There are now various attachments that allow you to capture microscope-scale images with your smartphone. Unfortunately, however, the limitations of the phone's lens and image sensor mean that those images still won't be as good as those obtained by a lab-grade microscope. That could change, however, thanks to a recent advance in artificial intelligence.

Led by Prof. Aydogan Ozcan, a team at the University of California Los Angeles (UCLA) started by taking microscope photos of lung tissue samples, blood and Pap smears. They did so two times, first using a standard laboratory-grade microscope, and then using a smartphone with a microscope attachment which they made with a 3D printer for under US$100.

Both sets of images were then uploaded to a laptop, which was running a deep-learning-based algorithm created by the scientists. Using the lab microscope photos as a reference, that algorithm learned how to rapidly enhance the smartphone photos to the point that they were similar in quality to those reference photos, possessing the level of resolution and color details needed for a laboratory analysis.

The system has since been shown to be capable of accurately enhancing other low-quality images, even when it doesn't have a corresponding high-quality image to use as a guide. It is now hoped that the technology could find use in resource-poor regions, where microscopes are in short supply.

"Using deep learning, we set out to bridge the gap in image quality between inexpensive mobile phone-based microscopes and gold-standard bench-top microscopes that use high-end lenses," says Ozcan. "We believe that our approach is broadly applicable to other low-cost microscopy systems that use, for example, inexpensive lenses or cameras, and could facilitate the replacement of high-end bench-top microscopes with cost-effective, mobile alternatives."

A paper on the research was recently published in the journal ACS Photonics.

Source: UCLA

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