A Novel Explainable Artificial Intelligence Model in Image Classification problem

In recent years, artificial intelligence is increasingly being applied widely in many different fields and has a profound and direct impact on human life. Following this is the need to understand the principles of the model making predictions. Since most of the current high-precision models are black boxes, neither the AI scientist nor the end-user deeply understands what's going on inside these models. Therefore, many algorithms are studied for the purpose of explaining AI models, especially those in the problem of image classification in the field of computer vision such as LIME, CAM, GradCAM. However, these algorithms still have limitations such as LIME's long execution time and CAM's confusing interpretation of concreteness and clarity. Therefore, in this paper, we propose a new method called Segmentation - Class Activation Mapping (SeCAM) that combines the advantages of these algorithms above, while at the same time overcoming their disadvantages. We tested this algorithm with various models, including ResNet50, Inception-v3, VGG16 from ImageNet Large Scale Visual Recognition Challenge (ILSVRC) data set. Outstanding results when the algorithm has met all the requirements for a specific explanation in a remarkably concise time.Comment: Published in the Proceedings of FAIC 202

G-CAME: Gaussian-Class Activation Mapping Explainer for Object Detectors

Nowadays, deep neural networks for object detection in images are very prevalent. However, due to the complexity of these networks, users find it hard to understand why these objects are detected by models. We proposed Gaussian Class Activation Mapping Explainer (G-CAME), which generates a saliency map as the explanation for object detection models. G-CAME can be considered a CAM-based method that uses the activation maps of selected layers combined with the Gaussian kernel to highlight the important regions in the image for the predicted box. Compared with other Region-based methods, G-CAME can transcend time constraints as it takes a very short time to explain an object. We also evaluated our method qualitatively and quantitatively with YOLOX on the MS-COCO 2017 dataset and guided to apply G-CAME into the two-stage Faster-RCNN model.Comment: 10 figure

VIETHERB: a database for Vietnamese herbal species

Vietnam carries a highly diverse practice of traditional medicine in which various combinations of herbs have been widely used as remedies for many types of diseases. Poor hand-written records and current text-based databases, however, perplex the process of conventionalizing and evaluating canonical therapeutic effects. In efforts to reorganize the valuable information, we provide the VIETHERB database (http://vietherb.com.vn/) for herbs documented in Vietnamese traditional medicines. This database is constructed with confidence to provide users with information on herbs and other side information including metabolites, diseases, morphologies, and geographical locations for each individual species. Our data in this release consist of 2,881 species, 10,887 metabolites, 458 geographical locations, and 8,046 therapeutic effects. The numbers of species-metabolite, species-therapeutic effect, species-morphology, and species-distribution binary relationships are 17,602, 2,718, 11,943, and 16,089, respectively. The information on Vietnamese herbal species can be easily accessed or queried using their scientific names. Searching for species sharing side information can be simply done by clicking on the data. The database primarily serves as an open source facilitating users in studies of modernizing traditional medicine, computer-aided drug design, conservation of endangered plants, and other relevant experimental sciences

High Prevalence of Infection Among School-Aged Children in Ho Chi Minh City, VietNam.

OBJECTIVES: There is no study on Helicobacter pylori (H. pylori) infection in pupils of Ho Chi Minh city (HCMC), the most overcrowded city in Vietnam. Therefore, the aim of this study was to estimate the prevalence of H. pylori and its geographical spread among school-aged children. METHODS: A school-based cross-sectional study was conducted among 1854 pupils across 24 districts of HCMC in 2019. Multiple-stage sampling method was used to enroll pupils. We built a four-points index for geographical division based on population density and employees density to evaluate the link between H. pylori and crowded level. Stool samples were analyzed by monoclonal enzyme-immunoassay stool antigen-test to assess the infection status. Logistic regression was performed to assess possible factors related to H. pylori infection. RESULTS: The overall prevalence of H. pylori was 87.7%. There was a linear increasing trend in the infection rate (p < 0.001) across the 4-points index of HCMC and this trend maintained within both age and gender subgroups (p = 0.02). CONCLUSION: Prevalence of H. pylori was high and it increased with population density or employees density. Therefore, it is crucial to plan and implement the reduction of H. pylori infection programs by targeting the highly concentrated population areas of HCMC

Effectiveness and Safety of Glucosamine in Osteoarthritis: A Systematic Review

Knee osteoarthritis is the most popular type of osteoarthritis that causes extreme pain in the elderly. Currently, there is no cure for osteoarthritis. To lessen clinical symptoms, glucosamine was suggested. The primary goal of our systematic review study is to evaluate the effectiveness and safety of glucosamine based on recent studies. Electronic databases such as PubMed, Scopus, and Cochrane were used to assess the randomized controlled trial (RCT). From the beginning through March 2023, the papers were checked, and if they fulfilled the inclusion criteria, they were then examined. The Western Ontario and McMaster Universities Osteoarthritis (WOMAC) and Visual Analog Scale (VAS) scales were considered the main outcome measures. A total of 15 studies were selected. Global pain was significantly decreased in comparison to placebo, as measured by the VAS index, with an overall effect size of standardized mean difference (SMD) of −7.41 ([95% CI] 14.31, 0.51). The WOMAC scale confirmed that pain, stiffness, and physical function had improved, however the effects were insufficient. A statistical update also revealed that there were no reports of serious medication interactions or significant adverse events. To summarize, glucosamine is more effective than a placebo at reducing pain in knee osteoarthritis patients. In long-term treatment, oral glucosamine sulfate 1500 mg/day is believed to be well tolerated

Cost-Effectiveness of Glucosamine in Osteoarthritis Treatment: A Systematic Review

Osteoarthritis (OA) is a chronic condition that most frequently affects older adults. It is currently the most common disability. The cost of treating an aging population places pressure on the healthcare budget. As a result, it is imperative to evaluate medicines’ cost-effectiveness and, accordingly, their influence on health resource allocation. Our study aims to summarize the cost and outcome of utilizing glucosamine in OA treatment. Databases like Medline, Cochrane, and Scopus were searched as part of the identification process up until April 2023. Our primary inclusion criteria centered on the economic evaluation of glucosamine in OA treatments, providing an incremental cost-effectiveness ratio (ICER). The Quality of Health Economic Studies (QHES) instrument was applied to grade the quality of the studies. Seven qualified studies that discussed the cost-effectiveness of glucosamine with or without other formulations were selected. All of them demonstrated that glucosamine was cost-effective. There was an increase in quality-adjusted life years (QALYs) when incorporating glucosamine in conventional care. Moreover, patented crystalline glucosamine sulfate (pCGS) was more cost-effective than the other formulations of glucosamine (OFG). Overall, utilizing pCGS was more beneficial than using OFG in terms both of cost and quality of life

The Distribution of Autoantibodies by Age Group in Children with Type 1 Diabetes versus Type 2 Diabetes in Southern Vietnam

Asian children are increasingly being diagnosed with type 1 diabetes (T1D) or type 2 diabetes (T2D), and the presence of coexisting islet autoimmune antibodies complicate diagnosis. Here, we aimed to determine the prevalence of islet cell autoantibodies (ICAs) and glutamic acid decarboxylase 65 autoantibodies (GADAs) in children with T1D versus T2D living in Vietnam. This cross-sectional study included 145 pediatric patients aged 10.3 ± 3.6 years, with 53.1% and 46.9% having T1D and T2D, respectively. ICAs were reported in only 3.9% of pediatric T1Ds, which was not significantly different from the 1.5% of those with T2D. Older children with T1D were positive for either ICAs, or ICAs and GADAs (5–9 and 10–15 years), whereas only a small proportion of children aged 0–4 years were positive for GADAs (18%). Notably, 27.9% of children with T2D aged 10–15 were positive for GADAs, and all were classified as overweight (n = 9) or obese (n = 10). GADAs were more commonly observed in T1D patients younger than four years than ICAs, which were more prevalent in older children (5–15 years). Even though few children with T2D carried ICAs and GADAs, finding a better biomarker or an appropriate time to confirm diabetes type may require further investigation

A NOVEL DATASET FOR VIETNAMESE NEW YEAR FOOD CLASSIFICATION

Food classification has always piqued the interest of both domestic and international researchers, but numerous challenges remain. We present the dataset UIT-TASTET21, which contains over 77,000 color images of 18 traditional Vietnamese Lunar New Year dishes. We have experimented with classification using feature vectors from network architectures such as VGG16, Inception-v3, ResNet-50, Xception, and MobileNet-v2 to train support vector machines (SVM), meeting the dataset’s challenges and laying the groundwork for the development of many optimal methods in the future that promise scientific breakthroughs in the service and commercial industries. At the same time, the authors desire to share a piece of Vietnamese cuisine’s distinctiveness with worldwide friends

Multimodal analysis of genome-wide methylation, copy number aberrations, and end motif signatures enhances detection of early-stage breast cancer

IntroductionBreast cancer causes the most cancer-related death in women and is the costliest cancer in the US regarding medical service and prescription drug expenses. Breast cancer screening is recommended by health authorities in the US, but current screening efforts are often compromised by high false positive rates. Liquid biopsy based on circulating tumor DNA (ctDNA) has emerged as a potential approach to screen for cancer. However, the detection of breast cancer, particularly in early stages, is challenging due to the low amount of ctDNA and heterogeneity of molecular subtypes.MethodsHere, we employed a multimodal approach, namely Screen for the Presence of Tumor by DNA Methylation and Size (SPOT-MAS), to simultaneously analyze multiple signatures of cell free DNA (cfDNA) in plasma samples of 239 nonmetastatic breast cancer patients and 278 healthy subjects.ResultsWe identified distinct profiles of genome-wide methylation changes (GWM), copy number alterations (CNA), and 4-nucleotide oligomer (4-mer) end motifs (EM) in cfDNA of breast cancer patients. We further used all three signatures to construct a multi-featured machine learning model and showed that the combination model outperformed base models built from individual features, achieving an AUC of 0.91 (95% CI: 0.87-0.95), a sensitivity of 65% at 96% specificity.DiscussionOur findings showed that a multimodal liquid biopsy assay based on analysis of cfDNA methylation, CNA and EM could enhance the accuracy for the detection of early- stage breast cancer

Global fertility in 204 countries and territories, 1950–2021, with forecasts to 2100: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

BackgroundAccurate assessments of current and future fertility—including overall trends and changing population age structures across countries and regions—are essential to help plan for the profound social, economic, environmental, and geopolitical challenges that these changes will bring. Estimates and projections of fertility are necessary to inform policies involving resource and health-care needs, labour supply, education, gender equality, and family planning and support. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 produced up-to-date and comprehensive demographic assessments of key fertility indicators at global, regional, and national levels from 1950 to 2021 and forecast fertility metrics to 2100 based on a reference scenario and key policy-dependent alternative scenarios. MethodsTo estimate fertility indicators from 1950 to 2021, mixed-effects regression models and spatiotemporal Gaussian process regression were used to synthesise data from 8709 country-years of vital and sample registrations, 1455 surveys and censuses, and 150 other sources, and to generate age-specific fertility rates (ASFRs) for 5-year age groups from age 10 years to 54 years. ASFRs were summed across age groups to produce estimates of total fertility rate (TFR). Livebirths were calculated by multiplying ASFR and age-specific female population, then summing across ages 10–54 years. To forecast future fertility up to 2100, our Institute for Health Metrics and Evaluation (IHME) forecasting model was based on projections of completed cohort fertility at age 50 years (CCF50; the average number of children born over time to females from a specified birth cohort), which yields more stable and accurate measures of fertility than directly modelling TFR. CCF50 was modelled using an ensemble approach in which three sub-models (with two, three, and four covariates variously consisting of female educational attainment, contraceptive met need, population density in habitable areas, and under-5 mortality) were given equal weights, and analyses were conducted utilising the MR-BRT (meta-regression—Bayesian, regularised, trimmed) tool. To capture time-series trends in CCF50 not explained by these covariates, we used a first-order autoregressive model on the residual term. CCF50 as a proportion of each 5-year ASFR was predicted using a linear mixed-effects model with fixed-effects covariates (female educational attainment and contraceptive met need) and random intercepts for geographical regions. Projected TFRs were then computed for each calendar year as the sum of single-year ASFRs across age groups. The reference forecast is our estimate of the most likely fertility future given the model, past fertility, forecasts of covariates, and historical relationships between covariates and fertility. We additionally produced forecasts for multiple alternative scenarios in each location: the UN Sustainable Development Goal (SDG) for education is achieved by 2030; the contraceptive met need SDG is achieved by 2030; pro-natal policies are enacted to create supportive environments for those who give birth; and the previous three scenarios combined. Uncertainty from past data inputs and model estimation was propagated throughout analyses by taking 1000 draws for past and present fertility estimates and 500 draws for future forecasts from the estimated distribution for each metric, with 95% uncertainty intervals (UIs) given as the 2·5 and 97·5 percentiles of the draws. To evaluate the forecasting performance of our model and others, we computed skill values—a metric assessing gain in forecasting accuracy—by comparing predicted versus observed ASFRs from the past 15 years (2007–21). A positive skill metric indicates that the model being evaluated performs better than the baseline model (here, a simplified model holding 2007 values constant in the future), and a negative metric indicates that the evaluated model performs worse than baseline. FindingsDuring the period from 1950 to 2021, global TFR more than halved, from 4·84 (95% UI 4·63–5·06) to 2·23 (2·09–2·38). Global annual livebirths peaked in 2016 at 142 million (95% UI 137–147), declining to 129 million (121–138) in 2021. Fertility rates declined in all countries and territories since 1950, with TFR remaining above 2·1—canonically considered replacement-level fertility—in 94 (46·1%) countries and territories in 2021. This included 44 of 46 countries in sub-Saharan Africa, which was the super-region with the largest share of livebirths in 2021 (29·2% [28·7–29·6]). 47 countries and territories in which lowest estimated fertility between 1950 and 2021 was below replacement experienced one or more subsequent years with higher fertility; only three of these locations rebounded above replacement levels. Future fertility rates were projected to continue to decline worldwide, reaching a global TFR of 1·83 (1·59–2·08) in 2050 and 1·59 (1·25–1·96) in 2100 under the reference scenario. The number of countries and territories with fertility rates remaining above replacement was forecast to be 49 (24·0%) in 2050 and only six (2·9%) in 2100, with three of these six countries included in the 2021 World Bank-defined low-income group, all located in the GBD super-region of sub-Saharan Africa. The proportion of livebirths occurring in sub-Saharan Africa was forecast to increase to more than half of the world's livebirths in 2100, to 41·3% (39·6–43·1) in 2050 and 54·3% (47·1–59·5) in 2100. The share of livebirths was projected to decline between 2021 and 2100 in most of the six other super-regions—decreasing, for example, in south Asia from 24·8% (23·7–25·8) in 2021 to 16·7% (14·3–19·1) in 2050 and 7·1% (4·4–10·1) in 2100—but was forecast to increase modestly in the north Africa and Middle East and high-income super-regions. Forecast estimates for the alternative combined scenario suggest that meeting SDG targets for education and contraceptive met need, as well as implementing pro-natal policies, would result in global TFRs of 1·65 (1·40–1·92) in 2050 and 1·62 (1·35–1·95) in 2100. The forecasting skill metric values for the IHME model were positive across all age groups, indicating that the model is better than the constant prediction. InterpretationFertility is declining globally, with rates in more than half of all countries and territories in 2021 below replacement level. Trends since 2000 show considerable heterogeneity in the steepness of declines, and only a small number of countries experienced even a slight fertility rebound after their lowest observed rate, with none reaching replacement level. Additionally, the distribution of livebirths across the globe is shifting, with a greater proportion occurring in the lowest-income countries. Future fertility rates will continue to decline worldwide and will remain low even under successful implementation of pro-natal policies. These changes will have far-reaching economic and societal consequences due to ageing populations and declining workforces in higher-income countries, combined with an increasing share of livebirths among the already poorest regions of the world. FundingBill & Melinda Gates Foundation