ArticleJournal of medical Internet research2024
Collaborative Human-Computer Vision Operative Video Analysis Algorithm for Analyzing Surgical Fluency and Surgical Interruptions in Endonasal Endoscopic Pituitary Surgery: Cohort Study.
Article in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06156020 (Analysis of Operative Outcomes Following Endoscopic Resection of Pituitary Adenomas), which is not on this map. Cited by 5 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Analysis of Operative Outcomes Following Endoscopic Resection of Pituitary Adenomas
Who cites it
5 citing papers in PubMed.
- Evaluation of nursing performance during coronary artery bypass grafting surgeries using machine learning analysis of surgical videos: a multicenter retrospective study.Journal of cardiothoracic surgery · 2026Article
- Decoding the OR Black Box and Similar Technologies and Considerations in Otolaryngologic Surgery.Laryngoscope investigative otolaryngology · 2026Review
- Surgical video-based temporal action analysis algorithm and competency assessment in laparoscopic cholecystectomy: development and exploratory evaluation.Surgical endoscopy · 2026Article
- Human-Computer Vision Collaborative Measurement of Surgical Exposure and Length in Endonasal Endoscopic Skull Base Surgery.IEEE open journal of engineering in medicine and biology · 2025Article
- The Impact of Surgical Telementoring on Reducing the Complication Rate in Endoscopic Endonasal Surgery of the Skull Base.Diagnostics (Basel, Switzerland) · 2024Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe 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.
objectiveThis 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.
methodsThe 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.
resultsA 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.
conclusionsCV-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 registrationClinicalTrials.gov NCT06156020; https://clinicaltrials.gov/study/NCT06156020.
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