Comprehensive evidence implies a higher social cost of CO2.

The social cost of carbon dioxide (SC-CO2) measures the monetized value of the damages to society caused by an incremental metric tonne of CO2 emissions and is a key metric informing climate policy. Used by governments and other decision-makers in benefit-cost analysis for over a decade, SC-CO2 estimates draw on climate science, economics, demography and other disciplines. However, a 2017 report by the US National Academies of Sciences, Engineering, and Medicine1 (NASEM) highlighted that current SC-CO2 estimates no longer reflect the latest research. The report provided a series of recommendations for improving the scientific basis, transparency and uncertainty characterization of SC-CO2 estimates. Here we show that improved probabilistic socioeconomic projections, climate models, damage functions, and discounting methods that collectively reflect theoretically consistent valuation of risk, substantially increase estimates of the SC-CO2. Our preferred mean SC-CO2 estimate is $185 per tonne of CO2 ($44-$413 per tCO2: 5%-95% range, 2020 US dollars) at a near-term risk-free discount rate of 2%, a value 3.6 times higher than the US government's current value of $51 per tCO2. Our estimates incorporate updated scientific understanding throughout all components of SC-CO2 estimation in the new open-source Greenhouse Gas Impact Value Estimator (GIVE) model, in a manner fully responsive to the near-term NASEM recommendations. Our higher SC-CO2 values, compared with estimates currently used in policy evaluation, substantially increase the estimated benefits of greenhouse gas mitigation and thereby increase the expected net benefits of more stringent climate policies.

UTE T2* cartilage mapping in the hip: a pilot study assessing cartilage in patients with femoroacetabular impingement.

BACKGROUND: UTE T2* cartilage mapping use in patients undergoing femoroacetabular impingement (FAI) has been lacking but may allow the detection of early cartilage damage. PURPOSE: To assess the reproducibility of UTE T2* cartilage mapping and determine the difference in UTE T2* values between FAI and asymptomatic patients and to evaluate the correlation between UTE T2* values and patient-reported symptoms. MATERIAL AND METHODS: Prospective evaluation of both hips (7 FAI and 7 asymptomatic patients). Bilateral hip 3-T MRI scans with UTE T2* cartilage maps were acquired. A second MRI scan was acquired 1-9 months later. Cartilage was segmented into anterosuperior, superior, and posterosuperior regions. Assessment was made of UTE T2* reproducibility (ICC). Mean UTE T2* values in patients were compared (t-tests) and correlation was made with patient-reported outcomes (Spearman's). RESULTS: ICCs of mean UTE T2* were as follows: acetabular, 0.82 (95% CI=0.50-0.95); femoral, 0.76 (95% CI=0.35-0.92). Significant strong correlation was found between mean acetabular UTE T2* values and iHOT12 (ρ = -0.63) and moderate correlation with mHHS (ρ = -0.57). There was no difference in mean UTE T2* values between affected vs. non-affected FAI hips. FAI-affected hips had significantly higher values in acetabulum vs. asymptomatic patients (13.47 vs. 12.55 ms). There was no difference in mean femoral cartilage values between the FAI-affected hips vs. asymptomatic patients. The posterosuperior femoral region had a higher mean value in non-affected FAI hips vs. asymptomatic patients (12.60 vs. 11.53 ms). CONCLUSION: UTE T2* cartilage mapping had excellent reproducibility. Affected FAI hips had higher mean acetabular UTE T2* values than asymptomatic patients. Severity of patient-reported symptoms correlates with UTE T2* acetabular cartilage values.

Acute patellar dislocation: how skeletal maturity affects patterns of injury.

OBJECTIVE: The main objective of this study was to understand the role of skeletal maturity in the different patterns of osteochondral and ligamentous injuries after an acute lateral patellar dislocation. MATERIALS AND METHODS: Two radiologists independently reviewed MRIs of 212 knees performed after an acute lateral patellar dislocation to evaluate the presence of high-grade patellar osteochondral injury, femoral osteochondral injury, and medial patellofemoral ligament injury. The association of skeletal maturity (indicated by a closed distal femoral physis), age, sex, and first-time versus recurrent dislocation with each of these various lesions was analyzed using Chi-square or T test, and multivariable logistic regression with estimation of odds ratios (OR). RESULTS: Skeletal maturity was significantly associated with high-grade patellar osteochondral injury [OR=2.72 (95% CI 1.00, 7.36); p=0.049] and femoral-side MPFL tear [OR=2.34 (95% CI 1.05, 5.25); p=0.039]. Skeletal immaturity was significantly associated with patellar-side MPFL tear [OR=0.35 (95% CI 0.14, 0.90); p=0.029]. CONCLUSION: Patterns of injury to the patella and medial patellofemoral ligament vary notably between the skeletally immature and mature, and these variations may be explained by the inherent weakness of the patellar secondary physis.

Utility of Zero-Echo time sequence as an adjunct for MR evaluation of degenerative disease in the cervical spine.

OBJECTIVE: Determine the utility of ZTE as an adjunct to routine MR for assessing degenerative disease in the cervical spine. METHODS: Retrospective study on 42 patients with cervical MR performed with ZTE from 1/1/2022 to 4/30/22. Fellowship trained radiologists evaluated each cervical disc level for neural foraminal (NF) narrowing, canal stenosis (CS), facet arthritis (FA), and presence of ossification of the posterior longitudinal ligament (OPLL). When NF narrowing and CS were present, the relative contributions of bone and soft disc were determined and a confidence level for doing so was assigned. Comparisons were made between assessments on routine MR without and with ZTE. RESULTS: With ZTE added, bone contribution as a cause of NF narrowing increased in 47% (n = 110) of neural foramina and decreased in 12% (n = 29) (p = < 0.001). Bone contribution as a cause of CS increased in 25% (n = 33) of disc levels and decreased in 10% (n = 13) (p = 0.013). Confidence increased in identifying the cause of NF narrowing (p = < 0.001)) and CS (p = 0.009) with ZTE. The cause of NF narrowing (p = 0.007) and CS (p = 0.041) changed more frequently after ZTE was added when initial confidence in making the determination was low. There was no change in detection of FA or presence of OPLL with ZTE. CONCLUSION: Addition of ZTE to a routine cervical spine MR changes the assessment of the degree of bone involvement in degenerative cervical spine pathology.

Artificial intelligence improves resident detection of pediatric and young adult upper extremity fractures.

PURPOSE: We wished to evaluate if an open-source artificial intelligence (AI) algorithm ( https://www.childfx.com ) could improve performance of (1) subspecialized musculoskeletal radiologists, (2) radiology residents, and (3) pediatric residents in detecting pediatric and young adult upper extremity fractures. MATERIALS AND METHODS: A set of evaluation radiographs drawn from throughout the upper extremity (elbow, hand/finger, humerus/shoulder/clavicle, wrist/forearm, and clavicle) from 240 unique patients at a single hospital was constructed (mean age 11.3 years, range 0-22 years, 37.9% female). Two fellowship-trained musculoskeletal radiologists, three radiology residents, and two pediatric residents were recruited as readers. Each reader interpreted each case initially without and then subsequently 3-4 weeks later with AI assistance and recorded if/where fracture was present. RESULTS: Access to AI significantly improved area under the receiver operator curve (AUC) of radiology residents (0.768 [0.730-0.806] without AI to 0.876 [0.845-0.908] with AI, P < 0.001) and pediatric residents (0.706 [0.659-0.753] without AI to 0.844 [0.805-0.883] with AI, P < 0.001) in identifying fracture, respectively. There was no evidence of improvement for subspecialized musculoskeletal radiology attendings in identifying fracture (AUC 0.867 [0.832-0.902] to 0.890 [0.856-0.924], P = 0.093). There was no evidence of difference between overall resident AUC with AI and subspecialist AUC without AI (resident with AI 0.863, attending without AI AUC 0.867, P = 0.856). Overall physician radiograph interpretation time was significantly lower with AI (38.9 s with AI vs. 52.1 s without AI, P = 0.030). CONCLUSION: An openly accessible AI model significantly improved radiology and pediatric resident accuracy in detecting pediatric upper extremity fractures.

Responsible north-south research and innovation: A framework for transdisciplinary research leadership and management.

The number, scale and ambition of transdisciplinary research initiatives between the global north and the global south is increasing, yet there is very little theoretical or empirical scholarship on how to lead and manage implementation to promote responsible practice. Within science, technology and innovation (STI) studies and decolonising research frameworks, and utilising collaborative autoethnography, this study codifies experience with implementing the 'Revitalising Informal Settlements and their Environments' (RISE) program (2017-2020). Our specific aim is to explore the leadership and management tensions and challenges of implementing transboundary transdisciplinary research. The findings reaffirm the importance of research leaders and managers carefully operationalising north-south research by critically reflecting on power asymmetries between disciplines, partners and locations, leveraging the potential for transdisciplinary consortia to build research capabilities in the global south, and creating a culture of reflexivity on the historical and social positionality in which research is designed, funded, implemented and evaluated. The findings foreground the role of boundary-spanning 'integrators' and 'pracademics', roles that have received little attention to date but are essential for effective delivery and societal impact beyond scientific advances. A framework for implementing north-south transdisciplinary research is outlined with five domains: (1) collaborative leadership; (2) agile management; (3) flexible consortia; (4) researcher positionality; and (5) co-design and participation. The framework can support efforts for responsibly designing and implementing large, transdisciplinary, cross-country research programs in line with ambitions for decolonising north-south research.

Artificial intelligence to identify fractures on pediatric and young adult upper extremity radiographs.

BACKGROUND: Pediatric fractures are challenging to identify given the different response of the pediatric skeleton to injury compared to adults, and most artificial intelligence (AI) fracture detection work has focused on adults. OBJECTIVE: Develop and transparently share an AI model capable of detecting a range of pediatric upper extremity fractures. MATERIALS AND METHODS: In total, 58,846 upper extremity radiographs (finger/hand, wrist/forearm, elbow, humerus, shoulder/clavicle) from 14,873 pediatric and young adult patients were divided into train (n = 12,232 patients), tune (n = 1,307), internal test (n = 819), and external test (n = 515) splits. Fracture was determined by manual inspection of all test radiographs and the subset of train/tune radiographs whose reports were classified fracture-positive by a rule-based natural language processing (NLP) algorithm. We trained an object detection model (Faster Region-based Convolutional Neural Network [R-CNN]; "strongly-supervised") and an image classification model (EfficientNetV2-Small; "weakly-supervised") to detect fractures using train/tune data and evaluate on test data. AI fracture detection accuracy was compared with accuracy of on-call residents on cases they preliminarily interpreted overnight. RESULTS: A strongly-supervised fracture detection AI model achieved overall test area under the receiver operating characteristic curve (AUC) of 0.96 (95% CI 0.95-0.97), accuracy 89.7% (95% CI 88.0-91.3%), sensitivity 90.8% (95% CI 88.5-93.1%), and specificity 88.7% (95% CI 86.4-91.0%), and outperformed a weakly-supervised model (AUC 0.93, 95% CI 0.92-0.94, P < 0.0001). AI accuracy on cases preliminary interpreted overnight was higher than resident accuracy (AI 89.4% vs. 85.1%, 95% CI 87.3-91.5% vs. 82.7-87.5%, P = 0.01). CONCLUSION: An object detection AI model identified pediatric upper extremity fractures with high accuracy.

Detecting pediatric wrist fractures using deep-learning-based object detection.

BACKGROUND: Missed fractures are the leading cause of diagnostic error in the emergency department, and fractures of pediatric bones, particularly subtle wrist fractures, can be misidentified because of their varying characteristics and responses to injury. OBJECTIVE: This study evaluated the utility of an object detection deep learning framework for classifying pediatric wrist fractures as positive or negative for fracture, including subtle buckle fractures of the distal radius, and evaluated the performance of this algorithm as augmentation to trainee radiograph interpretation. MATERIALS AND METHODS: We obtained 395 posteroanterior wrist radiographs from unique pediatric patients (65% positive for fracture, 30% positive for distal radial buckle fracture) and divided them into train (n = 229), tune (n = 41) and test (n = 125) sets. We trained a Faster R-CNN (region-based convolutional neural network) deep learning object-detection model. Two pediatric and two radiology residents evaluated radiographs initially without the artificial intelligence (AI) assistance, and then subsequently with access to the bounding box generated by the Faster R-CNN model. RESULTS: The Faster R-CNN model demonstrated an area under the curve (AUC) of 0.92 (95% confidence interval [CI] 0.87-0.97), accuracy of 88% (n = 110/125; 95% CI 81-93%), sensitivity of 88% (n = 70/80; 95% CI 78-94%) and specificity of 89% (n = 40/45, 95% CI 76-96%) in identifying any fracture and identified 90% of buckle fractures (n = 35/39, 95% CI 76-97%). Access to Faster R-CNN model predictions significantly improved average resident accuracy from 80 to 93% in detecting any fracture (P < 0.001) and from 69 to 92% in detecting buckle fracture (P < 0.001). After accessing AI predictions, residents significantly outperformed AI in cases of disagreement (73% resident correct vs. 27% AI, P = 0.002). CONCLUSION: An object-detection-based deep learning approach trained with only a few hundred examples identified radiographs containing pediatric wrist fractures with high accuracy. Access to model predictions significantly improved resident accuracy in diagnosing these fractures.

Factors influencing patient understanding of information on radiology examinations.

PURPOSE: To determine which factors influence patient understanding of information documents on radiology examinations. MATERIALS AND METHODS: This is a randomized prospective study with 361 consecutive patients. Documents with information on 9 radiology exams were obtained ( www.radiologyinfo.org ). Three versions of each of these were written at low (below 7th grade), middle (8-12th grade), and high (college) reading grades. Before their scheduled radiology exam, patients were randomized to read one document. Their subjective and objective understanding of the information was assessed. Statistics including logistic regression used to assess relationships between demographic factors and document grade level and understanding. RESULTS: Twenty-eight percent (100/361) of patients completed the study. More females vs. males (85% vs. 66%) read their entire document (p = 0.042). Document grade level was not associated with understanding (p > 0.05). Correlation between college degrees and subjective understanding was positive (r = 0.234, p = 0.019). More females (74% vs. 54%, p = 0.047) and patients with college degrees (72% vs. 48%, p = 0.034) had higher objective understanding. Controlling for document grade level and demographics, patients with college degrees were more likely to have subjective understanding of at least half of the document (OR 7.97, 95% CI [1.24, 51.34], p = 0.029) and females were more likely to have higher objective understanding (OR 2.65, 95% CI [1.06, 6.62], p = 0.037). CONCLUSION: Patients with college degrees understood more on information documents. Females read more of the documents than males and had a higher objective understanding. Reading grade level did not affect understanding.

A Comparison of 2 Abbreviated Methods for Assessing Adolescent Bone Age: The Shorthand Bone Age Method and the SickKids/Columbia Method.

BACKGROUND: Radiographic assessment of bone age is critically important to decision-making on the type and timing of operative interventions in pediatric orthopaedics. The current widely accepted method for determining bone age is time and resource-intensive. This study sought to assess the reliability and accuracy of 2 abbreviated methods, the Shorthand Bone Age (SBA) and the SickKids/Columbia (SKC) methods, to the widely accepted Greulich and Pyle (GP) method. METHODS: Standard posteroanterior radiographs of the left hand of 125 adolescent males and 125 adolescent females were compiled, with bone ages determined by the GP method ranging from 9 to 16 years for males and 8 to 14 years for females. Blinded to the chronologic age and GP bone age of each child, the bone age for each radiograph was determined using the SBA and SKC methods by an orthopaedic surgery resident, 2 pediatric orthopaedic surgeons, and a musculoskeletal radiologist. Measurements were then repeated 2 weeks later after rerandomization of the radiographs. Intrarater and interrater reliability for the 2 abbreviated methods as well as the agreement between all 3 methods were calculated using weighted κ values. Mean absolute differences between methods were also calculated. RESULTS: Both bone age methods demonstrated substantial to almost perfect intrarater reliability, with a weighted κ ranging from 0.79 to 0.93 for the SBA method and from 0.82 to 0.96 for the SKC method. Interrater reliability was moderate to substantial (weighted κ: 0.55 to 0.84) for the SBA method and substantial to almost perfect (weighted κ: 0.67 to 0.92) for the SKC method. Agreement between the 3 methods was substantial for all raters and all comparisons. The mean absolute difference, been GP-derived and SBA-derived bone age, was 7.6±7.8 months, as compared with 8.8±7.4 months between GP-derived and SKC-derived bone ages. CONCLUSIONS: The SBA and SKC methods have comparable reliability, and both correlate well to the widely accepted GP methods and to each other. However, they have relatively large absolute differences when compared with the GP method. These methods offer simple, efficient, and affordable estimates for bone age determination, but at best provide an estimate to be used in the appropriate setting. LEVEL OF EVIDENCE: Diagnostic study-level III.

Feasibility of ultrashort echo time (UTE) T2* cartilage mapping in the hip: a pilot study.

BACKGROUND: Ultrashort echo time (UTE) T2* is sensitive to molecular changes within the deep calcified layer of cartilage. Feasibility of its use in the hip needs to be established to determine suitability for clinical use. PURPOSE: To establish feasibility of UTE T2* cartilage mapping in the hip and determine if differences in regional values exist. MATERIAL AND METHODS: MRI scans with UTE T2* cartilage maps were prospectively acquired on eight hips. Hip cartilage was segmented into whole and deep layers in anterosuperior, superior, and posterosuperior regions. Quantitative UTE T2* maps were analyzed (independent one-way ANOVA) and reliability was calculated (ICC). RESULTS: UTE T2* mean values (anterosuperior, superior, posterosuperior): full femoral layer (19.55, 18.43, 16.84 ms) (P=0.004), full acetabular layer (19.37, 17.50, 16.73 ms) (P=0.013), deep femoral layer (18.68, 17.90, 15.74 ms) (P=0.010), and deep acetabular layer (17.81, 16.18, 15.31 ms) (P=0.007). Values were higher in anterosuperior compared to posterosuperior regions (mean difference; 95% confidence interval [CI]): full femur layer (2.71 ms; 95% CI 0.91-4.51: P=0.003), deep femur layer (2.94 ms; 95% CI 0.69-5.19; P=0.009), full acetabular layer (2.63 ms 95% CI 0.55-4.72; P=0.012), and deep acetabular layer (2.50 ms; 95% CI 0.69-4.30; P=0.006). Intra-reader (ICC 0.89-0.99) and inter-reader reliability (ICC 0.63-0.96) were good to excellent for the majority of cartilage layers. CONCLUSION: UTE T2* cartilage mapping was feasible in the hip with mean values in the range of 16.84-19.55 ms in the femur and 16.73-19.37 ms in the acetabulum. Significantly higher values were present in the anterosuperior region compared to the posterosuperior region.

Athletic Injuries of the Thoracic Cage.

A variety of sports require exposure to high-impact trauma or characteristic repetitive movements that predispose to injuries around the thorax. Appropriate prognostication and timely management are vital, as untreated or undertreated injuries can lead to pain, disability, loss of playing time, or early termination of sports participation. The authors review common athletic injuries of the thoracic cage, encompassing muscular, osseous, and vascular conditions, with an emphasis on mechanism, imaging features, and management. The authors also review pertinent soft-tissue and bony anatomy, along with relevant sports biomechanics. Generalized muscle trauma and more specific injuries involving the pectoralis major, latissimus dorsi, teres major, pectoralis minor, lateral abdominal wall and intercostals, serratus anterior, and rectus abdominis muscles are discussed. Osseous injuries such as stress fractures, sternoclavicular dislocation, costochondral fractures, and scapular fractures are included. Finally, thoracic conditions such as snapping scapula, thoracic outlet syndrome, and Paget-Schroetter syndrome are also described. Specific MRI protocols are highlighted to address imaging challenges such as the variable anatomic orientation of thoracic structures and artifact from breathing motion. Athletes are susceptible to a wide range of musculoskeletal thoracic trauma. An accurate imaging diagnosis of thoracic cage injury and assessment of injury severity allow development of an adequate treatment plan. This can be facilitated by an understanding of functional anatomy, sports biomechanics, and the unique injuries for which athletes are at risk. ©RSNA, 2021.

A Pilot Study on Feasibility of Ultrashort Echo Time T2* Cartilage Mapping in the Sacroiliac Joints.

PURPOSE: Assess feasibility of ultrashort echo time (UTE) T2* cartilage mapping in sacroiliac (SI) joints. METHODS: Prospective magnetic resonance imagings with UTE T2* cartilage maps obtained on 20 SI joints in 10 subjects. Each joint was segmented into thirds by 2 radiologists. The UTE T2* maps were analyzed; reliability and differences in UTE T2* values between radiologists were assessed. RESULTS: Mean UTE T2* value was 10.44 ± 0.60 ms. No difference between right/left SI joints (median, 10.52 vs 10.45 ms; P = 0.940), men/women (median, 10.34 vs. 10.57 ms; P = 0.174), or different anatomic regions (median range 10.55-10.69 ms; P = 0.805). Intraclass correlation coefficients were 0.94 to 0.99 (intraobserver) and 0.91 to 0.96 (interobserver). Mean bias ± standard deviation on Bland-Altman was -0.137 ± 0.196 ms (limits of agreement -0.521 and 0.247) without proportional bias (ß = 0.148, P = 0.534). CONCLUSIONS: The UTE T2* cartilage mapping in the SI joints is feasible with high reader reliability.

Tennis overuse injuries in the upper extremity.

OBJECTIVE: Tennis is a popular sport with high levels of participation. This article aims to describe how upper extremity overuse injuries occur in relation to tennis biomechanics and to review their imaging characteristics and implications for management. In particular, we will review the imaging patterns of internal impingement, scapular dyskinesis, lateral and medial epicondylitis, ulnar collateral ligament insufficiency, valgus extension overload, capitellar osteochondritis dissecans, extensor carpi ulnaris tendinosis and instability, tenosynovitis, triangular fibrocartilage complex injuries, and carpal stress injuries. CONCLUSION: Tennis is a complex and physically demanding sport with a wide range of associated injuries. Repetitive overloading commonly leads to injuries of the upper extremity. An understanding of the underlying mechanisms of injury and knowledge of these injury patterns will aid the radiologist in generating the correct diagnosis in both the professional and recreational tennis athlete.

Assessing the readability of patient-targeted online information on musculoskeletal radiology procedures.

OBJECTIVE: To assess the readability of patient-targeted online information on musculoskeletal radiology procedures. METHODS: Eleven common musculoskeletal radiology procedures were queried in three online search engines (Google, Yahoo!, Bing). All unique patient-targeted websites were identified (n = 384) from the first three pages of search results. The reading grade level of each website was calculated using 6 separate validated metrics for readability assessment. Analysis of word and sentence complexity was also performed. Results were compared between academic vs. non-academic websites and between websites found on different pages of the search results. Statistics were performed using a t test. RESULTS: The mean reading grade level across all procedures was 10th-14th grade. Webpages for nerve block were written at a higher reading grade level on non-academic websites (p = 0.025). There was no difference in reading grade levels between academic and non-academic sources for all other procedures. There was no difference in reading grade levels between websites found on the first page of search results compared with the second and third pages. Across all websites, 16-22% of the words used had 3+ syllables and 31-43% of the words used had 6+ characters (complex words); 13-24% of the sentences used had 22+ words (complex sentences). CONCLUSION: Patient-targeted online information on musculoskeletal radiology procedures are written at the 10th-14th grade reading level, which is well beyond the AMA and NIH recommendation. Readability can be lowered by decreasing text complexity through limitation of high-syllable words and reduction in word and sentence length.

MRI following medial patellofemoral ligament reconstruction: assessment of imaging features found with post-operative pain, arthritis, and graft failure.

OBJECTIVE: To assess MR features following MPFL reconstruction and determine their influence on post-operative pain, progressive arthritis, or graft failure. MATERIALS AND METHODS: Retrospective study on 38 patients with MPFL reconstruction and a post-operative MRI between January 2010 and June 2019. Two radiologists assessed MPFL graft signal, graft thickness, femoral screw, femoral tunnel widening, and patellofemoral cartilage damage. The third performed patellofemoral instability measurements. All three assessed femoral tunnel position with final result determined by majority consensus. Imaging findings were evaluated in the setting of post-operative pain, patellofemoral arthritis, and MPFL graft failure including need for MPFL revision. Statistics included chi-square, Fisher's exact test, t test, and kappa. RESULTS: Mean graft thickness was 6.0 ± 1.8 mm; 24% of the grafts were diffusely hypointense. Mean femoral tunnel widening was 2.5 ± 1.8 mm; 34% of the femoral screws were broken or extruded. Fifty-two percent of the patients had no interval cartilage change. Non-anatomic femoral tunnels were found in 66% of patients, including in all 9 patients requiring revision MPFL reconstruction (p = 0.013). Revised MPFL grafts had more abnormal femoral screws compared to those that did not (67% vs. 24%) (p = 0.019). Other MR features did not significantly influence the evaluated outcomes. CONCLUSION: The need for revision MPFL reconstruction occurs more frequently when there is a non-anatomic femoral tunnel and broken or extruded femoral screws. The appearance of the MPFL graft itself is not an influencing factor for post-operative pain, progression of patellofemoral arthritis, or graft failure.

Trends of Follow-Up Recommendations Made on Musculoskeletal MRI Reports.

OBJECTIVE. The purpose of this article was to analyze trends in follow-up recommendations made on musculoskeletal MRI reports. MATERIALS AND METHODS. An IRB-approved retrospective study identified 790 musculoskeletal MRI reports from our database between January 1, 2016, and January 1, 2018, containing follow-up recommendations made by the interpreting radiologist. Metadata were automatically extracted and classification of the recommendations was performed by manual review. Clinical outcome data were collected from the electronic health record. After exclusion criteria were applied, 654 reports were included in the study. Descriptive statistics, Fisher exact tests, and chi-square tests were used for analysis. RESULTS. Clinicians acknowledged 83% and followed 73% of the recommendations. Follow-up compliance varied with the type of recommendation made: 98% for clinical intervention versus 67% for additional imaging (p < 0.001). Subspecialties acknowledged and followed recommendations at different rates: 92% and 85% for internists versus 76% and 64% for orthopedists (p < 0.001 and p < 0.001), respectively. Patient age, practice setting, radiologist experience, recommendation conditionality, and specified follow-up time intervals made no difference in compliance rate (all p > 0.05). There was no difference in compliance rate among various pathologic findings of concern (p = 0.995). Compliance rate increased significantly after direct communication between the radiologist and clinician compared with when there was no direct communication (93% vs 71%, p < 0.001). Concern for neoplasm comprised the greatest number of unacknowledged recommendations (73%). CONCLUSION. Musculoskeletal MRI recommendations are followed independent of the finding of concern and compliance is lowest for requests of additional imaging. Direct communication improves compliance and may be particularly helpful for orthopedic referrers.

The prevalence of athletic pubalgia imaging findings on MRI in patients with femoroacetabular impingement.

OBJECTIVE: To determine the prevalence of athletic pubalgia imaging findings on MRI in patients with femoroacetabular impingement and assess for correlative risk factors. MATERIALS AND METHODS: A retrospective search identified 156 hips with femoroacetabular impingement and a control group of 113 without femoroacetabular impingement that had an MRI performed between January 1, 2015, and January 1, 2018. Two fellowship-trained musculoskeletal radiologists reviewed studies for the presence of acute osteitis pubis, chronic osteitis pubis, adductor tendinosis, and tendon tear; rectus abdominis tendinosis and tendon tear; and aponeurotic plate tear. Findings were correlated with various clinical and imaging risk factors. Univariate and multivariate statistical analyses were performed. RESULTS: Imaging findings of adductor tendinosis (p = 0.02) and chronic osteitis pubis (p = 0.01) were more prevalent in FAI patients than controls. Univariate analyses in FAI patients showed that an alpha angle ≥ 60° had a higher prevalence of aponeurotic plate tears (p = 0.02) and adductor tendinosis (p = 0.049). Multivariate analyses showed that an alpha angle ≥ 60° had a higher prevalence of chronic osteitis pubis (OR = 2.27, p = 0.031), sports participation had a higher prevalence of adductor tendon tears (OR = 4.69, p = 0.013) and chronic osteitis pubis (OR = 2.61, p = 0.0058), and males had a higher prevalence of acute osteitis pubis (OR = 5.17, p = 0.032). CONCLUSION: Sports participation, alpha angle ≥ 60°, and male sex predict a higher prevalence of athletic pubalgia imaging findings in patients with femoroacetabular impingement.

Advanced Deep Learning Techniques Applied to Automated Femoral Neck Fracture Detection and Classification.

To use deep learning with advanced data augmentation to accurately diagnose and classify femoral neck fractures. A retrospective study of patients with femoral neck fractures was performed. One thousand sixty-three AP hip radiographs were obtained from 550 patients. Ground truth labels of Garden fracture classification were applied as follows: (1) 127 Garden I and II fracture radiographs, (2) 610 Garden III and IV fracture radiographs, and (3) 326 normal hip radiographs. After localization by an initial network, a second CNN classified the images as Garden I/II fracture, Garden III/IV fracture, or no fracture. Advanced data augmentation techniques expanded the training set: (1) generative adversarial network (GAN); (2) digitally reconstructed radiographs (DRRs) from preoperative hip CT scans. In all, 9063 images, real and generated, were available for training and testing. A deep neural network was designed and tuned based on a 20% validation group. A holdout test dataset consisted of 105 real images, 35 in each class. Two class prediction of fracture versus no fracture (AUC 0.92): accuracy 92.3%, sensitivity 0.91, specificity 0.93, PPV 0.96, NPV 0.86. Three class prediction of Garden I/II, Garden III/IV, or normal (AUC 0.96): accuracy 86.0%, sensitivity 0.79, specificity 0.90, PPV 0.80, NPV 0.90. Without any advanced augmentation, the AUC for two-class prediction was 0.80. With DRR as the only advanced augmentation, AUC was 0.91 and with GAN only AUC was 0.87. GANs and DRRs can be used to improve the accuracy of a tool to diagnose and classify femoral neck fractures.

Effect of Analytics-Driven Worklists on Musculoskeletal MRI Interpretation Times in an Academic Setting.

OBJECTIVE. The objective of this study was to determine how use of analytics-driven worklists for MRI based on relative individual interpretation time affects the overall group interpretation time in an academic musculoskeletal practice. SUBJECTS AND METHODS. In this prospective study, interpretation times for all MRI studies signed by three musculoskeletal fellowship-trained radiologists during 2016 were calculated from initial study view and report signing times. Custom worklists were made for each radiologist with body parts ordered from the fastest to the slowest based on relative interpretation time. These worklists were then used for a trial period of 7 consecutive days. The difference in mean interpretation times between the trial period and baseline and the differences in volume distribution were calculated. Changes in individual interpretation time were assessed by z-score with statistical significance set at ≤ 0.05. RESULTS. Across all readers, total interpretation time decreased by a mean of 29.5 minutes per day during the trial period. Only two types of studies were read with an individual interpretation time significantly different from baseline (wrist studies for reader 1 were 10 minutes slower [p = 0.01] and cervical spine studies for reader 3 were 9 minutes faster [p < 0.01]). Volume distributions changed across various body parts (-3% to 4% for reader 1, -13% to 14% for reader 2, and -24% to 10% for reader 3). CONCLUSION. Analytics-driven worklists for MRI may decrease overall group interpretation time without significant alteration in individual speed, though a change in volume distribution is required.