To explore the application value of a fecal analysis instrument with morphological artificial intelligence (AI) recognition in the screening of intestinal parasites in feces.

A retrospective analysis was conducted using 9 845 fecal specimens collected from health examination participants in various towns of Tianmen City and received by the Laboratory Department of the First People′s Hospital of Tianmen from November 2023 to June 2025. Conventional manual microscopic examination and an AI based fecal analyzer were used to compare the detection rates of intestinal parasites. The consistency between the 2 methods was evaluated using the Kappa test. In addition, detection rates by sex, parasite species, and township distribution were analyzed.

The AI fecal analyzer identified a total of 294 positive samples for parasites, with a positive rate of 2.99% (294/9 845). After manual re-inspection, 80 cases were confirmed positive, with a positive rate of 0.81%. The false positive rate of AI identification was 2.19% (214/9 765). The positive rate of conventional manual microscopic examination was 0.56% (55/9 845), and the missed detection rate was 31.25% (25/80). There was a statistically significant difference in the detection rates between the two methods (χ²=21.330, P<0.01). There was no statistically significant difference in parasite detection rates between males and females for either the AI+ manual re-inspection method or the manual microscopic examination (χ²=0.109, 0.110, all P>0.05). The detection rates of parasites by simple AI identification and AI identification+ manual re-inspection reached a moderate consistency (Kappa=0.417, P<0.01), with a sensitivity of 98.75%, a specificity of 97.80%, a positive predictive value of 26.87%, and a negative predictive value of 99.99%. The detection rates of parasites by simple manual microscopic examination and AI identification+ manual re-inspection were highly consistent (Kappa=0.800, P<0.01), with a sensitivity of 67.50%, a specificity of 99.99%, a positive predictive value of 98.18%, and a negative predictive value of 99.73%. Both methods successfully detected the Clonorchis sinensis eggs, Giardia lamblia, pinworm eggs, and Blastocystis hominis, AI identification was also able to detect the Strongyloides stercoralis, Trichuris trichiura eggs, hookworm eggs, Entamoeba histolytica, and Entamoeba coli.

The morphology-based AI recognition system achieved a high detection rate for intestinal parasite screening and effectively reduced the risk of missed diagnoses. However, relying solely on AI recognition has a certain rate of false-positive results and therefore requiring manual verification.