The Uses and Advances in Imaging for Psoriatic Arthritis: A Scoping Review
Bibliographic record
Abstract
Objectives Psoriatic arthritis (PsA) is a chronic inflammatory condition characterized by variable involvement of the skin and musculoskeletal system. It is associated with significant comorbidities leading to disability, poor mental and physical health outcomes, and decreased quality of life.[1] With advances in treatment, early diagnosis has been key in early intervention and stopping disease progression.[2] Our review aims to map the literature on clinical trials with a focus on imaging modalities, advancements in imaging techniques, and identify gaps to propose directions for future research. Methods Following the Arksey and O’Malley framework for scoping reviews we conducted a comprehensive search of databases including MEDLINE, Embase, PubMed Central, CINAHL, Academic Search Complete, and ScienceDirect, covering the period from January 1, 2000, to June 27, 2024.[3] After deduplication, title and abstract screening, full-text review, and citation searching, a total of 53 peer-reviewed articles met our inclusion criteria for data extraction and analysis. Results Our search captured 1961 articles and after deduplication, title and abstract screening, and full-text review, 53 studies were included for analysis (Figure 1). Radiographs were used in 32/53 studies (60%) and the majority of these were randomized controlled trials (RCTs) (53%). Radiographic progression was most frequently measured with the PsA-modified Sharp/van der Heijde score (69%). Ultrasound (US), which included power doppler US (PD-US) and grayscale US (GS-US), was used in 11/53 studies (21%) to assess for synovial hypertrophy and inflammation. Though standardized scoring systems such as the GLOESS are available, they were only used in 3/11 studies (27%). Magnetic resonance imaging (MRI) was used in 16/53 (30%) studies and evaluated both peripheral and axial disease in PsA. Validated scoring systems such as PsAMRIS and SPARCC are becoming widely adopted in more recent trials. Dynamic contrast enhanced MRI (DCE-MRI) was also compared to computed tomography (CT) and demonstrated high sensitivity for bone erosions and inflammation. Multimodal imaging was used in 7/53 studies (13%) and CT was only used in 2/53 (4%). Fig 1. PRISMA flow chart of the review process Conclusion The development of PsA-specific scoring systems for X-ray and MRI has been instrumental in advancing imaging assessment in PsA. However, their application remains limited, particularly in ultrasound, where further standardization is needed. Future clinical trials should focus on increasing the adoption of PsA-specific scoring systems across modalities, exploring novel imaging techniques such as DCE-MRI, and using multi-modal imaging to improve disease monitoring in PsA. [1.] Haugeberg G. RMD Open 2020;6:e001223. [2.] Crespo-Rodríguez AM. Insights Imaging 2005;12:12. [3.] Arksey H. Int J Soc Res Method 2005:8:19-32.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.033 | 0.030 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".