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Updated: Jan 12, 2026
Comparative Analysis of Automatic Fecal Analyzer versus Direct Wet Smear Microscopy for Detecting Parasitic Infections in Stool Samples
Published on: April 25, 2025
[Effectiveness of an artificial intelligence-enabled microscopic imaging recognition system for detection of
1General Hospital of The Yangtze River Shipping, Wuhan, Hubei 430019, China.
Objective:
To evaluate the effectiveness of an artificial intelligence (AI)-enabled microscopic imaging recognition system integrated in the modified Kato-Katz thick smear technique for detection of Schistosoma japonicum eggs, so as to provide insights into precise control and elimination of schistosomiasis.
Methods:
In October 2023, 20 fecal samples were collected from healthy residents negative for S. japonicum infection in Wuhan City, and each fecal sample was prepared into 4 Kato-Katz test slides, with 3 slides added S. japonicum egg suspensions with concentrations of approximately 25, 10, and 5 eggs per 10 μL, respectively, and one untreated. A total of 80 Kato-Katz test slides were prepared, and were divided into mild, moderate, and severe infection groups, and a negative control group, according to the number of eggs per gram of feces on each slide, with 20 slides in each group. S. japonicum eggs were detected on 80 Kato-Katz test slides with the AI-enabled microscopic imaging recognition system and manual microscopy, and the differences were compared between the two methods in terms of average detection time, accurate rate of qualitative detection, accurate rate of quantitative detection, percentage of missed detection, and percentage of false detection.
Results:
The average detection time of the imaging recognition system was longer than manual microscopy for detection of S. japonicum eggs on Kato-Katz test slides in all groups [(16.70 ± 0.01) min vs. (15.78 ± 2.11) min; t = 3.90, P <0.05]. The detection time of the imaging recognition system was shorter than manual microscopy for detection of S. japonicum eggs on Kato-Katz test slides in the severe infection group (t = -3.91, P < 0.05), but was longer than manual microscopy in the the mild infection group (t = 5.03, P < 0.05) and the negative control group (t = 8.37, P < 0.05), while there was no significant difference in the detection time between the two methods in the moderate infection group (t = -0.09, P > 0.05). In addition, the imaging recognition system [97.50% (78/80) and 91.67% (55/60)] had higher accurate rates of both qualitative and quantitative detections than manual microscopy [81.25% (65/80) and 31.67% (19/60)] (χ2 = 11.08 and 34.11, both P values < 0.05), and the imaging recognition system had a lower percentage of missed detection in the infection groups [3.33% (2/60)] and a lower percentage of false detection in the negative control group (0) than manual microscopy [13.33% (8/60) and 35.00% (7/20)] (χ2 = 6.07, 5.14, both P values < 0.05).
Conclusions:
The AI-enabled microscopic imaging recognition system is effective to improve the accuracy for detection of S. japonicum eggs with the Kato-Katz technique, and is accurate to quantify and simple to perform, which may provide technical support for diagnosis of schistosomiasis and other parasitic diseases.
