Alteration of anterior cruciate ligament orientation in knees with trochlear dysplasia: description of a novel angle on MRI.

AIM: To assess changes in anterior cruciate ligament (ACL) geometry and inclination in trochlear dysplasia (TD) and analyse their significance. MATERIALS AND METHODS: Ninety-nine consecutive knees with TD and 23 normal knee magnetic resonance imaging (MRI) examinations were included as controls (n=122). Varying degrees of TD were classified into four distinct groups (A-D) according to the Dejour classification. MRI images were reviewed independently to measure four ACL angles. Interobserver and intra-observer agreements with statistical significance were determined for TD and various angles. RESULTS: A significant association was found between TD and two measured angles compared with the control group (sagittal ACL and anteromedial ACL angles, p<0.001 for each). The results indicate that TD can predispose to more vertical ACL inclination as measured in the coronal plane on MRI. No association was found with the Blumenstat angle. CONCLUSION: The present study found significant associations with TD and steeper sagittal ACL, which have been implicated in ACL failure. A novel angle (anteromedial ACL angle) is described which has significant association with TD and is specific for the anteromedial bundle as measured in the coronal plane. Careful consideration of ACL fibre orientation in the coronal plane on MRI is suggested in knees with TD and the use of this newly described angle in assessing ACL reconstruction (ACLR) grafts.

Vasovagal reactions in ultrasound guided musculoskeletal injections: A study of 2,462 procedures.

OBJECTIVE: to determine the rate of the vasovagal reaction (VVR) in ultrasound guided musculoskeletal injections (USGIs) and to investigate effect of injection site, age, and gender on this rate. MATERIAL AND METHODS: Retrospective analysis of all USGIs performed from the 1st of January 2019 to the 31st of December 2019 in single tertiary orthopaedic hospital. Two thousand four hundred and sixty two consecutive subjects undergoing USGIs were included. Statistical analysis used to determine the rate of the overall VVR in USGIs and to determine if site of the injection or joint injected has an effect on this rate as well as age and gender effect. RESULTS: Overall rate of VVR was 2.3% with shoulder and small joints of the hands and feet are more commonly affected than other sites. Females and patients aged younger than 65 years may be subjected to higher rate of VVR. CONCLUSIONS: VVR has an overall low occurrence in USGI. The higher rate of VVR for shoulder and small joints of hands and feet procedures. Care should be taken when positioning a patient prior to the procedure to allow for a VVR in case it happens. VVR are more likely to occur in females and less likely in age more than 65 years.

Automated characterisation of ultrasound images of ovarian tumours: the diagnostic accuracy of a support vector machine and image processing with a local binary pattern operator.

INTRODUCTION: Preoperative characterisation of ovarian masses into benign or malignant is of paramount importance to optimise patient management. OBJECTIVES: In this study, we developed and validated a computerised model to characterise ovarian masses as benign or malignant. MATERIALS AND METHODS: Transvaginal 2D B mode static ultrasound images of 187 ovarian masses with known histological diagnosis were included. Images were first pre-processed and enhanced, and Local Binary Pattern Histograms were then extracted from 2 × 2 blocks of each image. A Support Vector Machine (SVM) was trained using stratified cross validation with randomised sampling. The process was repeated 15 times and in each round 100 images were randomly selected. RESULTS: The SVM classified the original non-treated static images as benign or malignant masses with an average accuracy of 0.62 (95% CI: 0.59-0.65). This performance significantly improved to an average accuracy of 0.77 (95% CI: 0.75-0.79) when images were pre-processed, enhanced and treated with a Local Binary Pattern operator (mean difference 0.15: 95% 0.11-0.19, p < 0.0001, two-tailed t test). CONCLUSION: We have shown that an SVM can classify static 2D B mode ultrasound images of ovarian masses into benign and malignant categories. The accuracy improves if texture related LBP features extracted from the images are considered.