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• W2761603723 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." @default.
• W2761603723 created "2017-10-20" @default.
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• W2761603723 date "2017-11-15" @default.
• W2761603723 modified "2023-09-25" @default.
• W2761603723 title "Real-Time <i>In Vivo</i> Intraocular Pressure Monitoring Using an Optomechanical Implant and an Artificial Neural Network" @default.
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• W2761603723 doi "https://doi.org/10.1109/jsen.2017.2760140" @default.
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