Evaluation of KU-F40 automated microscope for parasitology: when artificial intelligence meets old school microscopy.

Intestinal parasitic infections (IPIs) have a worldwide distribution and have a major impact on health, work capacity, and economy in many countries. Light microscopy is still considered the reference method for IPI diagnosis but is labor-intensive. KU-F40, an automated feces analyzer, combines automated microscopic examination of stool samples and deep learning artificial intelligence. The aim of this study is to evaluate the performance of KU-F40 for the diagnosis of IPI. A random collection of stool samples prescribed for IPI investigation was retrospectively collected from six clinical laboratories in Belgium along with external quality controls. All samples were analyzed in our laboratory by wet mount preparation using classic light microscopy as reference. We assessed the sensitivity and specificity for parasite detection/identification. Finally, we studied the improvement in parasite detection rate when increasing the number of pictures taken to 150% and 200% of the standard settings. A total of 267 clinical stool samples were included. Using standard settings, overall sensitivity and specificity were 86% and 45%, respectively. When considering only clinically relevant parasites, sensitivity was 95%. Increasing the number of pictures allowed to improve detection rate, but it remained under 90% for several targets. KU-F40 offers an innovative approach and provides welcome automation in the diagnosis of IPI. Currently, its performance does not allow it to be used as a screening tool with automatic validation of negative results. Critical missing features could enhance its performance, including the addition of a 10x magnification objective and additional parasites currently absent from the database.IMPORTANCEIntestinal parasitic infections have a worldwide distribution and are a global health concern in many countries. Light microscopy is still considered the reference method for diagnosis but is labor-intensive, time-consuming, and requires highly skilled and motivated technologists. In this paper, we evaluate the KU-F40, an automated feces analyzer designed to diagnose intestinal parasitic infections by combining automated light microscopy and deep learning artificial intelligence for detection and presumptive identification of several protozoans and helminths. As it relies on microscopy, this method enables the detection and identification of a predefined panel of parasites, whose morphology is known to the system and included in the database, without requiring prior diagnostic suspicion, similarly to multiplex PCR assays. The automation could improve the quality, standardization, and turnaround time of stool parasitology. This study is the first to evaluate the performance of the KU-F40 on a wide range of parasites, collected from six Belgian hospitals, including our two national reference centers.