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Objective: To describe the morphologic sonographic appearances and frequency of the "halo sign" in the setting of fat necrosis on shear wave elastography (SWE). Methods: Patients with clinically suspected fat necrosis were prospectively scanned using SWE in addition to standard gray‐scale and Doppler images. Cases were qualitatively grouped into one of three sonographic appearances: focal hypoechoic lesion with increased internal tissue stiffness ("focal stiffness"), focal hypoechoic lesion with isoechoic or hyperechoic periphery demonstrating increased tissue stiffness relative to the central hypoechoic lesion ("halo stiffness"), heterogeneously echogenic lesion with diffusely increased stiffness ("heterogeneous stiffness"). Results: Exactly 19 patients met inclusion criteria (female n = 14; male n = 5). Shear wave velocities were recorded and retrospectively evaluated. The mean clinical follow‐up was 11.4 months (range 3.0–25.5). Lesions demonstrated higher average tissue stiffness than background tissue (overall mass shear wave velocity 3.26 m/s, background 1.42 m/s, P <.001; lesion Young's modulus 40.85 kPa vs background 7.22 kPa, P <.001). The halo sign was identified in 10/19 (55%) patients. Conclusion: The halo sign is a potentially useful sign in the setting of fat necrosis seen in the majority of clinically suspected cases. [ABSTRACT FROM AUTHOR]

Background There is interest in using convolutional neural networks (CNNs) to analyze medical imaging to provide computer-aided diagnosis (CAD). Recent work has suggested that image classification CNNs may not generalize to new data as well as previously believed. We assessed how well CNNs generalized across three hospital systems for a simulated pneumonia screening task. Methods and findings A cross-sectional design with multiple model training cohorts was used to evaluate model generalizability to external sites using split-sample validation. A total of 158,323 chest radiographs were drawn from three institutions: National Institutes of Health Clinical Center (NIH; 112,120 from 30,805 patients), Mount Sinai Hospital (MSH; 42,396 from 12,904 patients), and Indiana University Network for Patient Care (IU; 3,807 from 3,683 patients). These patient populations had an age mean (SD) of 46.9 years (16.6), 63.2 years (16.5), and 49.6 years (17) with a female percentage of 43.5%, 44.8%, and 57.3%, respectively. We assessed individual models using the area under the receiver operating characteristic curve (AUC) for radiographic findings consistent with pneumonia and compared performance on different test sets with DeLong's test. The prevalence of pneumonia was high enough at MSH (34.2%) relative to NIH and IU (1.2% and 1.0%) that merely sorting by hospital system achieved an AUC of 0.861 (95% CI 0.855-0.866) on the joint MSH-NIH dataset. Models trained on data from either NIH or MSH had equivalent performance on IU (P values 0.580 and 0.273, respectively) and inferior performance on data from each other relative to an internal test set (i.e., new data from within the hospital system used for training data; P values both Conclusion Pneumonia-screening CNNs achieved better internal than external performance in 3 out of 5 natural comparisons. When models were trained on pooled data from sites with different pneumonia prevalence, they performed better on new pooled data from these sites but not on external data. CNNs robustly identified hospital system and department within a hospital, which can have large differences in disease burden and may confound predictions., Author(s): John R. Zech 1, Marcus A. Badgeley 2, Manway Liu 2, Anthony B. Costa 3, Joseph J. Titano 4, Eric Karl Oermann 3,* Introduction There is significant interest in [...]

BACKGROUND . MRI utility for patients 45 years old and older with hip or knee pain is not well established. OBJECTIVE . We performed this systematic review to assess whether MRI-diagnosed hip or knee pathology in patients 45 years old and older correlates with symptoms or benefits from arthroscopic surgery. EVIDENCE ACQUISITION . A literature search (PubMed, Web of Science, Embase) of articles published before October 3, 2022, was performed to identify original research pertaining to the study question. Publication information, study design, cohort size, osteoarthritis severity, age (range, mean), measured outcomes, minimum follow-up length, and MRI field strength were extracted. Study methods were appraised with NIH's study quality assessment tools. EVIDENCE SYNTHESIS . The search yielded 1125 potential studies, of which 31 met the inclusion criteria (18 knee, 13 hip). Knee studies (10 prospective, eight retrospective) included 5907 patients (age range, 45-90 years). Bone marrow edema-like lesions, joint effusions, and synovitis on MRI were associated with symptoms. In patients with osteoarthritis, meniscal tears were less likely to be symptom generators and were less likely to respond to arthroscopic surgery with osteoarthritis progression. Hip studies (11 retrospective, two prospective) included 6385 patients (age range, 50 to ≥ 85 years). Patients with Tönnis grade 2 osteoarthritis and lower with and without femoroacetabular impingement (FAI) showed improved outcomes after arthroscopy, suggesting a role for MRI in the diagnosis of labral tears, chondral lesions, and FAI. Although this group benefited from arthroscopic surgery, outcomes were inferior to those in younger patients. Variability in study characteristics, follow-up, and outcome measures precluded a meta-analysis. CONCLUSION . In patients 45 years old and older, several knee structural lesions on MRI correlated with symptoms, representing potential imaging biomarkers. Meniscal tear identification on MRI likely has diminished clinical value as osteoarthritis progresses. For the hip, MRI can play a role in the diagnosis of labral tears, chondral lesions, and FAI in patients without advanced osteoarthritis. CLINICAL IMPACT . Several structural lesions on knee MRI correlating with symptoms may represent imaging biomarkers used as treatment targets. Osteoarthritis, not age, may play the greatest role in determining the utility of MRI for patients 45 years old and older with hip or knee pain.

Artificial intelligence (AI) is increasingly used in clinical practice for musculoskeletal imaging tasks, such as disease diagnosis and image reconstruction. AI applications in musculoskeletal imaging have focused primarily on radiography, CT, and MRI. Although musculoskeletal ultrasound stands to benefit from AI in similar ways, such applications have been relatively underdeveloped. In comparison with other modalities, ultrasound has unique advantages and disadvantages that must be considered in AI algorithm development and clinical translation. Challenges in developing AI for musculoskeletal ultrasound involve both clinical aspects of image acquisition and practical limitations in image processing and annotation. Solutions from other radiology subspecialties (e.g., crowdsourced annotations coordinated by professional societies), along with use cases (most commonly rotator cuff tendon tears and palpable soft-tissue masses), can be applied to musculoskeletal ultrasound to help develop AI. To facilitate creation of high-quality imaging datasets for AI model development, technologists and radiologists should focus on increasing uniformity in musculoskeletal ultrasound performance and increasing annotations of images for specific anatomic regions. This Expert Panel Narrative Review summarizes available evidence regarding AI's potential utility in musculoskeletal ultrasound and challenges facing its development. Recommendations for future AI advancement and clinical translation in musculoskeletal ultrasound are discussed.

Fractures are common injuries that can be difficult to diagnose, with missed fractures accounting for most misdiagnoses in the emergency department. Artificial intelligence (AI) and, specifically, deep learning have shown a strong ability to accurately detect fractures and augment the performance of radiologists in proof-of-concept research settings. Although the number of real-world AI products available for clinical use continues to increase, guidance for practicing radiologists in the adoption of this new technology is limited. This review describes how AI and deep learning algorithms can help radiologists to better diagnose fractures. The article also provides an overview of commercially available U.S. FDA-cleared AI tools for fracture detection as well as considerations for the clinical adoption of these tools by radiology practices.