Computational Metrics Can Provide Quantitative Values to Characterize Arthroscopic Field of View

Abstract

Purpose: 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.

Methods: 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.

Results: Inter-rater reliability for grading video quality had an intraclass correlation of .974. Improved image quality rating was positively correlated with increased entropy (.8142; P < .001), contrast (.8013; P < .001), and brightness (.6120; P < .001), and negatively correlated with redness (-.8626; P < .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.

Conclusions: 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.

Clinical relevance: 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.

Fig 1

Standard arthroscopic setup at our institution.

Fig 2

Representative screenshots from videos that received unanimous grading for visualization quality by all 5 participants. (A) Unanimous score of 4: excellent view—no limitation of view, procedure unimpeded. This screen shot is taken from the beach chair position using the posterolateral viewing portal, looking at the rotator cuff during a rotator cuff repair. (B) Unanimous score of 3: good view—slightly limited, procedure unimpeded. This screen shot is taken from the beach chair position using posterolateral viewing portal, looking at acromion during acromioplasty. (C) Unanimous score of 2: fair view—limited, procedure impeded slightly. This screen shot is taken from the beach chair position using the posterolateral viewing portal. (D) Unanimous score of 1: poor view—limited, procedure impeded markedly. This screen shot is taken from the beach chair position using the posterolateral viewing portal, looking at the subacromial space during a rotator cuff repair.

Fig 3

Illustration of masking process used to identify valid pixels for processing in each video frame.

Fig 4

Two images to demonstrate image entropy. The first image has entropy of 5.5128. The second is a posterized version of the first, with a reduced the number of grayscale values in the image, which has entropy of 1.5023.

Fig 5

Scatterplots for each image metric against average user rating, with least squares line of best fit. Each circle represents a video and is colored by average user rating value.

Fig 6

Added variable plot of whole linear regression model, illustrating that the model is significant.