Emerg Microbes Infect. 2026 Jul 1:2698240. doi: 10.1080/22221751.2026.2698240. Online ahead of print.
ABSTRACT
AbstractThe comprehensive method to detect gastrointestinal infections is still microscopic examination of stool specimens. However, manual microscopic examination is laborious, and its accuracy is highly observer-dependent. Therefore, we have developed a deep-learning approach integrated with a digital microscope scanner to detect gastro-intestinal helminths in digital images, which allows faster and more precise detection of gastro-intestinal helminths. A novel in vitro diagnostic (IVD) system (ParaScout) was developed that consists of an affordable, commercially available microscope scanner and a cloud-based deep-learning algorithm for accurate identification of gastro-intestinal helminths in stool. The performance of the ParaScout system was compared to manual examination by 4 expert technicians, using 50 validated stool specimens containing either no gastro-intestinal helminths or one or more of the 15 gastro-intestinal helminth species for which the ParaScout system had been trained. In the 45 positive stool specimens, 63 gastro-intestinal helminth species were present, and therefore, examination by 4 technicians could have revealed 252 gastro-intestinal helminth identifications (4*63). In total, the manual examinations produced 16 false negative and 11 false positive results, resulting in a sensitivity of 93.7% and a specificity of 99.6%. When ParaScout was employed to highlight suspected structures for human expert confirmation, these values increased to 98.8% and 99.9%, respectively. This study demonstrated that automated scanning in combination with a deep-learning algorithm can detect gastro-intestinal helminths in stool samples with high sensitivity, while resulting in minimal false positive detections if combined with expert confirmation. This approach shows high promise for automating and improving the quality of gastro-intestinal helminth detection in patient diagnostics.
PMID:42384752 | DOI:10.1080/22221751.2026.2698240