Deep tech innovation for parasite diagnosis: New dimensions and opportunities

Abstract

By converging advanced science, engineering, and design, deep techs are bringing a great wave of future innovations by mastering challenges and problem complexity across sectors and the field of parasitology is no exception. Remarkable research and advancements can be seen in the field of parasite detection and diagnosis through smartphone applications. Supervised and unsupervised data deep learnings are heavily exploited for the development of automated neural network models for the prediction of parasites, eggs, etc., From microscopic smears and/or sample images with more than 99% accuracy. It is expected that several models will emerge in the future wherein greater attention is being paid to improving the model’s accuracy. Invariably, it will increase the chances of adoption across the commercial sectors dealing in health and related applications. However, parasitic life cycle complexity, host range, morphological forms, etc., need to be considered further while developing such models to make the deep tech innovations perfect for bedside and field applications. In this review, the recent development of deep tech innovations focusing on human parasites has been discussed focusing on the present and future dimensions, opportunities, and applications.