Automatic Detection of Lung Ultrasound Artifacts using a Deep Neural Networks approach

The COVID-19 pandemic has challenged many of the healthcare systems around the world. Many patients who have been hospitalized due to this disease develop lung damage. In low and middle-income countries, people living in rural and remote areas have very limited access to adequate health care. Ultrasound is a safe, portable and accessible alternative;however, it has limitations such as being operator-dependent and requiring a trained professional. The use of lung ultrasound volume sweep imaging is a potential solution for this lack of physicians. In order to support this protocol, image processing together with machine learning is a potential methodology for an automatic lung damage screening system. In this paper we present an automatic detection of lung ultrasound artifacts using a Deep Neural Network, identifying clinical relevant artifacts such as pleural and A-lines contained in the ultrasound examination taken as part of the clinical screening in patients with suspected lung damage. The model achieved encouraging preliminary results such as sensitivity of 94%, specificity of 81%, and accuracy of 89% to identify the presence of A-lines. Finally, the present study could result in an alternative solution for an operator-independent lung damage screening in rural areas, leading to the integration of AI-based technology as a complementary tool for healthcare professionals. © 2023 SPIE.

COVID bell - A smart doorbell solution for prevention of COVID-19

The article introduces a novel strategy for efficiently mitigating COVID-19 distribution at the local level due to contact with any surfaces. Our project aims to be a critical safety shield for the general people in the fight against the epidemic. An ultrasonic sensor is integrated with the automated doorbell system to ring the doorbell with a hand motion. A temperature sensor Mlx90614 is also included in the system, which records the temperature of the person standing in front of the door. The device also includes a camera module that captures the image of the person standing at the front entrance. The captured image is processed through an ML model which runs at over 30 fps to detect whether or not the person is wearing a mask. The image and the temperature of the person standing outside are sent to the owner through the configured iOS application. If the person outside is wearing a mask, one can open the door through the app itself and permit the entry of the person standing outside thereby integrating the edge device with an app for a better user experience. The system helps in reducing physical contact, and the results obtained are at par with the already existing solutions and provide a few advantages over them. © 2023 the author(s), published by De Gruyter.

An IoT based Smart Robot that Aids in the Prevention of COVID19 Spread

The Internet of Things revolution is transforming current healthcare practices by combining technological, economic, and social aspects. Since December 2019, the global spread of COVID19 has influenced the global economy. The COVID19 epidemic has forced governments all around the world to implement lockdowns to prevent viral infections. Wearing a face mask in a public location, according to survey results, greatly minimizes the risk of infection. The suggested robotics design includes an IoT solution for facemask detection, body temperature detection, an automatic dispenser for hand sanitizing, and a social distance monitoring system that can be used in any public space as a single IoT solution. Our goal was to use IoT-enabled technology to help prevent the spread of COVID19, with encouraging results and a future Smart Robot that Aids in COVID19 Prevention. Arduino NANO, MCU unit, ultrasonic sensor, IR sensor, temperature sensor, and buzzer are all part of our suggested implementation system. Our system's processing components, the Arduino UNO and MCU modules are all employed to process and output data. Countries with large populations, such as India and Bangladesh, as well as any other developing country, will benefit from using our cost-effective, trustworthy, and portable smart robots to effectively reduce COVID-19 viral transmission. © 2022 IEEE.

Croup disease classification using VGG19 and ResNet50 transfer learning method

Detection of any disease in the early stage can save a life. There are many medical imaging modalities like MRI, FMRI, ultrasound, CT, and X-ray used in the detection of disease. In the last decades, neural network-based methods are effective in detecting and classifying the disease based on abnormalities present in the medical images. Acute laryngotracheobronchitis (croup) is one of the common diseases seen in children among the 0.5-3 years age group which infects the respiratory system that can cause the larynx, trachea, and bronchi. Prior detection can lower the risk of spreading and can be treated accurately by a pediatrician. Commonly this infection can be diagnosed though physical examination. But due to the similarity of Covid-19 symptoms urges the physicians to get accurate detection of this disease using X-ray and CT images of the infant's chest and throat. The proposed work aims to develop a croup diagnose system (CDS) which identify the Croup infection through post anterior (PA) view of pediatric X-ray using deep learning algorithm. We used the well-known transfer learning algorithm VGG19 and ResNet50. Data augmentation being adapted for reducing the overfitting and to improve the quantity of image samples. We show that the proposed transfer learning based CDS method can be used to classify the X-ray images into two classes namely, croup and normal. The experiment results confirm that VGG19 performs better than ResNet50 with promising classification accuracy (90.91%.). The results show that the proposed CDS models can be used for more pediatric medical image classification problem. © 2024 World Scientific Publishing Company.

An intelligent platform of services based on multimedia understanding and telehealth for supporting the management of SARS-CoV-2 multi-pathological patients

The combination of pervasive sensing and multimedia understanding with the advances in communications makes it possible to conceive platforms of services for providing telehealth solutions responding to the current needs of society. The recent outbreak has indeed posed several concerns on the management of patients at home, urging to devise complex pathways to address the Severe Acute Respiratory Syndrome (SARS) in combination with the usual diseases of an increasingly elder population. In this paper, we present TiAssisto, a project aiming to design, develop, and validate an innovative and intelligent platform of services, having as its main objective to assist both Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) multi-pathological patients and healthcare professionals. This is achieved by researching and validating new methods to improve their lives and reduce avoidable hospitalisations. TiAssisto features telehealth and telemedicine solutions to enable high-quality standards treatments based on Information and Communication Technologies (ICT), Artificial Intelligence (AI) and Machine Learning (ML). Three hundred patients are involved in our study: one half using our telehealth platform, while the other half participate as a control group for a correct validation. The developed AI models and the Decision Support System assist General Practitioners (GPs) and other healthcare professionals in order to help them in their diagnosis, by providing suggestions and pointing out possible presence or absence of signs that can be related to pathologies. Deep learning techniques are also used to detect the absence or presence of specific signs in lung ultrasound images. © 2022 IEEE.

Covid-19 Vest for Social distancing and Monitoring

In recent years, due to the rise in the number of novel coronaviruses across the globe nations step forward to stop the crisis. With guidelines of the WHO many methodologies came into existence to prevent the spreading of coronavirus. My SD: A Smart Social distance and Monitoring System takes advantage of the features of the smartphone's hardware which usually has Bluetooth transmitter-receiver, like GPS to determine the safe distance and required level of compliance. Through artificial intelligence, this new smart device helps maintain uniform social distance and detect COVID 19 patients. In these COVID 19 environments, everyone knows how safe they are. In this paper, we have automated the process whereby the layman can control himself without any priming which makes the system more user-friendly for the public. The user himself or herself can monitor body temperature, social distancing and get an alert in abnormal selfisolation conditions using contactless thermometer, ultrasonic sensors, and GSM modules. © 2022 IEEE.

Surveillance Rover to Maintain Social Distancing in Crowded Areas

COVID-19 is making a huge impact both in terms of the economy and human lives. Many lost their lives due to COVID-19 which is found in most of the nations. The number of positive symptoms is increasing rapidly all over the world. To safeguard us from the virus, some protocols have been addressed by WHO in which people has to wear a mask and make a social distancing when moved in public. Therefore, social distancing places an important role in preventing us from the spread of the diseases. The minimum distance between to be maintained is informed at 6 feet informed by the health organizations. When people gathered on a group social distancing could not be maintained even if manual or any kind of technology implemented. Temperature measurement on mass gathering was also a tedious process where the monitoring is essential. Multiple methods such as thermal cameras, temperature sensors for monitoring the personnel has not been efficient. In the proposed work to monitor the social distancing between the persons an ultrasonic sensor is placed to detect the obstacle and an IR sensor to make the rover move. An encoder is used to calculate the distance based on the rpm of the wheel. Based on this input the distance is checked within this limit the obstacle is detected, an alert signal is made using the buzzer. A thermal sensor is used to measure the temperature of the person and an LCD display shows the temperature of the person and distance between obstacles. The proposed system has resulted in identifying the distance and helps in reducing the spread during the pandemic situation. © 2023 IEEE.

IoT-Based E-Parking System for Multiplexes and Shopping Malls

This paper helps in automating process of car parking in shopping malls. It helps in making parking more efficient by burning of less fuel. This system is useful for places with large number of people considering less people-to-people contact considering Covid Pandemic and making a safe system for minimal infection transmission from people to people. This paper aims at developing a IoT-based E-parking system. This project uses Micro-controller (ATtiny85) for controlling of sensors. Set of multiple ultrasonic sensors are put on ceilings per floor with multiple slots for detection of vehicles in parked spaces with threshold set for cars. Multiple Wi-Fi modules are used for wirelessly uploading the values of vehicles parked in different floors to cloud from where the Wi-Fi module at entrance extracts data and displays on central display at entrance for assigning empty parking slots to new vehicles on arrival. Entrance display displays number of empty slots on every floor to new customer entering mall parking system. This project achieved objective of making a system which can be used in times of Covid-19 for better safety of people. This paper has been able to achieve its main objectives of making a safe, affordable, scalable parking system which can be used in shopping malls and multiplexes. It can be scaled to large usable parking systems using better sensors and better computing devices. It can provide means of work or business to youth of city for building and selling smart vehicle parking systems and deploy them to multiple malls and multiplexes using help from staff and sell at affordable rates. It can also help make more customizable and modular smart parking systems tailored to use of system in any buildings. Arduino IDE has been used for uploading code to cloud modules in project. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

COVID-19 Detection Using Deep Learning Based Medical Image Segmentation

COVID-19 is a rapidly spreading illness around the globe, yet healthcare resources are limited. Timely screening of people who may have had COVID-19 is critical in reducing the virus's spread considering the lack of an effective treatment or medication. COVID-19 patients should be diagnosed as well as isolated as early as possible to avoid the infection from spreading and levelling the pandemic arc. To detect COVID-19, chest ultrasound tomography seems to be an option to the RT-PCR assay. The Ultrasound of the lung is a very precise, quick, relatively reliable surgical assay that can be used in conjunction with the RT PCR (Reverse Transcription Polymerase Chain Reaction) assay. Differential diagnosis is difficult due to large differences in structure, shape, and position of illnesses. The efficiency of conventional neural learning-based Computed tomography scans feature extraction is limited by discontinuous ground-glass and acquisitions, as well as clinical alterations. Deep learning-based techniques, primarily Convolutional Neural Networks (CNN), had successfully proved remarkable therapeutic outcomes. Moreover, CNNs are unable to capture complex features amongst images examples, necessitating the use of huge databases. In this paper semantic segmentation method is used. The semantic segmentation architecture U-Net is applied on COVID-19 CT images as well as another method is suggested based on prior semantic segmentation. The accuracy of U-Net is 87% and by using pre-trained U-Net with convolution layers gives accuracy of 89.07%. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

COVID Detection Using ECG Image Reports: A Survey

COVID-19 is one of the greatest pandemics that threaten individuals, especially the elders. It was first reported in Wuhan, China in 2019. It was discovered recently that COVID-19 disease can be detected using three main protocols. The first protocol is based on Polymerase Chain reaction (PCR), while the second protocol is based on lung chest (ultrasound, X-ray, and CT-Scan), and the final protocol is based on the ECG image reports. This review aims to present a survey on the methodologies and algorithms applied for the detection of COVID disease using electrocardiogram (ECG). In this study, various papers were presented for determining how the COVID can be diagnosed using ECG image reports relying on symptoms and changes in the ECG peaks and intervals. In addition to this, other studies are presented on techniques applied to the ECG reports for the detection of COVID. Also, the main limitations and future works are illustrated. It can be concluded that COVID can be detected with high accuracy using ECG reports and it is even more efficient than other protocols. Finally, based on the performance of the studies it can be shown that the ECG image report is close to an acceptable level in the detection of COVID disease. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Graph and Capsule Convolutional Neural Network Based Classification of Lung Cancer, Pneumonia, COVID-19 using Lung CT and Ultrasound Radiography Imaging

Pulmonary / Lung nodules are a sign of lung cancer. Pneumonia, Lung nodules show up on imaging scans like X-rays, CT or ultrasound scans. The healthcare team may refer to the growth as a spot on the lung, coin lesion, or shadow. Coronavirus (COVID-19) has been identified as a worldwide epidemic, affecting individuals all over the nation. It is vital to identify COVID-19-affected persons to limit the virus's spread. According to the latest study, radiographic approaches can be used to diagnose contamination utilizing deep learning (DL) methods. Considering that DL is a valuable approach and methodology for image analysis, various studies on COVID-19 case detection utilizing radiographs to train DL networks have been conducted. Although just a handful of studies presume to have excellent prediction results, their proposed systems may suffer from a restricted amount of data. Employing graph and capsule, Convolutional Neural Network (CNN) can overcome the shortcomings by predicting multiple disorders using a single network implemented in a hospital. We present a novel comparative method that has paved the way for an open-source COVID-19 case classification approach based on graph and capsule images with CT and ultrasound. Experimental results show that the Capsule network attained the best 98.93% AUC, 99.2% accuracy, 98.4% Fl-score, 98.40% sensitivity, 98.40% specificity, 9S.4l% precision using CT labels. Whereas the ultrasound test set the graph network performed well with 96.93% AUC, 97.26% accuracy, 95.92% Fl-score, 95.90% sensitivity, 97.94% specificity, 96.08% precision. © 2022 IEEE.

Viral Pneumonia and Covid Screening on Lung Ultrasound

The rise of Covid-19 pandemic has exaggerated the necessity for safe, quick and sensitive diagnostic tools to confirm the protection of tending employees and patients. Although ML has shown success in medical imaging, existing studies concentrate on Covid-19 medicine victimization using Deep Learning (DL) with X-ray and computed axial Tomography (CT) scans. During this study we tend to aim to implement CNN model on Lung Ultrasound (LUS), to assist doctors with the designation of Covid-19 patients. We selected LUS since it's quicker, cheaper and additional out there in rural areas compared to CT and X- ray. We have used the biggest public dataset containing LUS pictures and videos of Covid, Pneumonia and healthy patients that has been collected from totally different resources. We tried out frame level approach that extracted 5 frames per patient video. We'll use this dataset to experiment with a CNN model that has hyper parameter calibration. We conjointly enclosed explainable AI using Grad-CAM that uses gradients of a selected target that flows through the convolutional network to localize and highlight regions of the target within the image. Moreover, we'll experiment with completely different data preprocessing techniques that may aid with pattern recognition and increasing the DL model's accuracy like histogram equalization, standardization, Principle Component Analysis (PCA) and Synthetic Minority Oversampling Technique (SMOTE). Lastly, we tend to create a straightforward application that diagnoses LUS videos with our CNN model, and shows the frame results with visual illustration of why the model has taken certain prediction with the help of Gradient-Weighted category Activation Mapping (Grad-CAM). © 2022 IEEE.

Self-Supervised Contrastive Learning for Covid-19 Classification from Computed Tomography Images

Computer-aided diagnosis (CAD) emerges as an exhaustive diagnostic tool in the Covid-19 pandemic outbreak and is enormously investigated for automatic and more accurate detections. Artificial intelligence (AI) based radiographic images (Computed Tomography, X-Ray, Lung Ultrasound) interpretation improves the overall diagnosis efficiency of Covid-19 infections. In this paper, CAD based deep meta learning approach has been discussed for automatically quick analysis of chest computed tomography (CT) images regarding the early detection of corona virus (Covid-19) presence inside a subject. We incorporated a self-supervised contrastive-learning neural network for unbiased feature representation and classifications using fine-tuned pre-trained Inception module on 28203 chest CT images. This trainable multi-shot end-to-end deep learning architecture is validated on public dataset of normal and covid-19 CT images obtaining normalized accuracy of 0.9708. Results verify our model to be able enough to assist radiologists and specialists in screening and correct diagnosis of Covid-19 patients in less span of time. © 2022 IEEE.

LFR Waiter: Arduino Based Android Application Controlled Waiter Robot

One of the most pressing challenges facing restaurants since the COVID-19 outbreak began is personnel. A staffing scarcity across the business has resulted in a slew of issues, including significantly longer wait times and irritated clients. A robot waiter may make a huge impact in a restaurant in this situation. This research led to the formation of a low-cost Arduino-based Android application control Robot that can work as a restaurant waiter. The proposed model can follow a path, avoid obstacles, serve meals to a specific consumer, and return to the kitchen on its own. To precisely follow the line, the PID algorithm is utilized. To detect potential obstructions, a sonar sensor is used. On an LCD, messages and warnings are displayed. An Android app that allows the chief to select a particular table for serving meals. For convenience, the robot's current state is displayed in the application. Our testing results show that the robot performs satisfactorily over 90% of the time. It should be emphasized that the offered model is adaptable to any restaurant. © 2022 IEEE.

Automatically Scoring Lung Ultrasound Videos of COVID-19 and post-COVID-19 Patients

Lung ultrasound (LUS) imaging is playing an important role in the current pandemic, allowing the evaluation of patients affected by COVID-19 pneumonia. However, LUS is limited to the visual inspection of ultrasound data, which negatively affects the reproducibility and reliability of the findings. For these reasons, we were the first to propose a standardized imaging protocol and a scoring system, from which we developed the first artificial intelligence (AI) models able to evaluate LUS videos. Furthermore, we demonstrated prognostic value of our approach and its utility for patients' stratification. In this study, we report on the level of agreement between AI and LUS clinical experts (MD) on LUS data acquired from both COVID-19 patients and post-COVID-19 patients. © 2022 IEEE.

Photoacoustic Detection of Nuclei Acid-Containing Water Droplets

It has been reported that the transmission route of SARS-CoV-2 is due to the transport and evolution of pathogen-containing aerosols and droplets. Detecting virus-laden aerosols and droplets could be valuable in reducing the pandemic of COVID-19. Coronaviruses have positive-sense single-stranded RNA (ssRNA) genomes. In this study, we explore the feasibility of photoacoustic (PA) detection of nucleic acid (NA) containing water droplets to model recent results acquired in our laboratory. A two-dimensional NA-containing water droplet model was implemented with randomly positioned water droplets. The diameter of water droplets was randomly distributed from 1 to 60 μm. The NA particles, simulating viral particles, were randomly positioned with the same number concentration (NC). Four different NCs were independently applied to investigate the effect of the NCs of NA particles on the PA signal generation. Monte-Carlo simulations were implemented using one million transmitted photons and a collimated laser beam (wavelengths from 100 to 370 nm). PA signals were computed based on Green's function approach, incorporating the directivity and the band limit of a single-element 300 kHz ultrasound transducer used in the experiments. The P A power was computed by using a root-mean-square. As a result, the total energy deposition increased with the NC of DNA at 260 nm, increasing the PA power. For 209 nm and 298 nm, on the other hand, the PA powers were the same regardless of the NCs of NA. This simulation study can provide insights into the PA sensing of viruses within aerosolized droplets. © 2022 IEEE.

Automatic Scoring of COVID-19 LUS Videos Using Cross-correlation-Based Features Aggregated from Frame-Level Confidence Levels Obtained by a Pre-trained Deep Neural Network

The emergence of COVID-19 has encouraged researchers to seek a method to detect and monitor patients infected with SARS-CoV 2. The use of lung ultrasound (LUS) in this setting is rapidly spreading because of its portability, cost-effectiveness, real-time imaging, and safety. LUS has demonstrated the potential to be widely used to assess the condition of the lungs in COVID-19 patients. Given frame-level labels provided by a pre-trained deep neural network (DNN), our goal is to identify an aggregation strategy that allows to move from frame-level to video-level, which is the output required by physicians for clinical evaluation. To achieve this goal, we propose a novel aggregation method based on the cross-correlation coefficients. The logic behind this idea is that, based on the similarity between the score's variables (at frame level), the cross-correlation should be informative as to how to discriminate at video level. We applied our approach to the LUS data from a multi-center study comprising of 283, 231, and 448 LUS videos from Lodi General, Gemelli, and San Matteo Hospital, respectively. Results show that the video-level agreement with clinical experts is obtained in 87.6% of the cases, which represents a promising video-level accuracy. © 2022 IEEE.

COVID Detection Using ECG Image Reports: A Survey

COVID-19 is one of the greatest pandemics that threaten individuals, especially the elders. It was first reported in Wuhan, China in 2019. It was discovered recently that COVID-19 disease can be detected using three main protocols. The first protocol is based on Polymerase Chain reaction (PCR), while the second protocol is based on lung chest (ultrasound, X-ray, and CT-Scan), and the final protocol is based on the ECG image reports. This review aims to present a survey on the methodologies and algorithms applied for the detection of COVID disease using electrocardiogram (ECG). In this study, various papers were presented for determining how the COVID can be diagnosed using ECG image reports relying on symptoms and changes in the ECG peaks and intervals. In addition to this, other studies are presented on techniques applied to the ECG reports for the detection of COVID. Also, the main limitations and future works are illustrated. It can be concluded that COVID can be detected with high accuracy using ECG reports and it is even more efficient than other protocols. Finally, based on the performance of the studies it can be shown that the ECG image report is close to an acceptable level in the detection of COVID disease. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

People Counting Automatic Barrier (CrowdStopper): An IoT Prototype Design

The emergence and occurrence of COVID-19 pandemic has affected the lives of many people around the world, implementing different protocols to further prevent the spread of this deadly virus. Overcrowding is one of the common reasons for the spread of any virus or diseases. The researchers aim to create a system that monitors crowd density inside a building or infrastructure to avoid overcrowding. This system primarily utilizes ultrasonic sensors to detect entry and exit of an individual. The feedback will be sent to the user and the data will be used to effectively monitor the number of people inside the building. © 2022 ACM.

Development of Android-Controlled Ultraviolet Floor Sanitizer Robot

Ultraviolet (UV) light radiation is very dangerous as it can irritate human skin and eyes in which long and direct exposure can lead to skin and eyes cancer. However, Ultraviolet C (UVC) light with a wavelength between 207nm-222nm could sanitize in which inactivate the bacteria such as superbug (the pathogen that already built immunity against chemical sanitizer) and contribute to the fight against Covid-19 viruses. Therefore, this development of UV sanitizer is to sanitize surface area effectively with a human alert system in which will activate the buzzer, turn off the UV light and stop moving when human motion is detected. The notification also will be sent to the user whenever a human motion is detected as a 360° sanitization area to inactivate pathogens. The precaution to avoid any accident. The device is equipped with one UVC light which provides, the body has ultrasonic sensors to detect obstacles and provide autonomous movement to the device while the PIR sensor is used to detect human motion and activate the human alert system. As the result, the UVC light effectiveness is determined based on the bacteria growth in the petri dish. After the sanitization process, the bacteria are significantly reduced and killed effectively in low areas such as floors but reduce their efficiency to the high area. Thus, this device is time efficient and able to reduce the cost of sanitization compared to chemical sanitizer. © 2022 IEEE.