, we conclude that if we follow the recommendations of various global governing bodies and use CT scan only in cases of moderate to severe COVID-related symptoms, adhere to the principle of 'as low as reasonably achievable' for radiation protection, and use LDCT scan protocols, we can significantly reduce the mean effective radiation dose delivered and the estimated cancer risk.Copyright © 2023 Cancer Research, Statistics, and Treatment. All rights reserved.

Sequence of actions for visualization of soft tissue hemorrhages/hematomas in elderly patients infected with SARS-CoV-2.

Aim. To determine ultrasound, computed tomography and angiographic image characteristics for soft tissue hemorrhages/hematomas, the sequence of using imaging methods in patients infected with SARS-CoV-2, to study the morphology of changes in soft tissues, to determine the essence of the concept and to develop treatment tactics for this complication of COVID-19. Material and methods. During 4 months of treatment of elderly patients (+60) infected with SARS-CoV-2, 40 patients were identified with soft tissue hemorrhages/hematomas, of which 26 (65%) patients with large hematomas (>10 cm in size and > 1000 ml in volume). The analysis of clinical and laboratory parameters, methods of instrumental diagnostics (ultrasound - 26 patients, CT - 10 patients, angiography - 9 patients, punctures - 6 patients) was carried out;autopsy material was studied in 11 cases. Results. Image characteristics of hemorrhages/hematomas of soft tissue density were obtained using modern instrumental methods, and the sequence of application of visualization methods was determined. A tactic for managing a patient with stopped and ongoing bleeding has been developed. The morphological substrate of hemorrhagic complications in a new viral infection was studied. All patients were treated with conservative and minimally invasive procedures (embolization, puncture with pressure bandage). 15 patients (57.7%) recovered, 11 patients (42.3%) died from the progression of COVID-19 complications. Conclusion. Comprehensive clinical and laboratory sequential instrumental diagnosis of soft tissue hemorrhages in COVID-19. Treatment should be conservative and significantly invasive. The use of the term "soft tissue hematoma" in SARS-CoV-2 infected patients is not a natural quality of the normal pathological process and should not be observed from our point of view.Copyright © 2022 Rostovskii Gosudarstvennyi Meditsinskii Universitet. All rights reserved.

Cough Detection for Prevention Against the COVID-19 Pandemic

This paper proposes a novel and robust technique for remote cough recognition for COVID-19 detection. This technique is based on sound and image analysis. The objective is to create a real-time system combining artificial intelligence (AI) algorithms, embedded systems, and network of sensors to detect COVID-19-specific cough and identify the person who coughed. Remote acquisition and analysis of sounds and images allow the system to perform both detection and classification of the detected cough using AI algorithms and image processing to identify the coughing person. This will give the ability to distinguish between a normal person and a person carrying the COVID-19 virus. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

2022 PeDRA Annual Conference

The proceedings contain 90 papers. The topics discussed include: characterization of nonalcoholic fatty liver disease in children with psoriasis: a pilot study;management of pediatric psoriasis: a representative US survey;severity and patient-related outcomes in atopic dermatitis do not correlate with deprivation index as an indicator of socioeconomic setting in a US metropolitan area;pediatric atopic dermatitis: assessment of burden based on lesional morphology;metered dose applicators: a potential solution for improving topical medication adherence in atopic dermatitis patients;serial staged punch excision technique for linear epidermal nevus and nevus sebaceous;the molecular basis of superficial vascular lesions of the skin: genotype-phenotype correlation of capillary malformations;utilization and effect of telehealth for the treatment of hemangioma before and after COVID;image analysis of port wine birthmarks using optical coherence tomography;image analysis of port wine birthmarks using optical coherence tomography;and responsiveness to change of the morphea activity measure.

A novel adaptive class weight adjustment-based multiclass segmentation error minimization technique for COVID-19 X-ray image analysis

Coronavirus outbreaks during the last couple of years created a huge health disaster for human lives. Diagnosis of COVID-19 infections is, thus, very important for the medical practitioners. For a quick detection, analysis of the COVID-19 chest X-ray images is inevitable. Therefore, there is a strong need for the development of a multiclass segmentation method for the purpose. Earlier techniques used for multiclass segmentation of images are mostly based on entropy measurements. Nonetheless, entropy methods are not efficient when the gray-level distribution of the image is nonuniform. To address this problem, a novel adaptive class weight adjustment-based multiclass segmentation error minimization technique for COVID-19 chest X-ray image analysis is investigated. Theoretical investigations on the first-hand objective functions are presented. The results on both the biclass and multiclass segmentation of medical images are enlightened. The key to our success is the adjustment of the pixel counts of different classes adaptively to reduce the error of segmentation. The COVID-19 chest X-ray images are taken from the Kaggle Radiography database for the experiments. The proposed method is compared with the state-of-the-art methods based on Tsallis, Kapur's, Masi, and Renyi entropy. The well-known segmentation metrics are used for an empirical analysis. Our method achieved a performance increase of around 8.03% in the case of PSNR values, 3.01% for FSIM, and 4.16% for SSIM. The proposed technique would be useful for extracting dots from micro-array images of DNA sequences and multiclass segmentation of the biomedical images such as MRI, CT, and PET.

Machine Learning Techniques on X-ray Images for Covid-19 Classification

The Public Health Commission of Hubei Province, China, at the end of 2019reported cases of severe and unknown pneumonia, marked by fever, malaise, dry cough, dyspnea, and respiratory failure, that occurred in the urban area of Wuhan, according to the World Health Organization (WHO). The lung infection, SARS-CoV-2, also known as COVID-19, was caused by a brand-new coronavirus (coronavirus disease 2019). Since then, infections have increased exponentially, and the WHO labeled the outbreak a worldwide emergency at the beginning of March 2020. Infected and asymptomatic individuals who can spread the virus are the main sources of it. The transmission occurs mainly by airthrough the air through the droplets, however indirect transmission is also possible, such as through contact with infected surfaces. It becomes essential to identify viral carriers as soon as possible in order to stop the spread of the disease and reduce morbidity and mortality. Imaging examinations, which are among the specific tests used to make the definite diagnosis, are crucial in the patient's management when COVID-19 is suspected. Numerous papers that use machine learning techniques discuss the use of X-ray chest radiographs as a component that aids in diagnosis and permits disease follow-up. The goal of this work is to supply the scientific community with information on the most widely used Machine Learning algorithms applied to chest X-ray images. © 2022 IEEE.

Detection of Covid-19 using Mask R-CNN

Deep learning has been widely used to analyze radiographic pictures such as chest scans. These radiographic pictures include a wealth of information, including patterns and cluster-like formations, which aid in the discovery and conformance of COVID-19-like pandemics. The COVID-19 pandemic is wreaking havoc on global well-being and public health. Until present, more than 27 million confirmed cases have been recorded globally. Due to the increasing number of confirmed cases and issues with COVID-19 variants, fast and accurate categorization of healthy and infected individuals is critical for COVID-19 management and treatment. In medical image analysis and classification, artificial intelligence (AI) approaches in general, and region-based convolutional neural networks (CNNs) in particular, have yielded promising results. In this study, a deep Mask R-CNN architecture based on chest image classification is suggested for the diagnosis of COVID-19. An effective and reliable Mask R-CNN classification was difficult due to a lack of sufficient size and high-quality chest image datasets. These complications are addressed with Mask Region-based convolutional neural networks (R-CNNs) as a framework for detecting COVID-19 patients from chest pictures using an open-source dataset. First, the model was evaluated using 100 photos from the original processed dataset, and it was found to be accurate. The model was then validated against an independent dataset of COVID-19 X-ray pictures. The suggested model outperformed all other models in general and specifically when tested using an independent testing set. © 2023 IEEE.

Understanding the Use of Images to Spread COVID-19 Misinformation on Twitter

While COVID-19 text misinformation has already been investigated by various scholars, fewer research efforts have been devoted to characterizing and understanding COVID-19 misinformation that is carried out through visuals like photographs and memes. In this paper, we present a mixed-method analysis of image-based COVID-19 misinformation in 2020 on Twitter. We deploy a computational pipeline to identify COVID-19 related tweets, download the images contained in them, and group together visually similar images. We then develop a codebook to characterize COVID-19 misinformation and manually label images as misinformation or not. Finally, we perform a quantitative analysis of tweets containing COVID-19 misinformation images. We identify five types of COVID-19 misinformation, from a wrong understanding of the threat severity of COVID-19 to the promotion of fake cures and conspiracy theories. We also find that tweets containing COVID-19 misinformation images do not receive more interactions than baseline tweets with random images posted by the same set of users. As for temporal properties, COVID-19 misinformation images are shared for longer periods of time than non-misinformation ones, as well as have longer burst times. we compare non-misinformation images instead of random images, and so it is not a direct comparison. When looking at the users sharing COVID-19 misinformation images on Twitter from the perspective of their political leanings, we find that pro-Democrat and pro-Republican users share a similar amount of tweets containing misleading or false COVID-19 images. However, the types of images that they share are different: while pro-Democrat users focus on misleading claims about the Trump administration's response to the pandemic, as well as often sharing manipulated images intended as satire, pro-Republican users often promote hydroxychloroquine, an ineffective medicine against COVID-19, as well as conspiracy theories about the origin of the virus. Our analysis sets a basis for better understanding COVID-19 misinformation images on social media and the nuances in effectively moderate them. © 2023 ACM.

Intelligent medicine and beyond

With the developments of medical artificial intelligence (AI), meta-data analysis, intelligence-aided drug design and discovery, surgical robots and image-navigated precision treatments, intelligent medicine (IM) as a new era evolved from ancient medicine and biomedical medicine, has become an emerging topic and important criteria for clinical applications. It is fully characterized by fundamental research-driven, new-generation technique-directed as well as state-of-the-art paradigms for advanced disease diagnosis and therapy leading to an even broader future of modern medicine. As a fundamental subject and also a practice-oriented field, intelligent medicine is highly trans-disciplinary and cross-developed, which has emerged the knowledge of modern medicine, basic sciences and engineering. Basically, intelligent medicine has three domains of intelligent biomaterials, intelligent devices and intelligent techniques. Intelligent biomaterials derive from traditional biomedical materials, and currently are endowed with multiple functionalities for medical uses. For example, micro-/nanorobots, smart responsive biomaterials and digital drugs are representative intelligent biomaterials which have been already commercialized and applied to clinical uses. Intelligent devices, such as surgical robots, rehabilitation robots and medical powered exoskeleton, are an important majority in the family of intelligent medicine. Intelligent biomaterials and intelligent devices are more and more closely integrated with each other especially on the occasions of intelligence acquisition, remote transmission, AI-aided analysis and management. In comparison, intelligent techniques are internalized in the former two domains and are playing a critical role in the development of intelligent medicine. Representative intelligent techniques of telemedicine, image-navigated surgery, virtual/augmented reality and AI-assisted image analysis for early-stage disease assessments have been employed in nowadays clinical operations which to a large extent relieved medical labors. In the past decades, China has been in the leading groups compared to international colleagues in the arena of intelligent medicine, and a series of eminent research has been clinically translated for practical uses in China. For instance, the first 5G-aided remote surgery has been realized in Fujian Province in January 2019, which for the first time validated their applicability for human uses. The surgical robots have found China as the most vigorous market, and more than 10 famous Chinese companies are developing versatile surgical robots for both Chinese people and people all over the world. China also applied AI techniques to new drug developments especially in early 2020 when COVID-19 epidemic roared, and several active molecules and drug motifs have been discovered for early-stage COVID-19 screening and treatments. Based on the significance of intelligent medicine and its rapid developments in both basic research and industrials, this review summarized the comprehensive viewpoints of the Y6 Xiangshan Science Conferences titled with Fundamental Principles and Key Technologies of Intelligent Medicine, and gave an in-depth discussion on main perspectives of future developments of the integration of biomaterial and devices, the integration of bioinformatics and medical hardware, and the synergy of biotechnology and intelligence information. It is expected that this featuring article will further promote intelligent medicine to an even broader community not only for scientists but also for industrials, and in the long run embrace a perspective future for its blooming and rich contributions in China in the coming 5 years. © 2023 Chinese Academy of Sciences. All rights reserved.

Autonomous Flight Using UWB-Based Positioning System with Optical Flow Sensors in a GPS-Denied Environment

This study presents the positioning method and autonomous flight of a quadrotor drone using ultra-wideband (UWB) communication and an optical flow sensor. UWB communication obtains the distance between multiple ground stations and a mobile station on a robot, and the position is calculated based on a multilateration method similar to global positioning system (GPS). The update rate of positioning using only UWB communication devices is slow;hence, we improved the update rate by combining the UWB and inertial measurement unit (IMU) sensor in the prior study. This study demonstrates the improvement of the positioning method and accuracy by sensor fusion of the UWB device, an IMU, and an optical flow sensor using the extended Kalman filter. The proposed method is validated by hovering and position control experiments and also realizes a sufficient rate and accuracy for autonomous flight.

Neuroradiological Findings in Headache Patients After COVID-19 Infection

The new coronavirus disease was declared by WHO as COVID-19 1 and the name of the virus causing this disease was defined as SARS-CoV-2 . The most common way of transmission of the virus is the close contact with infected people and respiratory droplets. Another common way of transmission is touching mouth, nose and eyes after touching surfaces contaminated with droplets shed by infected people. According to the results of the studies, the virus has a durability between 2-72 hours on different surfaces and items..Copyright © 2022, Universidade de Sao Paulo. Museu de Zoologia. All rights reserved.

COVID-19 Diagnostic System Using Medical Image Classification and Retrieval: A Novel Method for Image Analysis

With the rapid increase in the number of people infected with COVID-19 disease in the entire world, and with the limited medical equipment used to detect it (testing kit), it becomes necessary to provide another detection method that mainly relies on Artificial Intelligence and radiographic Image Analysis to determine the disease infection. In this study, we proposed a diagnosis system that detects the COVID-19 using chest X-ray or computed tomography (CT) scan images knowing that this system does not eliminate the reverse transcription-polymerase chain reaction test but rather complements it. The proposed system consists of the following steps, starting with extracting the image's features using Visual Words Fusion of ResNet-50 (deep neural network) and Histogram of Oriented Gradient descriptors based on Bag of Visual Word methodology. Then training the Adaptive Boosting classifier to classify the image to COVID-19 or NOTCOVID-19 and finally retrieving the most similar images. We implemented our work on X-ray and CT scan databases, and the experimental results demonstrate the effectiveness of the proposed system. The performance of the classification task in terms of accuracy was as follows: 100% for classifying the input image to X-ray or CT scan, 99.18% for classifying X-ray image to COVID-19 or NOTCOVID-19 and 97.84% for classifying CT scan to COVID-19 or NOTCOVID-19. © 2021 The British Computer Society.

AAPM task group report 273: Recommendations on best practices for AI and machine learning for computer-aided diagnosis in medical imaging.

Rapid advances in artificial intelligence (AI) and machine learning, and specifically in deep learning (DL) techniques, have enabled broad application of these methods in health care. The promise of the DL approach has spurred further interest in computer-aided diagnosis (CAD) development and applications using both "traditional" machine learning methods and newer DL-based methods. We use the term CAD-AI to refer to this expanded clinical decision support environment that uses traditional and DL-based AI methods. Numerous studies have been published to date on the development of machine learning tools for computer-aided, or AI-assisted, clinical tasks. However, most of these machine learning models are not ready for clinical deployment. It is of paramount importance to ensure that a clinical decision support tool undergoes proper training and rigorous validation of its generalizability and robustness before adoption for patient care in the clinic. To address these important issues, the American Association of Physicists in Medicine (AAPM) Computer-Aided Image Analysis Subcommittee (CADSC) is charged, in part, to develop recommendations on practices and standards for the development and performance assessment of computer-aided decision support systems. The committee has previously published two opinion papers on the evaluation of CAD systems and issues associated with user training and quality assurance of these systems in the clinic. With machine learning techniques continuing to evolve and CAD applications expanding to new stages of the patient care process, the current task group report considers the broader issues common to the development of most, if not all, CAD-AI applications and their translation from the bench to the clinic. The goal is to bring attention to the proper training and validation of machine learning algorithms that may improve their generalizability and reliability and accelerate the adoption of CAD-AI systems for clinical decision support.

Vision Transformers in medical computer vision-A contemplative retrospection

Vision Transformers (ViTs), with the magnificent potential to unravel the information contained within images, have evolved as one of the most contemporary and dominant architectures that are being used in the field of computer vision. These are immensely utilized by plenty of researchers to perform new as well as former experiments. Here, in this article, we investigate the intersection of vision transformers and medical images. We proffered an overview of various ViT based frameworks that are being used by different researchers to decipher the obstacles in medical computer vision. We surveyed the applications of Vision Transformers in different areas of medical computer vision such as image-based disease classification, anatomical structure segmentation, registration, region-based lesion detection, captioning, report generation, and reconstruction using multiple medical imaging modalities that greatly assist in medical diagnosis and hence treatment process. Along with this, we also demystify several imaging modalities used in medical computer vision. Moreover, to get more insight and deeper understanding, the self-attention mechanism of transformers is also explained briefly. Conclusively, the ViT based solutions for each image analytics task are critically analyzed, open challenges are discussed and the pointers to possible solutions for future direction are deliberated. We hope this review article will open future research directions for medical computer vision researchers.