Standardized reporting of spine and sacroiliac joints in axial spondyloarthritis MRI: from the ESSR-Arthritis Subcommittee.

OBJECTIVES: Apply a modified Delphi-based approach and produce a practical, radiology-specific set of definitions for interpretation and standardization of the multiple MRI findings in axial spondyloarthritis (ax-SpA), specifically to aid the general radiologist with a musculoskeletal interest, working with gold standard basic MRI protocols. MATERIALS AND METHODS: We report the results of a modified Delphi-based consensus of 35 experts from 13 countries in the Arthritis Subcommittee of the European Society of Musculoskeletal Radiology (ESSR). Seventeen definitions were created (i.e., nine for the spine and eight for the sacroiliac joint) and two Delphi rounds were conducted on an electronic database, collated and revised by the project leader with agreement. Group leads were appointed for each definition following the first round. Final definitions included only those that reached a consensus > 80%; if > 50% agreed on exclusion consensus, definitions were excluded. Final results have been shared during the Arthritis meeting at the Annual ESSR Congress. RESULTS: Fourteen definitions, eight for the spine and six for the sacroiliac joint were agreed for standardized reporting. Andersson's, anterior corner sclerotic and costovertebral joint inflammatory lesions of the spine, with active and non-active erosions, and fat metaplasia of the sacroiliac joint reaching the highest consensus (≥ 95%). More than 50% of the experts agreed to exclude joint space inflammation in the sacroiliac joint and tissue backfill. Syndesmophytes reached 76% agreement. CONCLUSIONS: Agreed definitions by expert radiologists using a modified Delphi process, should allow standardized actionable radiology reports and clarity in reporting terminology of ax-SpA. CLINICAL RELEVANCE STATEMENT: The proposed definitions will support reporting from musculoskeletal and general radiologists working with gold-standard basic MRI, improve confidence in lesion assessment, and standardize terminology to provide actionable reports on MRI in patients with ax-SpA. KEY POINTS: Experts applied a modified Delphi method to optimize the definitions of MRI findings of ax-SpA. After two Delphi rounds and one in-person meeting, fourteen definitions reached the agreement threshold. These consensus-based definitions will aid in actionable reporting specifically for the general radiologist with a musculoskeletal interest.

Patellar Instability: Imaging Findings.

Patellofemoral instability results from impaired engagement of the patella in the trochlear groove at the start of flexion and may lead to pain and lateral patellar dislocation. It occurs most frequently in adolescents and young adults during sporting activities. Trochlear dysplasia, patella alta, and excessive lateralization of the tibial tuberosity are the most common risk factors for patellar instability. The main role of imaging is to depict and assess these anatomical factors and highlight features indicating previous lateral dislocation of the patella.

Elbow trauma in children: development and evaluation of radiological artificial intelligence models.

Rationale and Objectives: To develop a model using artificial intelligence (A.I.) able to detect post-traumatic injuries on pediatric elbow X-rays then to evaluate its performances in silico and its impact on radiologists' interpretation in clinical practice. Material and Methods: A total of 1956 pediatric elbow radiographs performed following a trauma were retrospectively collected from 935 patients aged between 0 and 18 years. Deep convolutional neural networks were trained on these X-rays. The two best models were selected then evaluated on an external test set involving 120 patients, whose X-rays were performed on a different radiological equipment in another time period. Eight radiologists interpreted this external test set without then with the help of the A.I. models . Results: Two models stood out: model 1 had an accuracy of 95.8% and an AUROC of 0.983 and model 2 had an accuracy of 90.5% and an AUROC of 0.975. On the external test set, model 1 kept a good accuracy of 82.5% and AUROC of 0.916 while model 2 had a loss of accuracy down to 69.2% and of AUROC to 0.793. Model 1 significantly improved radiologist's sensitivity (0.82 to 0.88, P = 0.016) and accuracy (0.86 to 0.88, P = 0,047) while model 2 significantly decreased specificity of readers (0.86 to 0.83, P = 0.031). Conclusion: End-to-end development of a deep learning model to assess post-traumatic injuries on elbow X-ray in children was feasible and showed that models with close metrics in silico can unpredictably lead radiologists to either improve or lower their performances in clinical settings.