Parameter estimation and model reduction for model predictive control in retinal laser treatment

authored by: Manuel Schaller, Mitsuru Wilson, Viktoria Kleyman, Mario Mordmüller, Ralf Brinkmann, Matthias A. Müller, Karl Worthmann
Abstract: Laser photocoagulation is one of the most frequently used treatment approaches for retinal diseases such as diabetic retinopathy and macular edema. The use of model-based control, such as Model Predictive Control (MPC), enhances a safe and effective treatment by guaranteeing temperature bounds. In general, real-time requirements for model-based control designs are not met since the temperature distribution in the eye fundus is governed by a heat equation with a nonlinear parameter dependency. This issue is circumvented by representing the model by a lower-dimensional system which well-approximates the original model, including the parametric dependency. We combine a global-basis approach with the discrete empirical interpolation method, tailor its hyperparameters to laser photocoagulation, and show its superiority in comparison to a recently proposed method based on Taylor-series approximation. Its effectiveness is measured in computation time for MPC. We further present a case study to estimate the range of absorption parameters in porcine eyes, and by means of a theoretical and numerical sensitivity analysis we show that the sensitivity of the temperature increase is higher with respect to the absorption coefficient of the retinal pigment epithelium (RPE) than of the choroid's.
Organisation(s): Institute of Automatic Control
External Organisation(s): Ilmenau University of Technology
Universität zu Lübeck
Lübeck Medical Laser Centre
Type: Article
Journal: Control engineering practice
Volume: 128
ISSN: 0967-0661
Publication date: 11.2022
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Control and Systems Engineering, Computer Science Applications, Electrical and Electronic Engineering, Applied Mathematics
Sustainable Development Goals: SDG 3 - Good Health and Well-being
Electronic version(s): https://doi.org/10.1016/j.conengprac.2022.105320 (Access: Closed)