Faculty Bibliography

Total hip arthroplasty and hip preservation surgeries have substantially increased over the past few decades. Musculoskeletal imaging and interventions are cornerstones of comprehensive postoperative care and surveillance in patients undergoing established and more recently introduced hip surgeries. Hence the radiologist's role continues to evolve and expand. A strong understanding of hip joint anatomy and biomechanics, surgical procedures, expected normal postoperative imaging appearances, and postoperative complications ensures accurate imaging interpretation, intervention, and optimal patient care. This article presents surgical principles and procedural details pertinent to postoperative imaging evaluation strategies after common hip surgeries, such as radiography, ultrasonography, computed tomography, and magnetic resonance imaging. We review and illustrate the expected postoperative imaging appearances and complications following chondrolabral repair, acetabuloplasty, osteochondroplasty, periacetabular osteotomy, realigning and derotational femoral osteotomies, and hip arthroplasty.

OBJECTIVE:To establish the effectiveness of cooled radiofrequency ablation in managing hip pain from osteoarthritis at 6 months after receiving treatment in patients who failed conservative treatments and are not surgical candidates due to comorbidities or unwillingness to undergo arthroplasty surgery by targeting the femoral and obturator branches and assessing the degree of hip pain relief and change of function. MATERIALS AND METHODS/METHODS:This prospective pilot study includes a total of 11 consecutive patients experiencing persistent chronic hip pain in the setting of advanced osteoarthritis. Patients initially underwent anesthetic blocks of the obturator and femoral nerve branches to determine cooled radiofrequency ablation candidacy. After adequate response to the anesthetic blocks (> 50% immediate pain relief), patients were subjected to the procedures 2-3 weeks later. Treatment response was evaluated utilizing clinically validated questionnaires and visual analog score in order to assess impact on pain severity, stiffness, and functional activities of daily living. Follow-up outcome scores were collected up to 6 months after cooled radiofrequency ablation procedure. RESULTS:A total of 11 hips were treated consecutively between August 2019 and March 2020 (mean patient age 61.4 years; 8 M:3F). The mean total HOOS score improved significantly from baseline at 17.0 ± 6.0 to 52.9 ± 5.4 at a mean of 6.2 months after treatment (p < 0.0001), with significant improvement in mean pain score from 16.1 ± 6.6 to 53.4 ± 7.4 (p < 0.0001) and mean stiffness score from 15.0 ± 8.1 to 53.6 ± 11.0 (p < 0.0001). No major complications were encountered. No patients went on to re-treatment, surgery, or other intervention. CONCLUSION/CONCLUSIONS:Image-guided obturator and femoral nerve cooled radiofrequency ablation is effective and safe in treating chronic hip pain/stiffness in the setting of advanced osteoarthritis.

OBJECTIVE:To compare computer-based 3D-analysis for quantification of the femorotibial joint space width (JSW) using weight-bearing cone beam CT (WB-CT), non-weight-bearing multi-detector CT (NWB-CT), and weight-bearing conventional radiographs (WB-XR). DESIGN/METHODS:Twenty-six participants prospectively underwent NWB-CT, WB-CT, and WB-XR of the knee. For WB-CT and NWB-CT, the average and minimal JSW was quantified by 3D-analysis of the minimal distance of any point of the subchondral tibial bone surface and the femur. Associations with mechanical leg axes and osteoarthritis were evaluated. Minimal JSW of WB-CT was further compared to WB-XR. Two-tailed p-values of <0.05 were considered significant. RESULTS:Significant differences existed of the average medial and lateral JSW between WB-CT and NWB-CT (medial: 4.7 vs 5.1 mm [P = 0.028], lateral: 6.3 vs 6.8 mm [P = 0.008]). The minimal JSW on WB-XR (medial:3.1 mm, lateral:5.8 mm) were significantly wider compared to WB-CT and NWB-CT (both medial:1.8 mm, lateral:2.9 mm, all p < 0.001), but not significantly different between WB-CT and NWB-CT (all p ≥ 0.869). Significant differences between WB-CT and NWB-CT existed in participants with varus knee alignment for the average and the minimal medial JSW (p = 0.004 and p = 0.011) and for participants with valgus alignment for the average lateral JSW (p = 0.013). On WB-CT, 25% of the femorotibial compartments showed bone-on-bone apposition, which was significantly higher when compared to NWB-CT (10%,P = 0.008) and WB-XR (8%,P = 0.012). CONCLUSION/CONCLUSIONS:Combining WB-CT with 3D-based assessment allows detailed quantification of the femorotibial joint space and the effect of knee alignment on JSW. WB-CT demonstrates significantly more bone-on-bone appositions, which are underestimated or even undetectable on NWB-CT and WB-XR.

Artificial intelligence (AI) and deep learning have multiple potential uses in aiding the musculoskeletal radiologist in the radiological evaluation of orthopedic implants. These include identification of implants, characterization of implants according to anatomic type, identification of specific implant models, and evaluation of implants for positioning and complications. In addition, natural language processing (NLP) can aid in the acquisition of clinical information from the medical record that can help with tasks like prepopulating radiology reports. Several proof-of-concept works have been published in the literature describing the application of deep learning toward these various tasks, with performance comparable to that of expert musculoskeletal radiologists. Although much work remains to bring these proof-of-concept algorithms into clinical deployment, AI has tremendous potential toward automating these tasks, thereby augmenting the musculoskeletal radiologist.

OBJECTIVE:To evaluate the behavior of a publicly available deep convolutional neural network (DCNN) bone age algorithm when presented with inappropriate data inputs in both radiological and non-radiological domains. METHODS:We evaluated a publicly available DCNN-based bone age application. The DCNN was trained on 12,612 pediatric hand radiographs and won the 2017 RSNA Pediatric Bone Age Challenge (concordance of 0.991 with radiologist ground-truth). We used the application to analyze 50 left-hand radiographs (appropriate data inputs) and seven classes of inappropriate data inputs in radiological (i.e., chest radiographs) and non-radiological (i.e., image of street numbers) domains. For each image, we noted if (1) the application distinguished between appropriate and inappropriate data inputs and (2) inference time per image. Mean inference times were compared using ANOVA. RESULTS:The 16Bit Bone Age application calculated bone age for all pediatric hand radiographs with mean inference time of 1.1 s. The application did not distinguish between pediatric hand radiographs and inappropriate image types, including radiological and non-radiological domains. The application inappropriately calculated bone age for all inappropriate image types, with mean inference time of 1.1 s for all categories (p = 1). CONCLUSION/CONCLUSIONS:A publicly available DCNN-based bone age application failed to distinguish between appropriate and inappropriate data inputs and calculated bone age for inappropriate images. The awareness of inappropriate outputs based on inappropriate DCNN input is important if tasks such as bone age determination are automated, emphasizing the need for appropriate oversight at the data input and verification stage to avoid unrecognized erroneous results.

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.