Relationship between neonatal respiratory distress syndrome pulmonary ultrasonography and respiratory distress score,oxygenation index,and chest radiography grading

BACKGROUND Accurate condition assessment is critical for improving the prognosis of neonatal respiratory distress syndrome(RDS),but current assessment methods for RDS pose a cumulative risk of harm to neonates.Thus,a less harmful method for assessing the health of neonates with RDS is needed.AIM To analyze the relationships between pulmonary ultrasonography and respiratory distress scores,oxygenation index,and chest X-ray grade of neonatal RDS to identify predictors of neonatal RDS severity.METHODS This retrospective study analyzed the medical information of 73 neonates with RDS admitted to the neonatal intensive care unit of Liupanshui Maternal and Child Care Service Center between April and December 2022.The pulmonary ultrasonography score,respiratory distress score,oxygenation index,and chest Xray grade of each newborn before and after treatment were collected.Spearman correlation analysis was performed to determine the relationships among these values and neonatal RDS severity.RESULTS The pulmonary ultrasonography score,respiratory distress score,oxygenation index,and chest X-ray RDS grade of the neonates were significantly lower after treatment than before treatment(P<0.05).Spearman correlation analysis showed that before and after treatment,the pulmonary ultrasonography score of neonates with RDS was positively correlated with the respiratory distress score,oxygenation index,and chest X-ray grade(ρ=0.429–0.859,P<0.05).Receiver operating characteristic curve analysis indicated that pulmonary ultrasonography screening effectively predicted the severity of neonatal RDS(area under the curve=0.805–1.000,P<0.05).CONCLUSION The pulmonary ultrasonography score was significantly associated with the neonatal RDS score,oxygenation index,and chest X-ray grade.The pulmonary ultrasonography score was an effective predictor of neonatal RDS severity.

Pulmonary Edema and Pleural Effusion Detection Using Efficient Net-V1-B4 Architecture and AdamW Optimizer from Chest X-Rays Images

This paper presents a novelmulticlass systemdesigned to detect pleural effusion and pulmonary edema on chest Xray images,addressing the critical need for early detection in healthcare.A new comprehensive dataset was formed by combining 28,309 samples from the ChestX-ray14,PadChest,and CheXpert databases,with 10,287,6022,and 12,000 samples representing Pleural Effusion,Pulmonary Edema,and Normal cases,respectively.Consequently,the preprocessing step involves applying the Contrast Limited Adaptive Histogram Equalization(CLAHE)method to boost the local contrast of the X-ray samples,then resizing the images to 380×380 dimensions,followed by using the data augmentation technique.The classification task employs a deep learning model based on the EfficientNet-V1-B4 architecture and is trained using the AdamW optimizer.The proposed multiclass system achieved an accuracy(ACC)of 98.3%,recall of 98.3%,precision of 98.7%,and F1-score of 98.7%.Moreover,the robustness of the model was revealed by the Receiver Operating Characteristic(ROC)analysis,which demonstrated an Area Under the Curve(AUC)of 1.00 for edema and normal cases and 0.99 for effusion.The experimental results demonstrate the superiority of the proposedmulti-class system,which has the potential to assist clinicians in timely and accurate diagnosis,leading to improved patient outcomes.Notably,ablation-CAM visualization at the last convolutional layer portrayed further enhanced diagnostic capabilities with heat maps on X-ray images,which will aid clinicians in interpreting and localizing abnormalities more effectively.

Optimal Synergic Deep Learning for COVID-19 Classification Using Chest X-Ray Images

A chest radiology scan can significantly aid the early diagnosis and management of COVID-19 since the virus attacks the lungs.Chest X-ray(CXR)gained much interest after the COVID-19 outbreak thanks to its rapid imaging time,widespread availability,low cost,and portability.In radiological investigations,computer-aided diagnostic tools are implemented to reduce intra-and inter-observer variability.Using lately industrialized Artificial Intelligence(AI)algorithms and radiological techniques to diagnose and classify disease is advantageous.The current study develops an automatic identification and classification model for CXR pictures using Gaussian Fil-tering based Optimized Synergic Deep Learning using Remora Optimization Algorithm(GF-OSDL-ROA).This method is inclusive of preprocessing and classification based on optimization.The data is preprocessed using Gaussian filtering(GF)to remove any extraneous noise from the image’s edges.Then,the OSDL model is applied to classify the CXRs under different severity levels based on CXR data.The learning rate of OSDL is optimized with the help of ROA for COVID-19 diagnosis showing the novelty of the work.OSDL model,applied in this study,was validated using the COVID-19 dataset.The experiments were conducted upon the proposed OSDL model,which achieved a classification accuracy of 99.83%,while the current Convolutional Neural Network achieved less classification accuracy,i.e.,98.14%.

Histogram Matched Chest X-Rays Based Tuberculosis Detection Using CNN

Tuberculosis(TB)is a severe infection that mostly affects the lungs and kills millions of people’s lives every year.Tuberculosis can be diagnosed using chest X-rays(CXR)and data-driven deep learning(DL)approaches.Because of its better automated feature extraction capability,convolutional neural net-works(CNNs)trained on natural images are particularly effective in image cate-gorization.A combination of 3001 normal and 3001 TB CXR images was gathered for this study from different accessible public datasets.Ten different deep CNNs(Resnet50,Resnet101,Resnet152,InceptionV3,VGG16,VGG19,DenseNet121,DenseNet169,DenseNet201,MobileNet)are trained and tested for identifying TB and normal cases.This study presents a deep CNN approach based on histogram matched CXR images that does not require object segmenta-tion of interest,and this coupled methodology of histogram matching with the CXRs improves the accuracy and detection performance of CNN models for TB detection.Furthermore,this research contains two separate experiments that used CXR images with and without histogram matching to classify TB and non-TB CXRs using deep CNNs.It was able to accurately detect TB from CXR images using pre-processing,data augmentation,and deep CNN models.Without histogram matching the best accuracy,sensitivity,specificity,precision and F1-score in the detection of TB using CXR images among ten models are 99.25%,99.48%,99.52%,99.48%and 99.22%respectively.With histogram matching the best accuracy,sensitivity,specificity,precision and F1-score are 99.58%,99.82%,99.67%,99.65%and 99.56%respectively.The proposed meth-odology,which has cutting-edge performance,will be useful in computer-assisted TB diagnosis and aids in minimizing irregularities in TB detection in developing countries.

COVID-19 Detection from Chest X-Ray Images Using Convolutional Neural Network Approach

COVID-19 is a respiratory illness caused by the SARS-CoV-2 virus, first identified in 2019. The primary mode of transmission is through respiratory droplets when an infected person coughs or sneezes. Symptoms can range from mild to severe, and timely diagnosis is crucial for effective treatment. Chest X-Ray imaging is one diagnostic tool used for COVID-19, and a Convolutional Neural Network (CNN) is a popular technique for image classification. In this study, we proposed a CNN-based approach for detecting COVID-19 in chest X-Ray images. The model was trained on a dataset containing both COVID-19 positive and negative cases and evaluated on a separate test dataset to measure its accuracy. Our results indicated that the CNN approach could accurately detect COVID-19 in chest X-Ray images, with an overall accuracy of 97%. This approach could potentially serve as an early diagnostic tool to reduce the spread of the virus.

Tuberculosis Diagnosis and Visualization with a Large Vietnamese X-Ray Image Dataset

Tuberculosis is a dangerous disease to human life,and we need a lot of attempts to stop and reverse it.Significantly,in theCOVID-19 pandemic,access to medical services for tuberculosis has become very difficult.The late detection of tuberculosis could lead to danger to patient health,even death.Vietnamis one of the countries heavily affected by the COVID-19 pandemic,andmany residential areas as well as hospitals have to be isolated for a long time.Reality demands a fast and effective tuberculosis diagnosis solution to deal with the difficulty of accessingmedical services,such as an automatic tuberculosis diagnosis system.In our study,aiming to build that system,we were interested in the tuberculosis diagnosis problem from the chest X-ray images of Vietnamese patients.The chest X-ray image is an important data type to diagnose tuberculosis,and it has also received a lot of attention from deep learning researchers.This paper proposed a novel method for tuberculosis diagnosis and visualization using the deeplearning approach with a large Vietnamese X-ray image dataset.In detail,we designed our custom convolutional neural network for the X-ray image classification task and then analyzed the predicted result to provide visualization as a heat-map.To prove the performance of our network model,we conducted several experiments to compare it to another study and also to evaluate it with the dataset of this research.To support the implementation,we built a specific annotation system for tuberculosis under the requirements of radiologists in the Vietnam National Lung Hospital.A large experiment dataset was also from this hospital,and most of this data was for training the convolutional neural network model.The experiment results were evaluated regarding sensitivity,specificity,and accuracy.We achieved high scores with a training accuracy score of 0.99,and the testing specificity and sensitivity scores were over 0.9.Based on the X-ray image classification result,we visualize prediction results as heat-maps and also analyze them in comparison with annotated symptoms of radiologists.

Transfer Learning Approach to Classify the X-Ray Image that Corresponds to Corona Disease Using ResNet50 Pre-Trained by ChexNet

The COVID-19 pandemic has had a widespread negative impact globally. It shares symptoms with other respiratory illnesses such as pneumonia and influenza, making rapid and accurate diagnosis essential to treat individuals and halt further transmission. X-ray imaging of the lungs is one of the most reliable diagnostic tools. Utilizing deep learning, we can train models to recognize the signs of infection, thus aiding in the identification of COVID-19 cases. For our project, we developed a deep learning model utilizing the ResNet50 architecture, pre-trained with ImageNet and CheXNet datasets. We tackled the challenge of an imbalanced dataset, the CoronaHack Chest X-Ray dataset provided by Kaggle, through both binary and multi-class classification approaches. Additionally, we evaluated the performance impact of using Focal loss versus Cross-entropy loss in our model.

Chest Radiography: General Practitioners’ Compliance with Recommendations

Introduction: Chest radiography is the most frequently prescribed imaging test in general practice in France. We aimed to assess the extent to which general practitioners follow the recommendations of the French National Authority for Health in prescribing chest radiography. Methodology: We conducted a retrospective analysis study, in two radiology centers belonging to the same group in Saint-Omer and Aire-sur-la-Lys, of requests for chest radiography sent by general practitioners over the winter period between December 22, 2013, and March 21, 2014, for patients aged over 18 years. Results: One hundred and seventy-seven requests for chest X-rays were analyzed, 71.75% of which complied with recommendations. The most frequent reason was the search for bronchopulmonary infection, accounting for 70.08% of prescriptions, followed by 11.2% for requests to rule out pulmonary neoplasia, whereas the latter reason did not comply with recommendations. Chest X-rays contributed to a positive diagnosis in 28.81% of cases. The positive diagnosis was given by 36.22% of the recommended chest X-rays, versus 10% for those not recommended. Conclusion: In most cases, general practitioners follow HAS recommendations for prescribing chest X-rays. Non-recommended chest X-rays do not appear to make a major contribution to diagnosis or patient management, confirming the value of following the recommendations of the French National Authority for Health.

Covid-19 Detection from Chest X-Ray Images Using Advanced Deep Learning Techniques

Like the Covid-19 pandemic,smallpox virus infection broke out in the last century,wherein 500 million deaths were reported along with enormous economic loss.But unlike smallpox,the Covid-19 recorded a low exponential infection rate and mortality rate due to advancement inmedical aid and diagnostics.Data analytics,machine learning,and automation techniques can help in early diagnostics and supporting treatments of many reported patients.This paper proposes a robust and efficient methodology for the early detection of COVID-19 from Chest X-Ray scans utilizing enhanced deep learning techniques.Our study suggests that using the Prediction and Deconvolutional Modules in combination with the SSD architecture can improve the performance of the model trained at this task.We used a publicly open CXR image dataset and implemented the detectionmodelwith task-specific pre-processing and near 80:20 split.This achieved a competitive specificity of 0.9474 and a sensibility/accuracy of 0.9597,which shall help better decision-making for various aspects of identification and treat the infection.

Automatic Detection of COVID-19 Using Chest X-Ray Images and Modified ResNet18-Based Convolution Neural Networks

The latest studies with radiological imaging techniques indicate that X-ray images provide valuable details on the Coronavirus disease 2019(COVID-19).The usage of sophisticated artificial intelligence technology(AI)and the radiological images can help in diagnosing the disease reliably and addressing the problem of the shortage of trained doctors in remote villages.In this research,the automated diagnosis of Coronavirus disease was performed using a dataset of X-ray images of patients with severe bacterial pneumonia,reported COVID-19 disease,and normal cases.The goal of the study is to analyze the achievements for medical image recognition of state-of-the-art neural networking architectures.Transfer Learning technique has been implemented in this work.Transfer learning is an ambitious task,but it results in impressive outcomes for identifying distinct patterns in tiny datasets of medical images.The findings indicate that deep learning with X-ray imagery could retrieve important biomarkers relevant for COVID-19 disease detection.Since all diagnostic measures show failure levels that pose questions,the scientific profession should determine the probability of integration of X-rays with the clinical treatment,utilizing the results.The proposed model achieved 96.73%accuracy outperforming the ResNet50 and traditional Resnet18 models.Based on our findings,the proposed system can help the specialist doctors in making verdicts for COVID-19 detection.

Diagnostic Value of the Thoracic Ultrasonography Compared to Conventional Chest X-Rays in Pneumonia for Children between 0 to 15 Years: Case Study in Two Hospitals in Yaoundé

Introduction: The diagnosis of pneumonia is usually made based on clinical manifestations and chest X-ray. The use of ultrasound in detecting pulmonary diseases in general, and especially consolidation syndrome has been demonstrated. The objective of this study was to determine the accuracy of thoracic ultrasound compared to chest X-ray in the diagnosis of infectious pneumonia in children. Methods: Children between 0 to 15 years were included in our study. The lung ultrasound results obtained were compared with those of the chest X-ray used as the reference. Our data were introduced into the EpiInfo 3.5.4 software and analyzed with the EpiInfo 3.5.4 and IBMSPSS Statistics version 20.0 softwares. Microsoft Office Excel 2016 was used to produce Charts. Continuous quantitative variables were presented. Cohen’s Kappa concordance test was applied with confidence interval of 95%. Results: 52 children were enrolled in the study. In imaging, the dominant sign was consolidation syndrome (75.0%) of cases by chest radiography, and in 78.8% of cases by lung ultrasound (p Conclusion: Our study demonstrated that lung echography is a non-ionizing and reliable tool in the diagnosis of childhood’s pneumonia.

Detecting Tuberculosis from Vietnamese X-Ray Imaging Using Transfer Learning Approach

Deep learning created a sharp rise in the development of autonomous image recognition systems,especially in the case of the medical field.Among lung problems,tuberculosis,caused by a bacterium called Mycobacterium tuberculosis,is a dangerous disease because of its infection and damage.When an infected person coughs or sneezes,tiny droplets can bring pathogens to others through inhaling.Tuberculosis mainly damages the lungs,but it also affects any part of the body.Moreover,during the period of the COVID-19(coronavirus disease 2019)pandemic,the access to tuberculosis diagnosis and treatment has become more difficult,so early and simple detection of tuberculosis has been more and more important.In our study,we focused on tuberculosis diagnosis by using the chestX-ray image,the essential input for the radiologist’s profession,and researched the effectiveness of the transfer learning approach in the case study of Vietnamese chest X-ray images.We proposed four strategies to clarify our hypothesis in different ways of applying transfer learning and different training set types.We also prepared a Vietnamese X-ray image dataset with the support of the VRPACS team to provide the basis for training and testing deep learning models.Our experiments were carried out by applying three different architectures,Alexnet,Resnet,and Densenet,on international,Vietnamese,and combined X-ray image datasets.After training,all models were verified on a pure Vietnamese X-rays set.The results show that transfer learning is suitable in the case study of Vietnamese chest X-ray images with high evaluating metrics in terms of AUC(Area under the Receiver Operating Characteristic Curve),sensitivity,specificity,and accuracy.In the best strategy,most of the scores were more than 0.93,and all AUCs were more than 0.98.

Prediction of Covid-19 Based on Chest X-Ray Images Using Deep Learning with CNN

The COVID-19 pandemic has caused trouble in people’s daily lives andruined several economies around the world, killing millions of people thus far. Itis essential to screen the affected patients in a timely and cost-effective manner inorder to fight this disease. This paper presents the prediction of COVID-19 withChest X-Ray images, and the implementation of an image processing systemoperated using deep learning and neural networks. In this paper, a Deep Learning,Machine Learning, and Convolutional Neural Network-based approach for predicting Covid-19 positive and normal patients using Chest X-Ray pictures is proposed. In this study, machine learning tools such as TensorFlow were used forbuilding and training neural nets. Scikit-learn was used for machine learning fromend to end. Various deep learning features are used, such as Conv2D, Dense Net,Dropout, Maxpooling2D for creating the model. The proposed approach had aclassification accuracy of 96.43 percent and a validation accuracy of 98.33 percentafter training and testing the X-Ray pictures. Finally, a web application has beendeveloped for general users, which will detect chest x-ray images either as covidor normal. A GUI application for the Covid prediction framework was run. Achest X-ray image can be browsed and fed into the program by medical personnelor the general public.

Pertinence of Children’s Chest X-Ray Request Form and Practice at the Regional Hospital of Ngaoundere Cameroon

Background: Chest X-ray is frequently performed for evaluation of chest disease in both adults and children. Children are more exposed to the adverse effects of radiation as compared to adults. During our daily practice, we noticed that most of children’s chest X-ray results were normal. Purpose: This study aimed to evaluate the indications, the technic, the irradiation and the result of chest X-rays in children in order to know if the practice of these X-rays was relevant. Method: Cross-sectional and descriptive study conducted at the Imaging Regional Center of Ngaoundere from April to August 2017. A total number of 145 radiographs and 140 X-ray requests of 140 children were considered in this work. The conformity of the request were verified according to the recommendations of the National Agency for Accreditation and Health Evaluation in France (NAAHE), technical condition of realization and results were appreciated and the entrance surface dose (ESD) of the patients was estimated using a mathematical algorithm. Results: Children under 5 years (63.5%) were more represented in our study. The main indications were: cough (22.1%), suspicion of pneumonia (16.4%) and bronchitis (15.7%). No indication was mentioned on 69.3% of the request forms. After confrontation to the “Guide for proper use of medical imaging examinations” (GPU), we only had 24% conformity of indications. 82.7% of the examinations required immobilization assistance by the parents. Most of the children were imaged in a standing-up position (82.9%) and the anterior-posterior view (77.9%) was more practiced. After the analysis of the pictures, 62% of them presented an optimal contrast, while 42.1% of X-ray were performed without beam collimation. 25 X-rays were repeated: 12 (48%) because of patient’s motion and 13 (52%) of mispositionning. After interpretation, 87 (62.14%) chest X-ray were normal. Main lesion observed were pneumonia (17.14%) followed by bronchopeumopathy (5.71%) and bronchitis (5%). The obtained ESD values were 0.11, 0.15 and 0.17 mGy respectively for the 0 - 1 year, 1 - 5 year and 5 - 10 year age groups;0.2 and 0.57 respectively for postero-anterior (PA) and lateral (LAT) view for the age group 10 - 15 years, which were slightly greater than the values in internationally published studies. Conclusion: The request for children chest X-ray is not relevant in terms of indication, technical conditions of realization and irradiation.

Usefulness of Chest Computed Tomography for Diagnosis of Idiopathic Pneumomediastinum with Negative Findings on Plain X-Ray

Idiopathic pneumomediastinum is rare in children. Few cases of patients with pneumomediastinum show negative findings on X-ray examination. Chest computed tomography (CT) was very useful for the diagnosis and evaluation of the extent of pneumomediastinum. We report here a case of idiopathic pneumomediastinum in a 15-year-old boy who exhibited no significant chest X-ray finding. The patient was referred to our institute for the further evaluation of pre-cordial pain and breathing difficulty. Precordial pain suddenly developed, when he was carrying a portable shrine on his shoulder (day of onset). He was admitted to another institute 3 days after onset because of worsening precordial pain. On admission, he presented with 98% saturation of hemoglobin in the peripheral blood under room air. Plain chest X-ray also revealed no abnormal findings. A half-dissolved gastrographin swallow showed no leakage of gastrographin from the pharynx and esophagus to the mediastinum, and no diverticulum within the esophagus. Plain chest CT revealed extensive emphysema around the trachea from the neck to the portion inferior to the carina of trachea. The patient was diagnosed with idiopathic pneumomediastinum because the cause was unclear. We decided to admit him to our institute under fasting conditions and rest. His symptoms improved 3 days after onset. The lesion had disap-peared 8 days after onset on chest CT. When young people experience precordial pain which increases on inspiration, we must consider pneumomediastinum in a differential diagnosis, and it is important to perform chest CT.

Chest X-rays in detecting injuries caused by blunt trauma

BACKGROUND:The appropriate sequence of different imagings and indications of thoracic computed tomography(TCT)in evaluating chest trauma have not yet been clarified at present.The current study was undertaken to determine the value of chest X-ray(CXR)in detecting chest injuries in patients with blunt trauma.METHODS:A total of 447 patients with blunt thoracic trauma who had been admitted to the emergency department(ED)in the period of 2009–2013 were retrospectively reviewed.The patients met inclusion criteria(age>8 years,blunt injury to the chest,hemodynamically stable,and neurologically intact)and underwent both TCT and upright CXR in the ED.Radiological imagings were re-interpreted after they were collected from the hospital database by two skilled radiologists.RESULTS:Of the 447 patients,309(69.1%)were male.The mean age of the 447 patients was 39.5±19.2(range 9 and 87 years).158(35.3%)patients were injured in motor vehicle accidents(MVA).CXR showed the highest sensitivity in detecting clavicle fractures[95%CI 78.3(63.6–89)]but the lowest in pneuomediastinum[95%CI 11.8(1.5–36.4)].The specificity of CXR was close to 100%in detecting a wide array of entities.CONCLUSION:CXR remains to be the first choice in hemodynamically unstable patients with blunt chest trauma.Moreover,stable patients with normal CXR are candidates who should undergo TCT if significant injury has not been ruled out.

A Novel Method for Automated Lung Region Segmentation in Chest X-Ray Images

Detecting and segmenting the lung regions in chest X-ray images is an important part in artificial intelligence-based computer-aided diagnosis/detection (AI-CAD) systems for chest radiography. However, if the chest X-ray images themselves are used as training data for the AI-CAD system, the system might learn the irrelevant image-based information resulting in the decrease of system’s performance. In this study, we propose a lung region segmentation method that can automatically remove the shoulder and scapula regions, mediastinum, and diaphragm regions in advance from various chest X-ray images to be used as learning data. The proposed method consists of three main steps. First, employ the simple linear iterative clustering algorithm, the lazy snapping technique and local entropy filter to generate an entropy map. Second, apply morphological operations to the entropy map to obtain a lung mask. Third, perform automated segmentation of the lung field using the obtained mask. A total of 30 images were used for the experiments. In order to verify the effectiveness of the proposed method, two other texture maps, namely, the maps created from the standard deviation filtering and the range filtering, were used for comparison. As a result, the proposed method using the entropy map was able to appropriately remove the unnecessary regions. In addition, this method was able to remove the markers present in the image, but the other two methods could not. The experimental results have revealed that our proposed method is a highly generalizable and useful algorithm. We believe that this method might act an important role to enhance the performance of AI-CAD systems for chest X-ray images.

The Role of Chest X-Ray in Monitoring Lung Changes among COVID-19 Patients in Gaza Strip

Objective: To investigate the time course and findings severity of COVID-19 infection at chest radiography based on a 6-point radiological severity score, and correlates these with patients’ age and gender. Methods: This is a retrospective study of COVID-19 patients who were admitted at European Gaza Hospital and evaluated between October 6, 2020, and November 30, 2020. Baseline and serial chest radiographs, up to 4 images per patient, were reviewed and assessed for predominant pattern, side, and location of lung opacity. Utilized a 6-point scoring system, which divides the chest X-ray into 6 zones, to assess chest X-ray changes and correlate them with the severity of infection, age, and gender of patients. Results: The study included 136 COVID-19 patients: (51/136, 37%) were males and (85/136, 62.5%) were females, while age ranged from 7 months to 90 years with a mean age of 41.7 ± (19.5) years. Negative Chest x-rays were more observed than positive images. Ground-glass opacity was the most frequent pattern with a decreasing trend from 1st to 4th chest X-ray (from 33.8% to 3.7%), followed by consolidation (from 16.2% to 2.9%). Also, the commonest pattern of opacity was seen in peripheral areas (27/136, 19.9%), lower zone location (23/136, 16.9%), and bilateral opacity involvement (43/136;31.6%). No significant correlation was noticed between the patient’s gender, age, and severity score (P > 0.05). Conclusions: The 6-point chest X-ray severity score as a predictive tool in assessing the severity due to provide an assessment of the progression or regression pathway.

Chest ultrasound in neonates:What neonatologists should know

For many years,ultrasound was thought to have no indications in pulmonary imaging because lungs are filled with air,creating no acoustic mismatch,as encountered by ultrasound wave beam.Lung ultrasound(LUS)was started in adult critical care settings to detect pleural effusion and acquired more indications over time.In the neonatal intensive care unit(NICU),the use of chest ultrasound has gained more attention during the last two decades.Being a radiation-free,bedside,rapid,and handy tool,LUS started to replace chest X-rays in NICU.Using LUS depends upon understanding the nature of normal lungs and the changes induced by different diseases.With the help of LUS,an experienced neonatologist can detect many of the respiratory problems so fast that interventional therapy can be introduced as early as possible.LUS can diagnose pleural effusion,pneumothorax,pneumonia,transient tachypnoea of the newborn,respiratory distress syndrome,pulmonary atelectasis,meconium aspiration syndrome,bronchopulmonary dysplasia,and some other disorders with very high accuracy.LUS will be helpful in initial diagnosis,follow-up,and predicting the need for further procedures such as mechanical ventilation,diuretic therapy,surfactant therapy,etc.There are some limitations to using LUS in some respiratory disorders such as bullae,interstitial emphysema,and other conditions.This review will highlight the importance of LUS,its uses,and limitations.

Chest Radiographs Based Pneumothorax Detection Using Federated Learning

Pneumothorax is a thoracic condition that occurs when a person’s lungs collapse,causing air to enter the pleural cavity,the area close to the lungs and chest wall.The most persistent disease,as well as one that necessitates particular patient care and the privacy of their health records.The radiologists find it challenging to diagnose pneumothorax due to the variations in images.Deep learning-based techniques are commonly employed to solve image categorization and segmentation problems.However,it is challenging to employ it in the medical field due to privacy issues and a lack of data.To address this issue,a federated learning framework based on an Xception neural network model is proposed in this research.The pneumothorax medical image dataset is obtained from the Kaggle repository.Data preprocessing is performed on the used dataset to convert unstructured data into structured information to improve the model’s performance.Min-max normalization technique is used to normalize the data,and the features are extracted from chest Xray images.Then dataset converts into two windows to make two clients for local model training.Xception neural network model is trained on the dataset individually and aggregates model updates from two clients on the server side.To decrease the over-fitting effect,every client analyses the results three times.Client 1 performed better in round 2 with a 79.0%accuracy,and client 2 performed better in round 2 with a 77.0%accuracy.The experimental result shows the effectiveness of the federated learning-based technique on a deep neural network,reaching a 79.28%accuracy while also providing privacy to the patient’s data.