Adaptive finite element eye model for the compensation of biometric influences on acoustic tonometry. (March 2021) - Osmers, Jan; Kaiser, Nils; Sorg, Michael; Fischer, Andreas
Highlights: Geometrically adaptive finite element eye model for tonometry. Validated IOP-dependent behavior of adaptive eye model. Influence of geometric uniqueness of human eyes on corneal deflection amplitude. Abstract: Background and objective: Glaucoma is currently a major cause for irreversible blindness worldwide. A risk factor and the only therapeutic control parameter is the intraocular pressure (IOP). The IOP is determined with tonometers, whose measurements are inevitably influenced by the geometry of the eye. Even though the corneal mechanics have been investigated to improve accuracy of Goldmann and air pulse tonometry, influences of geometric properties of the eye on an acoustic self-tonometer approach are still unresolved. Methods: In order to understand and compensate for measurement deviations resulting from the geometric uniqueness of eyes, a finite element eye model is designed that considers all relevant eye components and is adjustable to all physiological shapes of the human eye. Results: The general IOP-dependent behavior of the eye model is validated by laboratory measurements on porcine eyes. The difference between simulation and measurement is below 8 µm for IOP levels from 5 to 40 mmHg. The adaptive eye model is then used to quantify systematic uncertainty contributions of a variation of eye length and central corneal thickness based on input statistics of a clinical trial series. The adaptive eye model provides the required relation between biometric eye parameters and the corneal deflection amplitude, which here is the measured quantity to trace back to the IOP. Implementing the relations provided by the eye model in a Gaussian uncertainty propagation calculation now allows the quantification of the uncertainty contributions of the biometric parameters on the overall measurement uncertainty of the acoustic self-tonometer. As a result, a systematic uncertainty contribution resulting from deviations in eye length dominate stochastic deviations of the sensor equipment by a factor of 3.5. Conclusion: As perspective, the proposed adaptive eye model provides the basis to compensate for systematic deviations of (but not only) the acoustic self-tonometer.