ArticleJOR spine2025
Comprehensive Evaluation of Facet Joints Osteoarthritis Radiological Features on Lumbar CT: A Multitask Deep Learning Approach.
Article in JOR spine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
Background: Accurately evaluating the radiological features of facet joint osteoarthritis (FJOA) may help to elucidate its relationship with pain. Multitask deep learning (DL) models have emerged as promising tools for this purpose. Materials and Methods: This retrospective study employed a dataset of 13 223 axial CT facet joint (FJ) patches cropped from 1 360 patients across two hospitals. At the image level, the dataset was categorized as training dataset ( Results: In this study, the accuracy of the model on the internal and external test datasets was 89.8% and 76.6% for joint space narrowing (JSN), 79.6% and 80.2% for osteophytes, 65.5% and 56% for hypertrophy, 88% and 89.6% for subchondral bone erosions, and 82.8% and 89.8% for subchondral cysts. The model's Gwet Conclusion: A multitask DL model is a viable method for assessing the severity of radiological features in FJOA, offering support to readers during image evaluation.
Indexed as
Identifiers
What Socratic 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 Socratic graph.