Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Jan 7, 2024
Open Peer Review Period: Jan 7, 2024 - Mar 3, 2024
Date Accepted: May 24, 2024
(closed for review but you can still tweet)
Human–computer vision collaborative operative video analysis algorithm for analyzing surgical fluency and surgical interruptions in endonasal endoscopic pituitary surgery
ABSTRACT
Background:
The endonasal endoscopic approach (EEA) is effective for pituitary adenoma resection. However, manual review of operative videos is a time-consuming task. The application of artificial intelligence (AI) and computer vision (CV) 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 develop and validate a CV-based video analysis system to detect surgical interruptions and analyze surgical fluency in EEA.
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.
Results:
A total of 36 EEA operative videos were analyzed, with an overall detection accuracy of 93.6 %. Compared with manual review, CV-based analysis reduced the time required for operative video review by 85 % (p < 0.001). Application of a human–computer collaborative strategy increased the overall accuracy to 98.5 %, with a 74 % reduction in the review time (p < 0.001). Analysis of the different surgical phases showed that the sellar phase had the lowest frequency and duration of surgical interruptions (p < 0.001). A comparison of the early and late EEA videos showed that increased surgical experience was associated with a decreased number (p = 0.028) and duration (p = 0.019) of surgical interruptions during the sellar phase.
Conclusions:
CV-based analysis reduces the time required to analyze the surgical fluency in EEA videos by detecting the number, frequency, and duration of surgical interruptions occurring during EEA. The application of AI and CV can facilitate the training of surgeons to overcome the learning curve of endoscopic skull base surgery. Clinical Trial: This study was registered on ClinicalTrials.gov (NCT06156020)
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