Background/Objectives: Tenosynovitis is a common feature of psoriatic arthritis (PsA) and is typically assessed using semi-quantitative magnetic resonance imaging (MRI) scoring. However, visual scoring s variability. This study evaluates a fully automated, deep-learning approach for ankle tenosynovitis segmentation and volume-based quantification from MRI in psoriatic arthritis (PsA) patients. Methods: We analyzed 364 ankle 3T MRI scans from 71 PsA patients. Four tenosynovitis pathologies were manually scored and used to create ground truth segmentations through a human–machine workflow. For each pathology, 30 annotated scans were used to train a deep-learning segmentation model based on the nnUNet framework, and 20 scans were used for testing, ensuring patient-level disjoint sets. Model performance was evaluated using Dice scores. Volumetric pathology measurements from test scans were compared to radiologist scores using Spearman correlation. Additionally, 218 serial MRI pairs were assessed to analyze the relationship between changes in pathology volume and changes in visual scores. Results: The segmentation model achieved promising performance on the test set, with mean Dice scores ranging from 0.84 to 0.92. Pathology volumes correlated with visual scores across all test MRIs (Spearman ρ = 0.52–0.62). Volume-based quantification captured changes in inflammation over time and identified subtle progression not reflected in semi-quantitative scores. Conclusions: Our automated segmentation tool enables fast and accurate quantification of ankle tenosynovitis in PsA patients. It may enhance sensitivity to disease progression and complement visual scoring through continuous, volume-based metrics.

Background: Lumbar interbody fusion (IF) is a common procedure to fuse the anterior spine. However, a lack of consensus on image-based fusion assessment limits the validity and comparison of IF studies. This systematic review aims to (1) report on IF assessment strategies and definitions and (2) summarize available literature on the diagnostic reliability and accuracy of these assessments. Methods: Two searches were performed according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. Search 1 identified studies on adult lumbar IF that provided a detailed description of image-based fusion assessment. Search 2 analyzed studies on the reliability of specific fusion criteria/classifications and the accuracy assessed with surgical exploration. Results: A total of 442 studies were included for search 1 and 8 studies for search 2. Fusion assessment throughout the literature was highly variable. Eighteen definitions and more than 250 unique fusion assessment methods were identified. The criteria that showed most consistent use were continuity of bony bridging, radiolucency around the cage, and angular motion <5°. However, reliability and accuracy studies were scarce. Conclusion: This review highlights the challenges in reaching consensus on IF assessment. The variability in IF assessment is very high, which limits the translatability of studies. Accuracy studies are needed to guide innovations of assessment. Future IF assessment strategies should focus on the standardization of computed tomography-based continuity of bony bridging. Knowledge from preclinical and imaging studies can add valuable information to this ongoing discussion.

Magnetic resonance Imaging is the gold standard for assessment of soft tissues; however, X-ray-based techniques are required for evaluating bone-related pathologies. This study evaluated the performance of synthetic computed tomography (sCT), a novel MRI-based bone visualization technique, compared with CT, for the scoring of knee osteoarthritis. sCT images were generated from the 3T T1-weighted gradient-echo MR images using a trained machine learning algorithm. Two readers scored the severity of osteoarthritis in tibiofemoral and patellofemoral joints according to OACT, which enables the evaluation of osteoarthritis, from its characteristics of joint space narrowing, osteophytes, cysts and sclerosis in CT (and sCT) images. Cohen's κ was used to assess the interreader agreement for each modality, and intermodality agreement of CT- and sCT-based scores for each reader. We also compared the confidence level of readers for grading CT and sCT images using confidence scores collected during grading. Inter-reader agreement for tibiofemoral and patellofemoral joints were almost-perfect for both modalities (κ = 0.83–0.88). The intermodality agreement of osteoarthritis scores between CT and sCT was substantial to almost-perfect for tibiofemoral (κ = 0.63 and 0.84 for the two readers) and patellofemoral joints (κ = 0.78 and 0.81 for the two readers). The analysis of diagnosis confidence scores showed comparable visual quality of the two modalities, where both are showing acceptable confidence levels for scoring OA. In conclusion, in this single-center study, sCT and CT were comparable for the scoring of knee OA.

Diagnosis of ankle impingement is performed primarily by clinical examination, whereas medical imaging is used for severity staging and treatment guidance. The association of impingement symptoms with regional three-dimensional (3D) bone shape variaties visible in medical images has not been systematically explored, nor do we know the type and magnitude of this relation. In this cross-sectional case-control study, we hypothesized that 3D talus bone shape could be used to quantitatively formulate the discriminating shape variations between ankles with impingement from ankles without impingement, and we aimed to characterize and quantify these variations. We used statistical shape modeling (SSM) methods to determine the most prevalent modes of shape variations that discriminate between the impinged and nonimpinged ankles. Results of the compactness and parallel analysis test on the statistical shape model identify 8 prominent shape modes of variations (MoVs) representing approximately 78% of the total 3D variations in the population of shapes, among which two modes captured discriminating features between impinged and nonimpinged ankles (p value of 0.023 and 0.042). Visual inspection confirms that these two shape modes, capturing abnormalities in the anterior and posterior parts of talus, represent the two main bony risk factors in anterior and posterior ankle impingement. In conclusion, in this research using SSM we have identified shape MoVs that were found to correlate significantly with bony ankle impingement. We also illustrated potential guidance from SSMs for surgical planning.