ArticleArthroscopy, sports medicine, and rehabilitation2022
Computational Metrics Can Provide Quantitative Values to Characterize Arthroscopic Field of View.
Article in Arthroscopy, sports medicine, and rehabilitation, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed, 2 citations in OpenAlex.
- The reliability of deep learning models in assessing the shoulder arthroscopic field's visual clarity in relation to bleeding.JSES international · 2025Article
- Validation of Novel Image Processing Method for Objective Quantification of Intra-Articular Bleeding During Arthroscopic Procedures.Journal of imaging · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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; 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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.