scholarly journals Differential X-ray diagnosis of pseudotuberculous scenario of pulmonary abscess with tuberculous cavities

2021 ◽  
Vol 29 (1) ◽  
pp. 9-20
Author(s):  
R. Yu. Churylin ◽  
I. O. Voronzhev ◽  
Yu. A. Kolomiichenko ◽  
О. О. Коvalova ◽  
V. V. Syrota
Keyword(s):  
Computed Tomography ◽  
Differential Diagnosis ◽  
Thoracic Surgery ◽  
Lung Abscess ◽  
Accurate Diagnosis ◽  
Tuberculosis Patients ◽  
X Ray ◽  
Pulmonary Abscess ◽  
Over Time

Background. Recent decades in Ukraine have been characterized by a significant increase in the number of tuberculosis patients, often with forming cavities of destruction. X-ray diagnosis of lung cavitary lesions is one of the current issues of modern pulmonology and thoracic surgery. Pulmonary abscesses resemble other diseases with destruction and cavities substantiating the need for differential diagnosis with tuberculosis. Purpose – specifying particular scenarios of X-ray presentation of lung abscess and determining the capability of differential diagnosis of pseudotuberculosis with cavities of tuberculosis etiology. Materials and methods. The paper deals with the analysis of X-ray examination of thoracic viscera provided for 252 patients with lung abscess, aged 18 and up to 78. X-ray radiography in two projections, linear and computed tomography (56 patients involved) were performed. All patients underwent a study over time. Results. Almost in most lung abscess cases, there is a need for differential diagnosis with a range of medical entities. The obtained data have made it possible to suggest a classification of X-ray scenarios of lung abscess. The scenarios of X-ray presentation of acute pulmonary abscess are typical and atypical, among those: cystoid, pseudotuberculous, affected 38 patients (15 %), and pulmonary-pleural. The peculiarities of X-ray presentation of pseudotuberculous scenario along with the differences and signs allowing to make an accurate diagnosis have been specified. Conclusions. X-ray study remains an essential in diagnosing purulent-destructive diseases. Being familiar with the scenarios mentioned above and pseudotuberculous one, in particular, will make it possible to significantly improve diagnosis as well as differential diagnosis of pulmonary abscess.

Differential diagnosis of hemoptysis

2021 ◽  
pp. 47-55
Author(s):  
Petr Arkadievich Ilyin
Keyword(s):  
Infectious Disease ◽  
Computed Tomography ◽  
Lung Cancer ◽  
Differential Diagnosis ◽  
Correct Interpretation ◽  
Diagnostic Investigation ◽  
Detailed History ◽  
X Ray ◽  
Chest X Ray ◽  
Upper Gastrointestinal

Blood expectoration or hemoptysis is the coughing up of sputum with blood from the larynx, bronchi or lungs. Hemoptysis is most often caused by diseases of the respiratory tract and lungs — bronchitis or pneumonia, as well as lung cancer, aspergilloma, tuberculosis, bronchiectasis, pulmonary embolism, etc. In the diagnostic investigation of the cause of hemoptysis, it is important to take a detailed history (in the case of an epidemiological history, a laboratory analysis of the secreted sputum for the detection of the causative agent of an infectious disease is necessary), to make the correct interpretation of the patient’s complaints and an assessment of the nature of the sputum (differential diagnosis with bleeding from the upper gastrointestinal tract). A chest X-ray is performed and, then, if indicated, computed tomography, bronchoscopy, and other studies are made. The article presents an algorithm for differential diagnostic investigation of hemoptysis in a patient

scholarly journals 4D Structural Root Architecture Modeling From Digital Twins By X-Ray Computed Tomography

2021 ◽  
Author(s):  
Monica Herrero ◽  
Valerian Meline ◽  
Anjali S. Iyer-Pascuzzi ◽  
Augusto M. Souza ◽  
Mitchell R. Tuinstra ◽  
...  
Keyword(s):  
Computed Tomography ◽  
High Throughput ◽  
Lateral Roots ◽  
Skeletal Structure ◽  
Coefficient Of Determination ◽  
Plant Phenotyping ◽  
X Ray ◽  
Digital Twin ◽  
Three Samples ◽  
Over Time

Abstract BackgroundBreakthrough imaging technologies are a potential solution to address the plant phenotyping bottleneck regarding marker-assisted breeding and genetic mapping. X-Ray CT (computed tomography) technology is able to acquire the digital twin of root system architecture (RSA) but computational methods to quantify RSA traits and analyze their changes over time are limited. RSA traits extremely affect agricultural productivity. We develop a spatial-temporal root architectural modeling method based on 4D data from X-ray CT. This novel approach is optimized for high-throughput phenotyping considering the cost-effective time to process the data and the accuracy and robustness of the results. Significant root architectural traits, including root elongation rate, number, length, growth angle, height, diameter, branching map, and volume of axial and lateral roots are extracted from the model based on the digital twin. Our pipeline is divided into two major steps: (i) first, we compute the curve-skeleton based on a constrained Laplacian smoothing algorithm. This skeletal structure determines the registration of the roots over time; (ii) subsequently, the RSA is robustly modeled by a cylindrical fitting. The experiment was carried out at the Ag Alumni Seed Phenotyping Facility (AAPF) from Purdue University in West Lafayette (IN, USA). ResultsRoots from three samples of tomato plants at two different times and three samples of corn plants at three different times were scanned. Regarding the first step, the PCA analysis of the skeleton is able to accurately and robustly register temporal roots. From the second step, the volume from the cylindrical model was compared against the root digital twin, reaching a coefficient of determination (R2) of 0.84 and a P < 0.001. ConclusionsThe results confirm the feasibility of the proposed methodology, providing scalability to a comprehensive analysis to high throughput root phenotyping.

scholarly journals A Hybrid Convolutional Neural Network-Support Vector Machine for X-ray Computed Tomography Images on CancerA Hybrid Convolutional Neural Network-Support Vector Machine for X-ray Computed Tomography Images on Cancer

2021 ◽  
Vol 9 (B) ◽  
pp. 1283-1289
Author(s):  
Jane Aurelia ◽  
Zuherman Rustam
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Computed Tomography ◽  
Support Vector Machine ◽  
Support Vector ◽  
X Ray ◽  
Network Support ◽  
Computed Tomography Images ◽  
X Ray Computed

BACKGROUND: Cancer is a major health problem not only in Indonesia but also throughout the world. Cancer is the growth and spread of abnormal cells that have distinctive characteristics, that if can no longer be controlled will usually cause death. The number of deaths due to cancer is generally caused by late diagnosis and inappropriate treatment. To reduce mortality from cancer, it is necessary to strive for early detection and monitoring of cancer in patients undergoing therapy. Convolutional neural networks (CNNs) as one of machine learning methods are designed to produce or process data from two dimensions that have a network tier and many applications carried out in the image. Moreover, support vector machines (SVMs) as a hypothetical space in the form of linear functions feature have high dimensions and trained algorithm based on optimization theory. AIM: In connection with the above, this paper discusses the role of the machine learning technique named a hybrid CNN-SVM. METHODS: The proposed method is used in the detection and monitoring of cancers by determining the classification of cancers in X-ray computed tomography (CT) patients’ images. Several types of cancer that used for determination in detection and monitoring of cancers diagnosis are also discussed in this paper, such as lung, liver, and breast cancer. RESULTS: From the discussion, the results show that the combining model of hybrid CNN-SVM has the best performance with 99.17% accuracy value. CONCLUSION: Therefore, it can be concluded that machine learning plays a very important role in the detection and management of cancer treatment through the determination of classification of cancers in X-ray CT patients’ images. As the proposed method can detect cancer cells with an effective mechanism of action so can has the potential to inhibit in the future studies with more extensive data materials and various diseases.

scholarly journals 4D Structural root architecture modeling from digital twins by X-Ray Computed Tomography

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Monica Herrero-Huerta ◽  
Valerian Meline ◽  
Anjali S. Iyer-Pascuzzi ◽  
Augusto M. Souza ◽  
Mitchell R. Tuinstra ◽  
...  
Keyword(s):  
Computed Tomography ◽  
High Throughput ◽  
Lateral Roots ◽  
Skeletal Structure ◽  
Coefficient Of Determination ◽  
Plant Phenotyping ◽  
X Ray ◽  
Digital Twin ◽  
Three Samples ◽  
Over Time

Abstract Background Breakthrough imaging technologies may challenge the plant phenotyping bottleneck regarding marker-assisted breeding and genetic mapping. In this context, X-Ray CT (computed tomography) technology can accurately obtain the digital twin of root system architecture (RSA) but computational methods to quantify RSA traits and analyze their changes over time are limited. RSA traits extremely affect agricultural productivity. We develop a spatial–temporal root architectural modeling method based on 4D data from X-ray CT. This novel approach is optimized for high-throughput phenotyping considering the cost-effective time to process the data and the accuracy and robustness of the results. Significant root architectural traits, including root elongation rate, number, length, growth angle, height, diameter, branching map, and volume of axial and lateral roots are extracted from the model based on the digital twin. Our pipeline is divided into two major steps: (i) first, we compute the curve-skeleton based on a constrained Laplacian smoothing algorithm. This skeletal structure determines the registration of the roots over time; (ii) subsequently, the RSA is robustly modeled by a cylindrical fitting to spatially quantify several traits. The experiment was carried out at the Ag Alumni Seed Phenotyping Facility (AAPF) from Purdue University in West Lafayette (IN, USA). Results Roots from three samples of tomato plants at two different times and three samples of corn plants at three different times were scanned. Regarding the first step, the PCA analysis of the skeleton is able to accurately and robustly register temporal roots. From the second step, several traits were computed. Two of them were accurately validated using the root digital twin as a ground truth against the cylindrical model: number of branches (RRMSE better than 9%) and volume, reaching a coefficient of determination (R2) of 0.84 and a P < 0.001. Conclusions The experimental results support the viability of the developed methodology, being able to provide scalability to a comprehensive analysis in order to perform high throughput root phenotyping.

scholarly journals Primary Nasopharyngeal Tuberculosis: A Case Report Focused on Nasopharyngoscopic Features and CT Findings

2020 ◽  
pp. 014556132093195
Author(s):  
Hyun Jin Min ◽  
Kyung Soo Kim
Keyword(s):  
Computed Tomography ◽  
Differential Diagnosis ◽  
Case Report ◽  
Rare Disease ◽  
Pulmonary Disease ◽  
Early Stage ◽  
Accurate Diagnosis ◽  
Tuberculosis Infection ◽  
Disease Entity ◽  
Radiological Findings

Primary nasopharyngeal tuberculosis, defined as an isolated tuberculosis infection of the nasopharynx without systemic or pulmonary disease, is rare, even in areas endemic for tuberculosis. It is challenging for ENT specialists to diagnose primary nasopharyngeal tuberculosis at an early stage. In this report, we describe a new case of primary nasopharyngeal tuberculosis, focusing on its nasopharyngoscopic features and radiological findings that can help the understanding and aid in accurate diagnosis of this unusual disease entity. Our experience suggests that although primary nasopharyngeal tuberculosis is a relatively rare disease, it must be included in the differential diagnosis of various nasopharyngeal lesions, particularly in patients with unusual nasopharyngoscopic and computed tomography findings.

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