Angiotensin-converting enzyme 2 (ACE2) in COVID-19 and other diseases.

The coronavirus SARS-CoV-2 was detected in isolates of pneumonia patients in January 2020. The virus cannot multiply extracellularly but requires access to the cells of a host organism. SARS-CoV-2 uses angiotensin-converting enzyme 2 (ACE2) as a receptor, to which it docks with its spikes. ACE2 belongs to the renin angiotensin system (RAS), whose inhibitors have been used for years against high blood pressure. Renin is an endopeptidase that is predominantly formed in the juxtaglomerular apparatus of the kidney and cleaves the decapeptide angiotensin I (Ang I) from angiotensinogen. Through the angiotensin-converting enzyme (ACE), another 2 C-terminal amino acids are removed from Ang I, so that finally the active octapeptide angiotensin II (Ang II) is formed. The biological effect of Ang II via the angiotensin II receptor subtype 1 (AT1-R) consists of vasoconstriction, fibrosis, proliferation, inflammation, and thrombosis formation. ACE2 is a peptidase that is a homolog of ACE. ACE2 is predominantly expressed by pulmonary alveolar epithelial cells in humans and has been detected in arterial and venous endothelial cells. In contrast to the dicarboxy-peptidase ACE, ACE2 is a monocarboxypeptidase that cleaves only one amino acid from the C-terminal end of the peptides. ACE2 can hydrolyze the nonapeptide Ang-(1-9) from the decapeptide Ang I and the heptapeptide Ang-(1-7) from the octapeptide Ang II. Ang-(1-7) acts predominantly antagonistically (vasodilatory, anti-fibrotic, anti-proliferative, anti-inflammatory, anti-thrombogenetically) via the G protein-coupled Mas receptor to the AT1-R-mediated effects of Ang II. In the pathogenesis of COVID-19 infection, it is therefore assumed that there is an imbalance due to overstimulation of the AT1 receptor in conjunction with a weakening of the biological effects of the Mas receptor.Copyright © 2022 Dustri-Verlag Dr. K. Feistle.

An observer comparison study to evaluate a machine learning model to quantify the infected pneumonia on lung CT images

Quantification of infected lung volume using computed tomography (CT) images can play a critical role in predicting the severity of pulmonary infectious disease. Manual segmentation of infected areas from several CT image slices, however, is not efficient and viable in clinical practice. To assist clinicians in overcoming this challenge, we developed a new method to automatically segment and quantify the percentage of the infected lung volume. First, we used a public dataset of 20 COVID-19 patients, which consists of manually annotated lung and infection masks, to train a new joint deep learning (DL) model for lung and infection segmentation. As for lung segmentation, a Mask-RCNN model was applied to the lung volume with a novel postprocessing technique. Following that, an ensemble model with a customized residual attention UNet model and feature pyramid network (FPN) models was employed for infection segmentation. Next, we assembled another set of 80 CT scans of Covid-19 patients. Two chest radiologists manually evaluated each CT scan and reported the infected lung volume percentage using a customized graphical user interface (GUI). The developed DL-model was also employed to process these CT images. Then, we compared the agreement between the radiologist (manual) and model-based (automated) percentages of diseased regions. Additionally, the GUI was used to let radiologists rate acceptance of the DL-model generated segmentation results. Analyzing the results demonstrate that the agreement between manual and automated segmentation is >95% in 28 testing cases. Furthermore, >53% of testing cases received the top assessment rating scores from two radiologists (between four-five- score). Thus, this study illustrates the feasibility of developing a DL-model based automated tool to effectively provide quantitative evaluation of infected lung regions to assist in improving the efficiency of radiologists in infection diagnosis. © COPYRIGHT SPIE. Downloading of the is permitted for personal use only.

A DEEP LEARNING APPROACH LUNG SEGMENTATION AND PNEUMONIA DETECTION FROM X-RAYS

Lung segmentation is a process of detection and identification of lung cancer and pneumonia with the help of image processing techniques. Deep learning algorithms can be incorporated to build the computer-aided diagnosis (CAD) system for detecting or recognizing broad objects like acute respiratory distress syndrome (ARDS), Tuberculosis, Pneumonia, Lung cancer, Covid, and several other respiratory diseases. This paper presents pneumonia detection from lung segmentation using deep learning methods on chest radiography. Chest X-ray is the most useful technique among other existing techniques, due to its lesser cost. The main drawback of a chest x-ray is that it cannot detect all problems in the chest. Thus, implementing convolutional neural networks (CNN) to perform lung segmentation and to obtain correct results. The 'lost' regions of the lungs are reconstructed by an automatic segmentation method from raw images of chest X-ray. © 2022 IEEE.

Do Preprocessing and Class Imbalance Matter to the Deep Image Classifiers for COVID-19 Detection? An Explainable Analysis

In a world withstanding the waves of a raging pandemic, respiratory disease detection from chest radiological images using machine-learning approaches has never been more important for a widely accessible and prompt initial diagnosis. A standard machine-learning disease detection workflow that takes an image as input and provides a diagnosis in return usually consists of four key components, namely input preprocessor, data irregularities (like class imbalance, missing and absent features, etc.) handler, classifier, and a decision explainer for better clarity. In this study, we investigate the impact of the three primary components of the disease-detection workflow leaving only the deep image classifier. We specifically aim to validate if the deep classifiers may significantly benefit from additional preprocessing and efficient handling of data irregularities in a disease-diagnosis workflow. To elaborate, we explore the applicability of seven traditional and deep preprocessing techniques along with four class imbalance handling approaches for a deep classifier, such as ResNet-50, in the task of respiratory disease detection from chest radiological images. While deep classifiers are more capable than their traditional counterparts, explaining their decision process is a significant challenge. Therefore, we also employ three gradient visualization algorithms to explain the decision of a deep classifier to understand how well each of them can highlight the key visual features of the different respiratory diseases. © 2020 IEEE.

A Survey on Diagnosing Pulmonary Diseases

Due to the increase in world population, a lot of research is being done in the medical sciences. Pandemics and epidemics have multiple outbreaks in many regions of the world. In order to solve the issue, creative probing is being used. Most of the illnesses in the group are obstructive and may result in a loss of life. Heart and lung conditions make up a large portion of the obstructive illnesses in this group. More than 5 lakh people die each year from lung illnesses, generally known as pulmonary disorders, with an equal proportion of men and women affected. Each disease has unique symptoms that are connected to it in the fields of medicine and healthcare. There are several new tests that are being developed to identify each of the dangerous diseases that are on the rise. This results from the necessity for quick illness prediction. This paper examines numerous studies and experiments carried out over a variety of timelines and approaches selected by various experiments, carefully examining the benefits and drawbacks of the approaches in order to construct an appropriate model for the cause. It focuses on the study of diagnosing pulmonary disorders and making the user's task easy in understanding the scanned images obtained. © 2023 IEEE.

A Novel Fusion Model of Hand-Crafted Features with Deep Convolutional Neural Networks for Classification of Several Chest Diseases using X-ray Images

With the continuing global pandemic of coronavirus (COVID-19) sickness, it is critical to seek diagnostic approaches that are both effective and rapid to limit the number of people infected with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The results of recent research suggest that radiological images include important information related to COVID-19 and other chest diseases. As a result, the use of deep learning (DL) to assist in the automated diagnosis of chest diseases may prove useful as a diagnostic tool in the future. In this study, we propose a novel fusion model of hand-crafted features with deep convolutional neural networks (DCNNs) for classifying ten different chest diseases such as COVID-19, lung cancer (LC), atelectasis (ATE), consolidation lung (COL), tuberculosis (TB), pneumothorax (PNET), edema (EDE), pneumonia (PNEU), pleural thickening (PLT), and normal using chest X-rays (CXR). The method that has been suggested is split down into three distinct parts. The first step involves utilizing the Info-MGAN network to perform segmentation on the raw CXR data to construct lung images of ten different chest diseases. In the second step, the segmented lung images are fed into a novel pipeline that extracts discriminatory features by using hand-crafted techniques such as SURF and ORB, and then these extracted features are fused to the trained DCNNs. At last, various machine learning (ML) models have been used as the last layer of the DCNN models for the classification of chest diseases. Comparison is made between the performance of various proposed architectures for classification, all of which integrate DCNNs, key point extraction methods, and ML models. We were able to attain a classification accuracy of 98.20% for testing by utilizing the VGG-19 model with a softmax layer in conjunction with the ORB technique. Screening for COVID-19 and other lung ailments can be accomplished using the method that has been proposed. The robustness of the model was further confirmed by statistical analyses of the datasets using McNemar’s and ANOVA tests respectively. Author

COVIDEffiNet: Pulmonary Diseases and COVID-19 Detection from Chest Radiographs Using EfficientNet Deep Learning Model

There have been attempts made previously to classify and determine the diagnosis of a disease of a patient based on the X-rays and computed tomography images of various parts of the body. In the field of lung disease diagnosis, there have been attempts to identify lungs infected with pneumonia, COVID-19, and tuberculosis, either individually classifying them into two groups of positive and negative of the given disease or in groups with multiple classes. These methods and approaches have used various deep learning models like CNNs, ResNet50, VGG19, Inception V3, MobileNet_V2, hybrid models, and ensemble learning methods. In this paper, we have proposed a model that takes an X-ray image of the lungs of the patients as input and classifies the result as one of the following classes: tuberculosis, pneumonia, COVID-19, or normal, that is, healthy lungs. What we have used here is transfer learning, with our base model being EfficientNet which gives an accuracy of 93%. For this, we have used different datasets of X-ray images of patients with different lung ailments, namely pneumonia, tuberculosis, and COVID. The dataset consists of images in four categories, the above-mentioned three diseases and a fourth category of normal healthy lungs. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Assessment of respiratory disease infection risk and natural ventilation intervention countermeasures in teaching spaces: A campus case study

The risk of indoor respiratory disease transmission can be significantly reduced through interventions that target the built environment. Several studies have successfully developed theoretical models to calculate the effects of built environment parameters on infection rates. However, current studies have mainly focused on calculating infection rate values and comparing pre- and post-optimization values, lacking a discussion of safe baseline values for infection rates with risk class classification. The purpose of this paper is to explore the design of interventions in the built environment to improve the ability of buildings to prevent virus transmission, with a university campus as an example. The study integrates the Wells-Riley model and basic reproduction number to identify teaching spaces with high infection risk on campus and proposes targeted intervention countermeasures based on the analysis of critical parameters. The results showed that teaching buildings with a grid layout pattern had a higher potential risk of infection under natural ventilation. By a diversity of building environment interventions designed, the internal airflow field of classrooms can be effectively organized, and the indoor virus concentration can be reduced. We can find that after optimizing the building mentioned above and environment intervention countermeasures, the maximum indoor virus infection probability can be reduced by 22.88%, and the basic reproduction number can be reduced by 25.98%, finally reaching a safe level of less than 1.0. In this paper, we support university campuses' respiratory disease prevention and control programs by constructing theoretical models and developing parametric platforms. © 2023 Elsevier Ltd

Advance IoT Intelligent Healthcare System for Lung Disease Classification Using Ensemble Techniques

In healthcare systems, the Internet of Things (IoT) innovation and development approached new ways to evaluate patient data. A cloud-based platform tends to process data generated by IoT medical devices instead of high storage, and computational hardware. In this paper, an intelligent healthcare system has been proposed for the prediction and severity analysis of lung disease from chest computer tomography (CT) images of patients with pneumonia, Covid-19, tuberculosis (TB), and cancer. Firstly, the CT images are captured and transmitted to the fog node through IoT devices. In the fog node, the image gets modified into a convenient and efficient format for further processing. advanced encryption Standard (AES) algorithm serves a substantial role in IoT and fog nodes for preventing data from being accessed by other operating systems. Finally, the preprocessed image can be classified automatically in the cloud by using various transfer and ensemble learning models. Herein different pre-trained deep learning architectures (Inception-ResNet-v2, VGG-19, ResNet-50) used transfer learning is adopted for feature extraction. The softmax of heterogeneous base classifiers assists to make individual predictions. As a meta-classifier, the ensemble approach is employed to obtain final optimal results. Disease predicted image is consigned to the recurrent neural network with long short-term memory (RNN-LSTM) for severity analysis, and the patient is directed to seek therapy based on the outcome. The proposed method achieved 98.6% accuracy, 0.978 precision, 0.982 recalls, and 0.974 F1-score on five class classifications. The experimental findings reveal that the proposed framework assists medical experts with lung disease screening and provides a valuable second perspective. © 2023 CRL Publishing. All rights reserved.

Deep Learning Based Automated Chest X-ray Abnormalities Detection

Abnormalities related to the chest are a fairly common occurrence in infants as well as adults. The process of identifying these abnormalities is relatively easy but the task of actually classifying them into specific labels pertaining to specific diseases is a much harder endeavour. COVID-19 sufferers are multiplying at an exponential rate, putting pressure on healthcare systems all around the world. Because of the limited number of testing kits available, it is impractical to test every patient with a respiratory ailment using traditional methods. Thus in such dire circumstances, we propose the use of modern deep learning techniques to help in the detection and classification of a number of different thoracic abnormalities from a chest radiograph. The goal is to be able to automatically identify and localize multiple points of interest in a provided chest X-ray and act as a second level of certainty after the radiologists. On our publically available chest radiograph dataset, our methods resulted in a mean average precision of 0.246 for the detection of 14 different thoracic abnormalities. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Android Application for Human Respiratory System Diagnosis: A Systematic Review

This paper presents a systematic review of android app respiratory system on smartphone. For some diseases, doctors have succeeded in inventing the necessary treatments that lasts for a short period, but in several cases, the treatment can stay for a lifetime. The goal of this system is to detect if a patient has any respiratory disease(s) by specifying the symptoms the patient encounters, schedules an appointement in the hospital for patient through the system to the linked specialist doctors to avoid contact in the case of Covid-19 patient. This research will help raise patient's awareness of the high risk of late discovery of having respiratory diseases (like Lung Cancer. corona virus etc), and also to develop a model that will help detect this disease early through mobile application. The focus of this review is to encourage medical institutions to adopt the health android app that can help patients in self-managing behavioral activities such as physical activities, using symptoms to determine the stage(early or critical) of the disease and drug suggestions with research evaluation using the app, this could help patients monitor and manage their health conditions. © 2022 IEEE.

Dhanvantari: An intelligent diagnosis tool to classify malignant skin disease and Lung conditions using Deep Learning

The pandemic due to COVID-19 has created a huge gap in the medical field leading to a reduction in the efficacy of this field. To improve this situation, we propose a solution 'Dhanvantari'. A medical app that is powered by Artificial Intelligence performs a task where the diagnosis is done by computer vision observing CT scans, MRIs, and also some skin diseases. Dhanvantari focuses mainly on the combination of CT scans and skin disease classifications. In this paper, a novel approach has been proposed for developing a supervised model for the classification of skin disease and lung ailments (that is to identify a healthy lung with an infected lung due to pneumonia) through analog to digital image processing. This app helps the user in analyzing conditions and if any abnormalities are detected then alerts the user about it. This is a primary service care application developed to reduce the number of false cases hence only alerting the user if a complication is observed. The proposed approach utilizes a camera and computational device or mobile. Two datasets from Kaggle that had 9 classes of malignant skin disease and 2 lung conditions were used to train the model. Design, training, and the testing of the algorithm were performed with the help of colab. Generally, a standard test for malignant skin disease requires sample gathering and conduction of various tests. All these consume a lot of time. The other method is laser or radiation-induced procedures that might be harmful and lead to exposure of unwanted radiation to patients. The proposed 'Dhanvantari' requires the patient/user to use a camera to take a picture of the affected area (in case of skin condition) and it provides the primary diagnosis. This approach aids the doctors in quick decision-making during diagnosis and reduce the time per patient which in house helps them to prioritize patients. © 2022 IEEE.

Identification of Respiratory Diseases and Diabetes by Non-invasive Method Using IoT

The objective of this paper is to identify respiratory diseases such as Asthma, Covid-19, pulmonary disease, and diabetes from the human breath odor using a non-invasive method. For detecting diseases using a non-invasive method, temperature sensor (to identify body temperature), pulse oximeter sensor (to identify blood oxygen level and heartbeat rate), and acetone sensor (to identify respiratory diseases from human breath odor) with Arduino ATMega328 microcontroller unit (MCU) were used. If the temperature is greater than 37.2 C, the heartbeat rate is greater than 100 bpm, and the oxygen level is less than 92% Covid-19 will be detected. If the oxygen level is less than 95% the heartbeat rate is at (100–125) bpm, and the temperature is at 36.1–37 C, asthma will be detected. If the heart rate is greater than 86 bpm, the temperature at 36.1–37 C, the oxygen level at 92–97% and the acetone level at (354–496) ppm, diabetes will be detected. If the oxygen level is less than 92% the temperature at 36.1–37 C, and the heartbeat rate is greater than 110 bpm, the pulmonary disease will be identified. After disease detection, suggestions will be provided to the patients based on their health reports. Finally, suggested medicines will be sent to the patient's registered mobile phones by connecting node MCU with blynk using IoT technology. The results will be stored and the patients can compare their health conditions for future analysis. The traditional method of laboratory tests is considered to consume more time. In our method, the duration of the detection process is less and the results help to identify health problems at early stages and predict diseases quickly compared to the traditional method. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Statistical Processing of Relationship Between Biomarkers and Disease Severity Caused by COVID-19 Infection-Delta Variant

Severe infectious disease caused by acute respiratory syndrome, COVID-19 (SARS-CoV-2), spread rapidly worldwide, infecting several million people. According to scientific data, the disease develops through several different stages. After 2–4 days of infection and disease development, the lower respiratory tract is attacked and in a relatively short time interstitial pneumonia develops in a certain number of patients (with genetic predisposition between 5 and 10% of cases). Patients infected with COVID-19 have symptoms such as very high temperatures, fever, persistent cough, joint and bone pain, in some cases diarrhea, and loss of appetite and taste. Disease monitoring should primarily include erythrocyte sedimentation rate, leukocyte count, leukocyte count formula, C-reactive protein (CRP), determination of troponin I (hsTnI) and T (cTnT) levels, N-terminal pro-B natriuretic peptide (NT-proBNP), fibrinogen, and D-dimer level. Previous studies have shown that in pneumonia developed from chronic and acute obstructive pulmonary infections, high levels of D-dimer are observed in patients, and it is suggested that this parameter can be used as a specific prognostic biomarker, and the values higher than > 1000 ng/ml represent increased risk factors for mortality in patients with COVID-19. Because vascular thrombosis affects the promotion of an unfavorable clinical progression for the patient, the identification of early and accurate predictors of the worst outcome seems to be essential for timely and appropriate anticoagulant treatment in patients with SARS-CoV-2 infection. Overall, these data suggest that acute myocardial damage, or heart failure, may be an important indicator of disease severity and adverse prognosis in patients with COVID-19. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

VAPOUR COUGH VISUALIZATION FOR COVID-19 – COMPUTATIONAL MODELLING APPROACH

New Zealand and many countries gained heightened awareness of indoor air quality (IAQ) issues, and increased investment, according to the World Health Organization (WHO) guidelines, to improve their IAQ and reduce air pollution in commercial and residential buildings. Additionally, some countries have introduced new standards for indoor environments, such as the New Zealand "healthy homes” standard. At the same time, COVID-19 pandemic forced many people to spend much more time in indoor spaces, due to stay-at-home, or lockdown orders by governments. This increased attention on other aspects of indoor environmental quality, such as occupants' satisfaction with thermal comfort parameters, presents an additional parameter for research and in the development of standards. From a medical perspectives, infectious respiratory diseases, such as influenza or COVID-19, are transmitted by airborne droplets. In this work, we assess a Polyester Filter and UV light (PFUV) dehumidifier device performance in an office with two occupants (one uninfected and the other one infected with a disease with airborne transmission using computational fluid dynamics (CFD) approach. Two positions for locating the PFUV dehumidifier in an office with a scenario in which one person is exhaling infected air and the other occupant must inhale and exhale from the shared air. The CFD model illustrated the best position of the device to distribute the air velocity contours. Further, based on the CFD model which was validated via the IAQ and comfort kit (Testo 400) thermal comfort analysis showed that the room is slightly cold. Copyright © 2022 by ASME.

Application of machine learning approaches to predict the impact of ambient air pollution on outpatient visits for acute respiratory infections

With a remarkable increase in industrialization among fast-developing countries, air pollution is rising at an alarming rate and has become a public health concern. The study aims to examine the effect of air pollution on patient's hospital visits for respiratory diseases, particularly Acute Respiratory Infections (ARI). Outpatient hospital visits, air pollution and meteorological parameters were collected from March 2018 to October 2021. Eight machine learning algorithms (Random Forest model, K-Nearest Neighbors regression model, Linear regression model, LASSO regression model, Decision Tree Regressor, Support Vector Regression, X.G. Boost and Deep Neural Network with 5-layers) were applied for the analysis of daily air pollutants and outpatient visits for ARI. The evaluation was done by using 5-cross-fold confirmations. The data was randomly divided into test and training data sets at a scale of 1:2, respectively. Results show that among the studied eight machine learning models, the Random Forest model has given the best performance with R2 = 0.606, 0.608 without lag and 1-day lag respectively on ARI patients and R2 = 0.872, 0.871 without lag and 1-day lag respectively on total patients. All eight models did not perform well with the lag effect on the ARI patient dataset but performed better on the total patient dataset. Thus, the study did not find any significant association between ARI patients and ambient air pollution due to the intermittent availability of data during the COVID-19 period. This study gives insight into developing machine learning programs for risk prediction that can be used to predict analytics for several other diseases apart from ARI, such as heart disease and other respiratory diseases. © 2022 Elsevier B.V.

Influences of obstacle factors on the transmission trends of respiratory infectious diseases in indoor public places

Public facilities are important transmission places for respiratory infectious diseases (e.g., COVID-19), due to the frequent crowd interactions inside. Usually, changes of obstacle factors can affect the movements of human crowds and result in different epidemic transmissions among individuals. However, most related studies only focus on the specific scenarios, but the common rules are usually ignored for the impacts of obstacles' spatial elements on epidemic transmission. To tackle these problems, this study aims to evaluate the impacts of three spatial factors of obstacles (i.e., size, quantity, and placement) on infection spreading trends in two-dimension, which can provide scientific and concise spatial design guidelines for indoor public places. Firstly, we used the obstacle area proportion as the indicator of the size factor, gave the mathematical expression of the quantity factor, and proposed the walkable-space distribution indicator to represent the placement factor by introducing the Space Syntax. Secondly, two spreading epidemic indicators (i.e., daily new cases and people's average exposure risk) were estimated based on the fundamental model named exposure risk with the virion-laden particles, which accurately forecasted the disease spreading between individuals. Thirdly, 120 indoor scenarios were built and simulated, based on which the value of independent and dependent variables can be measured. Besides, structural equation modeling was employed to examine the effects of obstacle factors on epidemic transmissions. Finally, three obstacle-related guidelines were provided for policymakers to mitigate the disease spreading: minimizing the size of obstacles, dividing the obstacle into more sub-ones, and placing obstacles evenly distributed in space. © 2022 Elsevier Ltd

Detecting COVID-19 from Lung Computed Tomography Images: A Swarm Optimised Artificial Neural Network Approach

COVID-19 has affected many people across the globe. Though vaccines are available now, early detection of the disease plays a vital role in the better management of COVID-19 patients. An Artificial Neural Network (ANN) powered Computer Aided Diagnosis (CAD) system can automate the detection pipeline accounting for accurate diagnosis, overcoming the limitations of manual methods. This work proposes a CAD system for COVID-19 that detects and classifies abnormalities in lung CT images using Artificial Bee Colony (ABC) optimised ANN (ABCNN). The proposed ABCNN approach works by segmenting the suspicious regions from the CT images of non-COVID and COVID patients using an ABC optimised region growing process and extracting the texture and intensity features from those suspicious regions. Further, an optimised ANN model whose input features, initial weights and hidden nodes are optimised using ABC optimisation classifies those abnormal regions into COVID and non-COVID classes. The proposed ABCNN approach is evaluated using the lung CT images collected from the public datasets. In comparison to other available techniques, the proposed ABCNN approach achieved a high classification accuracy of 92.37% when evaluated using a set of 470 lung CT images. Author

Classification of Respiratory Diseases and COVID-19 from Respiratory and Cough Sounds

Infectious and non-infectious respiratory diseases are among the primary reasons for deaths, financial and social crises around the world. In this study, we present a comparative analysis of various deep learning techniques for respiratory disease and COVID-19 identification methods from respiratory and cough sound recordings. Our experiments demonstrate that artificial intelligence can help tackle the global crisis by providing an alternative disease diagnosis method. We conduct numerous experiments using deep learning models and model training techniques to find the most efficient disease detection and classification system. We first propose procedures to extract image representations of audio features such as Mel-Spectrograms and Mel-frequency Cepstral Coefficients (MFCC) from each sound recording. Afterward, we compare the performance of the audio features and ten different convolutional neural network (CNN) models on disease classification. We also compare and analyze the performance of various model training methodologies, such as the 1cycle policy, transfer learning, and balanced mini-batch training, to determine the most effective way to train the models. In our experiment, we classify respiratory diseases with 94.57% accuracy and Area under the Receiver Operating Characteristic Curve (AUC) value of 0.93 and COVID-19 infected and healthy patients' cough recordings with 85.62% accuracy and 0.84 AUC value. © 2022 IEEE.

Organoids Technology: A Pathway to the Permanent Treatment of Pulmonary Diseases

Organoids are a miniature, simplified version of a human organ that are produced in three dimensions in the laboratory and show the true anatomical array. These organelles originate from one or more cells - embryonic stem cells or induced multipotent stem cells - that can organize themselves in three-dimensional culture media. The use of stem cells due to the unlimited capacity of tissue division and regeneration is a great promise as a therapeutic tool. These three-dimensional models of human tissue can be used to test drugs before they are tested on humans. Lung organoids are one of the different types of organoids that, like other organoids, can be formed through a process of self-organizing stem cells or specific parts of an organ. These organoids can also be utilized as a useful tool for screening drugs and vaccines for infections such as the novel SARS-COV-2 infection. The aim of this study was to investigate the potential of lung organoids in the treatment of pulmonary diseases. Copyright 2022, Journal of Iranian Medical Council. All rights reserved.