  Today, cataract surgery is the most frequently performed ophthalmic surgery
in the world. The cataract, a developing opacity of the human eye lens,
constitutes the world's most frequent cause for blindness. During surgery, the
lens is removed and replaced by an artificial intraocular lens (IOL). To
prevent patients from needing strong visual aids after surgery, a precise
prediction of the optical properties of the inserted IOL is crucial. There has
been lots of activity towards developing methods to predict these properties
from biometric eye data obtained by OCT devices, recently also by employing
machine learning. They consider either only biometric data or physical models,
but rarely both, and often neglect the IOL geometry. In this work, we propose
OpticNet, a novel optical refraction network, loss function, and training
scheme which is unsupervised, domain-specific, and physically motivated. We
derive a precise light propagation eye model using single-ray raytracing and
formulate a differentiable loss function that back-propagates physical
gradients into the network. Further, we propose a new transfer learning
procedure, which allows unsupervised training on the physical model and
fine-tuning of the network on a cohort of real IOL patient cases. We show that
our network is not only superior to systems trained with standard procedures
but also that our method outperforms the current state of the art in IOL
calculation when compared on two biometric data sets.
