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Los Angeles
Scientists have discovered that deep learning, a powerful form of artificial intelligence, can discern and enhance microscopic details in photos taken by smartphones to such an extent that they can match the quality of images from laboratory-grade microscopes.
The advance could help bring high-quality medical diagnostics into resource-poor regions, where people otherwise do not have access to high-end diagnostic technologies, said the researchers from the University of California, Los Angeles (UCLA) in the US.
The technique used attachments that can be inexpensively produced with a 3D printer, according to a study published in the journal ACS Photonics.
“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,” said Aydogan Ozcan from UCLA.
“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,” Ozcan said.
He said the new technique could find numerous applications in global health, telemedicine and diagnostics-related applications.
The researchers shot images of lung tissue samples, blood and Pap smears, first using a standard laboratory-grade microscope, and then with a smartphone with the 3D-printed microscope attachment.
Then they fed the pairs of corresponding images into a computer system that “learns” how to rapidly enhance the mobile phone images.
