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Phacoemulsification cataract surgery (PCS) is a routine procedure conductedusing a surgical microscope, heavily reliant on the skill of theophthalmologist. While existing PCS guidance systems extract valuableinformation from surgical microscopic videos to enhance intraoperativeproficiency, they suffer from non-phasespecific guidance, leading to redundantvisual information. In this study, our major contribution is the development ofa novel phase-specific augmented reality (AR) guidance system, which offerstailored AR information corresponding to the recognized surgical phase.Leveraging the inherent quasi-standardized nature of PCS procedures, we proposea two-stage surgical microscopic video recognition network. In the first stage,we implement a multi-task learning structure to segment the surgical limbusregion and extract limbus region-focused spatial feature for each frame. In thesecond stage, we propose the long-short spatiotemporal aggregation transformer(LS-SAT) network to model local fine-grained and global temporal relationships,and combine the extracted spatial features to recognize the current surgicalphase. Additionally, we collaborate closely with ophthalmologists to design ARvisual cues by utilizing techniques such as limbus ellipse fitting and regionalrestricted normal cross-correlation rotation computation. We evaluated thenetwork on publicly available and in-house datasets, with comparison resultsdemonstrating its superior performance compared to related works. Ablationresults further validated the effectiveness of the limbus region-focusedspatial feature extractor and the combination of temporal features.Furthermore, the developed system was evaluated in a clinical setup, withresults indicating remarkable accuracy and real-time performance. underscoringits potential for clinical applications.

Phase-Specific Augmented Reality Guidance for Microscopic Cataract Surgery Using Long-Short Spatiotemporal Aggregation Transformer