CaltechAUTHORS
  A Caltech Library Service

Real-Time In Vivo Intraocular Pressure Monitoring using an Optomechanical Implant and an Artificial Neural Network

Kim, Kun Ho and Lee, Jeong Oen and Du, Juan and Sretavan, David and Choo, Hyuck (2017) Real-Time In Vivo Intraocular Pressure Monitoring using an Optomechanical Implant and an Artificial Neural Network. IEEE Sensors Journal, 17 (22). pp. 7394-7404. ISSN 1530-437X. PMCID PMC5798645. https://resolver.caltech.edu/CaltechAUTHORS:20171012-103313071

[img] PDF - Accepted Version
See Usage Policy.

902kB

Use this Persistent URL to link to this item: https://resolver.caltech.edu/CaltechAUTHORS:20171012-103313071

Abstract

Optimized glaucoma therapy requires frequent monitoring and timely lowering of elevated intraocular pressure (IOP). A recently developed microscale IOP-monitoring implant, when illuminated with broadband light, reflects a pressure-dependent optical spectrum that is captured and converted to measure IOP. However, its accuracy is limited by background noise and the difficulty of modeling non-linear shifts of the spectra with respect to pressure changes. Using an end-to-end calibration system to train an artificial neural network (ANN) for signal demodulation we improved the speed and accuracy of pressure measurements obtained with an optically probed IOP-monitoring implant and make it suitable for real-time in vivo IOP monitoring. The ANN converts captured optical spectra into corresponding IOP levels. We achieved an IOP-measurement accuracy of ±0.1 mmHg at a measurement rate of 100 Hz, which represents a ten-fold improvement from previously reported values. This technique allowed real-time tracking of artificially induced sub-1 s transient IOP elevations and minor fluctuations induced by the respiratory motion of the rabbits during in vivo monitoring. All in vivo sensor readings paralleled those obtained concurrently using a commercial tonometer and showed consistency within ±2 mmHg. Real-time processing is highly useful for IOP monitoring in clinical settings and home environments and improves the overall practicality of the optical IOP-monitoring approach.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1109/JSEN.2017.2760140DOIArticle
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5798645PubMed CentralArticle
ORCID:
AuthorORCID
Kim, Kun Ho0000-0002-9271-9631
Lee, Jeong Oen0000-0001-5060-8370
Du, Juan0000-0001-7428-0576
Choo, Hyuck0000-0002-8903-7939
Alternate Title:Real-Time In Vivo Intraocular Pressure Monitoring using an Optomechanical Implant and an Artificial Neural Network ab1 ab2
Additional Information:© 2017 IEEE. Manuscript submitted August 1, 2017. Date of Publication: 05 October 2017. Manuscript received September 12, 2017; revised September 27, 2017; accepted September 27, 2017. Date of publication October 5, 2017; date of current version October 24, 2017. This work was supported by the National Institute of Health under Grant EY024582. The work of D. Sretavan was supported by the Research to Prevent Blindness Stein Innovation Award. The work of H. Choo was supported in part by the Research to Prevent Blindness Stein Innovation Award, in part by the Research to Prevent Blindness unrestricted grant to the UCSF Department of Ophthalmology, in part by the HMRI Investigator Award, Caltech CI2 Program, and in part by the Powell Foundation Award. The associate editor coordinating the review of this paper and approving it for publication was Dr. Cheng-Ta Chiang. (Corresponding author: Hyuck Choo.) The authors would like to extend a special thanks to Yisong Yue at the California Institute of Technology for discussions on the algorithm design. We also thank, William Kettyle, and Nauman Javed at the Harvard School of Medicine for helpful discussions on the influence of respiration on IOP levels. We finally thank the veterinarians at the UCSF ophthalmology department for assisting our experiments.
Group:Heritage Medical Research Institute
Funders:
Funding AgencyGrant Number
NIHEY024582
Research to Prevent BlindnessUNSPECIFIED
Heritage Medical Research InstituteUNSPECIFIED
Caltech Innovation Initiative (CI2)UNSPECIFIED
Charles Lee Powell FoundationUNSPECIFIED
Subject Keywords:Glaucoma, intraocular pressure, implant, in vivo, optical sensing, biomedical signal processing, neural network
Issue or Number:22
PubMed Central ID:PMC5798645
Record Number:CaltechAUTHORS:20171012-103313071
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20171012-103313071
Official Citation:K. H. Kim, J. O. Lee, J. Du, D. Sretavan and H. Choo, "Real-Time In Vivo Intraocular Pressure Monitoring Using an Optomechanical Implant and an Artificial Neural Network," in IEEE Sensors Journal, vol. 17, no. 22, pp. 7394-7404, Nov.15, 15 2017. doi: 10.1109/JSEN.2017.2760140. URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8058438&isnumber=8081774
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:82308
Collection:CaltechAUTHORS
Deposited By: Ruth Sustaita
Deposited On:12 Oct 2017 18:14
Last Modified:15 Apr 2020 18:17

Repository Staff Only: item control page