Abstract

We present a cost-effective mobile sensor based on 3D imaging of contact lenses and machine learning for quantification of Staphylococcus aureus. Compatible with human tear, this wearable-sensor can detect various pathogens and analytes in tear.

© 2018 The Author(s)

PDF Article
More Like This
Image-based Fluorescence Recovery After Photobleaching (FRAP) to dissect vancomycin diffusion-reaction processes in Staphylococcus aureus biofilms

S. Daddi Oubekka, R. Briandet, F. Waharte, M.-P. Fontaine-Aupart, and K. Steenkeste
80871I European Conference on Biomedical Optics (ECBO) 2011

Contact lens-based sensing of lysozyme in tear fluid using a mobile well-plate reader

Zachary Ballard, Sarah Bazargan, Diane Jung, Shyama Sathianathan, Ashley Clemens, Daniel Shir, Saba Al-Hashimi, and Aydogan Ozcan
AW3T.6 CLEO: Applications and Technology (CLEO_AT) 2021

Automated Detection and Enumeration of Waterborne Pathogens Using Mobile Phone Microscopy and Machine Learning

Hatice Ceylan Koydemir, Steve Feng, Kyle Liang, Rohan Nadkarni, Parul Benien, and Aydogan Ozcan
SM2C.3 CLEO: Science and Innovations (CLEO_SI) 2017

References

You do not have subscription access to this journal. Citation lists with outbound citation links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription