Microscope Uses Machine Learning To Optimize Illumination Settings

Engineers at Duke University (Durham, NC) have developed a microscope that adapts its lighting angles, colors, and patterns while teaching itself the optimal settings needed to complete a given diagnostic task.

In the engineering team’s study, the microscope simultaneously developed a lighting pattern and classification system that allowed it to quickly identify red blood cells infected by the malaria parasite more accurately than trained physicians and other machine learning approaches.

“A standard microscope illuminates a sample with the same amount of light coming from all directions, and that lighting has been optimized for human eyes over hundreds of years,” says Roarke Horstmeyer, assistant professor of biomedical engineering at Duke University, who is leading the work. However, Horstmeyer explains that computers are able to see things that humans cannot—so he and his team’s hardware redesign provides a diverse range of lighting options, which allows the microscope to optimize the illumination for itself.

Rather than diffusing white light from below to evenly illuminate the slide, the engineers developed a bowl-shaped light source with LEDs embedded throughout its surface. This allows samples to be illuminated from different angles up to nearly 90 degrees with different colors, which essentially casts shadows and highlights different features of the sample depending on the pattern of LEDs used.

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