METHODS: We investigated the existing body of evidence and applied Preferred Reporting Items for Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to search records in IEEE, Google scholar, and PubMed databases. We identified 65 papers that were published from 2013 to 2022 and these papers cover 67 different studies. The review process was structured according to the medical data that was used for disease detection. We identified six main categories, namely air flow, genetic, imaging, signals, and miscellaneous. For each of these categories, we report both disease detection methods and their performance.
RESULTS: We found that medical imaging was used in 14 of the reviewed studies as data for automated obstructive airway disease detection. Genetics and physiological signals were used in 13 studies. Medical records and air flow were used in 9 and 7 studies, respectively. Most papers were published in 2020 and we found three times more work on Machine Learning (ML) when compared to Deep Learning (DL). Statistical analysis shows that DL techniques achieve higher Accuracy (ACC) when compared to ML. Convolutional Neural Network (CNN) is the most common DL classifier and Support Vector Machine (SVM) is the most widely used ML classifier. During our review, we discovered only two publicly available asthma and COPD datasets. Most studies used private clinical datasets, so data size and data composition are inconsistent.
CONCLUSIONS: Our review results indicate that Artificial Intelligence (AI) can improve both decision quality and efficiency of health professionals during COPD and asthma diagnosis. However, we found several limitations in this review, such as a lack of dataset consistency, a limited dataset and remote monitoring was not sufficiently explored. We appeal to society to accept and trust computer aided airflow obstructive diseases diagnosis and we encourage health professionals to work closely with AI scientists to promote automated detection in clinical practice and hospital settings.
METHODS: A rapid online survey comprising 22 items was administered during the rapid outbreak of COVID-19 in Pakistan. Questions were focused on the prevention, transmission, clinical features, and control of COVID-19. In addition, the attitudes and practices of the participants were explored. Descriptive statistics, Mann-Whitney tests, Kruskal-Wallis tests, and regression analysis were carried out during data analysis.
RESULTS: A total of 1257 respondents participated in this study. Most of the respondents had good knowledge (good = 64.8%, average = 30.5%, poor = 4.7%) of COVID-19. Gender, marital status, education, and residence were observed to have a significant association with the knowledge score. A vast majority of the survey respondents (77.0%) believed that COVID-19 would be controlled successfully in Pakistan. The practices of wearing a mask (85.8%) and handwashing (88.1%) were common among the participants.
CONCLUSION: The participants demonstrated good knowledge and reasonable attitudes and practices toward most aspects of the COVID-19 outbreak. Improvements in certain areas could be made by mass-level education.
RESULTS: We found evidence of genetic influx from Indians to Malays, more in Melayu Kedah and Melayu Kelantan which are genetically different from the other Malay sub-ethnic groups, but similar to Thai Pattani. More than 98% of these northern Malays haplotypes could be found in either Indians or Chinese populations, indicating a highly admixture pattern among populations. Nevertheless, the ancestry lines of Malays, Indonesians and Thais were traced back to have shared a common ancestor with the Proto-Malays and Chinese.
CONCLUSIONS: These results support genetic admixtures in the Peninsular Malaysia Malay populations and provided valuable information on the enigmatic demographical history as well as shed some insights into the origins of the Malays in the Malay Peninsula.
METHODS: A total of 2231 higher vocational students from Shandong Province were surveyed by means of Academic Self-efficacy Questionnaire, Meaning in Life Questionnaire, and Test Anxiety Scale.
RESULTS: There were significant negative correlations among academic self-efficacy, sense of life meaning, and test anxiety. Fear of failure was positively correlated with test anxiety. Sense of life meaning and fear of failure played a mediating role in the relationship between academic self-efficacy and test anxiety. The chain mediating effect was significant only in the female group, not in the male group. In contrast, academic self-efficacy indirectly predicted test anxiety by the independent mediating effect of sense of life meaning or fear of failure in the male group.
CONCLUSION: Academic self-efficacy may influence test anxiety through the independent mediating effect of sense of life meaning, fear of failure, and the chain mediating effect, and there is a gender difference in these effects.
METHODS: In this study, we propose a multi-view secondary input residual (MV-SIR) convolutional neural network model for 3D lung nodule segmentation using the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset of chest computed tomography (CT) images. Lung nodule cubes are prepared from the sample CT images. Further, from the axial, coronal, and sagittal perspectives, multi-view patches are generated with randomly selected voxels in the lung nodule cubes as centers. Our model consists of six submodels, which enable learning of 3D lung nodules sliced into three views of features; each submodel extracts voxel heterogeneity and shape heterogeneity features. We convert the segmentation of 3D lung nodules into voxel classification by inputting the multi-view patches into the model and determine whether the voxel points belong to the nodule. The structure of the secondary input residual submodel comprises a residual block followed by a secondary input module. We integrate the six submodels to classify whether voxel points belong to nodules, and then reconstruct the segmentation image.
RESULTS: The results of tests conducted using our model and comparison with other existing CNN models indicate that the MV-SIR model achieves excellent results in the 3D segmentation of pulmonary nodules, with a Dice coefficient of 0.926 and an average surface distance of 0.072.
CONCLUSION: our MV-SIR model can accurately perform 3D segmentation of lung nodules with the same segmentation accuracy as the U-net model.
METHODS: The main outcome measures were disability-adjusted life-years (DALYs) and mortality (deaths) attributable to high fasting plasma glucose (HFPG), high systolic blood pressure (HSBP), high low-density lipoprotein (HLDL) cholesterol, high body-mass index (HBMI), kidney dysfunction (KDF), and low bone mineral density (LBMD). The average annual percent change (AAPC) between 1990 and 2019 was analyzed using Joinpoint regression.
RESULTS: For all six metabolic risk factors, the rate of DALYs and death increased with age, accelerating for individuals older than 60 and 70 for DALYs and death, respectively. The AAPC value in rate of DALYs and death were higher in male patients than in female patients across 20 age groups. A double-peak pattern was observed for AAPC in the rate of DALYs and death, peaking at age 20-49 and at age 70-95 plus. The age-standardized rate of DALYs increased for HBMI and LBMD, decreased for HFPG, HSBP, KDF, and remained stable for HLDL from 1990 to 2019. In terms of age-standardized rate of DALYs, there was an increasing trend of neoplasms and neurological disorders attributable to HFPG; diabetes and kidney diseases, neurological disorders, sense organ diseases, musculoskeletal disorders, neoplasms, cardiovascular diseases, digestive diseases to HBMI; unintentional injuries to LBMD; and musculoskeletal disorders to KDF. Among 19 countries of Group 20, in 2019, the age-standardized rate of DALYs and death were ranked fourth to sixth for HFPG, HSBP, and HLDL, but ranked 10th to 15th for LBMD, KDF, and HBMI, despite the number of DALYs and death ranked first to second for six metabolic risk factors.
CONCLUSIONS: Population aging continuously accelerates the metabolic risk factor driven disease burden in China. Comprehensive and tight control of metabolic risk factors before 20 and 70 may help to mitigate the increasing disease burden and achieve healthy aging, respectively.