Relation between the Lung Capacity, Oximetric Rebreath Test and Lung Field Area as Measured on the Chest X-ray Films in Three Phases of Respiration in Normal Subjects, the Aged and Patients with Chronic Pulmonary Emphysema

1964 ◽  
Vol 28 (9) ◽  
pp. 685-692
Author(s):  
SHIGEKI WATANABE
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Kosaku Komiya ◽  
Ryosuke Hamanaka ◽  
Hisayuki Shuto ◽  
Hiroki Yoshikawa ◽  
Atsushi Yokoyama ◽  
...  

Abstract Background Re-expansion pulmonary edema is an uncommon complication following drainage of a pneumothorax or pleural effusion. While pneumothorax is noted to complicate COVID-19 patients, no case of COVID-19 developing re-expansion pulmonary edema has been reported. Case representation A man in his early 40 s without a smoking history and underlying pulmonary diseases suddenly complained of left chest pain with dyspnea 1 day after being diagnosed with COVID-19. Chest X-ray revealed pneumothorax in the left lung field, and a chest tube was inserted into the intrathoracic space without negative pressure 9 h after the onset of chest pain, resulting in the disappearance of respiratory symptoms; however, 2 h thereafter, dyspnea recurred with lower oxygenation status. Chest X-ray revealed improvement of collapse but extensive infiltration in the expanded lung. Therefore, the patient was diagnosed with re-expansion pulmonary edema, and his dyspnea and oxygenation status gradually improved without any intervention, such as steroid administration. Abnormal lung images also gradually improved within several days. Conclusions This case highlights the rare presentation of re-expansion pulmonary edema following pneumothorax drainage in a patient with COVID-19, which recovered without requiring treatment for viral pneumonia. Differentiating re-expansion pulmonary edema from viral pneumonia is crucial to prevent unnecessary medication for COVID-19 pneumonia and pneumothorax.


Author(s):  
Muntasir Al-Asfoor

Abstract During the times of pandemics, faster diagnosis plays a key role in the response efforts to contain the disease as well as reducing its spread. Computer-aided detection would save time and increase the quality of diagnosis in comparison with manual human diagnosis. Artificial Intelligence (AI) through deep learning is considered as a reliable method to design such systems. In this research paper, an AI based diagnosis approach has been suggested to tackle the COVID-19 pandemic. The proposed system employs a deep learning algorithm on chest x-ray images to detect the infected subjects. An enhanced Convolutional Neural Network (CNN) architecture has been designed with 22 layers which is then trained over a chest x-ray dataset. More after, a classification component has been introduced to classify the x-ray images into two categories (Covid-19 and not Covid-19) of infection. The system has been evaluated through a series of observations and experimentation. The experimental results have shown a promising performance in terms of accuracy. The system has diagnosed Covid-19 with accuracy of 95.7% and normal subjects with accuracy of 93.1 while it showed 96.7 accuracy on Pneumonia.


2009 ◽  
Vol 17 (1-2) ◽  
pp. 29-31 ◽  
Author(s):  
Aleksandra Karapandzic ◽  
Milana Panjkovic ◽  
Zivka Eri ◽  
Istvan Klem ◽  
Nevena Djukic

Minute pulmonary meningothelial-like nodules (MPMNs) are relatively rare lesions that located at the pleura or parenchyma of the lung. They are usually found incidentally at autopsy or in surgical specimens. We presented a case of asymptomatic 47-year-old woman with an abnormal shadow in the right upper lung field found by a routine chest X ray. A computed tomography (CT) scan of the thorax revealed a hyperdense subpleural mass, which histologically conformed to adenocarcinoma. A resection of the right upper lobe discovered preponderance of small multiple lesions under the pleura. Microscopically, they were an interstitial nodular proliferation of oval or spindle-shape cells arranged in a zellenballen nesting pattern near small veins. Immunohistochemical and cytological analyses confirmed the diagnosis of MPMNs. Coexistence of multiple MPMNs and lung adenocarcinoma can be a differential diagnostic problem due to suspected metastasis of the primary carcinoma. To obtain an accurate diagnosis, the clinical findings should completely conform to histological, immunohistochemical, and cytological ones.


1986 ◽  
Vol 31 (2) ◽  
pp. 99-102 ◽  
Author(s):  
B. Teklu ◽  
W.M. Gray ◽  
R.J. Mills ◽  
F. Moran

We have demonstrated that the total lung capacities of normal Caucasian adults, can be satisfactorily estimated by means of a regression equation derived from a single set of three measurements taken from a specially exposed plain X-ray film of the chest. However, these equations differ from those found by workers who have studied patients with lung disease. It is concluded from this that different regression equations are to be expected for normal and diseased lungs, and that, the appropriate equation will depend not only on the presence of disease, but also on its type and severity. It is also concluded that the rapid radiographic method of estimating total lung capacity can make little contribution to the diagnostic assessment of lung disease. However, the regression equations appropriate to particular ethnic groups could be used to measure TLC in normal subjects rapidly and inexpensively in places where specialised respiratory equipment and trained personnel are lacking.


2020 ◽  
Vol 7 (1A) ◽  
pp. 189-194
Author(s):  
Bambang Satoto ◽  
Maya Nuriya Widyasari ◽  
Apriansah Apriansah

Pendahuluan SARS-CoV-2 merupakan virus RNA yang terutama menginfeksi sel-sel pada saluran napas pelapis alveoli. Virus SARS-CoV-2 yang terhirup mengikat sel epitel di rongga hidung dan mulai bereplikasi. Virus ini menyebar serta bermigrasi ke saluran pernapasan, memicu respons imun bawaan dan pada akhirnya berkembang menjadi Acute Respiratory Distress Syndrome (ARDS). Gambaran ground glass infiltrates dapat terdeteksi pada pencitraan toraks. Pemeriksaan X-ray toraks dan MSCT toraks memegang peranan penting dalam deteksi dan follow up COVID-19. Metode dan Bahan Laporan kasus 2 pasien laki-laki yang terkonfirmasi COVID-19 umur 43 tahun dan 48 tahun dengan keluhan utama sesak napas, batuk dan demam. Pasien pertama mempunyai riwayat perjalanan ke Amerika Serikat 3 minggu sebelum masuk rumah sakit, sedangkan pasien kedua mempunyai riwayat kontak dengan pasien terkonfirmasi COVID-19. Pada pemeriksaan X-ray toraks kedua pasien menunjukkan gambaran konsolidasi disertai air bronchogram pada lapangan paru bilateral yang tampak dominan pada perifer. Berdasarkan pedoman Severe Acute Respiratory Syndrome (SARS) terdahulu, evaluasi dapat dilakukan 2 bulan dan 6 bulan setelah terinfeksi. Dua bulan setelah terinfeksi COVID-19 dilakukan pemeriksaan HRCT toraks dengan hasil normal. Kesimpulan Lesi berupa konsolidasi disertai air bronchogram dengan distribusi yang dominan pada perifer merupakan gambaran radiologis yang khas pada pasien Covid-19 seperti yang ditemukan pada kedua kasus yang dipaparkan dalam artikel ini. Evaluasi sequele dengan pemeriksaan HRCT yang dilakukan 2 bulan pasca penyembuhan menunjukkan gambaran paru paru yang normal, tidak ada infiltrat maupun fibrosis pada kedua pasien tersebut. Kata kunci X-ray toraks, konsolidasi, air bronchogram, COVID-19   Introduction SARS-CoV-2 is an RNA virus that mainly infects cells in the alveoli lining airways. The inhaled virus binds to epithelial cells in the nasal cavity then begins to replicate. This virus spreads, migrates to the respiratory tract, triggering an innate immune response, and develop to Acute Respiratory Syndrome. The ground-glass opacities can be detected in thoracic imaging eventually. Chest X-ray and CT-scan have an important role in the detection and follow-up of COVID-19. Materials and Methods The case report of 2 male patients confirmed COVID-19 aged 43 years and 48 years with major complaints of shortness of breath, coughing, and fever. The first patient had a history of raveling to the United States 3 weeks before hospitalization, while the second patient had a history of contact with a confirmed COVID-19 patient. On chest X-ray examination, both patients showed multiple consolidation with air bronchogram in bilateral lung field which appeared dominant in the periphery. According to the previous Severe Acute Respiratory Syndrome (SARS) guideline, evaluation for patients can be done in two months and six months after firstly infected. Two months after COVID-19 infection, a chest HRCT examination was performed with normal results. Conclusion Consolidation with air bronchogram which dominantly seen in peripheral distribution is a typical radiological picture in COVID-19 patients as found in two cases described in this article. Sequelae evaluation with chest HRCT conducted 2 months after healing showed normal lung appearance with no sign of infiltrates or fibrosis seen in both patients. Keywords:  Chest X-ray, consolidation, air bronchogram, COVID-19


Author(s):  
Rashid S. Al Umairi ◽  
Ishaq Al Salmi ◽  
Jokha Al Kalbani ◽  
Atheel Kamona ◽  
Saqar Al Tai ◽  
...  

Objectives: The aim of this study is to assess the correlation between the severity of the initial chest x-ray abnormalities in patients with confirmed diagnosis of coronavirus disease 2019 (COVID-19) and the final outcomes. Methods: Retrospectively, we identified serial chest radiographs of 64 patients (57 men, 7 women, with mean age of 50 years) admitted to the Royal Hospital between March 15, 2020 and May 30, 2020 with confirmed diagnosis of COVID-19. The chest radiographs were examined for presence, extent, distribution and progression pattern of radiological abnormalities. Each lung field was divided into 3 zones on each CXR and a score was allocated for each zone. The scores (0 [normal], 1 [mild] to 4 [severe]) for all six zones per chest radiographic examination were summed to provide a cumulative chest radiographic score (range, 0–24). Results: The initial CXR was abnormal in 60 patients (93.8%). The most common finding was ground glass opacity (58/64, 90.6%), followed by consolidation (50/64, 78.1%). The majority of the patients had bilateral (51/64, 85%), multifocal (57/64 95%) combined central and peripheral (36/64, 60%) lung abnormalities. The median score of initial CXR for deceased patients was significantly higher than those who recovered (17 vs 11 respectively; P = 0.009). Five CXR evolution patterns were identified: type I (initial radiograph deteriorates then improves), type II (fluctuate), type III (static), type IV (progressive deterioration) and type V (progressive improvement). Conclusion: Higher baseline chest radiograph score is associated with higher mortality rate and poor prognosis in those with COVID-19 pneumonia. Keywords: SARS-CoV-2; COVID-19; Chest X-ray; Scoring System; Pneumonia; Prognosis; Outcome; Severity; Consolidation; Ground-glass.


2021 ◽  
Author(s):  
Wufeng Liu ◽  
Jiaxin Luo ◽  
Yan Yang ◽  
Wenlian Wang ◽  
Junkui Deng ◽  
...  

Abstract Automatic and highly accurate lung segmentation in chest X-ray (CXR) images is the basis of computer-aided diagnosis systems, because the lung is the region of interest of many diseases, and it can show useful information through its contours. However, automatic lung segmentation is immensely challenging due to extreme variations in the shape, obscure lung area, or opacity caused by lung diseases reaches high-intensity values. In the face of these severe situations, the model may segment the lung boundary incorrectly. We designed an improved U-Net network: using the pre-training Efficientnet-b4 as the encoder, and the residual block and LeakyRelu activation function are used in the decoder. The network can not only extract features with high efficiency but also avoid the gradient explosion caused by the multiplication effect in gradient backpropagation. We constructed a CXR lung field segmentation dataset (Haut) based on the NIH CXR dataset. In particular, this lung segmentation dataset contains some serious abnormal cases, such as lung deformation, pleural effusion, covered by foreign matters, or CXR blur caused by severe lung disease. The improved U-Net is evaluated on Haut, JSRT, and Montgomery County (MC) datasets. Experimental results show that our network can achieve high-precision lung segmentation.


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