Development and Validation of a Computer Vision-Based Artificial Intelligence System (FenoParasite) for the Microscopic Detection of Ascaris lumbricoides and Giardia lamblia in Stool Samples.

BACKGROUND

Intestinal parasitic infections caused by Giardia lamblia and Ascaris lumbricoides remain a public-health problem in resource-limited settings, where conventional microscopy depends on expert personnel and shows variable sensitivity. Artificial intelligence (AI) could standardize and decentralize diagnosis. This study aimed to design and validate FenoParasite, an AI object-detection software (Azure Custom Vision) for the microscopic identification of G. lamblia cysts and trophozoites and A. lumbricoides eggs in stool samples.

METHODS

Diagnostic-accuracy study. The reference standard was consensus microscopy by two expert readers, with a third reader for discordant cases. An internal validation set (n = 159) and a prospective pilot validation set (n = 31) were analyzed. Sensitivity, specificity, positive predictive value, negative predictive value, accuracy (95% Clopper-Pearson CI), AUC-ROC (bootstrap), and Cohen's kappa were computed; the confidence threshold was 90%.

RESULTS

The model achieved a mAP@0.30 of 98.3% (precision 98.2%; recall 96.4%). In internal validation, G. lamblia showed 100% sensitivity, 98.9% specificity, 99.4% accuracy, and AUC 0.98; A. lumbricoides reached 100% across all indices (AUC 1.00). In the prospective pilot validation, sensitivity and specificity were 100% for both taxa; however, the confidence intervals were very wide (lower sensitivity bounds of 39.8% and 75.3%, based on only 4 and 13 reference-positive specimens, respectively), and these estimates are correspondingly imprecise.

CONCLUSIONS

FenoParasite showed high agreement with consensus expert microscopy for both a protozoan and a helminth. Because the reference standard was expert microscopy rather than a molecular assay, the reported performance estimates quantify agreement with microscopic reading rather than absolute diagnostic accuracy. These findings remain preliminary. Multicenter validation against molecular reference standards is required before clinical implementation can be considered.