Chest X-ray In Patients Research Articles

DESCRIPTION OF COVID-19 PATIENTS WITH VENTILATORS AT GATOT SOEBROTO ARMY HOSPITAL FOR THE PERIOD OF JULY-SEPTEMBER 2021

COVID-19 is a disease caused by the SARS-CoV-2 virus. More than 90% of patients on ventilators with COVID-19 infection develop pneumonia and 40% develop ARDS. The severity of the disease is influenced by several things such as age and comorbid factors. In July 2021 Indonesia experienced a very high increase, namely 948,467 new positive cases with 20,877 deaths. This study aims to find out the description of COVID-19 patients with ventilators at the Gatot Soebroto Army Hospital for the period July-September 2021. The research method used is descriptive. Data collection in this study used secondary data in the form of patient medical records. The sample of this research is those who fulfill the requirements by using the total sampling technique. The sample size in the study was 76 people who were COVID-19 patients on ventilators at the Gatot Soebroto Army Hospital for the period July-September 2021. The results showed that the majority of COVID-19 patients were adults (60.5%) with male sex (53.9%). The results of the CT-value examination showed that 63.2% of patients had CT values <29, including strong positives. The results of D-dimer examination found 94.7% of patients had abnormal results ≥500 ng/dL and radiological examination results found 96.1% of patients indicated pneumonia. Comorbid patients were 46.1% DM comorbid patients, 44.7% hypertension comorbid patients, 31.6% obese patients and 5.3% cardiovascular disease comorbid patients. The patient mortality rate at discharge was 80.3% of patients who died. Males and the adult age group had the highest number of COVID-19 patients on ventilators with an average patient's chest X-ray showing indications of pneumonia with CT<29. Patients who have comorbidities increase the severity of the disease three times that of those who do not have comorbidities and increase the patient's mortality rate

Performance Analysis of Different Convolutional Neural Network (CNN) Models with Optimizers in Detecting Tuberculosis (TB) from Various Chest X-ray Images

Tuberculosis (TB) is one of the top 10 infectious disease-related deaths. This paper uses Convolutional Neural Networks (CNN) to investigate the accuracy and performance of three pre-trained models with different optimizers and loss functions to diagnose tuberculosis based on the patient's chest X-ray scans. The odds of treating and curing tuberculosis (TB) are better if the disease is diagnosed early in a patient. Early detection of tuberculosis could lead to a decreased overall mortality rate. The best and quickest way to identify tuberculosis is to look at the patient's chest X-Ray image (CXR). A qualified professional Radiologist is required to make an accurate diagnosis. But do not have qualified doctor or radiologist everywhere. On the other hand, it is quite difficult for a doctor or radiologist to diagnose from any x-ray images with open eyes. 914 normal chest x-ray images and 892 TB infected images were used from different sources to train and evaluate these images to detect the exact x-ray of Tuberculosis infected people. Different famous pre-trained models like VGG16, InceptionV3 and Xception etc. were applied. Approximately 80% of the data was used for training and the remaining 20% was used for validation. From all of these datasets, randomly 190 images from normal and 180 images from TB chest x-ray images have been taken. Those randomized 370 (190 for TB and 180 for normal) images were used to evaluate the data finally. Performance of different algorithm like VGG16, InceptionV3 and Xception by applying different optimizers (Adam, Adadelta, Adagrad, Adamax, RMSprop, Nadam, SGD), different loss functions (Binary Cross Entropy, Hinge, Squared Hinge), varying input image size and also varying batch size were also been recorded. Note that, huge variations of performance for different combinations of algorithm, optimizer, loss function, input image size, batch size have been observed. Confusion matrix, precision, recall, f1-score value have also been recorded to understand and justify how accurately the model is predicting the disease from different angles.

Retrosternal goiter masquerading as type II respiratory failure. A case report.

Introduction and importanceRetrosternal Goiter (RG) represents a challenging clinical entity for surgeons. Although the vast majority of cases are successfully operated via a cervical access, there still remains a small minority that require an extra-cervical approach, even in experienced hands. Factors that shift the odds towards an extra-cervical approach are mainly related to the anatomic characteristics of the retrosternal mass.Case presentationWe herein report a case of RG presenting as type 2 respiratory failure without a palpable neck mass, in an 81-year-old female. Despite her history of subtotal thyroidectomy for Graves' disease, the patient's chest x-ray showed a central mediastinal mass shifting the trachea to the left. The retrosternal mass extended below the aortic arch, and carina on computed tomography. It also extended into the posterior mediastinum. All these anatomical features of the RG along with the patient's previous neck surgery were in favor of an extra-cervical approach. Nevertheless, a cervical approach was attempted, and was concluded successfully.ConclusionCT plays a key role in determining the likelihood of requiring an extra-cervical approach in RG. Even if the odds seem to be in favor of an extra-cervical approach, an attempt to remove the goiter through a cervical incision should always be made by an experienced surgeon, using all available techniques, and taking all required precautions, on account of less risk of surgical and aesthetic damage obtained with this approach.

The Impact of COVID-19 Infection on a Neurologically Compromised Male With Fahr’s Disease Presenting With Acute Delirium and Aspiration Pneumonia: A Case Report

Fahr’s disease or idiopathic basal ganglia calcification is a rare, sporadic, genetically dominant, and inherited neurological condition that manifests with dysphagia and Parkinson’s disease. The computed tomography (CT) scan is the method of choice to diagnose basal ganglia calcifications seen in Fahr’s disease. This case report elaborates on the emergency management of a 58-year-old male patient with acute respiratory distress, acute delirium, schizophrenia, Fahr’s syndrome, and history of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (coronavirus disease 2019 or COVID-19) infection. The patient’s chest X-ray, laboratory workup, and vital signs were suggestive of aspiration pneumonia-induced sepsis and acute hypoxemic respiratory failure. Post-admission antibiotic management reduced sepsis complications without improving the altered mental status. A comprehensive clinical assessment suggested the attribution of Fahr’s disease to the patient’s aspiration pneumonia and other clinical complications. In addition, COVID-19 infection, sepsis-induced inflammatory processes, and pre-existing neurological compromise possibly deteriorated the patient’s neurological outcomes, overall prognosis, and recovery.

Machine Learning Approach using CNN for COVID-19 Pandemic Detection

Data science and machine learning are domain names in which data generation can assist with inside the fight towards the disease. Early caution systems which can are expecting how much a disease might effect society and permit the authorities to take suitable measures without disrupting the economy are extremely important. In the confrontation towards COVID-19 methods for forecasting the future cases primarily based totally on present data are extremely beneficial. The preceding are three strategies of machine learning which are discussed: Two for predicting the wide variety of positive cases in the coming ten days, and one for identifying COVID-19 infection via way of means of analyzing the patient's chest x-ray image. Various algorithms had been tested, and the only that produced the maximum accurate consequences become selected for use on this take a look at to forecast confirmed cases in India. Various government entities can leverage the findings to take corrective action. Now that methods for forecasting infectious disease are available, COVID-19 can be less complicated to combat.

CORONAVIRUS Diagnosis Based on Chest X-Ray Images and Pre-trained DenseNet-121

A serious global problem called COVID-19 has killed a great number of people and rendered many projects useless. The obtained individual's identification at the appropriate time is one of the crucial methods to reduce losses. By detecting and recognizing contaminated individuals in the early stages, artificial intelligence can help many associations in these situations. In this study, we offer a fully automated method to identify COVID-19 from a patient's chest X-ray images without the need for a clinical expert's assistance. A new dataset was released, which consists of 300 chest X-ray images from 100 healthy individuals, 100 individuals who were infected with Covid 19, and 100 images of viral pneumonitis. 100 more for testing, too. In order to attain an F1 score of 0.98, a Recall of 0.98, and also an Accuracy of 0.98 with this dataset, a classification method deep learning-based learning algorithm DenseNet-121, transfer learning, as well as data augmentation techniques were implemented. Therefore, even though there are not enough training photos, these findings are far better than other state-of-the-art.

SARS-Net: COVID-19 detection from chest x-rays by combining graph convolutional network and convolutional neural network

COVID-19 has emerged as one of the deadliest pandemics that has ever crept on humanity. Screening tests are currently the most reliable and accurate steps in detecting severe acute respiratory syndrome coronavirus in a patient, and the most used is RT-PCR testing. Various researchers and early studies implied that visual indicators (abnormalities) in a patient's Chest X-Ray (CXR) or computed tomography (CT) imaging were a valuable characteristic of a COVID-19 patient that can be leveraged to find out virus in a vast population. Motivated by various contributions to open-source community to tackle COVID-19 pandemic, we introduce SARS-Net, a CADx system combining Graph Convolutional Networks and Convolutional Neural Networks for detecting abnormalities in a patient's CXR images for presence of COVID-19 infection in a patient.In this paper, we introduce and evaluate the performance of a custom-made deep learning architecture SARS-Net, to classify and detect the Chest X-ray images for COVID-19 diagnosis. Quantitative analysis shows that the proposed model achieves more accuracy than previously mentioned state-of-the-art methods. It was found that our proposed model achieved an accuracy of 97.60% and a sensitivity of 92.90% on the validation set.

A deep learning-based COVID-19 automatic diagnostic framework using chest X-ray images

The lethal novel coronavirus disease 2019 (COVID-19) pandemic is affecting the health of the global population severely, and a huge number of people may have to be screened in the future. There is a need for effective and reliable systems that perform automatic detection and mass screening of COVID-19 as a quick alternative diagnostic option to control its spread. A robust deep learning-based system is proposed to detect the COVID-19 using chest X-ray images. Infected patient's chest X-ray images reveal numerous opacities (denser, confluent, and more profuse) in comparison to healthy lungs images which are used by a deep learning algorithm to generate a model to facilitate an accurate diagnostics for multi-class classification (COVID vs. normal vs. bacterial pneumonia vs. viral pneumonia) and binary classification (COVID-19 vs. non-COVID). COVID-19 positive images have been used for training and model performance assessment from several hospitals of India and also from countries like Australia, Belgium, Canada, China, Egypt, Germany, Iran, Israel, Italy, Korea, Spain, Taiwan, USA, and Vietnam. The data were divided into training, validation and test sets. The average test accuracy of 97.11 ± 2.71% was achieved for multi-class (COVID vs. normal vs. pneumonia) and 99.81% for binary classification (COVID-19 vs. non-COVID). The proposed model performs rapid disease detection in 0.137 s per image in a system equipped with a GPU and can reduce the workload of radiologists by classifying thousands of images on a single click to generate a probabilistic report in real-time.

PDCOVIDNet: a parallel-dilated convolutional neural network architecture for detecting COVID-19 from chest X-ray images.

The COVID-19 pandemic continues to severely undermine the prosperity of the global health system. To combat this pandemic, effective screening techniques for infected patients are indispensable. There is no doubt that the use of chest X-ray images for radiological assessment is one of the essential screening techniques. Some of the early studies revealed that the patient’s chest X-ray images showed abnormalities, which is natural for patients infected with COVID-19. In this paper, we proposed a parallel-dilated convolutional neural network (CNN) based COVID-19 detection system from chest X-ray images, named as Parallel-Dilated COVIDNet (PDCOVIDNet). First, the publicly available chest X-ray collection fully preloaded and enhanced, and then classified by the proposed method. Differing convolution dilation rate in a parallel form demonstrates the proof-of-principle for using PDCOVIDNet to extract radiological features for COVID-19 detection. Accordingly, we have assisted our method with two visualization methods, which are specifically designed to increase understanding of the key components associated with COVID-19 infection. Both visualization methods compute gradients for a given image category related to feature maps of the last convolutional layer to create a class-discriminative region. In our experiment, we used a total of 2905 chest X-ray images, comprising three cases (such as COVID-19, normal, and viral pneumonia), and empirical evaluations revealed that the proposed method extracted more significant features expeditiously related to suspected disease. The experimental results demonstrate that our proposed method significantly improves performance metrics: the accuracy, precision, recall and F1 scores reach 96.58%, 96.58%, 96.59% and 96.58%, respectively, which is comparable or enhanced compared with the state-of-the-art methods. We believe that our contribution can support resistance to COVID-19, and will adopt for COVID-19 screening in AI-based systems.

An ST-Elevation Myocardial Infarction In Situs Inversus Totalis (DEXTROCARDIA).

A 63-year-old woman with medical history significant for peripheral artery disease, hypertension, hyperlipidema and a 20 pack-year smoking history presented to the emergency department after waking from sleep at 3 am with severe indigestion, nausea, and emesis. On arrival the patient was mildly hypotensive to 102/61 mm Hg with a heart rate of 70 beats/min, and saturating 94% on room air. She had had a dobutamine stress echocardiogram 1 month prior, which was “unremarkable.” The patient added, during electrocardiogram (ECG) lead placement, that her “heart is on the right side” and the ECG was obtained with right-sided precordial leads and standard limb leads (Figure 1). The ECG was repeated with purposeful limb lead reversal in addition to right-sided reversed precordial leads (Figure 2). The patient's chest xray can be seen in Figure 3. Figure 2Electrocardiogram with complete appropriate reversal including limb leads and right anterior precordial leads. Note appropriate QRS in aVL and aVR. Resolved global negativity in lead I. View Large Image Figure Viewer Download Hi-res image Figure 3Chest x-ray study showing dextrocardia. View Large Image Figure Viewer Download Hi-res image

Recognition of COVID-19 disease from X-ray images by hybrid model consisting of 2D curvelet transform, chaotic salp swarm algorithm and deep learning technique.

The novel coronavirus disease 2019 (COVID-19), detected in Wuhan City, Hubei Province, China in late December 2019, is rapidly spreading and affecting all countries in the world. Real-time reverse transcription-polymerase chain reaction (RT-PCR) test has been described by the World Health Organization (WHO) as the standard test method for the diagnosis of the disease. However, considering that the results of this test are obtained between a few hours and two days, it is very important to apply another diagnostic method as an alternative to this test. The fact that RT-PCR test kits are limited in number, the test results are obtained in a long time, and the high probability of healthcare personnel becoming infected with the disease during the test, necessitates the use of other diagnostic methods as an alternative to these test kits. In this study, a hybrid model consisting of two-dimensional (2D) curvelet transformation, chaotic salp swarm algorithm (CSSA) and deep learning technique is developed in order to determine the patient infected with coronavirus pneumonia from X-ray images. In the proposed model, 2D Curvelet transformation is applied to the images obtained from the patient's chest X-ray radiographs and a feature matrix is formed using the obtained coefficients. The coefficients in the feature matrix are optimized with the help of the CSSA and COVID-19 disease is diagnosed by the EfficientNet-B0 model, which is one of the deep learning methods. Experimental results show that the proposed hybrid model can diagnose COVID-19 disease with high accuracy from chest X-ray images.

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Sinus Arrest and Asystole Caused by a Peripherally Inserted Central Catheter

Transient bradycardia in the critical care setting is frequently caused by hypoxemia or oropharyngeal manipulation. Central lines have been associated with a variety of cardiac arrhythmias, but sinus arrest and asystole have not been previously reported. A 38-year-old woman with multisystem organ failure had several episodes of prolonged sinus arrest, slow junctional escape rhythm, and periods of asystole lasting over 6 seconds. The cause of the repetitive bradyarrhythmia was evaluated by clinical observation including the response to parasympatholytic agents, by detailed analysis of rhythm strips, and review of cardiac imaging studies. The episodes of bradycardia did not coincide with orotracheal manipulation, were not prevented by escalating doses of glycopyrrolate, and were not accompanied by AV conduction disturbance as is frequently seen during a transient increase in vagal tone. Review of the patient's chest X-ray and chest CT revealed that the tip of a peripherally inserted central catheter migrated to the vicinity of the sinoatrial node. Removal of the catheter resulted in prompt resolution of the episodes of sinus arrest. This case demonstrates that migration of a peripherally inserted central catheter to the sinoatrial node can provoke prolonged sinus bradycardia, sinus arrest and asystole.

Pancreaticopleural Fistula Complicating Alcoholic Pancreatitis and Pancreas Divisum Successfully Treated with Minor Papillotomy and Stent Placement

Introduction: Pancreaticopleural fistulas are an infrequent complication of pancreatitis, pancreatic trauma, or pancreatic surgery. Seventy to ninety percent of pancreaticopleural fistulas occur as sequelae to chronic pancreatitis. Several case reports have described the success of ERCP in the management of pancreaticopleural fistulas. However, we are aware of only two case reports of pancreaticopleural fistulas effectively managed with ERCP in the setting of pancreas divisum or pseudodivisum. We describe a case of pancreaticopleural fistula in the setting of alcohol pancreatitis and pancreas divisum successfully treated with ERCP and dorsal pancreatic duct stenting. Case Presentation: A 31-year-old female with a history of alcoholic pancreatitis presented with a recurrent left-sided pleural effusion and epigastric pain. On admission, her amylase was 819 U/L. Her chest X-ray was remarkable for a large left pleural effusion with lung collapse. The MRCP was notable for pancreas divisum and inflammation of the pancreatic head. Fluid was also seen tracking superiorly and communicating with the left pleural effusion. A left chest tube was placed and 2 liters of fluid was drained. Fluid analysis was notable for an amylase of 24608 IU/L and a lipase of 39595 U/L, confirming the diagnosis of pancreaticopleural fistula. ERCP demonstrated a normal biliary tree. On pancreaticogram, the ventral pancreatic duct was completely disrupted with contrast filling a fistulous tract. The minor papilla was then cannulated and pancreaticogram demonstrated a normal caliber dorsal pancreatic duct communicating with the same fistulous tract at the level of the genu of the pancreas. After minor papillotomy, a 5 Fr x 9 cm pancreatic stent with a single internal flap was placed through the minor papilla and into the dorsal duct bridging the disrupted area. After ERCP, the patient was started on parenteral nutrition and octreotide. The patient's chest X-ray remained clear 6 days after the stent was placed with minimal drainage from the chest tube. The chest tube was then removed and she was discharged home. Parenteral nutrition and octreotide were stopped 2 weeks later. Seven weeks after her ERCP, the pancreatic stent was removed. Pancreaticogram through the minor papilla demonstrated no extravasation of contrast and a healed fistulous tract. Discussion: Pancreaticopleural fistulas are rare with the majority of cases occurring in the setting of chronic pancreatitis. We describe the formation of a pancreaticopleural fistula in the setting alcoholic pancreatitis, pancreas divisum, and disrupted ventral and dorsal ducts. The pancreaticopleural fistula was successfully managed with trans-minor-papillary stenting of the dorsal pancreatic duct.

Bilateral Chylothorax After Severe Vomiting in a Child

In the etiology of chylothorax, traumas and malignancies are the first two leading causes. Today in pediatric patients, the most common cause of chylothorax includes the complications secondary to cardiothoracic operations. Bilateral chylothorax is rarely observed after severe vomiting leading to increase in intrathoracic pressure. In idiopathic chylothorax, bilateral localization is dominant. A 9-year-old girl who presented to our emergency department with the complaints of dyspnea and back pain following severe vomiting received a diagnosis of bilateral chylothorax. The patient was treated with the insertion of a bilateral chest tube, and pleurodesis was performed in left hemithorax. Examination did not reveal a pathology to this condition, except the vomiting observed 2 days previously after the meal.

Recurrence of sarcoidosis: The follow-up of splenic involvement

We report the case of a 53-year-old woman who initially presented with an intermittent dry cough that had lasted for 6 months. An investigation into these symptoms with thoracic computed tomography demonstrated hyperdense shadows in her lungs, and ultrasonography showed the presence of multiple hypoechoic nodules on her spleen. A lung biopsy was then performed, which revealed noncaseating epithelioid cell granulomas. Magnetic resonance imagining of her spleen was performed, which showed low signal intensity. The patient was treated with glucocorticoids, and 3 months later the lesions in her lungs had disappeared completely. Five months later, the lesions in her spleen had disappeared also. However, after 13 months of low-dose prednisone, a miliary pattern was observed on the patient's chest x-ray, although no lesions were observed in her spleen. Laboratory tests demonstrated that her blood level of angiotensin-converting enzyme had increased to 96 IU/L. The dose of prednisone was then increased, and the lung images returned to normal after 2 months of therapy.

CSF hydrothorax after ventriculoperitoneal shunt without catheter migration: a case report

Ventriculoperitoneal (VP) shunting is the most common procedure performed for the management of hydrocephalus. VP shunt related complications remain a persistent problem in current clinical practice. Five-year-old female patient was admitted to our hospital with persistent dyspnea complaint. The patient was operated at the age of 3 months and a VP shunt established in a different clinic due to hydrocephalus associated with Dandy-Walker malformation. The patient's chest X-ray revealed right sided pleural effusion. Thorasentesis was performed and the effusion was drained with a chest tube. The discharged liquid was consistent with CSF. Scintigraphic radionuclide shunt analyses were performed and CSF passage from abdomen to chest and lower mediastinal region was determined in the late static images. The patient was operated and the incorporated ventriculoperitoneal shunt was removed. Hydrothorax was completely resolved after early postoperative stage. CSF hydrothorax especially without catheter migration is an unusual but potentially serious-clinical complication.

디지털 흉부영상에서 주성분분석을 이용한 폐암인식

흉부의 폐질환으로 폐암발생은 꾸준히 증가하고 있다. 일차적인 폐암진단 방법에는 흉부X선영상이다. 흉부X선영상 이용하여 폐암진단을 하기 위해서는 임상경험이 풍부한 의사가 필요하다. 그러나 풍부한 경험을 가진 의사라도 오진이 발생할 수 있고 이한 폐암의 조기진단과 생존률을 낮게 한다. 본 논문에서는 주성분분석을 이용하여 학습영상의 데이터베이스와 질병이 있는 흉부영상을 진단함으로써 컴퓨터보조진단의 기반을 마련하고자 한다. 이를 의사가 진단하기 전의 예비판독의 단계로 이용한다면 오진으로 인한 환자의 조기 진단률의 감소를 줄일 수가 있다. 실험은 정상흉부X선영상과 악성폐암인 기관지암(Bronchogenic Carcinoma)과 양성종양인 육아종(Granuloma)으로 실험하였다. 영상은 주성분분석 후 정상영상과 질환 영상의 고유영상을 추출하고 상호 비교한 뒤 인식효율을 비교하였다. 결과로는 정상영상과 질환영상간의 인식률은 높았으나 질환간의 인식효율은 정상에 비해 다소 떨어지는 것으로 나타났다. 흉부질환간의 인식효율을 높이기 위해서 관련 알고리즘에 관한 연구가 계속 이어진다면 컴퓨터보조진단에 좋은 연구기반이 되리라 생각한다. Risk of lung cancer among lung-related diseases has gradually increased during last decades. The chest digital radiography is the primary diagnosis method for lung cancer. Diagnosing lung cancer using this method requires doctors of ripe experience. Despite their experience there are often wrong diagnoses, which decrease early diagnosis and survival rates of patients. The aim of this study was intended to establish the base on the Computer Aided Diagnosis (CAD) by analyzing Image Recognition Algorithm using Principle component Analysis (PCA) and diagnosing patient's chest X-ray image. The database obtained through this approach enables a doctor to significantly reduce misdiagnosis during the early diagnosis stage, if he or she utilizes it as the preliminary reading step. Case studies were carried out using normal organ, and organs suffering from bronchogenic carcinoma and granuloma. A normal image and unique disease images were extracted after PCA analysis, and their cross-recognition efficiency were compared each other. The result revealed that the recognition rate was much high between normal and disease images, but relatively low between two disease images. In order to increase the recognition efficiency among chest diseases the related algorithms have to be developed continuously in the future study, and such effort will establish the resolute base for CAD.

Case Study: Asphyxia Caused by Inspissated Oral and Nasopharyngeal Secretions

Case Study: Asphyxia Caused by Inspissated Oral and Nasopharyngeal Secretions

Digital Afterthoughts on Single Sign-on Technology

Digital Afterthoughts on Single Sign-on Technology