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Aydogan Ozcan, Ph.D.

AIMBE College of Fellows Class of 2017
For his pioneering contributions to bio-photonics, computational imaging, sensing and diagnostics technologies impacting telemedicine, mobile-health and global health applications.

Recurrent neural network advances 3D fluorescence imaging

Via EurekAlert | March 23, 2021

Rapid 3D microscopic imaging of fluorescent samples has gained increasing importance in numerous applications in physical and biomedical sciences. Given the limited axial range that a single 2D image can provide, 3D fluorescence imaging often requires time-consuming mechanical scanning of samples using a dense sampling grid. In addition to being slow and tedious, this approach also introduces additional light exposure on the sample, which might be toxic and cause unwanted damage, such as photo-bleaching.

By devising a new recurrent neural network, UCLA researchers have demonstrated a deep learning-enabled volumetric microscopy framework for 3D imaging of fluorescent samples. This new method only requires a few 2D images of the sample to be acquired for reconstructing its 3D image, providing ~30-fold reduction in the number of scans required to image a fluorescent volume… Continue reading.

Deep learning enables early detection and classification of live bacteria using holography

Via Biophotonics World | July 10, 2020

Waterborne diseases affect more than 2 billion people worldwide, causing substantial economic burden. For example, the treatment of waterborne diseases costs more than $2 billion annually in the United States alone, with 90 million cases recorded per year. Among waterborne pathogen-related problems, one of the most common public health concerns is the presence of total coliform bacteria and Escherichia coli (E. coli) in drinking water, which indicates fecal contamination. Traditional culture-based bacteria detection methods often take 24-48 hours, followed by visual inspection and colony counting by an expert, according to the United States Environmental Protection Agency (EPA) guidelines. Alternatively, molecular detection methods based on, for example, the amplification of nucleic acids, can reduce the detection time to a few hours, but they generally lack the sensitivity for detecting bacteria at very low concentrations, and are not capable of differentiating between live and dead microorganisms. Furthermore, there is no EPA-approved nucleic acid-based method for detecting coliform bacteria in water samples… Continue reading.

Aydogan Ozcan, Ph.D. To be Inducted into Medical and Biological Engineering Elite

Via AIMBE | March 1, 2017

WASHINGTON, D.C.— The American Institute for Medical and Biological Engineering (AIMBE) has announced the pending induction of Aydogan Ozcan, Ph.D., Professor, Electrical Engineering and Bioengineering Departments, California NanoSystems Institute, University of California, Los Angeles, to its College of Fellows. Dr. Ozcan was nominated, reviewed, and elected by peers and members of the College of Fellows For his pioneering contributions to bio-photonics, computational imaging, sensing and diagnostics technologies impacting telemedicine, mobile-health and global health applications..