How Lens-free Vision System Technology and Deep Learning Innovations Can Speed Up Medical Diagnostics

lens vision systemsLens-free vision systems promise to have a massive impact on the speed of medical diagnostics. Currently, patients may have to wait several days or longer to find out if they have meningitis, get blood test results, or other diagnosis. But with lens-free technology, physicians could get results in a fraction of the time.

Lens-Free Vision System Primer

The lens-free microscope lacks an objective lens and is able to screen 10,000 cells in real-time at several frames per second. Instead of using an objective lens, the system reconstructs an image by means of a holographic pattern and algorithm. Although lens-free technology has been around for about a decade, only now has deep learning been added to the mix.

Deep learning algorithms are able to identify, count, and track different cell types in real-time. In the past, it could take 30 minutes to reconstruct just a single image. Deep learning can reconstruct the same image in just one second.

Cell cultures can cover 400 different cell lines. When using traditional image processing techniques, a dedicated algorithm would need to be written for each cell line. But deep learning is able to differentiate between the cells and number them on its own.

Another major advantage of lens-free technology is cost. The decrease in processing time is sure to save money for a lot of diagnostics labs. The integration of deep learning cuts down on the costs associated with programming as well.

Lens-Free Point of Care Analysis

Point-of-care analysis could be a game-changer for diagnostics. Lens-free vision systems may allow physicians to have a lot more testing done on-site instead of having to send samples off to a lab. The lowered costs made possible can make onsite medical diagnostics more accessible.

Lens-free vision systems are also compact. Doctors could run their own blood counts, spinal fluid tests, and blood coagulation tests. Some other applications include the monitoring of bioprocesses in bioreactors for the Big Pharma. The technology also has applications in biological research and drug screening.


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