Summary
Eye surgeries involve very quick and precise movements that are hard to analyze because current computer models struggle to understand long and detailed sequences without slowing down too much. The authors created OphBiWSSD, a method that looks at surgical videos both forwards and backwards in time, which helps it spot important moments more accurately and faster than previous systems. This new approach uses a clever way to handle information efficiently, making it possible to monitor surgical steps closely without using too much computer memory. Their tests show it works better than earlier methods on a benchmark for eye surgery videos. This research could help build smarter tools that assist surgeons during operations.
What this means in practice
- •For clinical video analytics teams: Develop surgical monitoring tools that detect detailed eye surgery actions with precise timing and lower computational cost than existing methods.
- •For medical device software developers: Integrate efficient temporal modeling algorithms into ophthalmic surgical assistance systems to improve real-time feedback on instrument usage and procedure phases.$Commercial implications: Enables advanced surgical intelligence products for eye surgery centers by providing fast, accurate action recognition critical for intraoperative guidance.
Authors
Yang Liu, Qionghong Ma, Joongwon Chae, Lihui Luo, Yibing Shen, Yulin Zhuo, Yingting Zhu, Jiashu Chang, Xiaoyun Zhong, Dongmei Yu, Peter E. Lobie, Peiwu Qin, Chengming Yang
Abstract
High-frequency surgical maneuvers in ophthalmology necessitate high-fidelity temporal modeling, yet characterizing long-range procedural dependencies remains computationally prohibitive for attention-based architectures. Existing models often require aggressive temporal downsampling, which compromises the detection of fine-grained action boundaries and instrument-tissue interactions. To address these scalability constraints, we present OphBiWSSD, a framework that reformulates surgical temporal action localization leveraging Bidirectional State Space Duality. By employing a weight-tied selective scan mechanism that incorporates both preceding and succeeding surgical contexts, our approach facilitates the global synthesis of non-causal temporal cues with linear complexity. This streamlined architecture is well-suited to capture the bidirectional dependencies present in ophthalmic workflows, effectively bridging the gap between local boundary precision and long-range procedural context without incurring the quadratic memory overhead of traditional Transformers. Extensive experiments on the OphNet benchmark demonstrate that OphBiWSSD achieves state-of-the-art temporal localization performance, with mean Average Precisions of 44.42% on phases and 43.08% on operations, surpassing the baselines by 6.80% and 6.66%, respectively. Empirical validation indicates that our approach ensures precise temporal localization and offers a computationally viable pathway for deploying surgical intelligence systems in clinical environments. The code is publicly available at https://github.com/yo3nglau/OphBiWSSD.