Section: New Results

A Combined Simulation and Machine Learning Approach for Force Classification during Robotized Intravitreal Injections

Intravitreal injection is one of the most common treatment strategies for chronic ophthalmic diseases. The last decade has seen the number of intravitreal injections dramatically increase, and with it, adverse effects and limitations. To overcome these issues, medical assistive devices for robotized injections have been proposed and are projected to improve delivery mechanisms for new generation of pharmacological solutions. In our work, we propose a method aimed at improving the safety features of such envisioned robotic systems. Our vision-based method uses a combination of 2D OCT data, numerical simulation and machine learning to estimate the range of the force applied by an injection needle on the sclera (see Fig. 6). We build a Neural Network (NN) to predict force ranges from Optical Coherence Tomography (OCT) images of the sclera directly. To avoid the need of large training data sets, the NN is trained on images of simulated deformed sclera. We validate our approach on real OCT images collected on five ex vivo porcine eyes using a robotically-controlled needle. Results show that the applied force range can be predicted with $94\%$ accuracy. Being real-time, this solution can be integrated in the control loop of the system, allowing for in-time withdrawal of the needle.

Figure 6. During the robotized intravitreal injection an OCT image of the deformed sclera is collected. The obtained image is processed and given a to a Neural Network that predicts the force range of the force applied by the robotically guided needle.

• Authors: Andrea Mendizabal and Stephane Cotin

• Type: Conference publication MICCAI 2018