A Freehand 3D Ultrasound Carotid Scanning System for Point-of-Care Ultrasonography

Stroke is a leading cause of morbidity and mortality throughout the world. Three-dimensional ultrasound (3DUS) imaging was shown to be more sensitive to treatment effect and more accurate in stratifying stroke risk than two-dimensional ultrasound (2DUS) imaging. Point-of-care ultrasound screening (POCUS) is important for patients with limited mobility and at times when the patients have limited access to the ultrasound scanning room, such as in the COVID-19 era. We used an optical tracking system to track the 3D position and orientation of the 2DUS frames acquired by a commercial wireless ultrasound system and subsequently reconstructed a 3DUS image from these frames. The tracking requires spatial and temporal calibrations. Spatial calibration is required to determine the spatial relationship between the 2DUS machine and the tracking system. Spatial calibration was achieved by localizing the landmarks with known coordinates in a custom-designed Z-fiducial phantom in an 2DUS image. Temporal calibration is needed to synchronize the clock of the wireless ultrasound system and the optical tracking system so that position and orientation detected by the optical tracking system can be registered to the corresponding 2DUS frame. Temporal calibration was achieved by initiating the scanning by an abrupt motion that can be readily detected in both systems. This abrupt motion establishes a common reference time point, thereby synchronizing the clock in both systems. We demonstrated that the system can be used to visualize the three-dimensional structure of a carotid phantom. The error rate of the measurements is 2.3%. Upon in-vivo validation, this system will allow POCUS carotid scanning in clinical research and practices. © 2023 SPIE.

Deep CNN Based Video Compression With Lung Ultrasound Sample

Video compression and transmission is an ever-growing area of research with continuous development in both software and hardware domain, especially when it comes to medical field. Lung ultra sound (LUS) is identified as one of the best, inexpensive and harmless option to identify various lung disorders including COVID-19. The paper proposes a model to compress and transfer the LUS sample with high quality and less encoding time than the existing models. Deep convolutional neural network is exploited to work on this, as it focusses on content, more than pixels. Here two deep convolutional neural networks, ie, P(prediction)-net and B(bi-directional)-net model are proposed that takes the input as Prediction, Bidirectional frame of existing Group of Pictures and learn. The network is trained with data set of lung ultrasound sample. The trained network is validated to predict the P, B frame from the GOP. The result is evaluated with 23 raw videos and compared with existing video compression techniques. This also shows that deep learning methods might be a worthwhile endeavor not only for COVID-19, but also in general for lung pathologies. The graph shows that the model outperforms the replacement of block-based prediction algorithm in existing video compression with P-net, B-net for lower bit rates. © The Author('s).

Multi-modal lung ultrasound image classification by fusing image-based features and probe information

Lung ultrasound is a widely used portable, cheap, and non-invasive medical imaging technology that can be used to identify various lung pathologies. In this work, we propose a multi-modal approach for lung ultrasound image classification that combines image-based features with information about the type of ultrasound probe used to acquire the input image. Experiments on a large lung ultrasound image dataset that contains images acquired with a linear or a convex ultrasound probe demonstrated the superiority of the proposed approach for the task of classifying lung ultrasound images as "COVID-19”, "Normal”, "Pneumonia”, or "Other”, when compared to simply using image-based features. Classification accuracy reached 99.98% using the proposed combination of the Xception pre-trained CNN model with the ultrasound probe information, as opposed to 96.81% when only the pre-trained EfficientNetB4 CNN model was used. Furthermore, the experimental results demonstrated a consistent improvement in classification performance when combining the examined base CNN models with probe information, indicating the efficiency of the proposed approach. © 2022 IEEE.

"Technological convergence" of preventive nutrition with non thermal processing. (Special Issue: Innovative methods in food process engineering.)

Post COVID-19 pandemic realization and expanding consumer demand for functional nutrition have compelled the food industry to focus on one, clean-label technologies to improve energy expenditure, microbial inactivation, shelf stability, and retention of functional nutrients, and second on the systematic evaluation of food matrices for bioactive potential (functionality) and designing novel food matrices and products healthier than the existing formats. The food industry is rapidly heading toward a "technological convergence" with the goal of establishing highly efficient processing technologies for safe, shelf-stable functional products. Novelty impact statement: In this review, we evaluated the utility and efficiency of various non-thermal processing technologies (cold plasma, ultra-sonication, high pressure, pulsed electric field, pulsed light processing) with respect to their capabilities to retain phytonutrient functionality and antioxidant potential in processed foods. The review also discusses existing gaps in current non thermal processing techniques and explores potential improvements necessary to foster reliable next-generation processing technologies.

Performance Analysis of Wiener filter in Restoration of Covid-19 Chest X-Ray Images, Ultrasound Images and Mammograms

Medical images such as X-Ray images, Mammograms and Ultrasound images are very useful diagnostic techniques used for understanding the functions of different internal organs, bones, tissues, etc. Most of the times these medical images are degraded by some noises and different kinds of blur. Image blurring and degradation leads to loss of quality of images which in hand causes difficulty in proper diagnosis. This paper emphases on the efficacy of Wiener filter in image de blurring and denoising Chest X-Ray of Covid-19 patients, ultrasound images of fetal abdominal cyst, umbilical cord cyst and Common Carotid Artery, Mammogram of both pathological and non-pathological breasts. Performance of Wiener filter is analyzed using image restoration parameters like Structural Similarity (SSIM), Histogram, Peak Signal to Noise Ratio and Mean Square Error. © 2022 IEEE.

COVID-19 Diagnosis Using VGG-16 with CT Scans

Artificial intelligence techniques known as deep learning are a collection of approaches that use interconnected networks to capture higher levels of ion in scene data. Analysis of vast amounts of data helps it learn ion. Imaging techniques such as x-ray, ultrasound, CT, MRI, and fluoroscopy are used in the medical field of radiology to diagnose and treat disease. Computer performance and networking advancements have made it possible for this field to benefit from the use of deep neural networks. When it comes to lung disease, the most common problem is that the symptoms aren't obvious to patients until they've progressed significantly. To prevent the spread of this disease, it is necessary to educate the public about its symptoms. Artificial intelligence approaches known as 'deep learning' use a network of interconnected nodes to extract higher levels of ion from raw data such as images and video. Analysis of vast amounts of data helps it learn ion. Imaging techniques such as x-ray, ultrasound, CT, MRI, and fluoroscopy are used in the medical field of radiology to diagnose and treat disease. Due to advances in computer performance and networking, deep neural networks are now possible in this field. More than 1.3 million people have been infected by COVID-19 (coronavirus disease 2019) and more than 106, 000 people have died. One of the biggest obstacles to containing the spread of this disease is the lack of effective and affordable medical diagnostics. There is a growing interest in developing deep learning methods for detecting COVID-19 from CT scans. These efforts, however, are difficult to replicate and adapt because the CT data used in their investigations is not publicly available.' A large number of CT scans are required for many projects to train appropriate diagnosis models, which are difficult to obtain. © 2022 IEEE.

Deep Learning-based Classification of COVID-19 Lung Ultrasound for Tele-operative Robot-assisted diagnosis

Despite the implementation of strict COVID-19 guideline, over 300,000 healthcare workers has been infected with COVID-19 globally with over 7,000 deaths. This risk of infection and loss of vital healthcare workers can be eliminated by deploying a deep learning enhanced teleoperated robot. The robot for this study was developed by Worchester Polytechnic Institute, US, to be deployed for COVID-19 at the Nigerian National Hospital Abuja. In this paper, we develop a deep learning-based automatic classification of lung ultrasound images for rapid, efficient and accurate diagnosis of patients for the developed teleoperated robot. Two lightweight models (SqueezeNet and MobileNetV2) were trained on COVID-US benchmark dataset with a computational-and memory-efficient mixed-precision training. The models achieve 99.74% (± 1) accuracy, 99.39% (± 1) recall and 99.58% (± 2) precision rate. We believe that a timely deployment of this model on the teleoperated robot will remove the risk of infection of healthcare workers. © 2021 IEEE.

Texture Analysis of Ultrasound Images to Differentiate Pneumonia and Covid-19

Lung ultrasound can potentially diagnose lung abnormalities such as pneumonia and covid-19, but it requires high experience. Covid-19, as a global pandemic, has similar common symptoms as pneumonia. The proper diagnosis of covid-19 and pneumonia necessitates clinicians' high expertise and skill to classify Covid-19 disease. This paper presents an approach to differentiate pneumonia and covid-19 based on texture analysis of ultrasound images. The proposed scheme is based on the Gray Level Co-occurrence Matrix (GLCM) features computing with Contrast Limited Adaptive Histogram Equalization (CLAHE) and gamma transformation for image enhancement. The results of the feature extraction analysis for lung ultrasound images suggest that differentiating pneumonia and Covid-19 is possible based on image texture features. © 2021 IEEE.

A Systematic Review and Analysis on Deep Learning Techniques Used in Diagnosis of Various Categories of Lung Diseases

One of the record killers in the world is lung disease. Lung disease denotes to many disorders affecting the lungs. These diseases can be identified through Chest X-Ray, Computed Tomography CT, Ultrasound tests. This study provides a systematic review on different types of Deep Learning (DL) designs, methods, techniques used by different researchers in diagnosing COVID-19, Pneumonia, Tuberculosis, Lung tumor, etc. In the present research study, a systematic review and analysis is carried by following PRISMA research methodology. For this study, more than 900 research articles are considered from various indexing sources such as Scopus and Web of Science. After several selection steps, finally a 40 quality research articles are included for detailed analysis. From this study, it is observed that majority of the research articles focused on DL techniques with Chest X-Ray images and few articles focused on CT scan images and very few have focused on Ultrasound images to identify the lung disease. © 2021, Brno University of Technology. All rights reserved.

Lessons Learned from the Development and Application of Medical Imaging-Based AI Technologies for Combating COVID-19: Why Discuss, What Next

The global COVID-19 pandemic has resulted in huge pressures on healthcare systems, with lung imaging, from chest radiographs (CXR) to computed tomography (CT) and ultrasound (US) of the thorax, playing an important role in the diagnosis and management of patients with coronavirus infection. The AI community reacted rapidly to the threat of the coronavirus pandemic by contributing numerous initiatives of developing AI technologies for interpreting lung images across the different modalities. We performed a thorough review of all relevant publications in 2020 [1] and identified numerous trends and insights that may help in accelerating the translation of AI technology in clinical practice in pandemic times. This workshop is devoted to the lessons learned from this accelerated process and in paving the way for further AI adoption. In particular, the objective is to bring together radiologists and AI experts to review the scientific progress in the development of AI technologies for medical imaging to address the COVID-19 pandemic and share observations regarding the data relevance, the data availability and the translational aspects of AI research and development. We aim at understanding if and what needs to be done differently in developing technologies of AI for lung images of COVID-19 patients, given the pressure of an unprecedented pandemic - which processes are working, which should be further adapted, and which approaches should be abandoned. © 2021, Springer Nature Switzerland AG.

Effect of H2O2 Antiseptic on Dispersal of Cavitation-Induced Microdroplets.

The persisting outbreak of SARS-CoV-2 has posed an enormous threat to global health. The sustained human-to-human transmission of SARS-CoV-2 via respiratory droplets makes the medical procedures around the perioral area vulnerable to the spread of the disease. Such procedures include the ultrasonic dental cleaning method, which occurs within the oral cavity and involves cavitation-induced sprays, thus increasing the risk of pathogen transmission via advection. To understand the associated health and safety risks for patients and clinicians, it is critical to understand the flow pattern of the spray cloud around the operating region, the size and velocity distribution of the emitted droplets, and the extent of fluid dispersion until ultimate deposit on surfaces or escape through air vents. In this work, the droplet size and velocity distributions of the spray emerging from the tip of a free-standing common ultrasonic dental cleaning device were characterized via high-speed imaging. Deionized water and 1.5% and 3% aqueous hydrogen peroxide (H2O2) solutions were used as working fluids, with the H2O2-an established oxidizing agent-intended to curb the survival of virus released in aerosols generated from dental procedures. The measurements reveal that the presence of H2O2 in the working fluid increases the mean droplet size and ejection velocity. Detailed computational fluid dynamic simulations with multiphase flow models reveal benefits of adding small amounts of H2O2 in the feed stream of the ultrasonic cleaner; this practice causes larger droplets with shorter residence times inside the clinic before settling down or escaping through air vents. The results suggest optimal benefits (in terms of fluid spread) of adding 1.5% H2O2 in the feed stream during dental procedures involving ultrasonic tools. The present findings are not specific to the COVID-19 pandemic but should also apply to future outbreaks caused by airborne droplet transmission.

Sources of SARS-CoV-2 and Other Microorganisms in Dental Aerosols.

On March 16, 2020, 198,000 dentists in the United States closed their doors to patients, fueled by concerns that aerosols generated during dental procedures are potential vehicles for transmission of respiratory pathogens through saliva. Our knowledge of these aerosol constituents is sparse and gleaned from case reports and poorly controlled studies. Therefore, we tracked the origins of microbiota in aerosols generated during ultrasonic scaling, implant osteotomy, and restorative procedures by combining reverse transcriptase quantitative polymerase chain reaction (to identify and quantify SARS-CoV-2) and 16S sequencing (to characterize the entire microbiome) with fine-scale enumeration and source tracking. Linear discriminant analysis of Bray-Curtis dissimilarity distances revealed significant class separation between the salivary microbiome and aerosol microbiota deposited on the operator, patient, assistant, or the environment (P < 0.01, analysis of similarities). We also discovered that 78% of the microbiota in condensate could be traced to the dental irrigant, while saliva contributed to a median of 0% of aerosol microbiota. We also identified low copy numbers of SARS-CoV-2 virus in the saliva of several asymptomatic patients but none in aerosols generated from these patients. Together, the bacterial and viral data encourage us to conclude that when infection control measures are used, such as preoperative mouth rinses and intraoral high-volume evacuation, dental treatment is not a factor in increasing the risk for transmission of SARS-CoV-2 in asymptomatic patients and that standard infection control practices are sufficiently capable of protecting personnel and patients from exposure to potential pathogens. This information is of immediate urgency, not only for safe resumption of dental treatment during the ongoing COVID-19 pandemic, but also to inform evidence-based selection of personal protection equipment and infection control practices at a time when resources are stretched and personal protection equipment needs to be prioritized.