Hybridizing Convolutional Neural Network for Classification of Lung diseases

Pulmonary disease is widespread worldwide. There is persistent blockage of the lungs, pneumonia, asthma, TB, etc. It is essential to diagnose the lungs promptly. For this reason, machine learning models were developed. For lung disease prediction, many deep learning technologies, including the CNN, and the capsule network, are used. The fundamental CNN has low rotating, inclined, or other irregular image orientation efficiency. Therefore by integrating the space transformer network (STN) with CNN, we propose a new hybrid deep learning architecture named STNCNN. The new model is implemented on the dataset from the Kaggle repository for an NIH chest X-ray image. STNCNN has an accuracy of 69% in respect of the entire dataset, while the accuracy values of vanilla grey, vanilla RGB, hybrid CNN are 67.8%, 69.5%, and 63.8%, respectively. When the sample data set is applied, STNCNN takes much less time to train at the cost of a slightly less reliable validation. Therefore both specialists and physicians are simplified by the proposed STNCNN System for the diagnosis of lung disease.

Low Circulating Monocytes Is in Parallel With Lymphopenia Which Predicts Poor Outcome in Anti-melanoma Differentiation-Associated Gene 5 Antibody-Positive Dermatomyositis-Associated Interstitial Lung Disease

Anti-melanoma differentiation-associated gene 5 (MDA5) antibody-positive dermatomyositis (DM)-associated interstitial lung disease (ILD) may progress rapidly and lead to high mortality within 6 or 12 months. Except for reported prognostic factors, simple but powerful prognostic biomarkers are still in need in practice. In this study, we focused on circulating monocyte and lymphocyte counts and their variation tendency in the early stage of ILD. A total of 351 patients from two inception anti-MDA5 antibody-positive cohorts were included in this study, with various treatment choices. Lymphocyte count remained lower in the first month after admission in the non-survivor patients. Although baseline monocyte count showed no significant differences, average monocyte count in the following 4 weeks was also lower in the non-survivor group. Based on the C-index and analysis by the “survminer” R package in the discovery cohort, we chose 0.24 × 109/L as the cutoff value for Mono W0-2, 0.61 × 109/L as the cutoff value for lymph W0-2, and 0.78 × 109/L as the cutoff value for peripheral blood mononuclear cell (PBMC) W0-2, to predict the 6-month all-cause mortality. The Kaplan–Meier survival curves and adjusted hazard ratio with age, gender, and the number of immunosuppressants used all validated that patients with lower average monocyte count, lower average lymphocyte count, or lower average PBMC count in the first 2 weeks after admission had higher 6-month death risk, no matter in the validation cohort or in the pooled data. Furthermore, flow cytometry figured out that non-classical monocytes in patients with anti-MDA5 antibody-positive DM were significantly lower than healthy controls and patients with DM without anti-MDA5 antibodies. In conclusion, this study elucidated the predictive value of monocyte and lymphocyte counts in the early stage and may help rheumatologists to understand the possible pathogenesis of this challenging disease.

Diagnosis, Clinical Features and Management of Interstitial Lung Diseases in Rheumatic Disorders: Still a Long Journey

Interstitial lung disease (ILD) is one of the most frequent pulmonary complications of autoimmune rheumatic diseases (ARDs), and it is mainly associated with connective tissue diseases (CTDs) and rheumatoid arthritis (RA) [...]

Subgroup analysis reveals higher reliability of the new comprehensive evaluation of Global Initiative for Chronic Obstructive Lung Disease 2019

To estimate the severity of the disease in outpatients with chronic obstructive pulmonary disease (COPD) in Hunan Province, China and use the subgroup analysis to evaluate the reliability of the new comprehensive evaluation of Global Initiative for Chronic Obstructive Lung Disease (GOLD). COPD outpatients from 12 medical centers in Hunan Province, China were stratified into groups A–D, and group D patients were further stratified into subgroups D1–D3 according to the GOLD 2016 and 2019 comprehensive assessment. Demography, clinical characteristics and medications were compared among groups. In 1017 COPD outpatients, the distribution from group A to D and subgroup D1 to D3 was 41 (4.0%), 249 (24.5%), 17 (1.7%), 710 (69.8%) and 214 (30.2%), 204 (28.7%), 292 (41.1%), according to GOLD 2016. In terms of demographic and clinical characteristics related to A–D groups, there was a significant difference in COPD assessment test (CAT), modified Medical British Research Council (mMRC), the clinical COPD questionnaire(CCQ), age, BMI, education level, smoking history, comorbidities, the course of chronic bronchitis/emphysema, number of exacerbations/hospitalisations in the previous year, treatment protocols, forced expiratory volume in one second (FEV1) % predicted, and FEV1/forced vital capacity (FVC) (p < 0.01). Furthermore, some patients in groups C–D regrouped to groups A–B were all C1 and D1 subgroups according to GOLD 2019. Comparing subgroup D1 with group B, subgroup D2 and subgroup D3, it was found that the demography, clinical characteristics and medications of subgroup D1 were the closest to group B, according to GOLD 2016 (p < 0.01). The disease severity of outpatients with COPD in Hunan Province was more pronounced in group B and D and patients in groups A–D had different demography, clinical characteristics and medications. Subgroup analysis can explain to a certain extent that GOLD2019’s new comprehensive assessment is more reliable than GOLD 2016.