Division of Neurosurgery, Department of Surgery, National Cheng Kung University Hospital, Tainan, Taiwan.
Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan.
J Med Internet Res. 2024 Jul 4;26:e56127. doi: 10.2196/56127.
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. TRIAL REGISTRATION: ClinicalTrials.gov NCT06156020; https://clinicaltrials.gov/study/NCT06156020.
背景：经鼻内镜手术（EEA）是治疗垂体腺瘤的有效方法。然而，手动审查手术视频非常耗时。计算机视觉（CV）算法的应用有可能减少手术视频审查所需的时间，并有助于培训外科医生以克服 EEA 的学习曲线。
目的：本研究旨在评估基于 OpenCV 算法的基于 CV 的视频分析系统在检测 EEA 中的手术中断和分析手术流畅性方面的性能。研究了基于 CV 的视频分析的准确性，并比较了基于 CV 的分析与手动审查所需的手术视频审查时间。
方法：使用 OpenCV 确定 EEA 视频中每一帧的主导颜色。我们开发了一种算法，如果主导颜色像素的变化达到一定阈值，则可以识别手术中断事件。通过使用 EEA 视频对当前算法进行训练来确定阈值。基于手动审查确定 CV 分析的准确性，并报告所花费的时间。
结果：共分析了 46 例 EEA 手术视频，训练集、测试 1 集和测试 2 集的准确率分别为 93.6%、95.1%和 93.3%。与手动审查相比，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 分钟和 CV 分析：22.6 分钟；P<.001）。应用人机协作策略将整体准确率提高到 98.5%，审查时间减少 74%（手动审查：166.8 分钟和人机协作：43.4 分钟；P<.001）。对不同手术阶段的分析表明，鞍区的中断频率（鼻腔期：14.9 次，蝶窦期：15.9 次，鞍区：4.9 次/10 分钟；P<.001）和持续时间（鼻腔期：67.4 秒，蝶窦期：77.9 秒，鞍区：31.1 秒/10 分钟；P<.001）最低。早期和晚期 EEA 视频的比较表明，随着手术经验的增加，中断次数（早期：4.9 次和晚期：2.9 次/10 分钟；P=.03）和持续时间（早期：41.1 秒和晚期：19.8 秒/10 分钟；P=.02）减少。
结论：基于 CV 的分析在检测 EEA 期间发生的手术中断的数量、频率和持续时间方面具有 93%至 98%的准确性。此外，与手动审查相比，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 的应用可以帮助培训外科医生以克服内镜颅底手术的学习曲线。
试验注册：ClinicalTrials.gov NCT06156020；https://clinicaltrials.gov/study/NCT06156020。
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