Automated parasite faecal egg counting using fluorescence labelling, smartphone image capture and computational image analysis.

This research study introduces a new method for identifying intestinal parasites in fecal samples. The method involves using a fluorescent protein, a smartphone for image capture, and computational image analysis to count the parasite eggs, with the aim to provide a convenient, more precise, and portable solution for parasite detection.

Background

• The study originates from the need to address the problems in detecting intestinal parasites, which pose a significant concern in veterinary medicine and human health in developing countries.
• The traditional method used for identifying these infections is the microscopic visualization of parasite eggs in fecal samples, but this technique is time-consuming, requires technical expertise, and is not practical for on-site use.
• This traditional approach has been leading to overuse of prophylactic treatments with anthelmintic drugs, promoting anthelmintic resistance. Thus, there is a strong need for a more convenient egg counting method.

Method

• The researchers used a fluorescent chitin-binding protein, a structural carbohydrate found in the shells of parasite eggs, to highlight the eggs in fecal samples.
• They then used a smartphone to capture images of these highlighted eggs, demonstrating the potential of smartphones as portable diagnostic devices.
• The study also utilized the computational power of smartphones to perform image analysis and count the parasite eggs, introducing a more automated process and reducing the need for manual intervention.

Findings

• The egg counts generated by the smartphone system showed a significant linear correlation with manual McMaster counts, implying a high level of accuracy. Moreover, the system had a significantly lower coefficient of variation, suggesting improved precision in egg counting.
• The system was also capable of differentiating between different types of parasite eggs, such as equine strongyle and ascarid eggs, demonstrating its robustness and applicability.
• The findings demonstrate the feasibility of a simple, automated, on-site test to detect and count parasite eggs without needing a laboratory microscope leading to the potential widespread adoption of this method in the future.