Helminth Egg Automatic Detector (HEAD): Improvements in development for digital identification and quantification of helminth eggs and their application online.

Helminths are parasitic worms that constitute a major public health problem. Conventional analytical techniques to evaluate helminth eggs in environmental samples rely on different steps, namely sedimentation, filtration, centrifugation, and flotation, to separate the eggs from a variety of particles and concentrate them in a pellet for direct observation under an optical microscope. To improve this process, a new approach was implemented in which various image processing algorithms were developed and implemented by a Helminth Egg Automatic Detector (HEAD). This allowed identification and quantification of pathogenic helminth eggs of global medical importance and it was found to be useful for relatively clean wastewater samples. After the initial version, two improvements were developed: first, a texture verification process that reduced the number of false positive results; and second, the establishment of the optimal thresholds (morphology and texture) for each helminth egg species. This second implementation, which was found to improve on the results of the former, was developed with the objective of using free software as a platform for the system. This does not require the purchase of a license, unlike the previous version that required a Mathworks® license to run. After an internal statistical verification of the system was carried out, trials in internationally recognized microbiology laboratories were performed with the aim of reinforcing software training and developing a web-based system able to receive images and perform the analysis throughout a web service. Once completed, these improvements represented a useful and cheap tool that could be used by environmental monitoring facilities and laboratories throughout the world; this tool is capable of identifying and quantifying different species of helminth eggs in otherwise difficult environmental samples: wastewater, soil, biosolids, excreta, and sludge, with a sensitivity and specificity for the TensorFlow (TF) model in the web service values of 96.82% and 97.96% respectively. Additionally, in the case of Ascaris, it may even differentiate between fertile and non-fertile eggs.