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Deep learning-based MRI diagnosis of internal joint derangement is an emerging field of artificial intelligence, which offers many exciting possibilities for musculoskeletal radiology. A variety of investigational deep learning algorithms have been developed to detect anterior cruciate ligament tears, meniscus tears, and rotator cuff disorders. Additional deep learning-based MRI algorithms have been investigated to detect Achilles tendon tears, recurrence prediction of musculoskeletal neoplasms, and complex segmentation of nerves, bones, and muscles. Proof-of-concept studies suggest that deep learning algorithms may achieve similar diagnostic performances when compared to human readers in meta-analyses; however, musculoskeletal radiologists outperformed most deep learning algorithms in studies including a direct comparison. Earlier investigations and developments of deep learning algorithms focused on the binary classification of the presence or absence of an abnormality, whereas more advanced deep learning algorithms start to include features for characterization and severity grading. While many studies have focused on comparing deep learning algorithms against human readers, there is a paucity of data on the performance differences of radiologists interpreting musculoskeletal MRI studies without and with artificial intelligence support. Similarly, studies demonstrating the generalizability and clinical applicability of deep learning algorithms using realistic clinical settings with workflow-integrated deep learning algorithms are sparse. Contingent upon future studies showing the clinical utility of deep learning algorithms, artificial intelligence may eventually translate into clinical practice to assist detection and characterization of various conditions on musculoskeletal MRI exams.

Periprosthetic joint infections are a devastating complication after arthroplasty and are associated with substantial patient morbidity. More than 25% of revisions are attributed to these infections, which are expected to increase. The increased prevalence of obesity, diabetes, and other comorbidities are some of the reasons for this increase. Recognition of the challenge of surgical site infections in general, and periprosthetic joint infections particularly, has prompted implementation of enhanced prevention measures preoperatively (glycaemic control, skin decontamination, decolonisation, etc), intraoperatively (ultraclean operative environment, blood conservation, etc), and postoperatively (refined anticoagulation, improved wound dressings, etc). Additionally, indications for surgical management have been refined. In this Review, we assess risk factors, preventive measures, diagnoses, clinical features, and treatment options for prosthetic joint infection. An international consensus meeting about such infections identified the best practices and further research needs. Orthopaedics could benefit from enhanced preventive, diagnostic, and treatment methods.

The Society of Skeletal Radiology (SSR) Practice Guidelines and Technical Standards Committee identified musculoskeletal infection as a White Paper topic, and selected a Committee, tasked with developing a consensus on nomenclature for MRI of musculoskeletal infection outside the spine. The objective of the White Paper was to critically assess the literature and propose standardized terminology for imaging findings of infection on MRI, in order to improve both communication with clinical colleagues and patient care.A definition was proposed for each term; debate followed, and the committee reached consensus. Potential controversies were raised, with formulated recommendations. The committee arrived at consensus definitions for cellulitis, soft tissue abscess, and necrotizing infection, while discouraging the nonspecific term phlegmon. For bone infection, the term osteitis is not useful; the panel recommends using terms that describe the likelihood of osteomyelitis in cases where definitive signal changes are lacking. The work was presented virtually to SSR members, who had the opportunity for review and modification prior to submission for publication.

ObjectiveTo develop and evaluate the performance of deep convolutional neural networks (DCNN) to detect and identify specific total shoulder arthroplasty (TSA) models.Materials and methodsWe included 482 radiography studies obtained from publicly available image repositories with native shoulders, reverse TSA (RTSA) implants, and five different TSA models. We trained separate ResNet DCNN–based binary classifiers to (1) detect the presence of shoulder arthroplasty implants, (2) differentiate between TSA and RTSA, and (3) differentiate between the five TSA models, using five individual classifiers for each model, respectively. Datasets were divided into training, validation, and test datasets. Training and validation datasets were 20-fold augmented. Test performances were assessed with area under the receiver-operating characteristic curves (AUC-ROC) analyses. Class activation mapping was used to identify distinguishing imaging features used for DCNN classification decisions.ResultsThe DCNN for the detection of the presence of shoulder arthroplasty implants achieved an AUC-ROC of 1.0, whereas the AUC-ROC for differentiation between TSA and RTSA was 0.97. Class activation map analysis demonstrated the emphasis on the characteristic arthroplasty components in decision-making. DCNNs trained to distinguish between the five TSA models achieved AUC-ROCs ranging from 0.86 for Stryker Solar to 1.0 for Zimmer Bigliani-Flatow with class activation map analysis demonstrating an emphasis on unique implant design features.ConclusionDCNNs can accurately identify the presence of and distinguish between TSA & RTSA, and classify five specific TSA models with high accuracy. The proof of concept of these DCNNs may set the foundation for an automated arthroplasty atlas for rapid and comprehensive model identification.

Flexible radiofrequency coils for magnetic resonance imaging (MRI) have garnered attention in research and industrial communities because they provide improved accessibility and performance and can accommodate a range of anatomic postures. Most recent flexible coil developments involve customized conductors or substrate materials and/or target applications at 3 T or above. In contrast, we set out to design a flexible coil based on an off-the-shelf conductor that is suitable for operation at 0.55 T (23.55 MHz). Signal-to-noise ratio (SNR) degradation can occur in such an environment because the resistance of the coil conductor can be significant with respect to the sample. We found that resonating a commercially available RG-223 coaxial cable shield with a lumped capacitor while the inner conductor remained electrically floating gave rise to a highly effective “cable coil.” A 10-cm diameter cable coil was flexible enough to wrap around the knee, an application that can benefit from flexible coils, and had similar conductor loss and SNR as a standard-of-reference rigid copper coil. A two-channel cable coil array also provided good SNR robustness against geometric variability, outperforming a two-channel coaxial coil array by 26 and 16% when the elements were overlapped by 20–40% or gapped by 30–50%, respectively. A 6-channel cable coil array was constructed for 0.55 T knee imaging. Incidental cartilage and bone pathologies were clearly delineated in T1- and T2-weighted turbo spin echo images acquired in 3–4 min with the proposed coil, suggesting that clinical quality knee imaging is feasible in an acceptable examination timeframe. Correcting for T1, the SNR measured with the cable coil was approximately threefold lower than that measured with a 1.5 T state-of-the-art 18-channel coil, which is expected given the threefold difference in main magnetic field strength. This result suggests that the 0.55 T cable coil conductor loss does not deleteriously impact SNR, which might be anticipated at low field.

Objective Compressed sensing (CS) acceleration has been theorized for slice encoding for metal artifact correction (SEMAC), but has not been shown to be feasible. Therefore, we tested the hypothesis that CS-SEMAC is feasible for MRI of metal-on-metal hip resurfacing implants. Materials and methods Following prospective institutional review board approval, 22 subjects with metal-on-metal hip resurfacing implants underwent 1.5 T MRI. We compared CS-SEMAC prototype, high-bandwidth TSE, and SEMAC sequences with acquisition times of 4–5, 4–5 and 10–12 min, respectively. Outcome measures included bone-implant interfaces, image quality, periprosthetic structures, artifact size, and signal- and contrast-to-noise ratios (SNR and CNR). Using Friedman, repeated measures analysis of variances, and Cohen’s weighted kappa tests, Bonferroni-corrected p -values of 0.005 and less were considered statistically significant. Results There was no statistical difference of outcomes measures of SEMAC and CS-SEMAC images. Visibility of implant-bone interfaces and pseudocapsule as well as fat suppression and metal reduction were “adequate” to “good” on CS-SEMAC and “non-diagnostic” to “adequate” on high-BW TSE ( p  < 0.001, respectively). SEMAC and CS-SEMAC showed mild blur and ripple artifacts. The metal artifact size was 63 % larger for high-BW TSE as compared to SEMAC and CS-SEMAC ( p  < 0.0001, respectively). CNRs were sufficiently high and statistically similar, with the exception of CNR of fluid and muscle and CNR of fluid and tendon, which were higher on intermediate-weighted high-BW TSE ( p  < 0.005, respectively). Conclusion Compressed sensing acceleration enables the time-neutral use of SEMAC for MRI of metal-on-metal hip resurfacing implants when compared to high-BW TSE and image quality similar to conventional SEMAC.

Objectives The purpose of this agreement was to establish evidence-based consensus statements on imaging of distal radioulnar joint (DRUJ) instability and triangular fibrocartilage complex (TFCC) injuries by an expert group using the Delphi technique. Methods Nineteen hand surgeons developed a preliminary list of questions on DRUJ instability and TFCC injuries. Radiologists created statements based on the literature and the authors’ clinical experience. Questions and statements were revised during three iterative Delphi rounds. Delphi panelists consisted of twenty-seven musculoskeletal radiologists. The panelists scored their degree of agreement to each statement on an 11-item numeric scale. Scores of “0,” “5,” and “10” reflected complete disagreement, indeterminate agreement, and complete agreement, respectively. Group consensus was defined as a score of “8” or higher for 80% or more of the panelists. Results Three of fourteen statements achieved group consensus in the first Delphi round and ten statements achieved group consensus in the second Delphi round. The third and final Delphi round was limited to the one question that did not achieve group consensus in the previous rounds. Conclusions Delphi-based agreements suggest that CT with static axial slices in neutral rotation, pronation, and supination is the most useful and accurate imaging technique for the work-up of DRUJ instability. MRI is the most valuable technique in the diagnosis of TFCC lesions. The main indication for MR arthrography and CT arthrography are Palmer 1B foveal lesions of the TFCC. Clinical relevance statement MRI is the method of choice for assessing TFCC lesions, with higher accuracy for central than peripheral abnormalities. The main indication for MR arthrography is the evaluation of TFCC foveal insertion lesions and peripheral non-Palmer injuries. Key points • Conventional radiography should be the initial imaging technique in the assessment of DRUJ instability. CT with static axial slices in neutral rotation, pronation, and supination is the most accurate method for evaluating DRUJ instability. • MRI is the most useful technique in diagnosing soft-tissue injuries causing DRUJ instability, especially TFCC lesions. • The main indications for MR arthrography and CT arthrography are foveal lesions of the TFCC.

Pelvic pain due to chronic pudendal nerve (PN) compression, when treated surgically, is approached with a transgluteal division of the sacrotuberous ligament (STL). Controversy exists as to whether the STL heals spontaneously or requires grafting. Therefore, the aim of this study was to determine how surgically divided and unrepaired STL heal. A retrospective evaluation of 10 patients who had high spatial resolution 3-Tesla magnetic resonance imaging (3T MRI) exams of the pelvis was done using an IRB-approved protocol. Each patient was referred for residual pelvic pain after a transgluteal STL division for chronic pudendal nerve pain. Of the 10 patients, 8 had the STL divided and not repaired, while 2 had the STL divided and reconstructed with an allograft tendon. Of the 8 that were left unrepaired, 6 had bilateral surgery. Outcome variables included STL integrity and thickness. Normative data for the STL were obtained through a control group of 20 subjects. STL integrity and thickness were measured directly on 3 T MR Neurography images, by two independent Radiologists. The integrity and thickness of the post-surgical STL was evaluated 39 months (range, 9-55) after surgery. Comparison was made with the native contra-lateral STL in those who had unilateral STL division, and with normal, non-divided STL of subjects of the control group. The normal STL measured 3 mm (minimum and maximum of absolute STL thickness, 2-3 mm). All post-operative STL were found to be continuous regardless of the surgical technique used. Measured at level of Alcock's canal in the same plane as the obturator internus tendon posterior to the ischium, the mean anteroposterior STL diameter was 5 mm (range, 4-5 mm) in the group of prior STL division without repair and 8 mm (range, 8-9 mm) in the group with the STL reconstructed with grafts (p<0.05). The group of healed STLs were significantly thicker than the normal STL (p<0.05). We conclude that a surgically divided STL will heal spontaneously and will be significantly thicker after healing.

Objectives The purpose of this agreement was to establish evidence-based consensus statements on imaging of scapholunate joint (SLJ) instability by an expert group using the Delphi technique. Methods Nineteen hand surgeons developed a preliminary list of questions on SLJ instability. Radiologists created statements based on the literature and the authors’ clinical experience. Questions and statements were revised during three iterative Delphi rounds. Delphi panellists consisted of twenty-seven musculoskeletal radiologists. The panellists scored their degree of agreement to each statement on an eleven-item numeric scale. Scores of ‘0’, ‘5’ and ‘10’ reflected complete disagreement, indeterminate agreement and complete agreement, respectively. Group consensus was defined as a score of ‘8’ or higher for 80% or more of the panellists. Results Ten of fifteen statements achieved group consensus in the second Delphi round. The remaining five statements achieved group consensus in the third Delphi round. It was agreed that dorsopalmar and lateral radiographs should be acquired as routine imaging work-up in patients with suspected SLJ instability. Radiographic stress views and dynamic fluoroscopy allow accurate diagnosis of dynamic SLJ instability. MR arthrography and CT arthrography are accurate for detecting scapholunate interosseous ligament tears and articular cartilage defects. Ultrasonography and MRI can delineate most extrinsic carpal ligaments, although validated scientific evidence on accurate differentiation between partially or completely torn or incompetent ligaments is not available. Conclusions Delphi-based agreements suggest that standardized radiographs, radiographic stress views, dynamic fluoroscopy, MR arthrography and CT arthrography are the most useful and accurate imaging techniques for the work-up of SLJ instability. Key Points • Dorsopalmar and lateral wrist radiographs remain the basic imaging modality for routine imaging work-up in patients with suspected scapholunate joint instability . • Radiographic stress views and dynamic fluoroscopy of the wrist allow accurate diagnosis of dynamic scapholunate joint instability . • Wrist MR arthrography and CT arthrography are accurate for determination of scapholunate interosseous ligament tears and cartilage defects .

Artificial intelligence (AI) has made tremendous advances in recent years and will presumably have a major impact in health care. These advancements are expected to affect different aspects of clinical medicine and lead to improvement of delivered care but also optimization of available resources. As a modern specialty that extensively relies on imaging, interventional radiology (IR) is primed to be on the forefront of this development. This is especially relevant since IR is a highly advanced specialty that heavily relies on technology and thus is naturally susceptible to disruption by new technological developments. Disruption always means opportunity and interventionalists must therefore understand AI and be a central part of decision-making when such systems are developed, trained, and implemented. Furthermore, interventional radiologist must not only embrace but lead the change that AI technology will allow. The CIRSE position paper discusses the status quo as well as current developments and challenges.