The aim of this study is to investigate the effect of arthroscopic synovectomy (AS) on disease status in patients with rheumatoid arthritis (RA). A prospective study was conducted among patients with RA who underwent AS of the elbow or knee joints due to persistent swelling following conventional treatment, as well as control individuals who only received conventional treatment. All patients were evaluated at baseline and 4, 12, 24, 48, 96 weeks after AS. The coprimary outcomes were week 96 disease activity score (DAS)-28 remission and American College of Rheumatology improvement criteria (ACR20/50/70) remission. Key secondary outcomes were changes from baseline to week 96 in Mayo score, Lysoholm score, Health Assessment questionnaire (HAQ), patient’s visual analogue scale, Magnetic Resonance Imaging in AS group. The correlation between synovial tissue biology and clinical response was analyzed. A total of 51 patients were included, with 17 accepted AS and conventional treatment, 34 only accepted conventional treatment. At week 96, more patients in AS group achieved DAS-28 remission [52.9

RATIONALE AND OBJECTIVES:To develop and validate a deep learning system with guided diffusion-based data augmentation for grading partial-thickness supraspinatus tendon (SST) tears and to compare its performance with experienced radiologists, including external validation. METHODS:This retrospective study included 1150 patients with arthroscopically confirmed SST tears, divided into a training set (741 patients), validation set (185 patients), and internal test set (185 patients). An independent external test set of 224 patients was used for generalizability assessment. To address data imbalance, MRI images were augmented using a guided diffusion model. A ResNet-34 model was employed for Ellman grading of bursal-sided and articular-sided partial-thickness tears across different MRI sequences (oblique coronal [OCOR], oblique sagittal [OSAG], and combined OCOR+OSAG). Performance was evaluated using AUC and precision-recall curves, and compared to three experienced musculoskeletal (MSK) radiologists. The DeLong test was used to compare performance across different sequence combinations. RESULTS:A total of 26,020 OCOR images and 26,356 OSAG images were generated using the guided diffusion model. For bursal-sided partial-thickness tears in the internal dataset, the model achieved AUCs of 0.99, 0.98, and 0.97 for OCOR, OSAG, and combined sequences, respectively, while for articular-sided tears, AUCs were 0.99, 0.99, and 0.99. The DeLong test showed no significant differences among sequence combinations (P=0.17, 0.14, 0.07). In the external dataset, the combined-sequence model achieved AUCs of 0.99, 0.97, and 0.97 for bursal-sided tears and 0.99, 0.95, and 0.95 for articular-sided tears. Radiologists demonstrated an ICC of 0.99, but their grading performance was significantly lower than the ResNet-34 model (P<0.001). The deep learning system improved grading consistency and significantly reduced evaluation time, while guided diffusion augmentation enhanced model robustness. CONCLUSION:The proposed deep learning system provides a reliable and efficient method for grading partial-thickness SST tears, achieving radiologist-level accuracy with greater consistency and faster evaluation speed.

Background:Bone marrow stimulation (BMS) is the most commonly performed surgery for osteochondral lesion of the talus (OLT), but there is a risk of poor outcome when cysts recur. The indications of BMS in the presence of cystic OLT remain controversial. Purpose:To investigate whether a new "jumping dot (JD) sign," manifesting as speckle-like areas of elevated signals surrounding the subchondral bone cyst (SBC) on preoperative magnetic resonance imaging (MRI) against the background of bone marrow edema (BME), could be a predictor of clinical outcome and recurrence of SBCs following BMS and to further propose a more precise indication regarding BMS surgery for cystic OLT. Study Design:Cohort study; Level of evidence, 3. Methods:Patients with cystic OLTs (<150 mm2) who received BMS between November 2016 and January 2021 were retrospectively studied. Visual analog scale for pain and American Orthopaedic Foot & Ankle Society (AOFAS) scores were assessed preoperatively and at follow-up. The normal bone marrow, BME, and SBCs (including size) were quantified, and the JD sign was evaluated on the preoperative MRI. Notably, the maximal vertical diameter of the cyst was rigorously defined as the greatest distance measured from the superior to the inferior margins of the cyst in this study. The MOCART (magnetic resonance observation of cartilage repair tissue) score and the cyst recurrence were evaluated, and multivariate analysis was performed to evaluate the association of the JD sign with outcomes at the final follow-up. Results:A total of 117 patients were divided into the JD sign group (n = 41) and no JD sign group (n = 76), and no significant difference was found for the follow-up duration (48.04 ± 14.78 months vs 48.46 ± 15.38 months; P = .89). Overall, the patients had significantly improved AOFAS scores (68.69 ± 7.46 vs 86.40 ± 10.75; P = .009) and lessened postoperative cysts (117/117 vs 43/117; P = .000). However, both uni- and multivariate analysis revealed that the JD sign was negatively associated with clinical outcomes following BMS (P < .05). Additionally, the JD sign group showed significantly higher cyst recurrence rate (75.60% vs 15.78%; P < .001) and lower MOCART score (73.04 ± 11.28 vs 80.59 ± 19.07; P = .008). When the maximal vertical diameter of the cyst was >5.4 mm, the JD sign showed excellent effectiveness in predicting the postoperative cyst recurrence (sensitivity, 81.4%; specificity, 68.9%; positive predictive value, 61.34%; and negative predictive value, 86.44%). Conclusion:The JD sign might be significantly associated with inferior clinical outcomes and higher SBC recurrence following BMS for cystic OLT. For those patients with the maximal vertical diameter of the cyst >5.4 mm and with JD signs, BMS may not be an appropriate option.

To establish an automated, multitask, MRI-based deep learning system for the detailed evaluation of supraspinatus tendon (SST) injuries. According to arthroscopy findings, 3087 patients were divided into normal, degenerative, and tear groups (groups 0–2). Group 2 was further divided into bursal-side, articular-side, intratendinous, and full-thickness tear groups (groups 2.1–2.4), and external validation was performed with 573 patients. Visual geometry group network 16 (VGG16) was used for preliminary image screening. Then, the rotator cuff multitask learning (RC-MTL) model performed multitask classification (classifiers 1–4). A multistage decision model produced the final output. Model performance was evaluated by receiver operating characteristic (ROC) curve analysis and calculation of related parameters. McNemar’s test was used to compare the differences in the diagnostic effects between radiologists and the model. The intraclass correlation coefficient (ICC) was used to assess the radiologists’ reliability. p < 0.05 indicated statistical significance. In the in-group dataset, the area under the ROC curve (AUC) of VGG16 was 0.92, and the average AUCs of RC-MTL classifiers 1–4 were 0.99, 0.98, 0.97, and 0.97, respectively. The average AUC of the automated multitask deep learning system for groups 0–2.4 was 0.98 and 0.97 in the in-group and out-group datasets, respectively. The ICCs of the radiologists were 0.97–0.99. The automated multitask deep learning system outperformed the radiologists in classifying groups 0–2.4 in both the in-group and out-group datasets (p < 0.001). The MRI-based automated multitask deep learning system performed well in diagnosing SST injuries and is comparable to experienced radiologists. Our study established an automated multitask deep learning system to evaluate supraspinatus tendon (SST) injuries and further determine the location of SST tears. The model can potentially improve radiologists’ diagnostic efficiency, reduce diagnostic variability, and accurately assess SST injuries. • A detailed classification of supraspinatus tendon tears can help clinical decision-making. • Deep learning enables the detailed classification of supraspinatus tendon injuries. • The proposed automated multitask deep learning system is comparable to radiologists.

BACKGROUNDCervical haemorrhage due to spontaneous rupture of a parathyroid adenoma is a rare complication that may cause life-threatening acute airway compromise.CASE SUMMARYA 64-year-old woman was admitted to the hospital 1 day after the onset of right neck enlargement, local tenderness, head-turning difficulty, pharyngeal pain, and mild dyspnoea. Repeat routine blood testing showed a rapid decrease in the haemoglobin concentration, indicating active bleeding. Enhanced computed tomography images showed neck haemorrhage and a ruptured right parathyroid adenoma. The plan was to perform emergency neck exploration, haemorrhage removal, and right inferior parathyroidectomy under general anaesthesia. The patient was administered 50 mg of intravenous propofol, and the glottis was successfully visualised on video laryngoscopy. However, after the administration of a muscle relaxant, the glottis was no longer visible and the patient had a difficult airway that prevented mask ventilation and endotracheal intubation. Fortunately, an experienced anaesthesiologist successfully intubated the patient under video laryngoscopy after an emergency laryngeal mask placement. Postoperative pathology showed a parathyroid adenoma with marked bleeding and cystic changes. The patient recovered well without complications.CONCLUSIONAirway management is very important in patients with cervical haemorrhage. After the administration of muscle relaxants, the loss of oropharyngeal support can cause acute airway obstruction. Therefore, muscle relaxants should be administered with caution. Anaesthesiologists should pay careful attention to airway management and have alternative airway devices and tracheotomy equipment available.

PURPOSE:To determine the ultrasound imaging manifestations associated with subspine impingement (SSI), including the osseous and soft-tissue injuries adjacent to anterior inferior iliac spine (AIIS) and to investigate the diagnostic value of ultrasound for SSI. METHODS:We retrospectively evaluated patients who attended the sports medicine department of our hospital and underwent arthroscopic treatment for femoroacetabular impingement (FAI) between September 2019 and October 2020, with preoperative hip joint ultrasound and computed tomography (CT) examination within 1 month before surgery. All of the FAI patients were divided into the SSI group and non-SSI group, according to the clinical and intraoperative findings. The preoperative ultrasound and CT findings were assessed. The sensitivity, specificity, and positive predictive value (PPV) of some indicators were calculated and compared. Multivariable logistic regression and receiver operating characteristic curve (ROC) were also used. RESULTS:A total of 71 hips were included, with a mean age of 35.4 ± 10.4 years, 56.3% were women. Of these, 40 hips had clinically confirmed SSI. The bone morphology type III, heterogeneous hypoecho in anterosuperior joint capsule and the direct head of rectus femoris (dRF) tendon adjacent to AIIS on the Standard Section of the dRF in ultrasound were associated with SSI. Among them, the heterogeneous hypoecho in the anterosuperior joint capsule had the best diagnostic value for the SSI (85.0% sensitivity, 58.1% specificity, AUC = 0.681). The AUC of the ultrasound composite indicators was 0.750. The AUC and PPV of CT low-lying AIIS for the SSI diagnosis was 0.733 and 71.7%, which could be improved when CT was combined with the ultrasound composite indicators with AUC = 0.831 and PPV = 85.7%. CONCLUSIONS:Bone morphology abnormalities and soft-tissue injuries adjacent to the AIIS through sonographic evaluation were associated with SSI. Ultrasound could be used as a feasible method to predict SSI. The diagnostic value for SSI could be improved when ultrasound is combined with CT. LEVEL OF EVIDENCE:Level IV, case series.

To investigate the potential applicability of AI-assisted compressed sensing (ACS) in knee MRI to enhance and optimize the scanning process. Volunteers and patients with sports-related injuries underwent prospective MRI scans with a range of acceleration techniques. The volunteers were subjected to varied ACS acceleration levels to ascertain the most effective level. Patients underwent scans at the determined optimal 3D-ACS acceleration level, and 3D compressed sensing (CS) and 2D parallel acquisition technology (PAT) scans were performed. The resultant 3D-ACS images underwent 3.5 mm/2.0 mm multiplanar reconstruction (MPR). Experienced radiologists evaluated and compared the quality of images obtained by 3D-ACS-MRI and 3D-CS-MRI, 3.5 mm/2.0 mm MPR and 2D-PAT-MRI, diagnosed diseases, and compared the results with the arthroscopic findings. The diagnostic agreement was evaluated using Cohen’s kappa correlation coefficient, and both absolute and relative evaluation methods were utilized for objective assessment. The study involved 15 volunteers and 53 patients. An acceleration factor of 10.69 × was identified as optimal. The quality evaluation showed that 3D-ACS provided poorer bone structure visualization, and improved cartilage visualization and less satisfactory axial images with 3.5 mm/2.0 mm MPR than 2D-PAT. In terms of objective evaluation, the relative evaluation yielded satisfactory results across different groups, while the absolute evaluation revealed significant variances in most features. Nevertheless, high levels of diagnostic agreement (κ: 0.81–0.94) and accuracy (0.83–0.98) were observed across all diagnoses. ACS technology presents significant potential as a replacement for traditional CS in 3D-MRI knee scans, allowing thinner MPRs and markedly faster scans without sacrificing diagnostic accuracy. 3D-ACS-MRI of the knee can be completed in the 160 s with good diagnostic consistency and image quality. 3D-MRI-MPR can replace 2D-MRI and reconstruct images with thinner slices, which helps to optimize the current MRI examination process and shorten scanning time. • AI-assisted compressed sensing technology can reduce knee MRI scan time by over 50 • 3D AI-assisted compressed sensing MRI and related multiplanar reconstruction can replace traditional accelerated MRI and yield thinner 2D multiplanar reconstructions. • Successful application of 3D AI-assisted compressed sensing MRI can help optimize the current knee MRI process.

Background: Diagnosing anterior talofibular ligament (ATFL) injuries differs among radiologists. Further assessment of ATFL tears is valuable for clinical decision-making.Purpose: To establish a deep learning method for classifying ATFL injuries based on magnetic resonance imaging (MRI).Study Type: Retrospective.Population: One thousand seventy-three patients from a single center with ankle MRI within 1 month of reference stan-dard arthroscopy (in-group dataset), were divided into training, validation, and test sets in a ratio of 8:1:1. Additionally, 167 patients from another center were used as an independent out-group dataset.Field Strength/Sequence: Fat-saturation proton density-weighted fast spin-echo sequence at 1.5/3.0 T.Assessment: Patients were divided into normal, strain and degeneration, partial tear and complete tear groups (groups 0-3). The complete tear group was divided into five sub-groups by location and the potential avulsion fracture (groups 3.1-3.5). All images were input into AlexNet, VGG11, Small-Sample-Attention Net (SSA-Net), and SSA-Net + Weight Loss for classification. The results were compared with four radiologists with 5-30 years of experience.Statistical Tests: Model performance was evaluated by the receiver operating characteristic (ROC) curve, the area under the ROC curve (AUC), and so on. McNemar's test was used to compare performance among the different models, and between the radiologists and models. The intraclass correlation coefficient (ICC) was used to assess the reliability of the radiologists. P < 0.05 was considered statistically significant.Results: The average AUC of AlexNet, VGG11, SAA-Net, and SSA-Net + Weight Loss was 0.95, 0.99, 0.99, 0.99 in groups 0-3 and 0.96, 0.99, 0.99, 0.99 in groups 3.1-3.5. The effect of SSA-Net + Weight Loss was similar to SSA-Net but better than AlexNet and VGG11. In the out-group test set, the AUC of SSA-Net + Weight Loss ranged from 0.89 to 0.99. The ICC of radiologists was 0.97-1.00. The effect of SSA-Net + Weight Loss was better than each radiologist in the in-group and out-group test sets.Data Conclusion: Deep learning has potential to be used for classifying ATFL injuries. SSA-Net + Weight Loss has a better diagnostic effect than radiologists with different experience levels.

Background:The classification of calcaneofibular ligament (CFL) injuries on magnetic resonance imaging (MRI) is time-consuming and subject to substantial interreader variability. This study explores the feasibility of classifying CFL injuries using deep learning methods by comparing them with the classifications of musculoskeletal (MSK) radiologists and further examines image cropping screening and calibration methods.Methods:The imaging data of 1,074 patients who underwent ankle arthroscopy and MRI examinations in our hospital were retrospectively analyzed. According to the arthroscopic findings, patients were divided into normal (class 0, n=475); degeneration, strain, and partial tear (class 1, n=217); and complete tear (class 2, n=382) groups. All patients were divided into training, validation, and test sets at a ratio of 8:1:1. After preprocessing, the images were cropped using Mask region-based convolutional neural network (R-CNN), followed by the application of an attention algorithm for image screening and calibration and the implementation of LeNet-5 for CFL injury classification. The diagnostic effects of the axial, coronal, and combined models were compared, and the best method was selected for outgroup validation. The diagnostic results of the models in the intragroup and outgroup test sets were compared with those results of 4 MSK radiologists of different seniorities.Results:The mean average precision (mAP) of the Mask R-CNN using the attention algorithm for the left and right image cropping of axial and coronal sequences was 0.90-0.96. The accuracy of LeNet-5 for classifying classes 0-2 was 0.92, 0.93, and 0.92, respectively, for the axial sequences and 0.89, 0.92, and 0.90, respectively, for the coronal sequences. After sequence combination, the classification accuracy for classes 0-2 was 0.95, 0.97, and 0.96, respectively. The mean accuracies of the 4 MSK radiologists in classifying the intragroup test set as classes 0-2 were 0.94, 0.91, 0.86, and 0.85, all of which were significantly different from the model. The mean accuracies of the MSK radiologists in classifying the outgroup test set as classes 0-2 were 0.92, 0.91, 0.87, and 0.85, with the 2 senior MSK radiologists demonstrating similar diagnostic performance to the model and the junior MSK radiologists demonstrating worse accuracy.Conclusions:Deep learning can be used to classify CFL injuries at similar levels to those of MSK radiologists. Adding an attention algorithm after cropping is helpful for accurately cropping CFL images.

Purpose MR arthrography (MRA) is the most accurate method for preoperatively diagnosing superior labrum anterior–posterior (SLAP) lesions, but diagnostic results can vary considerably due to factors such as experience. In this study, deep learning was used to facilitate the preliminary identification of SLAP lesions and compared with radiologists of different seniority. Methods MRA data from 636 patients were retrospectively collected, and all patients were classified as having/not having SLAP lesions according to shoulder arthroscopy. The SLAP-Net model was built and tested on 514 patients (dataset 1) and independently tested on data from two other MRI devices (122 patients, dataset 2). Manual diagnosis was performed by three radiologists with different seniority levels and compared with SLAP-Net outputs. Model performance was evaluated by the receiver operating characteristic (ROC) curve, area under the ROC curve (AUC), etc. McNemar’s test was used to compare performance among models and between radiologists’ models. The intraclass correlation coefficient (ICC) was used to assess the radiologists’ reliability. p < 0.05 was considered statistically significant. Results SLAP-Net had AUC = 0.98 and accuracy = 0.96 for classification in dataset 1 and AUC = 0.92 and accuracy = 0.85 in dataset 2. In dataset 1, SLAP-Net had diagnostic performance similar to that of senior radiologists ( p = 0.055) but higher than that of early- and mid-career radiologists ( p = 0.025 and 0.011). In dataset 2, SLAP-Net had similar diagnostic performance to radiologists of all three seniority levels ( p = 0.468, 0.289, and 0.495, respectively). Conclusions Deep learning can be used to identify SLAP lesions upon initial MR arthrography examination. SLAP-Net performs comparably to senior radiologists.

Abstract Background High-spatial resolution magnetic resonance imaging (MRI) is essential for imaging ankle joints. However, the clinical application of fast spin-echo sequences remains limited by their lengthy acquisition time. Artificial intelligence-assisted compressed sensing (ACS) technology has been recently introduced as an integrative acceleration solution. We compared ACS-accelerated 3-T ankle MRI to conventional methods of compressed sensing (CS) and parallel imaging (PI) . Methods We prospectively included 2 healthy volunteers and 105 patients with ankle pain. ACS acceleration factors for ankle protocol of T1-, T2-, and proton density (PD)-weighted sequences were optimized in a pilot study on healthy volunteers (acceleration factor 3.2–3.3×). Images of patients acquired using ACS and conventional acceleration methods were compared in terms of acquisition times, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), subjective image quality, and diagnostic agreement. Shapiro-Wilk test, Cohen κ, intraclass correlation coefficient, and one-way ANOVA with post hoc tests (Tukey or Dunn) were used. Results ACS acceleration reduced the acquisition times of T1-, T2-, and PD-weighted sequences by 32−43%, compared with conventional CS and PI, while maintaining image quality (mostly higher SNR with p < 0.004 and higher CNR with p < 0.047). The diagnostic agreement between ACS and conventional sequences was rated excellent (κ = 1.00). Conclusions The optimum ACS acceleration factors for ankle MRI were found to be 3.2–3.3× protocol. The ACS allows faster imaging, yielding similar image quality and diagnostic performance. Relevance statement AI-assisted compressed sensing significantly accelerates ankle MRI times while preserving image quality and diagnostic precision, potentially expediting patient diagnoses and improving clinical workflows. Key points • AI-assisted compressed sensing (ACS) significantly reduced scan duration for ankle MRI. • Similar image quality achieved by ACS compared to conventional acceleration methods. • A high agreement by three acceleration methods in the diagnosis of ankle lesions was observed. Graphical Abstract

Background Subspine impingement is considered a source of residual hip symptoms after primary hip arthroscopy, and the role of the subspine space and soft tissue is not clear. The purpose of this study was to analyze the relationship between the subspine space and labrum size in subspine impingement patients. Methods We performed a retrospective study of patients with femoroacetabular impingement between July 2016 and July 2020. Sixteen patients without hip symptom relief after primary hip arthroscopic treatment of femoroacetabular impingement and undergoing revision surgery for anterior inferior iliac spine compression were included as the study group. Forty-eight matched patients who underwent only primary surgery and whose hip discomfort was relieved without a diagnosis of subspine impingement were included as the control group. The patients’ preoperative computerized tomography data were reviewed, and the anterior inferior iliac spine dimensions and the size of the subspine space were measured. The size of the labrum at the 11:30, 1:30, and 3 o’clock positions was measured with the use of magnetic resonance imaging. The ratio of the subspine space to the labrum was also calculated. Results There was no significant difference in anterior inferior iliac spine dimensions between these two groups ( p > 0.05). A relatively narrow subspine space was found in the study group, especially in the direction of the anterior inferior iliac spine. Compared with the control group, subspine impingement patients were identified with larger labrums at 11:30 (8.20 ± 1.95 mm vs. 6.81 ± 0.50 mm, p = 0.016), 1:30 (7.83 ± 1.61 mm and 6.25 ± 0.78 mm, p = 0.001) and 3:00 (9.50 ± 1.73 mm vs. 7.48 ± 0.99 mm, p = 0.001). A relative mismatch between the subspine space and the labrum was also identified in the study group. The ratios of the labrum width to the subspine area were significantly larger in the study group than in the control group. Conclusion This study reported potential additional criteria for subspine impingement—a large labrum and a relatively narrow subspine space—instead of abnormal anterior inferior iliac spine dimensions. For those with a large labrum and narrow subspine space, the diagnosis of subspine impingement should be carefully made, and arthroscopic anterior inferior iliac spine decompression may be important.

Background The diagnosis of labral injury on MRI is time‐consuming and potential for incorrect diagnoses. Purpose To explore the feasibility of applying deep learning to diagnose and classify labral injuries with MRI. Study Type Retrospective. Population A total of 1016 patients were divided into normal (n = 168, class 0) and abnormal labrum ( n = 848) groups. The abnormal group consisted of n = 111 with class 1 (degeneration), n = 437 with class 2 (partial or complete tear), and n = 300 with unclassified injury. Patients were randomly divided into training, validation, and test cohort according to the ratio of 55%:15%:30%. Field Strength/Sequence Fat‐saturation proton density‐weighted fast spin‐echo sequence at 3. 0 T . Assessment Convolutional neural network‐6 ( CNN ‐6) was used to extract, discriminate, and detect oblique coronal ( OCOR ) and oblique sagittal ( OSAG ) images. Mask R‐CNN was used for segmentation. LeNet ‐5 was used to diagnose and classify labral injuries. The weighting method combined the models of OCOR and OSAG . The output–input connection was used to correlate the whole diagnosis/classification system. Four radiologists performed subjective diagnoses to obtain the diagnosis results. Statistical Tests CNN ‐6 and LeNet ‐5 were evaluated by area under the receiver operating characteristic ( ROC ) curve and related parameters. The mean average precision ( MAP ) evaluated the Mask R‐CNN . McNemar 's test was used to compare the radiologists and models. A P value < 0.05 was considered statistically significant. Results The area under the curve ( AUC ) of CNN ‐6 was 0.99 for extraction, discrimination, and detection. MAP values of Mask R‐CNN for OCOR and OSAG image segmentation were 0.96 and 0.99. The accuracies of LeNet ‐5 in the diagnosis and classification were 0.94/0.94 ( OCOR ) and 0.92/0.91 ( OSAG ), respectively. The accuracy of the weighted models in the diagnosis and classification were 0.94 and 0.97, respectively. The accuracies of radiologists in the diagnosis and classification of labrum injuries ranged from 0.85 to 0.92 and 0.78 to 0.94, respectively. Data Conclusion Deep learning can assist radiologists in diagnosing and classifying labrum injuries. Evidence Level 3 Technical Efficacy Stage 2

PURPOSE:To investigate the clinical outcomes and radiologic evaluation of an all-arthroscopic Latarjet procedure with modified button fixation. METHODS:Patients who received all-arthroscopic Latarjet procedure with modified suture button fixation between September 2015 to September 2016 were retrospectively reviewed. Indications for surgery were recurrent anterior shoulder dislocation with any 1 of these 3 conditions: glenoid defect >15%, contact-sport athlete, or failure after Bankart repair. Inclusion criteria included cases who received this surgery. Clinical outcomes were evaluated by University of California Los Angeles, ASES and Rowe score with a minimal follow-up of 3 years. Radiologic assessment on 3D computed tomography scan was performed preoperatively and postoperatively at different time points. Complications were also recorded. RESULTS:A total of 30 patients were eventually included in this study. The mean follow-up time was 38.0 ± 2.5 months. There were 25 patients who performed contact sports. Of them, 10 patients were without glenoid defect >15% or failed Bankart repair. The remaining 20 patients had glenoid defect >15%, including 2 failed Bankart cases. Ten patients had glenoid defect < 13.5%, and the rest 20 patients had > 13.5%. UCLA, American Shoulder and Elbow Surgeons, and Rowe score significantly improved during follow-up, and the improvement exceeded MCID for all patients. No severe complications were noted. In total, 86.7% of the graft positioning was measured as flush and 13.3% as medial. The bone union rate was 96.7% at 3 months postoperatively and at final follow-up. The remodeling process for the restoration of the normal anatomy of the lower part of glenoid was noted. CONCLUSIONS:All-arthroscopic Latarjet with modified suture button fixation can achieve stable fixation of the coracoid, good clinical outcomes (all patients with improvement exceeding MCID), low complications rate. Furthermore, the bone remodeling process contributes to the recovery of the normal anatomy of anteroinferior glenoid. STUDY DESIGN:Case series; Level of evidence, 4.

Chronic lateral ankle instability (CLAI) could accompany with latent syndesmotic diastasis (LSD), which is difficult to distinguish before surgery. Tibiofibular interval width and extravasation of joint fluid (‘lambda sign’) on MRI are widely used in the diagnosis of syndesmotic injury, but the reliability of these methods in distinguishing the associated LSD in CLAI was rarely studied. Our objective was to compare the diagnostic value of the measurement of the transverse tibiofibular interval and ‘lambda sign’ on MRI in distinguishing LSD in CLAI and to investigate the radiological predictor that best matched the intraoperatively measured syndesmotic width. 138 CLAI patients undergoing arthroscopy in our institute from March 2017 to June 2020 were enrolled (CLAI group). Anterior space width (ASW) and posterior space width (PSW) at 10 mm layer above tibial articular and fluid height above tibial articular surface (FH) were measured on preoperative MRI. The same parameters were measured on MRI of 50 healthy volunteers as control group. At arthroscopy, syndesmotic width was measured and the patients were divided into arthroscopic widening (AW) and arthroscopic normal (AN) subgroup taking 2 mm as critical value. The CLAI group was compared with the control group to explore the interval changes related to CLAI. The AW and AN subgroups were compared to explore the potential diagnostic indicators and reference values for the LSD. All parameters showed significant difference between CLAI group and control group (p < 0.05), but only PSW (p = 0.004) showed significant difference between AW and AN subgroups other than FH (p = 0.461). Only PSW was involved in formula of multiple-factor analysis (p = 0.005; OR, 1.819; 95%CI, 1.196–2.767). ROC analysis showed critical value of PSW was 3.8 mm (sensitivity, 66%; specificity, 66%; accuracy, 66.7%), while accuracy of lambda sign was 41.3%. Transverse tibiofibular interval measurements were more reliable than the ‘lambda sign’ in distinguishing associated LSD in CLAI patients. The PSW ≥ 3.8 mm could be a predictor of syndesmotic diastasis.

To compare the mid- to long-term clinical and radiological outcomes of the confluent L-shaped tunnel technique with the Y-graft technique for anatomic lateral ankle ligament reconstruction. This retrospective study involved 41 patients who underwent lateral ankle ligament reconstruction between 2013 and 2018. Based on the tunnel direction and tendon fixation method at the fibula side, patients were divided into two groups, with 17 patients in the L-shaped tunnel group and 24 patients in the Y-graft group. The American Orthopaedic Foot and Ankle Society (AOFAS) score, visual analogue scale (VAS) pain score, Tegner score, and Karlsson score were evaluated and compared preoperatively and at follow-up. Anterior talar translation and talar tilt at stress radiographs, postoperative sprain recurrence, range of motion (ROM) restriction, sensory disturbance, etc., were also collected and compared. The mean follow-up times were 72 and 42 months for the L-shaped group and Y-graft group, respectively. The median VAS pain score, Tegner score, AOFAS score, Karlsson score significantly improved from a preoperative level in both groups (all with p < 0.01). No significant difference was found between the two groups regarding the changes from preoperatively to postoperatively except for the VAS pain score reduction (1.58 ± 1.58 in the L-shaped group vs. 2.53 ± 1.29 in the Y-graft group, p = 0.035). The incidence of flexion–extension ROM restriction (≥ 5°) was significantly higher in the Y-graft group (41.2%) than in the L-shaped group (12.5%) (p = 0.035). Both the confluent L-shaped tunnel technique and the Y-graft technique significantly improved symptoms, ankle function, and radiographic outcomes in patients with chronic lateral ankle instability (CLAI) at mid- to long-term follow-up. The confluent L-shaped tunnel technique resulted in lower rates of flexion–extension ROM restriction, while the Y-graft technique showed better VAS pain reduction. This result could provide further evidence for the surgical treatment of CLAI. III.

Objective: To explore a modified CT scoring system, its feasibility for disease severity evaluation and its predictive value in coronavirus disease 2019 (COVID-19) patients. Methods: This study was a multi-center retrospective cohort study. Patients confirmed with COVID-19 were recruited in three medical centers located in Beijing, Wuhan and Nanchang from January 27, 2020 to March 8, 2020. Demographics, clinical data, and CT images were collected. CT were analyzed by two emergency physicians of more than ten years' work experience independently through a modified scoring system. Final score was determined by average score from the two reviewers if consensus was not reached. The lung was divided into 6 zones (upper, middle, and lower on both sides) by the level of trachea carina and the level of lower pulmonary veins. The target lesion types included ground-glass opacity (GGO), consolidation, overall lung involvement, and crazy-paving pattern. Bronchiectasis, cavity, pleural effusion, etc., were not included in CT reading and analysis because of low incidence. The reviewers evaluated the extent of the targeted patterns (GGO, consolidation) and overall affected lung parenchyma for each zone, using Likert scale, ranging from 0-4 (0=absent; 1=1%-25%; 2=26%-50%; 3=51%-75%; 4=76%-100%). Thus, GGO score, consolidation score, and overall lung involvement score were sum of 6 zones ranging from 0-24. For crazy-paving pattern, it was only coded as absent or present (0 or 1) for each zone and therefore ranging from 0-6. Results: A total of 197 patients from 3 medical centers and 522 CT scans entered final analysis. The median age of the patients was 64 years, and 54.8% were male. There were 76(38.8%) patients had hypertension and 30(15.3%) patients had diabetes mellitus. There were 75 of the patients classified as moderate cases, as well as 95 severe cases and 27 critical cases. As initial symptom, dry cough occurred in 170 patients, 134 patients had fever, and 125 patients had dyspnea. Reparatory rate, oxygen saturation, lymphocyte count and CURB 65 score on admission day varied among patients with different disease severity scale. There were 50 of the patients suffered from deterioration during hospital stay. The median time consumed for each CT by clinicians was 86.5 seconds. Cronbach's alpha for GGO, consolidation, crazy-paving pattern, and overall lung involvement between two clinicians were 0.809, 0.712, 0.678, and 0.906, respectively, showing good or excellent inter-rater correlation. There were 193 (98.0%) patients had GGO, 147 (74.6%) had consolidation, and 126(64.0%) had crazy-paving pattern throughout clinical course. Bilateral lung involvement was observed in 183(92.9%) patients. Median time of interval for CT scan in our study was 7 days so that the whole clinical course was divided into stages by week for further analysis. From the second week on, the CT scores of various types of lesions in severe or critically patients were higher than those of moderate cases. After the fifth week, the course of disease entered the recovery period. The CT score of the upper lung zones was lower than that of other zones in moderate and severe cases. Similar distribution was not observed in critical patients. For moderate cases, the ground glass opacity score at the second week had predictive value for the escalation of the severity classification during hospitalization. The area under the receiver operating characteristic curve was 0.849, the best cut-off value was 5 points, with sensitivity of 84.2% and specificity of 75.0%. Conclusions: It is feasible for clinicians to use the modified semi-quantitative CT scoring system to evaluate patients with COVID-19. Severe/critical patients had higher scores for ground glass opacity, consolidation, crazy-paving pattern, and overall lung involvement than moderate cases. The ground glass opacity score in the second week had an optimal predictive value for escalation of disease severity during hospitalization in moderate patients on admission. The frequency of CT scan should be reduced after entering the recovery stage.

OBJECTIVE:To introduce an arthroscopic "inlay" Bristow procedure based on the Mortise-Tenon joint structure concept using suture button fixation, and to evaluate its clinical and radiology results postoperatively with a minimal 3-year follow-up. METHODS:A total of 56 patients who received arthroscopic "inlay" Bristow procedure with suture button fixation between June 2015 to June 2016 were eventually enrolled in this study. Radiological assessment on the 3D CT scan was performed preoperatively, immediately after operation, and postoperatively at the end of 3 months, 6 months and the final follow-up. Complications postoperatively were also recorded. RESULTS:A total of 56 patients were finally included in this study. The mean follow-up time was (36.1±3.7) months. Coracoid grafts (middle point) were positioned at about 4 o'clock (123.8°±12.3°) in the En-face view. In the axial view, 95% (53/56) of the grafts positioning were measured as flush, 5% (3/56) as medial. Bone union rate was 96.4% at the final follow-up. At the end of 3 months, 6 months, and the final follow-up, the length of the coracoid graft was 96.9%±4.9%, 91.9%±6.2%, and 91.6%±6.6% of the immediate postoperative length, respectively. Compared with the immediate postoperative length, the length measured at the end of 3 months shortened not significantly (t=2.12, P > 0.05). The coracoid graft shortened more pronouncedly 6 months postoperatively (t=4.98, P < 0.05) and then remained almost constant over time (t=-0.75, P > 0.05), with all grafted coracoid graft retaining more than 90% of their initial length by the 3-year follow-up. And new bone formation at the junction between the coracoid graft and glenoid neck in the axial view were obviously noted in 25 cases. The quantitative evaluation showed that the glenoid area in En-face view was significantly increased at the final follow-up than that immediately after surgery [(9.72±1.22) cm2 vs. (9.42±1.11) cm2]. No degenerative changes were noted on CT images in all the patients at the final follow-up. CONCLUSION:This study reported a series of "inlay" Bristow procedure with suture button fixation for recurrent shoulder dislocation, providing satisfactory union rate and excellent graft positioning. And using suture button fixation instead of screw can reduce osteolysis and complications related to hardware implantation. Moreover, the bone remodeling between the coracoid process and glenoid could be beneficial to restoring the anterior stability of shoulder joint in a long term follow-up.