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

• Published in: IEEE sensors journal Vol. 17; no. 22; pp. 7394 - 7404
• Main Authors: Kim, Kun Ho, Lee, Jeong Oen, Du, Juan, Sretavan, David, Choo, Hyuck
• Format: Journal Article
• Language: English
• Published: United States IEEE 15.11.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">in vivo; Accuracy; Artificial neural networks; Background noise; Biomedical optical imaging; biomedical signal processing; Broadband; Demodulation; Glaucoma; implant; Implants; Intraocular pressure; Light; Monitoring; neural network; Neural networks; Nonlinear optics; Optical reflection; optical sensing; Optical sensors; Rabbits; Real time; Surgical implants; Glaucoma; intraocular pressure; biomedical signal processing; optical sensing; in vivo; implant; neural network
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2017.2760140

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.