- Article
Artificial Intelligence-Based Detection of Clonorchis sinensis and Metagonimus spp. Eggs Using an Automated Microscope Solution
- Hee-Eun Shin,
- Young-Ju Lee and
- Myoung-Ro Lee
- + 5 authors
Clonorchis sinensis and Metagonimus spp. are prevalent parasites in Korea, and accurate diagnosis is essential because treatment dosages differ between infections. However, their eggs are morphologically similar under light microscopy, making differentiation difficult and dependent on examiner expertise. To address this limitation, we evaluated an artificial intelligence (AI)-based automated microscope solution for the simultaneous detection and discrimination of both parasites. Microscopic images from 170 stool samples were analyzed using an AI system based on You Only Look Once version 5. The annotated dataset comprised 9455 egg images (6494 C. sinensis and 2961 Metagonimus spp.), randomly divided at the slide/patient level into training (6862), validation (1301), and test (1292) sets. Diagnostic performance was evaluated using mean average precision, confusion matrix analysis, and correlation with conventional microscopy. The model achieved a classification accuracy of up to 97.8%. C. sinensis showed higher recall and F1 scores, whereas Metagonimus spp. showed higher precision and specificity. Species identification showed complete concordance with conventional microscopy, and egg quantification was strongly correlated. These results indicate that the proposed AI system may serve as a supportive diagnostic tool comparable to conventional microscopy.
28 January 2026



![Geographic distribution of locations where tick infestation occurred in patients from North Macedonia and Serbia (2022–2024). Each point represents a probable tick infestation site reported by patients. Red bubble size indicates the number of ticks collected per site, and color intensity reflects relative abundance. The shape file for mapping at district and municipality levels is available at the GADM database of Global Administrative Areas (v4.1, https://gadm.org/, accessed on 22 July 2022). The map was generated using QGIS v3.12 [25].](https://mdpi-res.com/cdn-cgi/image/w=281,h=192/https://mdpi-res.com/parasitologia/parasitologia-06-00006/article_deploy/html/images/parasitologia-06-00006-g001-550.jpg)

