背景:经鼻内镜入路(EEA)可有效切除垂体腺瘤。然而,手术视频的手动审查是耗时的。计算机视觉(CV)算法的应用可能会减少手术视频审查所需的时间,并促进外科医生的培训以克服EEA的学习曲线。
目的:本研究旨在评估基于CV的视频分析系统的性能,基于OpenCV算法,在EEA中检测手术中断并分析手术流畅性。研究了基于CV的视频分析的准确性,并将使用基于CV的分析进行手术视频审查所需的时间与手动审查所需的时间进行了比较。
方法:使用OpenCV确定EEA视频中每个帧的主色。我们开发了一种算法,如果主色像素的变化达到某些阈值,则可以识别手术中断事件。通过使用EEA视频训练当前算法来确定阈值。CV分析的准确性是通过人工审查确定的,并报告了花费的时间。
结果:共分析了46个EEA手术视频,93.6%,95.1%,培训准确率为93.3%,测试1和测试2数据集,分别。与人工审核相比,基于CV的分析将手术视频审查所需的时间减少了86%(手动审查:166.8和CV分析:22.6分钟;P<.001)。人机协同策略的应用使整体准确率提高到98.5%,审查时间减少了74%(人工审查:166.8和人类CV协作:43.4分钟;P<.001)。对不同手术阶段的分析表明,鞍相的频率最低(鼻相:14.9,蝶形相:15.9,鞍相:4.9中断/10分钟;P<.001)和持续时间(鼻相:67.4,蝶形相:77.9,鞍相:31.1秒/10分钟;P<.001)。早期和晚期EEA视频的比较表明,手术经验的增加与鞍期手术中断的数量减少(早期:4.9和晚期:2.9中断/10分钟;P=.03)和持续时间(早期:41.1和晚期:19.8秒/10分钟;P=.02)相关。
结论:基于CV的分析在检测数字方面具有93%至98%的准确性,频率,和在EEA期间发生的手术中断的持续时间。此外,与手动检查相比,基于CV的分析减少了分析EEA视频中手术流畅性所需的时间。CV的应用可以促进外科医生的培训,以克服内窥镜颅底手术的学习曲线。
背景:ClinicalTrials.govNCT06156020;https://clinicaltrials.gov/study/NCT06156020。
BACKGROUND: The endonasal endoscopic approach (EEA) is effective for pituitary adenoma resection. However, manual review of operative videos is time-consuming. The application of a computer vision (CV) algorithm could potentially reduce the time required for operative video review and facilitate the training of surgeons to overcome the learning curve of EEA.
OBJECTIVE: This
study aimed to evaluate the performance of a CV-based video analysis system, based on OpenCV algorithm, to detect surgical interruptions and analyze surgical fluency in EEA. The accuracy of the CV-based video analysis was investigated, and the time required for operative video review using CV-based analysis was compared to that of manual review.
METHODS: The dominant color of each frame in the EEA video was determined using OpenCV. We developed an algorithm to identify events of surgical interruption if the alterations in the dominant color pixels reached certain thresholds. The thresholds were determined by training the current algorithm using EEA videos. The accuracy of the CV analysis was determined by manual review, and the time spent was reported.
RESULTS: A total of 46 EEA operative videos were analyzed, with 93.6%, 95.1%, and 93.3% accuracies in the training, test 1, and test 2 data sets, respectively. Compared with manual review, CV-based analysis reduced the time required for operative video review by 86% (manual review: 166.8 and CV analysis: 22.6 minutes; P<.001). The application of a human-computer collaborative strategy increased the overall accuracy to 98.5%, with a 74% reduction in the review time (manual review: 166.8 and human-CV collaboration: 43.4 minutes; P<.001). Analysis of the different surgical phases showed that the sellar phase had the lowest frequency (nasal phase: 14.9, sphenoidal phase: 15.9, and sellar phase: 4.9 interruptions/10 minutes; P<.001) and duration (nasal phase: 67.4, sphenoidal phase: 77.9, and sellar phase: 31.1 seconds/10 minutes; P<.001) of surgical interruptions. A comparison of the early and late EEA videos showed that increased surgical experience was associated with a decreased number (early: 4.9 and late: 2.9 interruptions/10 minutes; P=.03) and duration (early: 41.1 and late: 19.8 seconds/10 minutes; P=.02) of surgical interruptions during the sellar phase.
CONCLUSIONS: CV-based analysis had a 93% to 98% accuracy in detecting the number, frequency, and duration of surgical interruptions occurring during EEA. Moreover, CV-based analysis reduced the time required to analyze the surgical fluency in EEA videos compared to manual review. The application of CV can facilitate the training of surgeons to overcome the learning curve of endoscopic skull base surgery.
BACKGROUND: ClinicalTrials.gov NCT06156020; https://clinicaltrials.gov/
study/NCT06156020.