MICROSCOPY & IMAGING
Image taken by NCSU’s microscope
the collection of a large amount of data so the team can train models capable of recognising large number of foram species. “We are focused on the development of an open-source and affordable platform that can be used by the scientific community. Our current prototypes make use of off-the-shelf microscopes, and we are integrating the robotic components to such platforms. Te robotic platform will consist mainly of 3D printed components and off-the-shelf servos and pumps,” says Lobaton. In terms of technology, Lobaton
also reveals the team uses the Amscope SE306R-P microscope, Amscope MU1803-HS-CK camera, Hiltec HS-
485HB servos and a Makeblock air pump motor – with most hardware components sourced from Servocity. “Te main advantages are that this system will minimise the need for manual sorting of samples of forams for scientific studies, which is the gold standard at this point. Manual sorting is a time- consuming and error-prone procedure. In terms of next steps, we are hoping to release our first robotic prototype for imaging of forams in the next months,” he adds.
PARASITE DETECTION Another interesting initiative is a joint ARUP Laboratories and Techcyte project
to develop the first ever AI-augmented ova and parasite detection tool. As Troy Bankhead, director at Techcyte Europe, explains, the company has found that a combination of the ability of a deep learning algorithm to quickly find and propose potential parasites with the expertise of parasitologists and medical technicians, enables ‘faster and more accurate results.’ After testing several device manufacturers, Bankhead reveals that the company created a partnership with Apacor, which manufactures a high-quality sample collection and filtration device. It then honed the sample collection and slide preparation process, resulting in a complete solution that can be ‘deployed much quicker and cheaper, but that produces results that are completely trustworthy for the labs.’ “Te deep neural networks that we use to create our algorithms are very good at finding and correctly identifying things like cells, organisms, particles, and the like. But this work all starts with a scanned image – and we work with digital slide manufacturers to adapt their software and enable us to obtain images that are optimised for AI,” says Bankhead. In his view, although humans are really good at making visual and informational associations computers are better at finding the proverbial ‘needle in a haystack’ and at being relentlessly consistent and rigorous.
The Techcyte Europe team 56
www.scientistlive.com
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