Comparative Analysis of Automatic Fecal Analyzer versus Direct Wet Smear Microscopy for Detecting Parasitic Infections in Stool Samples

With socio-economic development, the prevalence of intestinal parasitic diseases has significantly decreased year by year. However, parasitic infections remain a major public health issue globally, particularly in developing countries and regions. Timely diagnosis and treatment are crucial for controlling the spread of these diseases. The traditional Direct Wet Smear Microscopy method, while widely used, is labor-intensive, prone to contamination, and dependent on the skills of the technician. This paper introduces an Automatic Fecal Analyzer, which automates the stool sample processing, offering advantages over the traditional Direct Wet Smear Microscopy method, such as ease of operation, rapid detection, a clean and hygienic working environment, and high sensitivity and specificity, thus enhancing diagnostic efficiency and accuracy. We compared three different methods for fecal analysis: direct wet smear microscopy method, automatic fecal analyzer (AI report), and automatic fecal analyzer (user audit). The AI report uses automated image analysis and machine learning algorithms to identify components like parasites and eggs in fecal samples. This method can process a large number of samples quickly, increasing efficiency. The User Audit also uses an automatic fecal analyzer but includes an additional step of user audit. Experienced technicians review the AI report to enhance the accuracy and reliability of the results.The analyzer demonstrated a sensitivity of 84.31% for AI report and 94.12% for user audits, along with a specificity of 98.71% for AI reports and 99.69% for user audits, making it an invaluable tool for the clinical diagnosis and treatment of parasitic infections.

Authors

Institutions

Publication Details

Journal
Journal of Visualized Experiments
Published
2025-04-25
DOI
https://doi.org/10.3791/67706
Primary Topic
Microbial infections and disease research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Comparative Analysis of Automatic Fecal Analyzer versus Direct Wet Smear Microscopy for Detecting Parasitic Infections in Stool Samples

Qianyun Deng, Guanghua Li, Xue Jia, Chun-Miao Wang et al.
Journal of Visualized Experiments
Microbial infections and disease research
article

Comparative Analysis of Automatic Fecal Analyzer versus Direct Wet Smear Microscopy for Detecting Parasitic Infections in Stool Samples

Qianyun Deng, Guanghua Li, Xue Jia, Chun-Miao Wang, Ziqing Deng, Caiping Gong, Yunhu Zhao, F Wang, Yujing Yang, Rongchun Huang
article en

Abstract

With socio-economic development, the prevalence of intestinal parasitic diseases has significantly decreased year by year. However, parasitic infections remain a major public health issue globally, particularly in developing countries and regions. Timely diagnosis and treatment are crucial for controlling the spread of these diseases. The traditional Direct Wet Smear Microscopy method, while widely used, is labor-intensive, prone to contamination, and dependent on the skills of the technician. This paper introduces an Automatic Fecal Analyzer, which automates the stool sample processing, offering advantages over the traditional Direct Wet Smear Microscopy method, such as ease of operation, rapid detection, a clean and hygienic working environment, and high sensitivity and specificity, thus enhancing diagnostic efficiency and accuracy. We compared three different methods for fecal analysis: direct wet smear microscopy method, automatic fecal analyzer (AI report), and automatic fecal analyzer (user audit). The AI report uses automated image analysis and machine learning algorithms to identify components like parasites and eggs in fecal samples. This method can process a large number of samples quickly, increasing efficiency. The User Audit also uses an automatic fecal analyzer but includes an additional step of user audit. Experienced technicians review the AI report to enhance the accuracy and reliability of the results.The analyzer demonstrated a sensitivity of 84.31% for AI report and 94.12% for user audits, along with a specificity of 98.71% for AI reports and 99.69% for user audits, making it an invaluable tool for the clinical diagnosis and treatment of parasitic infections.

Journal of Visualized Experiments(218)
Guangdong Academy of Medical Sciences (CN), Guangdong Provincial People's Hospital (CN), Southern Medical University (CN)
Zero hunger
Openalex Percentile: Top 91%
Microbial infections and disease research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.