Decision-support system for live detection of Leishmania parasites from microscopic images with deep learning

Abstract Background Leishmaniasis is a vector-borne parasitic disease caused by Leishmania protozoa. The disease manifests in several clinical presentations including cutaneous, mucocutaneous, and visceral leishmaniasis. The diagnosis of leishmaniasis is complex and often requires a combination of clinical assessment, microscopy, serological tests, and molecular techniques especially in immunocompromised cases. However, traditional diagnostic methods have limitations in terms of accuracy, sensitivity, and the expertise required, leading to an urgent need for advanced, automated diagnostic tools. The aim of this research is to develop a deep learning-based decision-support system for the microscopic examination of tissue samples to support non-experts with the live diagnosis of the disease. Methods Tissue samples from lesions were collected from patients diagnosed with cutaneous leishmaniasis in Libya and Palestine for the purpose of preparing microscopic slides. The samples were then visualized using a high-performance laboratory microscope and a mobile, low-cost device. The captured images were subsequently used to train the object detection framework YOLOv8 with the aim of identifying Leishmania parasites. A graphical user interface was developed for the application of the deep learning model, which enables real-time detection of the parasites using a microscope camera, as well as recognition from previously generated images and videos. Results The deep learning YOLOv8 framework was successfully trained using data generated by the advanced microscope and employed for the detection of Leishmania parasites. Subsequent finetuning with a combined set containing the aforementioned data and microscopic images generated with the low-cost device resulted in a considerable improvement in accuracy. The efficacy of the model was demonstrated through its successful operation on previously unseen data. Object detection yielded a mean average precision of 0.78 for the combined datasets. The evaluation process for determining the presence of parasites in an image resulted in 91% accuracy, 91% sensitivity, 90% specificity and 94% precision on the test data. Conclusions Deep learning-based YOLOv8 achieved accurate Leishmania detection in tissue samples, enhancing decision-support for non-experts via real-time graphical user interface support. This innovation can simplify diagnostics by addressing traditional method limitations, enabling early, accessible leishmaniasis detection in resource-limited settings, and potentially inspiring similar applications in other parasitic diseases. Clinical trial number Not applicable.