Normal X-ray Research Articles

Direct Experimental Evidence for Substrate Adatom Incorporation into a Molecular Overlayer.

While the phenomenon of metal substrate adatom incorporation into molecular overlayers is generally believed to occur in several systems, the experimental evidence for this relies on the interpretation of scanning tunneling microscopy (STM) images, which can be ambiguous and provides no quantitative structural information. We show that surface X-ray diffraction (SXRD) uniquely provides unambiguous identification of these metal adatoms. We present the results of a detailed structural study of the Au(111)-F4TCNQ system, combining surface characterization by STM, low-energy electron diffraction, and soft X-ray photoelectron spectroscopy with quantitative experimental structural information from normal incidence X-ray standing wave (NIXSW) and SXRD, together with dispersion-corrected density functional theory (DFT) calculations. Excellent agreement is found between the NIXSW data and the DFT calculations regarding the height and conformation of the adsorbed molecule, which has a twisted geometry rather than the previously supposed inverted bowl shape. SXRD measurements provide unequivocal evidence for the presence and location of Au adatoms, while the DFT calculations show this reconstruction to be strongly energetically favored.

Fractura de estrés del cuello del fémur. A propósito de un caso.

A fatigue fracture, also known as a stress fracture of the femoral neck, is an uncommon type of fracture that happens when a patient engages in strenuous physical activity or overuse. This case involves a 31-year-old woman who consults the hospital on several occasions with creeping pain in her groin and inner thigh. Following normal X-rays and the absence of clinical improvement, an MRI and a fresh X-ray were conducted, revealing a loss of continuity of the cortical bone of the femur's neck, which was classified as a stress fracture. The most common consequence of this type of fracture is femoral head necrosis due to delayed diagnosis. As a result, it's critical to get a diagnosis as soon as possible.

Adaptive deep learning for deep COVID-19 diagnosis

PurposeCOVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The main purpose of this work is, COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The COVID-19 diagnosis is required to detect at the beginning stage and special attention should be given to them. The fastest way to detect the COVID-19 infected patients is detecting through radiology and radiography images. The few early studies describe the particular abnormalities of the infected patients in the chest radiograms. Even though some of the challenges occur in concluding the viral infection traces in X-ray images, the convolutional neural network (CNN) can determine the patterns of data between the normal and infected X-rays that increase the detection rate. Therefore, the researchers are focusing on developing a deep learning-based detection model.Design/methodology/approachThe main intention of this proposal is to develop the enhanced lung segmentation and classification of diagnosing the COVID-19. The main processes of the proposed model are image pre-processing, lung segmentation and deep classification. Initially, the image enhancement is performed by contrast enhancement and filtering approaches. Once the image is pre-processed, the optimal lung segmentation is done by the adaptive fuzzy-based region growing (AFRG) technique, in which the constant function for fusion is optimized by the modified deer hunting optimization algorithm (M-DHOA). Further, a well-performing deep learning algorithm termed adaptive CNN (A-CNN) is adopted for performing the classification, in which the hidden neurons are tuned by the proposed DHOA to enhance the detection accuracy. The simulation results illustrate that the proposed model has more possibilities to increase the COVID-19 testing methods on the publicly available data sets.FindingsFrom the experimental analysis, the accuracy of the proposed M-DHOA–CNN was 5.84%, 5.23%, 6.25% and 8.33% superior to recurrent neural network, neural networks, support vector machine and K-nearest neighbor, respectively. Thus, the segmentation and classification performance of the developed COVID-19 diagnosis by AFRG and A-CNN has outperformed the existing techniques.Originality/valueThis paper adopts the latest optimization algorithm called M-DHOA to improve the performance of lung segmentation and classification in COVID-19 diagnosis using adaptive K-means with region growing fusion and A-CNN. To the best of the authors’ knowledge, this is the first work that uses M-DHOA for improved segmentation and classification steps for increasing the convergence rate of diagnosis.

Performance Comparison of Deep Learning Models to Detect Covid-19 Based on X-Ray Images

The SARS-Cov-2 outbreak caused by a coronavirus infection shocked dozens of countries. This disease has spread rapidly and become a new pandemic, a serious threat and even destroys various sectors of life. Along with technological developments, various deep learning models have been developed to classify between Covid-19 and Normal X-ray images of lungs, such as Inception V3, Inception V4 and MobileNet. These models have been separately reported to perform good classification on Covid-19. However, there is no comparison of their performance in classifying Covid-19 on the same data. This research aims to compare the performance of the three mentioned deep learning models in classifying Covid-19 based on X-ray images. The methods involve data collection, pre-processing, training, and testing using the three models. According to 2,169 dataset, the InceptionV3 model obtained an average accuracy value of 99.62%, precision value 99.65%, recall value 99.5%, specificity value 99.5%, and f-score value 99.52%; while the InceptionV4 model obtained an average accuracy value of 97.79%, precision value 98.11%, recall value 90.18%, specificity value 90.18%, and f-score value 97.25%; and the MobileNet model obtained an average accuracy value of 99.67%, precision value 99.77%, recall value 99.38%, specificity value 99.38%, and f-score value of 99.67%. The three models can classify the Covid-19 and Normal X-ray images based on research results, while the MobileNet model achieved the best performance. The model has stable performance in achieving graphic results and has extensive layers; the more layers there are to achieve better accuracy results.

Adolescent medial epicondyle trash lesion clinic radiological evaluation and treatment

<p class="abstract">Elbow injures in children accounts for 10% of all fractures, ranges from simple sprain to elbow fracture dislocation. Ligamentous injury accounts for 2-3% only and are mostly sports related seen in overhead athletes. Trash lesions (the radiological appearances seemed harmless) around the elbow are reported in children under 10 years which are high energy trauma with initial normal looking X-rays. These are osteochondral or physical injures presenting with soft tissue swelling without any fractures. A high index of suspicion, early additional imaging such as MRI, CT scan, ultrasound examination and aggressive surgical care are usually necessary for prompt diagnosis and successful treatment of these injuries before complications arise. We presented a case of an elbow injury in a 15 year old girl, following a fall on her outstretched hand with initial normal findings in her X-rays. 3 months following the injury she had persisting pain and instability with ulnar nerve impingement symptoms. Her elbow CT scan, nerve conduction study confirmed the avulsion fracture of medial epicondyle with ulnar nerve neuritis and cubital tunnel syndrome. Valgus stress test proved the extent of additional medial instability. Medial epicondyle avulsion fractures are always challenging for fixation. The retracting UCL forces prevents healing and were prone for failure with non-operative splinting once instability is defined. Attempted fixation can also result in failure of fixation. Our case study highlights the possibility of such trash lesion in adolescent and also the modified technique of ulnar collateral ligament (UCL) reconstruction in a cost effective manner.</p>

Recognizing COVID-19 from chest X-ray images for people in rural and remote areas based on deep transfer learning model.

In this article, we propose Deep Transfer Learning (DTL) Model for recognizing covid-19 from chest x-ray images. The latter is less expensive, easily accessible to populations in rural and remote areas. In addition, the device for acquiring these images is easy to disinfect, clean and maintain. The main challenge is the lack of labeled training data needed to train convolutional neural networks. To overcome this issue, we propose to leverage Deep Transfer Learning architecture pre-trained on ImageNet dataset and trained Fine-Tuning on a dataset prepared by collecting normal, COVID-19, and other chest pneumonia X-ray images from different available databases. We take the weights of the layers of each network already pre-trained to our model and we only train the last layers of the network on our collected COVID-19 image dataset. In this way, we will ensure a fast and precise convergence of our model despite the small number of COVID-19 images collected. In addition, for improving the accuracy of our global model will only predict at the output the prediction having obtained a maximum score among the predictions of the seven pre-trained CNNs. The proposed model will address a three-class classification problem: COVID-19 class, pneumonia class, and normal class. To show the location of the important regions of the image which strongly participated in the prediction of the considered class, we will use the Gradient Weighted Class Activation Mapping (Grad-CAM) approach. A comparative study was carried out to show the robustness of the prediction of our model compared to the visual prediction of radiologists. The proposed model is more efficient with a test accuracy of 98%, an f1 score of 98.33%, an accuracy of 98.66% and a sensitivity of 98.33% at the time when the prediction by renowned radiologists could not exceed an accuracy of 63.34% with a sensitivity of 70% and an f1 score of 66.67%.

Clinico-Radiological Profile and Outcome of Airway Foreign Body Aspiration in Children: A Single-Center Experience From a Tertiary Care Center in Eastern India.

BackgroundForeign body aspiration is one of the leading causes of childhood morbidity and mortality among older infants and toddler age groups. Missed and delayed diagnosis of foreign body aspiration can lead to increased incidence of complications. Early diagnosis can prevent life-threatening complications and morbidity. In this study, we aimed to evaluate the clinical and radiological details, types, localization of foreign bodies, complications, and outcomes in pediatric patients who presented to our hospital with foreign body aspiration.MethodologyWe conducted a retrospective analysis of hospital case records of children aged one month to 14 years who were admitted to the Department of Pediatrics between June 2018 and May 2020, with clinical suspicion of foreign body aspiration.ResultsA total of 22 children with a diagnosis of airway foreign body were included. The mean age of presentation was three years (SD: ±2.22), with a boy-to-girl ratio of 3.4:1. Cough (81.8%) and tachypnea (72.7%) were the most common clinical symptoms. The median duration between symptom onset and diagnosis was three (interquartile range: 6) days. Unilateral reduced breath sound (81.8%) was the most common clinical examination finding. The common site of impaction was the right main bronchus in 59.1% of cases. The foreign bodies retrieved during bronchoscopy were organic substances in 63.6% of cases, with peanuts being the most common (31.8%). Chest radiographs were normal in 36.3% of cases, and common abnormalities included hyperinflation, collapse, consolidation, and mediastinal shift. Mechanical ventilation was required in 54.5% of cases. The mean duration of hospitalization was five (SD: ±2.84) days. Complications such as pneumothorax were seen in one (4.5%) case. Mortality was seen in 4.54% of cases during the bronchoscopic procedures.ConclusionsForeign body aspiration was common in young male children, with cough being the common symptom. Normal X-rays of the chest were seen in one-third of cases. The common site of impaction was the right main bronchus, and organic substances such as peanuts were common foreign bodies retrieved. Strong clinical suspicion of foreign body aspiration should be kept in cases with acute onset of cough in young children. Prompt medical attention is needed to reduce the morbidity and mortality associated with foreign body aspiration.

The Link Between Skull Fracture and Extradural Hematoma in Head Injury Patients who came to a Tertiary Care Hospital in Pakistan

Aim: The purpose of our current study is to see if there is any connection between skull fracture and extradural hematoma in head injury patients who came to the tertiary care hospital in Pakistan. Methods: From January 2020 to December 2021, this descriptive research was conducted at Chandka Medical College, Shaheed Mohtrama Benazir Bhutto Medical University Larkana. The research comprised those patients who have undergone extradural hematoma surgery during study period. Normal skull X-rays being taken, and the type and position of fractures were documented on the X-rays, CT scan, and during surgery. The frequency of skull fracture linked through extradural hematoma was studied in different ages. SPSS version 24.0 was utilized to investigate our current findings. Results: A sum of 130 individuals were operated on for extradural hematoma. In 70 cases, a linear fracture was seen, and in 18 individuals, a depressed skull fracture was seen. Another ten individuals with no radiologically evident fracture reported discovered to also have a fracture line intra-operatively. As a result, 87 (78 percent) of the participants suffered a skull fracture. Here remained no statistically important variation in occurrence of skull fracture among age ranges. Conclusion: Although there is a significant link between skull fracture and extradural hematoma, a normal X-ray does not rule out extradural hematoma. Keywords: Skull fracture, extradural hematoma, head injury, Tertiary Care Hospital, Larkana, Pakistan.

Diagnosis of hypercritical chronic pulmonary disorders using dense convolutional network through chest radiography.

Lung-related ailments are prevalent all over the world which majorly includes asthma, chronic obstructive pulmonary disease (COPD), tuberculosis, pneumonia, fibrosis, etc. and now COVID-19 is added to this list. Infection of COVID-19 poses respirational complications with other indications like cough, high fever, and pneumonia. WHO had identified cancer in the lungs as a fatal cancer type amongst others and thus, the timely detection of such cancer is pivotal for an individual’s health. Since the elementary convolutional neural networks have not performed fairly well in identifying atypical image types hence, we recommend a novel and completely automated framework with a deep learning approach for the recognition and classification of chronic pulmonary disorders (CPD) and COVID-pneumonia using Thoracic or Chest X-Ray (CXR) images. A novel three-step, completely automated, approach is presented that first extracts the region of interest from CXR images for preprocessing, and they are then used to detects infected lungs X-rays from the Normal ones. Thereafter, the infected lung images are further classified into COVID-pneumonia, pneumonia, and other chronic pulmonary disorders (OCPD), which might be utilized in the current scenario to help the radiologist in substantiating their diagnosis and in starting well in time treatment of these deadly lung diseases. And finally, highlight the regions in the CXR which are indicative of severe chronic pulmonary disorders like COVID-19 and pneumonia. A detailed investigation of various pivotal parameters based on several experimental outcomes are made here. This paper presents an approach that detects the Normal lung X-rays from infected ones and the infected lung images are further classified into COVID-pneumonia, pneumonia, and other chronic pulmonary disorders with an utmost accuracy of 96.8%. Several other collective performance measurements validate the superiority of the presented model. The proposed framework shows effective results in classifying lung images into Normal, COVID-pneumonia, pneumonia, and other chronic pulmonary disorders (OCPD). This framework can be effectively utilized in this current pandemic scenario to help the radiologist in substantiating their diagnosis and in starting well in time treatment of these deadly lung diseases.

The role of computed tomography in the diagnosis of lung cancer - a case report


 Introduction
 Lung cancer has been the main oncological problem in the world for years. It is extremely important to use appropriate diagnostic methods that enable its detection and implementation of appropriate treatment.
 
 
 Aim
 The presented case shows the advantage of computed tomography over chest X-ray (X-ray) in visualizing neoplastic changes in the lungs.
 
 
 Case Study
 The paper presents a description of a patient diagnosed with centrally located advanced lung adenocarcinoma with a strong expression of PD-L1 qualified for treatment with pembrolizumab.
 
 
 Results and Discussion
 Presented case confirms that X-ray is less sensitive, especially in the case of centrally located tumors. Therefore, the emergence of a new cough in a smoker or ex-smoker should raise concerns related to lung cancer despite a normal X-ray image. The central location of the tumor may cause dramatic course of the symptoms. In the presented case, a sudden significant deterioration of the condition was observed due to atelectasis of the entire lung. Haemoptysis observed during hospitalization was another symptom of centraly located tumor mass.
 
 
 Conclusions
 In conclusion, the history of cigarette smoking, presence of typical symptoms should provide an in-depth diagnosis of lung cancer, despite normal X-ray. Diagnostic procedures include computed tomography in the first place. The course of centrally localized disease may change rapidly during on first cycle of treatment. Due to the possibility of serious complications of the ongoing neoplastic disease, the patient should be under constant medical supervision.

Classification of healthy and affected lungs by pneumonia disease from x-ray images of lungs and gene sequencing using inception model

This project is entitled to predict the lung disease using chest x-rays by deep learning technique. Lung disease is a term that refers to improper functioning of lungs. There are many diseases which occur due to the abnormal functioning of lungs. It includes tuberculosis, pneumonia, lung cancer, asthma. The infection can be bacterial, viral or fungal. It causes inflammation of trachea and respiratory failure. If found earlier it can be cured else it can even lead to death. This project classifies the normal and abnormal x-ray with a percentage of accuracy so that we can give the treatment to the patient accordingly by seeing the x-ray. The algorithms used are Convolutional Neural Network (CNN) Inception Neural Network (INN) and Tensor Flow which is Google's open-source algorithm. The project is helpful for finding lung disease using chest x-ray.

Covid-19 detection on x-ray images using a deep learning architecture

Recently, coronavirus disease (Covid-19) has become a serious public health threat, spreading worldwide in a very short time and threatening the lives of millions. With the increasing number of cases and mutations, medical resources are being drained day by day due to the rapid transmission of the disease, and the health systems of many countries are negatively affected. For this reason, it is very important to use available resources appropriately and timely for the detection and treatment of the disease. In this study, VGG16 and ResNet50 deep learning models were used to quickly evaluate x-ray images and to make the pre-diagnosis of Covid-19, and an alternative model (IsVoNet) was proposed. As a result of the training of the models, success accuracy of 99.92% in the VGG16 model, 99.65% in the ResNet50 model and 99.76% in the proposed model were obtained. According to the results, it was observed that the models classified Covid-19 and normal lung x-ray images with high accuracy and the proposed model showed a high success rate at lower time complexity than other models.

CheXImageNet: a novel architecture for accurate classification of Covid-19 with chest x-ray digital images using deep convolutional neural networks.

Many countries around the world have been influenced by Covid-19 which is a serious virus as it gets transmitted by human communication. Although, its syndrome is quite similar to the ordinary flu. The critical step involved in Covid-19 is the initial screening or testing of the infected patients. As there are no special detection tools, the demand for such diagnostic tools has been increasing continuously. So, it is eminently admissible to find out positive cases of this disease at the earliest so that the spreading of this dangerous virus can be controlled. Although, some methods for the detection of Covid-19 patients are available, which are performed upon respiratory based samples and among them, a critical approach for treatment is radiologic imaging or X-ray imaging. The latest conclusions obtained from X-ray digital imaging based algorithms and techniques recommend that such type of digital images may consist of significant facts regarding the SARS-CoV-2 virus. The utilization of Deep Neural Networks based methodologies clubbed with digital radiological imaging has been proved useful for accurately identifying this disease. This could also be adjuvant in conquering the problem of dearth of competent physicians in far-flung areas. In this paper, a CheXImageNet model has been introduced for detecting Covid-19 disease by using digital images of Chest X-ray with the help of an openly accessible dataset. Experiments for both binary class and multi-class have been performed in this work for benchmarking the effectiveness of the proposed work. An accuracy of 100% is reported for both binary classification (having cases of Covid-19 and Normal X-Ray) and classification for three classes (including cases of Covid-19, Normal X-Ray and, cases of Pneumonia disease) respectively.

Prediction Model Using Reinforcement Deep Learning Technique for Osteoarthritis Disease Diagnosis

Osteoarthritis is the most common class of arthritis that involves tears down the soft cartilage between the joints of the knee. The regeneration of this cartilage tissue is not possible, and thus physicians typically suggest therapeutic measures to prevent further deterioration over time. Normally, bringing about joint replacement is a remedial course of action. Expose itself in joint pain recognized with a normal X-ray. Deep learning plays a vital role in predicting the early stages of osteoarthritis by using the MRI pictures of muscles of the knee muscle. It can be used to accurately measure the shape and texture of biological structures can be measured consistently from X-ray images. Moreover, deep learning-based computation can be used to design framework to predict whether a given patient will develop osteoarthritis. Such a framework can identify clear biochemical changes in the focal point of ligaments of the knees of patients who have exhibit pre-indications in standard imaging. This study proposes framework to identify cases of osteoarthritis by using deep learning and reinforcement learning. It can be used as a clinical mechanism to predict the occurrence of osteoarthritis so that patients can benefit from early intervention.

A Deep Learning Model for Coronavirus 2019 Pneumonia Screening

This paper proposes an automatic COVID-19 detection model using chest X-ray images. The proposed model is developed to give accurate diagnostics for multiclass classification (COVID vs. PNEUMONIA vs. NORMAL). Our model produced a classification accuracy of 96%. Evaluation has been done on publicly available databases containing covid19, pneumonia and normal X-ray images. The proposed approach uses the VGG-16 model with pre-trained weights in the initialization step.

Multi-class Chest X-ray classification of Pneumonia, Tuberculosis and Normal X-ray images using ConvNets

Pneumonia and Tuberculosis (TB) are two serious and life-threatening diseases that are caused by a bacterial or viral infection of the lungs and have the potential to result in severe consequences within a short period of time. Therefore, early diagnosis is a significant factor in terms of a successful treatment process. Chest X-Rays which are used to diagnose Pneumonia and/or Tuberculosis need expert radiologists for evaluation. Thus, there is a need for an intelligent and automatic system that has the capability of diagnosing chest X-rays, and to simplify the disease detection process for experts and novices. This study aims to develop a model that will help with the classification of chest X-ray medical images into normal vs Pneumonia or Tuberculosis. Medical organizations take a minimum of one day to classify the diagnosis, while our model could perform the same classification within a few seconds. Also, it will display a prediction probability about the predicted class. The model had an accuracy, precision and recall score over 90% which indicates that the model was able to identify patterns. Users can upload their respective chest X-ray image and the model will classify the uploaded image into normal vs abnormal.

Ultrasonography and Magnetic Resonance Imaging Findings of an Occult Tibial Plateau Fracture

We describe a case of posttraumatic lateral tibial plateau fracture that was not demonstrated by conventional radiography. Knee joint ultrasound showed mild lateral meniscal protrusion suggestive of an intra-articular injury, which was confirmed at magnetic resonance imaging to represent an occult lateral tibial plateau fracture. A high index of suspicion for occult fractures should be maintained in posttraumatic patients with ultrasound findings of meniscal protrusion, in spite of apparently normal X-ray findings.

Particularities of radiological lesions in smear-positive and HIV-positive tuberculosis patients in the era of triple antiretroviral therapy at Jamot Yaoundé hospital

Introduction: The aim of this work was to compare the radiological lesions during bacilliferous tuberculosis between HIV positive patients under ARV treatment and HIV negative patients. Methodology: This was a, descriptive and comparative study conducted from December 1, 2018 to May 31, 2019 (6 months) at Jamot Yaoundé Hospital. Smearpositive tuberculosis patients meeting our inclusion criteria were enrolled in the study. We split them into two groups, HIV + and HIV -. The comparison of the data was made by the Chi² test, that of the quantitative data with the Student's T test. A p-value less than 0.05 was considered significant. Results: In total, we recruited 145 patients divided into 2 groups of 63 HIV + patients and 82 HIV-patients. The radiological peculiarities found in HIV were as follows: Normal radiography was more frequent, P = 0.004. Interstitial syndrome was less common. Bilateral involvement was predominant. Right localization and upper lobe lesions were less frequent, P <0.05. Micronodular lesions were more frequent, P <0.05. During the alveolar syndrome, the left side was less affected and the upper lobe was the most; p <0.05. Cavitary, pleural, mediastinal and bronchial syndromes did not show statistically significant differences. Conclusion: Although a normal x-ray does not exclude pulmonary tuberculosis, the x-ray lesions are multiple but are dominated by the interstitial syndrome. In patients immunocompromised to HIV, bilateral involvement predominates. The right localization and lesions of the upper lobe are less frequent. Micronodular lesions are frequent.

Adaptive UNet-based Lung Segmentation and Ensemble Learning with CNN-based Deep Features for Automated COVID-19 Diagnosis.

COVID-19 disease is a major health calamity in twentieth century, in which the infection is spreading at the global level. Developing countries like Bangladesh, India, and others are still facing a delay in recognizing COVID-19 cases. Hence, there is a need for immediate recognition with perfect identification of infection. This clear visualization helps to save the life of suspected COVID-19 patients. With the help of traditional RT-PCR testing, the combination of medical images and deep learning classifiers delivers more hopeful results with high accuracy in the prediction and recognition of COVID-19 cases. COVID-19 disease is recently researched through sample chest X-ray images, which have already proven its efficiency in lung diseases. To emphasize corona virus testing methods and to control the community spreading, the automatic detection process of COVID-19 is processed through the detailed medication reports from medical images. Although there are numerous challenges in the manual understanding of traces in COVID-19 infection from X-ray, the subtle differences among normal and infected X-rays can be traced by the data patterns of Convolutional Neural Network (CNN). To improve the detection performance of CNN, this paper plans to develop an Ensemble Learning with CNN-based Deep Features (EL-CNN-DF). In the initial phase, image scaling and median filtering perform the pre-processing of the chest X-ray images gathered from the benchmark source. The second phase is lung segmentation, which is the significant step for COVID detection. It is accomplished by the Adaptive Activation Function-based U-Net (AAF-U-Net). Once the lungs are segmented, it is subjected to novel EL-CNN-DF, in which the deep features are extracted from the pooling layer of CNN, and the fully connected layer of CNN are replaced with the three classifiers termed “Support Vector Machine (SVM), Autoencoder, Naive Bayes (NB)”. The final detection of COVID-19 is done by these classifiers, in which high ranking strategy is utilized. As a modification, a Self Adaptive-Tunicate Swarm Algorithm (SA-TSA) is adopted as a boosting algorithm to enhance the performance of segmentation and detection. The overall analysis has shown that the precision of the enhanced CNN by using SA-TSA was 1.02%, 4.63%, 3.38%, 1.62%, 1.51% and 1.04% better than SVM, autoencoder, NB, Ensemble, RNN and LSTM respectively. The comparative performance analysis on existing model proves that the proposed algorithm is better than other algorithms in terms of segmentation and classification of COVID-19 detection.

537 Oversensing due to micro-air bubbles leading to inappropriate shocks in patient with subcutaneous implantable cardioverter defibrillator

Abstract Aims Subcutaneous air has been reported as uncommon cause of early device malfunction after implantation of subcutaneous implantable cardioverter defibrillator (S-ICD). Methods and results A 43-year-old man was admitted to our hospital due to syncopal episodes. During a brain computed tomography (CT), he was resuscitated from cardiac arrest due to ventricular fibrillation. Further investigations excluded ischaemic heart disease and cardiac magnetic resonance showed signs of prior myocarditis. After successful screening, an S-ICD was inserted for secondary prevention using the standard technique. The chest X-ray excluded acute post-procedural complications. Three days after implantation, he experienced two inappropriate shocks while conscious. Device interrogation revealed frequent high-amplitude signals followed by two shocks (Figure 1A and B) that could be interpreted as oversensing. During provocative maneuvers on the generator case, minimal and intermittent noise only on the secondary sensing vector was observed. Lateral and antero-posterior chest X-ray excluded subcutaneous air. However, the general agreement based on previous reports was that oversensing as observed in Figures 1A and B was typical for noise due to micro-air bubbles in the connection between the lead and the generator case. As a consequence, the sensing vector was successfully changed and no other inappropriate shocks were observed during follow-up. Conclusions Entrapped micro-air bubbles in the connection between the leads and the generator case can cause inappropriate shocks in the early period after S-ICD implantation. Timely recognition of this complication is important to prevent inappropriate shocks. It can occur shortly after the procedure even if the intra-procedural parameters were normal and chest X-ray excluded acute complications. As demonstrated in our case, the device can be reprogrammed using another sensing vector to solve these oversensing problems.