Automated detection of surgical errors can improve robotic-assisted surgery.
Despite promising progress, existing methods still face challenges in capturing
rich temporal context to establish long-term dependencies while maintaining
computational efficiency. In this paper, we propose a novel hierarchical model
named SEDMamba, which incorporates the selective state space model (SSM) into
surgical error detection, facilitating efficient long sequence modelling with
linear complexity. SEDMamba enhances selective SSM with bottleneck mechanism
and fine-to-coarse temporal fusion (FCTF) to detect and temporally localize
surgical errors in long videos. The bottleneck mechanism compresses and
restores features within their spatial dimension, thereby reducing
computational complexity. FCTF utilizes multiple dilated 1D convolutional
layers to merge temporal information across diverse scale ranges, accommodating
errors of varying durations. Besides, we deploy an established observational
clinical human reliability assessment tool (OCHRA) to annotate the errors of
suturing tasks in an open-source radical prostatectomy dataset (SAR-RARP50),
constructing the first frame-level in-vivo surgical error detection dataset to
support error detection in real-world scenarios. Experimental results
demonstrate that our SEDMamba outperforms state-of-the-art methods with at
least 1.82% AUC and 3.80% AP performance gain with significantly reduced
computational complexity.

研究表明，使用名为 SEDMamba 的新颖分层模型，通过将选择性状态空间模型 (SSM) 与外科手术错误检测相结合，可以有效地进行长序列建模和定位手术错误，从而提高机器辅助手术的自动错误检测性能。

SEDMamba: 加强选择性状态空间建模，利用瓶颈机制和细粗时间融合实现机器人辅助手术的高效错误检测

SEDMamba: Enhancing Selective State Space Modelling with Bottleneck  Mechanism and Fine-to-Coarse Temporal Fusion for Efficient Error Detection in  Robot-Assisted Surgery