  Phacoemulsification cataract surgery (PCS) is a routine procedure conducted
using a surgical microscope, heavily reliant on the skill of the
ophthalmologist. While existing PCS guidance systems extract valuable
information from surgical microscopic videos to enhance intraoperative
proficiency, they suffer from non-phasespecific guidance, leading to redundant
visual information. In this study, our major contribution is the development of
a novel phase-specific augmented reality (AR) guidance system, which offers
tailored AR information corresponding to the recognized surgical phase.
Leveraging the inherent quasi-standardized nature of PCS procedures, we propose
a two-stage surgical microscopic video recognition network. In the first stage,
we implement a multi-task learning structure to segment the surgical limbus
region and extract limbus region-focused spatial feature for each frame. In the
second 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 surgical
phase. Additionally, we collaborate closely with ophthalmologists to design AR
visual cues by utilizing techniques such as limbus ellipse fitting and regional
restricted normal cross-correlation rotation computation. We evaluated the
network on publicly available and in-house datasets, with comparison results
demonstrating its superior performance compared to related works. Ablation
results further validated the effectiveness of the limbus region-focused
spatial feature extractor and the combination of temporal features.
Furthermore, the developed system was evaluated in a clinical setup, with
results indicating remarkable accuracy and real-time performance. underscoring
its potential for clinical applications.
