Automated Coverslipping Design to Prevent Image Disturbances in Tissue Scans Used for Neurological Disorder Diagnosis

Biomedical Engineering

Sydney Begin, Ian Berkram, Eiven Mugo, Keyonna Pollins

Abstract

Our concept works to prevent image disturbances in pathology scans by making an automated design for coverslipping. The design will work to create reproducible quality slides through increased consistency via robotic activity. CND Life Sciences aids in the diagnosis of Parkinson’s through skin biopsy in order to successfully provide a diagnosis before clinical onset. Current Laboratory processes are extremely manual with errors due to human variability. These errors cause delays and can result in the need for new biopsies, longer waits, and higher expenses for patients. These errors damage CND Life Science’s profit margin, and the prevention of them would be a significant ROI, demonstrating a large need for automated devices in the pathology laboratory market.

Our design seeks to fill this market with an automated coverslipping process, ensuring slides are 95%+ readable with little to no need for technician input and preventing disruption of laboratory procedures outside of coverslipping. Our device is designed to be a discrete entity, ensuring it is easily inputted into any laboratory without having to adjust other procedures. The device moves the slides automatically between each step of coverslipping to avoid technician intervention. The device is consistent with a tight margin of errors such that the vast majority of slides have no clinically significant sample damage. Simple physics will be utilized to manipulate the slide. The dropper uses gravity to manipulate the fluid and the tilt table uses capillary forces to apply a uniform film. Suction cups that manipulate air pressure to control coverslip application. Pistons and conveyor belts use simple Newtonian mechanics.

Our design for manufacturing took into account each individual component and their materials. We suspect the overall costs for production to be around $165 not including labor we plan for our device to have a 20% profit margin.

Video

Research poster

Faculty mentor

Portrait of Xiaojun Tian

Xiaojun Tian

Associate Professor

School of Biological and Health Systems Engineering

[email protected]

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