Proceedings on Automation in Medical Engineering
Vol. 3 No. 1 (2026): Proc AUTOMED
https://doi.org/10.18416/AUTOMED.2026.2467
Towards data-driven predictive temperature control for retinal laser treatments
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Copyright (c) 2026 Proceedings on Automation in Medical Engineering
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Abstract
Laser-induced coagulation is a common treatment of retinal diseases. Manual dosing is prone to error and can lead to extended
damage of the neural retina. We elaborated a novel data-driven predictive temperature control (DPC). To identify controller
hyperparameters with minimal deviation from the target temperature extensive simulation was carried out, based on 206
measured data sets, each applied pulse energy and induced temperature rise. This resulted in a median and mean deviation
from the target temperature of 1.6 °C and 2.6 °C (95 % CI = 2.3 - 3.1 °C), respectively. In addition, we developed a detection
algorithm for eye movements (saccades), optimized with Bayesian Optimization that achieved detection sensitivity of 92 % and
specificity of 100 %. The experience with Bayesian Optimization shall be used to further optimize the DPC and to evaluate
clinical data.