School of Biomedical Engineering, Jimma University, Jimma, Ethiopia.
Department of Chemical and Biomedical Engineering, West Virginia University, Morgantown, WV, 26505, USA.
BMC Med Imaging. 2024 Jun 18;24(1):152. doi: 10.1186/s12880-024-01333-1.
Leishmaniasis is a vector-born neglected parasitic disease belonging to the genus Leishmania. Out of the 30 Leishmania species, 21 species cause human infection that affect the skin and the internal organs. Around, 700,000 to 1,000,000 of the newly infected cases and 26,000 to 65,000 deaths are reported worldwide annually. The disease exhibits three clinical presentations, namely, the cutaneous, muco-cutaneous and visceral Leishmaniasis which affects the skin, mucosal membrane and the internal organs, respectively. The relapsing behavior of the disease limits its diagnosis and treatment efficiency. The common diagnostic approaches follow subjective, error-prone, repetitive processes. Despite, an ever pressing need for an accurate detection of Leishmaniasis, the research conducted so far is scarce. In this regard, the main aim of the current research is to develop an artificial intelligence based detection tool for the Leishmaniasis from the Geimsa-stained microscopic images using deep learning method.
Stained microscopic images were acquired locally and labeled by experts. The images were augmented using different methods to prevent overfitting and improve the generalizability of the system. Fine-tuned Faster RCNN, SSD, and YOLOV5 models were used for object detection. Mean average precision (MAP), precision, and Recall were calculated to evaluate and compare the performance of the models.
The fine-tuned YOLOV5 outperformed the other models such as Faster RCNN and SSD, with the MAP scores, of 73%, 54% and 57%, respectively.
The currently developed YOLOV5 model can be tested in the clinics to assist the laboratorists in diagnosing Leishmaniasis from the microscopic images. Particularly, in low-resourced healthcare facilities, with fewer qualified medical professionals or hematologists, our AI support system can assist in reducing the diagnosing time, workload, and misdiagnosis. Furthermore, the dataset collected by us will be shared with other researchers who seek to improve upon the detection system of the parasite. The current model detects the parasites even in the presence of the monocyte cells, but sometimes, the accuracy decreases due to the differences in the sizes of the parasite cells alongside the blood cells. The incorporation of cascaded networks in future and the quantification of the parasite load, shall overcome the limitations of the currently developed system.
利什曼病是一种由媒介传播的被忽视的寄生虫病，属于利什曼属。在 30 种利什曼原虫中，有 21 种会导致人类感染，影响皮肤和内脏。全球每年报告的新感染病例约为 70 万至 100 万例，死亡病例为 2.6 万至 6.5 万例。该疾病有三种临床表现，即皮肤利什曼病、黏膜利什曼病和内脏利什曼病，分别影响皮肤、黏膜膜和内脏器官。该疾病反复发作，限制了其诊断和治疗效果。常见的诊断方法如下：主观、易出错、重复的过程。尽管人们迫切需要准确检测利什曼病，但迄今为止的研究还很少。在这方面，当前研究的主要目的是使用深度学习方法从吉姆萨染色的显微镜图像中开发一种基于人工智能的利什曼病检测工具。
本地获取染色的显微镜图像，并由专家进行标记。通过使用不同的方法对图像进行扩充，以防止过拟合并提高系统的通用性。微调后的 Faster RCNN、SSD 和 YOLOV5 模型用于目标检测。计算平均精度（MAP）、精度和召回率以评估和比较模型的性能。
微调后的 YOLOV5 模型的表现优于 Faster RCNN 和 SSD 等其他模型，MAP 得分分别为 73%、54%和 57%。
目前开发的 YOLOV5 模型可以在临床上进行测试，以协助实验室人员从显微镜图像中诊断利什曼病。特别是在资源较少的医疗设施中，合格的医疗专业人员或血液学家较少，我们的人工智能支持系统可以帮助减少诊断时间、工作量和误诊。此外，我们收集的数据集将与其他寻求改进寄生虫检测系统的研究人员共享。当前的模型甚至可以在存在单核细胞的情况下检测寄生虫，但有时由于寄生虫细胞与血细胞的大小差异，准确性会降低。在未来，将级联网络纳入并量化寄生虫负荷，将克服当前系统的局限性。
