arXiv cs.CL· Arda Mamur, Matthias Seibold, Christoph Laux, Florian Wanivenhaus, Philipp F\"urnstahl·· 12 小时前AI 评分20
手术室内实时术中专家口述内容的系统性分析
A Systematic Analysis of Live Intraoperative Surgical Expert Narrations
AI 导读
研究分析了两名资深脊柱外科医生实施的13台内镜下腰椎减压手术（13.2小时）的实时口述音频，经无标注流水线将语音转为带时间戳的手术线索。3171条话语中1201条（37.9%）被保留为程序性线索，其中60.7%提及器械、48.0%提及解剖结构、57.1%提及手术动作，39%为指令。全部467条经人工核验的器械请求均早于器械出现在视野中，中位提前8.2秒，但18.0%的保留线索存在领域术语转写错误。
正文 · 原文
Abstract:Manual annotation remains the principal bottleneck in surgical video understanding. Live intraoperative narration by the operating surgeon is a scalable alternative that requires no manual annotation, but the information it contains has not been characterized. We analyzed 13 endoscopic lumbar spine decompression procedures (13.2 h) by two senior spine surgeons, using time-synchronized close-mic German audio. An annotation-free pipeline (word-level speech recognition, deterministic correction of surgical-term errors, utterance fusion, LLM-based translation to English, and relevance filtering) converted speech into time-stamped procedural cues labelled with the surgical entities they reference and their speech act. Of 3,171 utterances, 1,201 (37.9%) were retained as procedural cues, while 57.6% were non-procedural and removed. Retained cues referenced instruments (60.7%), anatomy (48.0%) and surgical actions (57.1%), and 98.5% carried at least one of these. 39% of cues (472 of 1,201) were directives, mainly instrument requests and commands. All 467 manually verified instrument requests preceded the instrument's appearance in the field, by a median of 8.2 s. Domain-term transcription corruption affected 18.0% of retained cues. Live narration is thus a dense, annotation-free supervisory signal describing instruments, anatomy, actions and team coordination, but it is noisy and temporally displaced from the events it names, and this displacement must be modelled before safe downstream use.
| Comments: | Accepted as abstract at the SafeSurg Workshop at MICCAI 2026 |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL) |
| ACM classes: | I.2.10; J.3 |
| Cite as: | arXiv:2611.00028 [cs.CV] |
| (or arXiv:2611.00028v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2611.00028 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Arda Mamur [view email]
[v1]
Mon, 5 Oct 2026 09:48:28 UTC (423 KB)
来源：arXiv cs.CL · arxiv.org