This study presents methodology for objectively quantifying the pulmonary region affected by emphysemic and fibrotic sequelae in treated patients with paracoccidioidomycosis. This methodology may also be applied to any other disease that results in these sequelae in the lungs.Pulmonary high-resolution computed tomography examinations of 30 treated paracoccidioidomycosis patients were used in the study. The distribution of voxel attenuation coefficients was analyzed to determine the percentage of lung volume that consisted of emphysemic, fibrotic, and normal tissue. Algorithm outputs were compared with subjective evaluations by radiologists using a scale that is currently used for clinical diagnosis.Affected regions in the patient images were determined by computational analysis and compared with estimates by radiologists, revealing mean (± standard deviation) differences in the scores for fibrotic and emphysemic regions of 0.1% ± 1.2% and −0.2% ± 1.0%, respectively.The computational results showed a strong correlation with the radiologist estimates, but the computation results were more reproducible, objective, and reliable.

The purpose of this work was to develop a quantitative method for evaluating the pulmonary inflammatory process (PIP) through the computational analysis of chest radiography exams in posteroanterior (PA) and lateral views. The quantification procedure was applied to patients with tuberculosis (TB) as the motivating application.A study of high-resolution computed tomography (HRCT) examinations of patients with TB was developed to establish a relation between the inflammatory process and the signal difference-to-noise ratio (SDNR) measured in the PA projection. A phantom essay was used to validate this relation, which was implemented using an algorithm that is able to estimate the volume of the inflammatory region based solely on SDNR values in the chest radiographs of patients.The PIP volumes that were quantified for 30 patients with TB were used for comparisons with direct HRCT analysis for the same patient. The Bland–Altman statistical analyses showed no significant differences between the 2 quantification methods. The linear regression line had a correlation coefficient of R2 = 0.97 and P < 0.001, showing a strong association between the volume that was determined by our evaluation method and the results obtained by direct HRCT scan analysis.Since the diagnosis and follow-up of patients with TB is commonly performed using X-rays exams, the method developed herein can be considered an adequate tool for quantifying the PIP with a lower patient radiation dose and lower institutional cost. Although we used patients with TB for the application of the method, this method may be used for other pulmonary diseases characterized by a PIP.

It is estimated that multiple sclerosis (MS) affects 35,000 Brazilians and 2.5 million individuals worldwide. Many studies have suggested a possible role of metallic elements in the etiology of MS, but their concentration in the blood of MS patients is nonetheless little investigated in Brazil. In this work, these elements were studied through inductively coupled plasma Mass Spectrometry (icp-MS), whose analysis provides a tool to quantify the concentrations of metal elements in the blood samples of individuals with neurodegenerative disorders. This study aimed to compare the concentration of metallic elements in blood samples from patients with MS and healthy individuals. Blood was collected from 30 patients with multiple sclerosis and compared with the control group. Blood samples were digested in closed vessels using a microwave and ICP-MS was used to determine the concentrations of 12 metallic elements (

Background: Background: Tuberculosis (TB) is an infectious lung disease with high worldwide incidence that severely compromises the quality of life in affected individuals. Clinical tests are currently employed to monitor pulmonary status and treatment progression. The present study aimed to apply a three-dimensional (3D) reconstruction method based on chest radiography to quantify lung-involvement volume of TB acute-phase patients before and after treatment. In addition, these results were compared with indices from conventional clinical exams to show the coincidence level. Methods: A 3D lung reconstruction method using patient chest radiography was applied to quantify lung-involvement volume using retrospective examinations of 50 patients who were diagnosed with pulmonary TB and treated with two different drugs schemes. Twenty-five patients were treated with Scheme I (rifampicin, isoniazid, and pyrazinamide), whereas twenty-five patients were treated with Scheme II (rifampicin, isoniazid, pyrazinamide, and ethambutol). Acute-phase reaction: Serum exams included C-reactive protein levels, erythrocyte sedimentation rate, and albumin levels. Pulmonary function was tested posttreatment. Results: We found strong agreement between lung involvement and serum indices pre- and posttreatment. Comparison of the functional severity degree with lung involvement based on 3D image quantification for both treatment schemes found a high correlation. Conclusions: The present 3D reconstruction method produced a satisfactory agreement with the acute-phase reaction, most notably a higher significance level with the C-reactive protein. We also found a quite reasonable coincidence between the 3D reconstruction method and the degree of functional lung impairment posttreatment. The performance of the quantification method was satisfactory when comparing the two treatment schemes. Thus, the 3D reconstruction quantification method may be useful tools for monitoring TB treatment. The association with serum indices are not only inexpensive and sensitive but also may be incorporated into the assessment of patients during TB treatment.

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Background: Multiple sclerosis (MS) is a neurologic disease of the central nervous system which affects almost three million people worldwide. MS is characterized by a demyelination process that leads to brain lesions, allowing these affected areas to be visualized with magnetic resonance imaging (MRI). Deep learning techniques, especially computational algorithms based on convolutional neural networks (CNNs), have become a frequently used algorithm that performs feature self-learning and enables segmentation of structures in the image useful for quantitative analysis of MRIs, including quantitative analysis of MS. To obtain quantitative information about lesion volume, it is important to perform proper image preprocessing and accurate segmentation. Therefore, we propose a method for volumetric quantification of lesions on MRIs of MS patients using automatic segmentation of the brain and lesions by two CNNs. Methods: We used CNNs at two different moments: the first to perform brain extraction, and the second for lesion segmentation. This study includes four independent MRI datasets: one for training the brain segmentation models, two for training the lesion segmentation model, and one for testing. Results: The proposed brain detection architecture using binary cross-entropy as the loss function achieved a 0.9786 Dice coefficient, 0.9969 accuracy, 0.9851 precision, 0.9851 sensitivity, and 0.9985 specificity. In the second proposed framework for brain lesion segmentation, we obtained a 0.8893 Dice coefficient, 0.9996 accuracy, 0.9376 precision, 0.8609 sensitivity, and 0.9999 specificity. After quantifying the lesion volume of all patients from the test group using our proposed method, we obtained a mean value of 17,582 mm3. Conclusions: We concluded that the proposed algorithm achieved accurate lesion detection and segmentation with reproducibility corresponding to state-of-the-art software tools and manual segmentation. We believe that this quantification method can add value to treatment monitoring and routine clinical evaluation of MS patients.

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