A new diagnostic method and tool for cutaneous leishmaniasis based on artificial intelligence techniques

Background

Cutaneous leishmaniasis (CL) is a parasitic disease caused by protozoan parasites of the genus Leishmania, leading to significant morbidity in endemic regions. While effective, traditional diagnostic methods often suffer from limitations such as the requirement for specialized expertise and prolonged processing times. Artificial intelligence (AI) methodologies have recently emerged to enhance CL's diagnostic accuracy and efficiency.

Objective

This project aims to develop and make available to biologists a new, rapid, more efficient, and more precise cutaneous leishmaniasis diagnosis method and tool based on the latest techniques of artificial intelligence AI and computer vision (CV).

Methods

We used a deep learning model (YOLO 8) to detect Leishmania parasite bodies in microscopic images; we trained the model on microscopic images collected at the Algerian Pasteur Institute, Annex of M'sila. We implemented the proposed model on a mobile application to validate its performance.

Results

YOLO v8's application to the detection of Leishmania parasite bodies in microscopic images gives a high accuracy of 97 % over the entire test dataset.

Conclusion

This research demonstrated the significant potential of AI-based object detection models, particularly YOLOv8, for accurately detecting Leishmania parasites in microscopic images. The obtained results pave the way for promising clinical applications and further research in this field.