Abstract
Phacoemulsification with intraocular lens (IOL) implantation is a widely used effective treatment for cataracts. However, the surgical outcome relies heavily on precise operations with marked eye location and orientation, which ideally require a high-precision navigation system for complete guidance of surgical procedure. However, both research and current commercial surgical microscopes still face substantial challenges in handling various complex clinical scenarios. Here we propose a neural network-powered surgical microscopic system that can benefit from big data to address the unmet clinical need. In this system, we designed an end-to-end navigation network for real-time positioning and alignment of IOL and then built a computer-assisted surgical microscope with a complete imaging and display platform integrating the control software and algorithms for surgical planning and navigation. The network used an attention-based encoder-decoder architecture with an edge padding mechanism and an MLP layer for eye center localization, and combined siamese network, correlation filter, and spatial transformation network to track eye rotation. Using computer-aided annotation, we collected and labeled 100 clinical surgery videos from 100 patients, and proposed a data augmentation method to enhance the diversity of training. We further evaluated the navigation performance of the microscopic system on a human eye model.