Spatiotemporal Motion Prediction of Intraocular Microsurgical Robot in Non-Visible Regions

Abstract

In intraocular microsurgery with minute operational scales, instruments pass through non-visible regions of the anterior segment, where robot-assisted surgery, which heavily relies on visual perception, fails to determine the instrument’s attitude relative to the eyeball. This compromises surgical flexibility, increases risks, and hinders autonomous surgery development. Therefore, a framework for predicting instrument trajectories in non-visible regions during robot-assisted microsurgery has been proposed to mitigate the risks of retinal and lens injuries caused by blind operations and enhance surgical procedures’ intelligence and autonomy. First, a lightweight reconstruction of the anterior segment environment is performed under controlled knowledge guidance to construct a global map. Second, the tip position of the surgical instrument is detected through multi-sensor fusion, enabling the perception of instrument-environment interactions under visual constraints. Based on this, a long short-term spatiotemporal aggregation algorithm for instrument trajectory prediction is proposed, which enhances surgical safety by providing high-precision predictions of the instrument tip’s motion trajectory. Experiments show that the framework achieved a 0.0435 mm average prediction error in non-visible regions, corresponding to 0.03% of the region in a single dimension and 7.25% of the surgical instrument’s diameter. This significantly enhances the precision of robot-assisted surgery under visual constraints and provides robust technical support for safe, intelligent, and autonomous intraocular robotic surgery.