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Summary

Worldwide medical blood parasites were automatically screened using simple steps on a low-code AI platform. The prospective diagnosis of blood films was improved by using an object detection and classification method in a hybrid deep learning model. The collaboration of active monitoring and well-trained models helps to identify hotspots of trypanosome transmission.

Abstract

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.

Introduction

Trypanosomiasis is a significant challenge to global health issues due to a variety of zoonotic species causing human disease with a wide range of geographical distribution outside the African and American continents, such as South and Southeast Asia1,2,3. Human African trypanosomiasis (HAT) or sleeping sickness, is caused by Trypanosoma brucei gambiense and T. b. rhodesiense which produce the chronic and acute forms, respectively, representing the major spread in Africa. The causative parasite belongs to the Salivaria group due to the transmission by infect....

Protocol

Archived blood films and project design were approved by the Institutional Biosafety Committee, the Institutional Animal Care and Use Committee of the Faculty of Veterinary Science, Chulalongkorn University (IBC No. 2031033 and IACUC No. 1931027), and Human Research Ethics Committee of King Mongkut's Institute of Technology Ladkrabang (EC-KMITL_66_014).

1. Preparation of raw images

  1. The image dataset preparation
    1. Obtain at least 13 positive slides wit.......

Representative Results

In this study, hybrid deep learning algorithms were proposed to help automatically predict the positivity of a blood sample with a trypanosome parasite infection. Archived, Giemsa-stained blood films were sorted to localize and classify the parasitized versus non-parasitic by using the object detection algorithm based on a darknet backbone neural network. Within any rectangular box prediction result obtained by the previous model, the best-selected classification model was developed to classify all three species of medic.......

Discussion

Microscopic observation for Trypanosoma protozoa infection is early and commonly used, especially during surveillance in remote areas where there is a lack of skilled technicians and labor-intensive and time-consuming processes that are all obstacles to reporting the health organization timely. Although molecular biology techniques such as immunology and polymerase chain reaction (PCR) have been approved as high-sensitivity methods to support the effectiveness of lab findings, expensive chemicals, apparatus, and professi.......

Acknowledgements

This work (Research grant for New Scholar, Grant No. RGNS 65 - 212) was financially supported by the Office of the Permanent Secretary, Ministry of Higher Education, Science, Research and Innovation (OPS MHESI), Thailand Science Research and Innovation (TSRI) and King Mongkut's Institute of Technology Ladkrabang. We are grateful to the National Research Council of Thailand (NRCT) [NRCT5-RSA63001-10] for funding the research project. M.K. was funded by Thailand Science Research and Innovation Fund Chulalongkorn University. We also thank the College of Advanced Manufacturing Innovation, King Mongkut's Institute of Technology, Ladkrabang who have provided the dee....

Materials

NameCompanyCatalog NumberComments
Darknet19, Darknet53 and Densenet201Gao Huang, Z. L., Laurens van der Maaten. Densely Connected Convolutional Networks. arXiv:1608.06993 [cs.CV]. (2016)https://github.com/liuzhuang13/DenseNet Deep convolutional neural network model that can function to  classification
Generic name: YOLO model/ detection model?
Olympus CX31 Model CX31RRBSFA Olympus, Tokyo, JapanSN 4G42178 A light microscope 
Olympus DP21-SAL U-TV0.5XC-3 Olympus, Tokyo, JapanSN 3D03838A digital camera
Generic name: Classification models/ densely CNNs
Window 10MicrosoftWindow 10Operation system in computers
YOLO v4-tiny Naing, K. M. et al. Automatic recognition of parasitic products in stool examination using object detection approach. PeerJ Comput Sci. 8 e1065, (2022).https://git.cira-lab.com/users/sign_inDeep convolutional neural network model that can function to both localization and also classification 
https://git.cira-lab.com/users/sign_in

References

  1. Kasozi, K. I., et al. Epidemiology of trypanosomiasis in wildlife-implications for humans at the wildlife interface in Africa. Frontiers in Veterinary Science. 8, 621699 (2021).
  2. Ola-Fadunsin, S. D., Gimba, F. I., Abdullah, D. A., Abdullah, F. J. F., Sani, R. A.

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