Proprioception After Primary Repair of the Anterior Cruciate Ligament.

BACKGROUND: Primary repair of the anterior cruciate ligament (ACL) has some potential advantages over the reconstruction technique, which include but are not limited to better knee sensation due to preservation of the natural ACL tissue in patients compared with tendon graft. Proprioception is impaired after ACL injuries and the sense of the joint position is lost. PURPOSE/HYPOTHESIS: The purpose of this study was to compare arthroscopic ACL primary repair and ACL reconstruction techniques clinically and functionally and analyze the differences in proprioception. It was hypothesized that primary repair would restore knee joint proprioception more successfully because the original tissue of the ACL is preserved. STUDY DESIGN: Cohort study; Level of evidence, 3. METHODS: A total of 63 patients (34 underwent reconstruction and 29 underwent primary repair between 2017 and 2020) and 33 healthy controls, as well as the healthy knees of the operated groups, were evaluated between 24 and 48 months (mean, 29 months) postoperatively. Patients with proximal femoral avulsion tears and stump quality suitable for repair underwent primary repair, and those with tears outside these criteria underwent reconstruction using hamstring tendon autograft. Proprioception was evaluated using the active joint position sensation method during weightbearing, with a digital inclinometer used to measure differences between the target and achieved flexion angles of 15°, 30°, and 60°. RESULTS: At 15° of knee flexion, the deviation angles for the healthy knee of the reconstruction and primary repair groups were significantly smaller than those of the control group (P < .001), but there was no statistically significant difference between the groups in terms of deviation angle at 30° and 60° of flexion. The deviation angle of the operated knees was statistically significantly larger in the reconstruction group than in the primary repair group at all angles. The deviation angles at 15°, 30°, and 60° were 2.83°, 2.66°, and 2.66° in the reconstruction group and 1.00°, 1.00°, and 1.33° in the primary repair group, respectively (P < .001). There was no statistically significant difference between the reconstruction and primary repair groups in terms of clinical scores. CONCLUSION: Primary ACL repair can preserve proprioception in a well-selected patient group. In short-term follow-up, primary repair of the ACL in patients with proximal femoral avulsion tears and stump quality suitable for repair appears to be proprioceptively protective. Future studies are needed to clarify the long-term consequences of primary repair on proprioception in a larger population.

Discoid Double-layered Lateral Meniscus: A Case Report.

Introduction: Abnormalities of the double-layered meniscus are rare and can be difficult to diagnose. The main pathology in this abnormality, which is predominantly seen in the lateral compartment, is an accessory meniscus lying over the normal meniscus. Anatomically, this structure can appear in different forms. Although other meniscal abnormalities associated with a double-layered meniscus have been reported, this case presents a previously undescribed type in the literature. Case Report: A 12-year-old girl presented to our clinic with complaints of knee locking and pain. When the patient underwent arthroscopy, an accessory incomplete discoid meniscus was found overlying the normal lateral meniscus. We performed an excision of the upper accessory meniscus and the patient had no problems during 3 years of follow-up after surgery. Conclusion: Abnormalities of the lateral meniscus are rare and can be difficult to diagnose. In cases where we suspect a meniscal abnormality, a proper arthroscopic examination should be performed and if a discoid meniscus is seen, it should be considered that this may be a double-layered meniscus.

The diagnosis of femoroacetabular impingement can be made on pelvis radiographs using deep learning methods.

OBJECTIVES: The aim of this study was to evaluate diagnostic ability of deep learning models, particularly convolutional neural network models used for image classification, for femoroacetabular impingement (FAI) using hip radiographs. MATERIALS AND METHODS: Between January 2010 and December 2020, pelvic radiographs of a total of 516 patients (270 males, 246 females; mean age: 39.1±3.8 years; range, 20 to 78 years) with hip pain were retrospectively analyzed. Based on inclusion and exclusion criteria, a total of 888 hip radiographs (308 diagnosed with FAI and 508 considered normal) were evaluated using deep learning methods. Pre-trained VGG-16, ResNet-101, MobileNetV2, and Inceptionv3 models were used for transfer learning. RESULTS: As assessed by performance measures such as accuracy, sensitivity, specificity, precision, F-1 score, and area under the curve (AUC), the VGG-16 model outperformed other pre-trained networks in diagnosing FAI. With the pre-trained VGG-16 model, the results showed 86.6% accuracy, 82.5% sensitivity, 89.6% specificity, 85.5% precision, 83.9% F1 score, and 0.92 AUC. CONCLUSION: In patients with suspected FAI, pelvic radiography is the first imaging method to be applied, and deep learning methods can help in the diagnosis of this syndrome.

Capsular remnant in the rotator cuff footprint is a novel arthroscopic finding may indicate the etiology of the tear.

PURPOSE: The aim of the study was that the capsule remnant is a common indicator of sub-acromial impingement syndrome and bursal side onset rotator cuff tears. METHODS: Sixty-three patients with capsule remnants in the rotator cuff footprint (Study group) were detected, while the 53 patients did not have any remnant on the tendon footprint (Control group) between 2015 and 2020 were included. Demographic data of patients, such as age, gender, and operated side information were obtained from the archive files. Acromion type, presence of osteophytes in the acromioclavicular joint, Acromiohumeral Distance (AHD), Acromial Index (AI), Critical Shoulder Angle (CSA), and Coracoacromial Ligament (CAL) degeneration values were evaluated from preoperative MRI, radiographic images, and arthroscopic video recordings. RESULTS: Severe stages of CAL degeneration were observed in 82.5% of the patients who had capsule remnant (p: 0.001). While type 2 acromion was found in 61.9%, and also acromioclavicular joint osteophyte was found in 58.7% of the patients in the study group. The mean AHD was 8.22 ± 1.56 mm in the study group and 9.2 ± 1.3 mm in the control group. The mean CSA was 43.3 ± 4.9 in the study group and 40.8 ± 4.2 in the control group. The AI was measured as 0.8 ± 0.1 in the study group and 0.8 ± 0.01 in the control group. As a result of these measurements, a statistical difference was found between the two groups in terms of type 2 acromion ratio (p < 0.001), presence of osteophytes in the acromioclavicular joint (p < 0.001), mean acromio-humeral distance (p < 0.001), critical shoulder angle (p = 0.004), and acromial index values (p < 0.001). CONCLUSION: The findings of sub-acromial impingement syndrome were found to be more prominent in patients with full-thickness degenerative tear and findings of capsular remnant in the footprint. If the presence of the current finding is detected during glenohumeral arthroscopy, sub-acromial impingement syndrome should be examined in more detail to reveal the underlying cause and prevent a recurrence.

Comparison of balance and function in the long term after all arthroscopic ATFL repair surgery.

INTRODUCTION: We compared the balance, ROM, clinical scores, kinesiophobia and functional outcomes of patients after all-arthroscopic ATFL repair surgery with the non-operated side and healthy control group. MATERIALS AND METHODS: Twenty-five patients with follow up time 37.32 ± 12.51 months and twenty-five healthy controls participated in the study. Postural stability was evaluated with the Biodex® balance system by measuring overall (OSI), anterior-posterior (API), and mediolateral (MLI) stability index. Dynamic balance and function were measured using the Y-balance test (YBT) and single-leg hop test (SLH). Limb symmetry index for SLH and contralateral comparisons (YBT, OSI, API, MLI) was performed. The AOFAS score and the Tampa Scale of Kinesiophobia (TSK) were applied. Two subgroups were created (with OLT and without OLT). RESULTS: There was no statistically significant difference between subgroups. There was no statistically significant difference between bilateral OSI, API, MLI values and YBT anterior reach distances of all groups. Single leg OSI (0.78 ± 0.27/0.55 ± 0.12), API (0.55 ± 0.22/0.41 ± 0.10), and MLI (0.40 ± 0.16/ 0.26 ± 0.08) values were significantly worse and YBT posteromedial (73.88 ± 15.70/89.62 ± 12.25), posterolateral reach (78.03 ± 14.08/92.62 ± 8.25) and the SLH distance (117.14 ± 27.84/165.90 ± 20.91) were significantly lower on the patients than controls (p < 0.05), respectively. In contralateral comparisons, all reach distances on YBT were similar and SLH limb symmetry index of the operated side was 98.25%. AOFAS scores of the patients were 92.62 ± 11.13, TSK scores were 46.45 ± 11.32, and 21 patients (84%) had kinesiophobia. CONCLUSION: AOFAS score, limb symmetry index, and bilateral balance of the patients were successful; however, there is single-leg postural stability insufficiency and kinesiophobia. Although the extremity symmetry index of the operated side of the patients was 98.25, the fact that these values are lower than those of the healthy control may have been caused by kinesiophobia. During the long-term rehabilitation, kinesiophobia should be considered and single-leg balance exercises should be monitored during the rehabilitation period. LEVEL OF EVIDENCE: Level III.

The Diagnosis of Developmental Dysplasia of the Hip From Hip Ultrasonography Images With Deep Learning Methods.

BACKGROUND: Hip ultrasonography is very important in the early diagnosis of developmental dysplasia of the hip. The application of deep learning-based medical image analysis to computer-aided diagnosis has the potential to provide decision-making support to clinicians and improve the accuracy and efficiency of various diagnostic and treatment processes. This has encouraged new research and development efforts in computer-aided diagnosis. The aim of this study was to evaluate hip sonograms using computer-assisted deep-learning methods. METHODS: The study included 376 sonograms evaluated as normal according to the Graf method, 541 images with dysplasia and 365 images with incorrect probe position. To classify the developmental hip dysplasia ultrasound images, transfer learning was applied with pretrained VGG-16, ResNet-101, MobileNetV2 and GoogLeNet networks. The performances of the networks were evaluated with the performance parameters of accuracy, sensitivity, specificity, precision, F1 score, and AUC (area under the ROC curve). RESULTS: The accuracy, sensitivity, specificity, precision, F1 score, and AUC results obtained by testing the VGG-16, ResNet-101, MobileNetV2, and GoogLeNet models showed performance >80%. With the pretrained VGG-19 model, 93%, 93.5%, 96.7%, 92.3%, 92.6%, and 0.99 accuracy, sensitivity, specificity, precision, F1 score, and AUC results were obtained, respectively. CONCLUSION: In this study, in addition to the ultrasonography images of dysplastic and healthy hips, images were also included of probe malpositioning, and these images were able to be successfully evaluated with deep learning methods. On the sonograms, which provided criteria appropriate for evaluation, successful differentiation could be made of healthy hips and dysplastic hips. LEVEL OF EVIDENCE: Level-IV; diagnostic studies.

Fracture Detection in Wrist X-ray Images Using Deep Learning-Based Object Detection Models.

Hospitals, especially their emergency services, receive a high number of wrist fracture cases. For correct diagnosis and proper treatment of these, images obtained from various medical equipment must be viewed by physicians, along with the patient's medical records and physical examination. The aim of this study is to perform fracture detection by use of deep-learning on wrist X-ray images to support physicians in the diagnosis of these fractures, particularly in the emergency services. Using SABL, RegNet, RetinaNet, PAA, Libra R-CNN, FSAF, Faster R-CNN, Dynamic R-CNN and DCN deep-learning-based object detection models with various backbones, 20 different fracture detection procedures were performed on Gazi University Hospital's dataset of wrist X-ray images. To further improve these procedures, five different ensemble models were developed and then used to reform an ensemble model to develop a unique detection model, 'wrist fracture detection-combo (WFD-C)'. From 26 different models for fracture detection, the highest detection result obtained was 0.8639 average precision (AP50) in the WFD-C model. Huawei Turkey R&D Center supports this study within the scope of the ongoing cooperation project coded 071813 between Gazi University, Huawei and Medskor.