Barnes Ryan H, Golden M Leslie, Borland David, Heckert Reed, Richardson Meghan, Creighton R Alexander, Spang Jeffrey T, Kamath Ganesh V
University of North Carolina at Chapel Hill, Department of Orthopaedics, Chapel Hill, North Carolina, U.S.A.
University of North Carolina at Chapel Hill, Renaissance Computing Institute (RENCI), Chapel Hill, North Carolina, U.S.A.
Arthrosc Sports Med Rehabil. 2021 Dec 7;4(2):e403-e409. doi: 10.1016/j.asmr.2021.10.017. eCollection 2022 Apr.
The purpose of this study was to determine the inter-rater reliability of arthroscopic video quality, determine correlation between surgeon rating and computational image metrics, and facilitate a quantitative methodology for assessing video quality.
Five orthopaedic surgeons reviewed 60 clips from deidentified arthroscopic shoulder videos and rated each on a four-point Likert scale from poor to excellent view. The videos were randomized, and the process was completed a total of three times. Each user rating was averaged to provide a user rating per clip. Each video frame was processed to calculate brightness, local contrast, redness (used to represent bleeding), and image entropy. Each metric was then averaged over each frame per video clip, providing four image quality metrics per clip.
Inter-rater reliability for grading video quality had an intraclass correlation of .974. Improved image quality rating was positively correlated with increased entropy (.8142; < .001), contrast (.8013; < .001), and brightness (.6120; < .001), and negatively correlated with redness (-.8626; < .001). A multiple linear regression model was calculated with the image metrics used as predictors for the image quality ranking, with an R-squared value of .775 and root mean square error of .42.
Our study demonstrates strong inter-rater reliability between surgeons when describing image quality and strong correlations between image quality and the computed image metrics. A model based on these metrics enables automatic quantification of image quality.
Video quality during arthroscopic cases can impact the ease and duration of the case which could contribute to swelling and complication risk. This pilot study provides a quantitative method to assess video quality. Future works can objectively determine factors that affect visualization during arthroscopy and identify options for improvement.
本研究旨在确定关节镜视频质量的评分者间信度，确定外科医生评分与计算图像指标之间的相关性，并促进一种评估视频质量的定量方法。
五名骨科医生查看了来自匿名关节镜肩部视频的60个片段，并根据从差到优的四点李克特量表对每个片段进行评分。视频是随机的，该过程总共完成了三次。每个用户的评分进行平均，以提供每个片段的用户评分。对每个视频帧进行处理，以计算亮度、局部对比度、红色度（用于表示出血）和图像熵。然后对每个视频片段的每一帧的每个指标进行平均，为每个片段提供四个图像质量指标。
视频质量分级的评分者间信度的组内相关性为0.974。图像质量评分的提高与熵增加（0.8142；P<0.001）、对比度增加（0.8013；P<0.001）和亮度增加（0.6120；P<0.001）呈正相关，与红色度呈负相关（-0.8626；P<0.001）。使用图像指标作为图像质量排名的预测因子计算了一个多元线性回归模型，R平方值为0.775，均方根误差为0.42。
我们的研究表明，外科医生在描述图像质量时具有很强的评分者间信度，并且图像质量与计算图像指标之间具有很强的相关性。基于这些指标的模型能够自动量化图像质量。
关节镜手术过程中的视频质量会影响手术的难易程度和持续时间，这可能会导致肿胀和并发症风险。这项初步研究提供了一种评估视频质量的定量方法。未来的工作可以客观地确定影响关节镜检查可视化的因素，并确定改进的选项。
