Osmers Jan, Kaiser Nils, Sorg Michael, Fischer Andreas
University of Bremen, Bremen Institute for Metrology, Automation and Quality Science (BIMAQ), Linzer Str. 13, Bremen 28359, Germany.
University of Bremen, Bremen Institute for Metrology, Automation and Quality Science (BIMAQ), Linzer Str. 13, Bremen 28359, Germany.
Comput Methods Programs Biomed. 2021 Mar;200:105930. doi: 10.1016/j.cmpb.2021.105930. Epub 2021 Jan 9.
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.
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.
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.
As perspective, the proposed adaptive eye model provides the basis to compensate for systematic deviations of (but not only) the acoustic self-tonometer.
青光眼是目前全球不可逆性失明的主要原因。眼压（IOP）是一个风险因素且是唯一的治疗控制参数。眼压通过眼压计测量，其测量不可避免地受到眼睛几何形状的影响。尽管已经对角膜力学进行了研究以提高戈德曼眼压计和空气脉冲眼压计的准确性，但眼睛几何特性对声学自眼压计方法的影响仍未解决。
为了理解和补偿因眼睛几何独特性导致的测量偏差，设计了一个有限元眼模型，该模型考虑了所有相关眼部组件，并可调整以适应人眼的所有生理形状。
通过对猪眼的实验室测量验证了眼模型一般的眼压依赖性行为。对于5至40 mmHg的眼压水平，模拟值与测量值之间的差异低于8 µm。然后，基于一个临床试验系列的输入统计数据，使用自适应眼模型来量化眼轴长度和中央角膜厚度变化的系统不确定性贡献。自适应眼模型提供了生物测量眼参数与角膜偏转幅度之间所需的关系，在此角膜偏转幅度是用于追溯到眼压的测量量。在高斯不确定性传播计算中应用眼模型提供的关系，现在可以量化生物测量参数对声学自眼压计整体测量不确定性的不确定性贡献。结果表明，眼轴长度偏差导致的系统不确定性贡献比传感器设备的随机偏差大3.5倍。
展望未来，所提出的自适应眼模型为补偿（但不限于）声学自眼压计的系统偏差提供了基础。
