A novel approach for identification of zoonotic trypanosome utilizing deep metric learning and vector database-based image retrieval system.

Trypanosomiasis, a significant health concern in South America, South Asia, and Southeast Asia, requires active surveys to effectively control the disease. To address this, we have developed a hybrid model that combines deep metric learning (DML) and image retrieval. This model is proficient at identifying Trypanosoma species in microscopic images of thin-blood film examinations. Utilizing the ResNet50 backbone neural network, a trained-model has demonstrated outstanding performance, achieving an accuracy exceeding 99.71 % and up to 96 % in recall. Acknowledging the necessity for automated tools in field scenarios, we demonstrated the potential of our model as an autonomous screening approach. This was achieved by using prevailing convolutional neural network (CNN) applications, and vector database based-images returned by the KNN algorithm. This achievement is primarily attributed to the implementation of the Triplet Margin Loss function as 98 % of precision. The robustness of the model demonstrated in five-fold cross-validation highlights the ResNet50 neural network, based on DML, as a state-of-the-art CNN model as AUC >98 %. The adoption of DML significantly improves the performance of the model, remaining unaffected by variations in the dataset and rendering it a useful tool for fieldwork studies. DML offers several advantages over conventional classification model to manage large-scale datasets with a high volume of classes, enhancing scalability. The model has the capacity to generalize to novel classes that were not encountered during training, proving particularly advantageous in scenarios where new classes may consistently emerge. It is also well suited for applications requiring precise recognition, especially in discriminating between closely related classes. Furthermore, the DML exhibits greater resilience to issues related to class imbalance, as it concentrates on learning distances or similarities, which are more tolerant to such imbalances. These contributions significantly make the effectiveness and practicality of DML model, particularly in in fieldwork research.

Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model.

Trypanosomiasis is a significant public health problem in several regions across the world, including South Asia and Southeast Asia. The identification of hotspot areas under active surveillance is a fundamental procedure for controlling disease transmission. Microscopic examination is a commonly used diagnostic method. It is, nevertheless, primarily reliant on skilled and experienced personnel. To address this issue, an artificial intelligence (AI) program was introduced that makes use of a hybrid deep learning technique of object identification and object classification neural network backbones on the in-house low-code AI platform (CiRA CORE). The program can identify and classify the protozoan trypanosome species, namely Trypanosoma cruzi, T. brucei, and T. evansi, from oil-immersion microscopic images. The AI program utilizes pattern recognition to observe and analyze multiple protozoa within a single blood sample and highlights the nucleus and kinetoplast of each parasite as specific characteristic features using an attention map. To assess the AI program's performance, two unique modules are created that provide a variety of statistical measures such as accuracy, recall, specificity, precision, F1 score, misclassification rate, receiver operating characteristics (ROC) curves, and precision versus recall (PR) curves. The assessment findings show that the AI algorithm is effective at identifying and categorizing parasites. By delivering a speedy, automated, and accurate screening tool, this technology has the potential to transform disease surveillance and control. It could also assist local officials in making more informed decisions on disease transmission-blocking strategies.

Automatic identification of medically important mosquitoes using embedded learning approach-based image-retrieval system.

Mosquito-borne diseases such as dengue fever and malaria are the top 10 leading causes of death in low-income countries. Control measure for the mosquito population plays an essential role in the fight against the disease. Currently, several intervention strategies; chemical-, biological-, mechanical- and environmental methods remain under development and need further improvement in their effectiveness. Although, a conventional entomological surveillance, required a microscope and taxonomic key for identification by professionals, is a key strategy to evaluate the population growth of these mosquitoes, these techniques are tedious, time-consuming, labor-intensive, and reliant on skillful and well-trained personnel. Here, we proposed an automatic screening, namely the deep metric learning approach and its inference under the image-retrieval process with Euclidean distance-based similarity. We aimed to develop the optimized model to find suitable miners and suggested the robustness of the proposed model by evaluating it with unseen data under a 20-returned image system. During the model development, well-trained ResNet34 are outstanding and no performance difference when comparing five data miners that showed up to 98% in its precision even after testing the model with both image sources: stereomicroscope and mobile phone cameras. The robustness of the proposed-trained model was tested with secondary unseen data which showed different environmental factors such as lighting, image scales, background colors and zoom levels. Nevertheless, our proposed neural network still has great performance with greater than 95% for sensitivity and precision, respectively. Also, the area under the ROC curve given the learning system seems to be practical and empirical with its value greater than 0.960. The results of the study may be used by public health authorities to locate mosquito vectors nearby. If used in the field, our research tool in particular is believed to accurately represent a real-world scenario.

Seroprevalence and associated risk factors of Toxoplasma gondii infection among slaughterhouse workers in Yangon Region, Myanmar: A cross-sectional study.

BACKGROUND: Toxoplasmosis, having the significant consequences affecting mortality and quality of life, is still prevalent in various places throughout the world. The major gap in surveillance for Toxoplasma gondii infection among high-risk population, slaughterhouse workers, is an obstacle for the effective policies formulation to reduce the burden of toxoplasmosis in Myanmar. Therefore, this study aimed to assess the seroprevalence of toxoplasmosis and associated factors of seropositivity among slaughterhouse workers in Yangon Region, Myanmar. METHODS: A cross-sectional study that was conducted from June to November 2020 included 139 slaughterhouse workers involving at five main slaughterhouses under Yangon City Development Committee, Myanmar. The presence of IgG and IgM anti-T. gondii antibodies in serum was detected using the OnSite Toxo IgG/IgM Combo Rapid Test. A face-to-face interview was also performed using pretested structured questionnaires to obtain the detail histories: sociodemographic characteristics, level of knowledge, occupational factors, and environmental factors related to T. gondii infection. Bivariate logistic regression was used to determine the factors associated with T. gondii infection. RESULTS: Of all participants, the overall seroprevalence of anti-T. gondii was 43.9% (95% CI: 35.5-52.5%), of whom 98.4% (95% CI: 91.2-100.0%) were reactive only for IgG antibody and 1.6% (95% CI: 0.0-8.8%) were reactive for IgG and IgM antibodies. The significant factors associated with the seropositivity of T. gondii antibodies were blood transfusion history (OR: 5.74, 95% CI: 1.17-28.09), low level of knowledge (OR: 2.91, 95% CI: 1.46-5.83), contact with animal organs, muscles or blood (OR: 14.29, 95% CI: 1.83-111.51), and animals most frequently slaughtered (cattle) (OR: 3.22, 95% CI: 1.16-8.93). CONCLUSIONS: A high seroprevalence of toxoplasmosis was detected among slaughterhouse workers in Yangon Region and it raises a significant public health concern. Therefore, providing health education regarding toxoplasmosis, enforcement of personal hygiene practices in workplaces, the establishment of training for occupational hygiene, and commencement of the risk assessment and serological screening for toxoplasmosis are crucial to curtail the prevalence of T. gondii infection among slaughterhouse workers.

Automatic recognition of parasitic products in stool examination using object detection approach.

Background: Object detection is a new artificial intelligence approach to morphological recognition and labeling parasitic pathogens. Due to the lack of equipment and trained personnel, artificial intelligence innovation for searching various parasitic products in stool examination will enable patients in remote areas of undeveloped countries to access diagnostic services. Because object detection is a developing approach that has been tested for its effectiveness in detecting intestinal parasitic objects such as protozoan cysts and helminthic eggs, it is suitable for use in rural areas where many factors supporting laboratory testing are still lacking. Based on the literatures, the YOLOv4-Tiny produces faster results and uses less memory with the support of low-end GPU devices. In comparison to the YOLOv3 and YOLOv3-Tiny models, this study aimed to propose an automated object detection approach, specifically the YOLOv4-Tiny model, for automatic recognition of intestinal parasitic products in stools. Methods: To identify protozoan cysts and helminthic eggs in human feces, the three YOLO approaches; YOLOv4-Tiny, YOLOv3, and YOLOv3-Tiny, were trained to recognize 34 intestinal parasitic classes using training of image dataset. Feces were processed using a modified direct smear method adapted from the simple direct smear and the modified Kato-Katz methods. The image dataset was collected from intestinal parasitic objects discovered during stool examination and the three YOLO models were trained to recognize the image datasets. Results: The non-maximum suppression technique and the threshold level were used to analyze the test dataset, yielding results of 96.25% precision and 95.08% sensitivity for YOLOv4-Tiny. Additionally, the YOLOv4-Tiny model had the best AUPRC performance of the three YOLO models, with a score of 0.963. Conclusion: This study, to our knowledge, was the first to detect protozoan cysts and helminthic eggs in the 34 classes of intestinal parasitic objects in human stools.

Involvement of high school teachers in Health Promoting School program in selected township, Yangon Region, Myanmar: A cross-sectional mixed methods study.

BACKGROUND: Schools provide a big opportunity for promoting the student's health, life skill, and behavior. Teachers play a fundamental role in the promotion and successful implementation of school health services. This study aimed to assess the level of involvement in the Health Promoting School program and its associated factors and to explore the benefits and barriers to involvement among high school teachers in Myanmar. METHODS: A mixed methods explanatory sequential study was conducted among 194 high school teachers in Thanlyin Township, Yangon Region, Myanmar, from June to August 2020. Quantitative data were collected with the pretested structural questionnaire and analyzed by Chi-square tests and Fisher's exact tests. A qualitative strand was added by conducting in-depth interviews (n = 15, five teachers from each level of involvement: poor, medium, and good), analyzed by thematic content analysis. RESULTS: Of the 194 teachers, 23.7% had a good level of involvement in the Health Promoting School program. The factor associated with involvement in Health Promoting School program were age (p value < 0.001), duration of services (p value = 0.001), and a number of accomplished training-related school health (p value = 0.008). Qualitative data revealed that improvement of the health knowledge and awareness on health problems, the progress of healthy behaviors, development of physical and mental health, prevention of the disease spread, achievement of healthy and productive learning environment, and development of academic achievement were major benefits of teachers' involvement. Moreover, the main barriers to involvement were insufficient materials and human resources, time constraints, incompetence of the teachers, poor cooperation of school health partnerships, and insufficient awareness of parents. CONCLUSIONS: The proportion of good involvement in the Health Promoting School program among high school teachers was low in this study area. Providing sufficient human resources and material, conducting the on-the-job and refresher training, enhancing parent-teacher cooperation, and strengthening the community partnerships were crucial to improve the level of involvement and reduced the barriers for the achievement of the Health Promoting School program.

Classification for avian malaria parasite Plasmodium gallinaceum blood stages by using deep convolutional neural networks.

The infection of an avian malaria parasite (Plasmodium gallinaceum) in domestic chickens presents a major threat to the poultry industry because it causes economic loss in both the quality and quantity of meat and egg production. Computer-aided diagnosis has been developed to automatically identify avian malaria infections and classify the blood infection stage development. In this study, four types of deep convolutional neural networks, namely Darknet, Darknet19, Darknet19-448 and Densenet201 are used to classify P. gallinaceum blood stages. We randomly collected a dataset of 12,761 single-cell images consisting of three parasite stages from ten-infected blood films stained by Giemsa. All images were confirmed by three well-trained examiners. The study mainly compared several image classification models and used both qualitative and quantitative data for the evaluation of the proposed models. In the model-wise comparison, the four neural network models gave us high values with a mean average accuracy of at least 97%. The Darknet can reproduce a superior performance in the classification of the P. gallinaceum development stages across any other model architectures. Furthermore, the Darknet has the best performance in multiple class-wise classification, with average values of greater than 99% in accuracy, specificity, and sensitivity. It also has a low misclassification rate (< 1%) than the other three models. Therefore, the model is more suitable in the classification of P. gallinaceum blood stages. The findings could help us create a fast-screening method to help non-experts in field studies where there is a lack of specialized instruments for avian malaria diagnostics.

Assessment of depressive symptoms in patients with COVID-19 during the second wave of epidemic in Myanmar: A cross-sectional single-center study.

BACKGROUND: Coronavirus disease 2019 (COVID-19) pandemic has had a great impact on every aspect of society. All countries launched preventive measures such as quarantine, lockdown, and physical distancing to control the disease spread. These restrictions might effect on daily life and mental health. This study aimed to assess the prevalence and associated factors of depressive symptoms in patients with COVID-19 at the Treatment Center. METHODS: A cross-sectional telephone survey was carried out at Hmawbi COVID-19 Treatment Center, Myanmar from December 2020 to January 2021. A total of 142 patients with COVID-19 who met the criteria were invited to participate in the study. A pre-tested Center for Epidemiologic Studies Depression Scale (CES-D) was used as a tool for depressive symptoms assessment. Data were analyzed by using binary logistic regression to identify associated factors of depressive symptoms. Adjusted odds ratio (AOR) with a 95% confidence interval (CI) was computed to determine the level of significance with a p < 0.05. RESULTS: The prevalence of depressive symptoms in patients with COVID-19 was 38.7%, with the means (± standard deviation, SD) subscale of somatic symptom, negative effect, and anhedonia were 4.64 (±2.53), 2.51 (± 2.12), and 5.01 (± 3.26), respectively. The patients with 40 years and older (AOR: 2.99, 95% CI: 1.36-6.59), < 4 of household size (AOR: 3.45, 95% CI: 1.46-8.15), ≤ 400,000 kyats of monthly family income (AOR: 2.38, 95% CI: 1.02-5.54) and infection to family members (AOR: 4.18, 95% CI: 1.74-10.07) were significant associated factors of depressive symptoms. CONCLUSION: The high prevalence of depressive symptoms, approximately 40%, was found in patients with COVID-19 in the Treatment Center. Establishments of psychosocial supports, providing psychoeducation, enhancing the social contact with family and friends, and using credible source of information related COVID-19 would be integral parts of mental health services in COVID-19 pandemic situation.