Imaging Techniques for Assessment of Dynamically Unstable Sports Related Foot and Ankle Injuries

Postoperative Magnetic Resonance Evaluation of Anterior Talofibular Ligament Following Arthroscopic Brostrom Procedure: Analysis and Outcomes of 40 Repairs at 12 Months

Lateral ankle sprains are one of the most common orthopedic injuries. When conservative treatment fails, surgical correction is often performed using either open or arthroscopic techniques. We hypothesize that MRI evaluation of the arthroscopic brostrom repair will show intact repair and decrease in thickness of the anterior talofibular ligament (ATFL) at 1 year, with statistically significant improvement of patient function and pain scores. Postoperative MRI was utilized at minimum 1-year follow-up to evaluate the integrity of the arthroscopic brostrom repair, as well as comparison of ATFL thickness to literature validated average thickness. A musculoskeletal fellowship trained radiologist performed all MRI reads. In addition, 3 fellowship trained foot and ankle specialists from a single institution all performed measurements of the ATFL. Surgical satisfaction using 1 to 100 scale, and Karlsson-Peterson (KP) were measured at 1 year postoperatively. In addition, pre- and postoperative Foot Function Index (FFI), American Orthopedic Foot and Ankle (AOFAS) hindfoot scores, and Visual Analog Scale (VAS) were measured using unpaired t tests. All repairs were shown to be intact at minimum 1-year follow-up via MRI evaluation, with ATFL thickness of 2.21 mm. Preoperative FFI, AOFAS, and VAS were 54.9, 46.4, and 7.1 respectively. Postoperative scores were 11.0, 91.7, and 1.3 respectively. Surgical satisfaction was 88.2, KP was 75.3. Comparison of pre- and postoperative scores (VAS, FFI, AOFAS) were shown to be statistically significant, p < .05. No significant difference in demographic data was observed at 1 year. The data from this study offers evidence that the arthroscopic brostrom repair provides patients with good outcomes as well as an intact ATFL with normal morphology at 1 year postoperatively.

Ankle Instability Update

Sprains are the most frequent injuries of the ankle, especially in sports. Up to 85% of cases affect the lateral ligament complex. Multi-ligament injuries with associated lesions of the external complex, deltoid, syndesmosis, and sinus tarsi ligaments are also common. Most ankle sprains respond to conservative treatment. However, up to 20 to 30% of patients can develop chronic ankle pain and instability.New concepts have been recently developed, based on arthroscopic advances, such as microinstability and rotatory ankle instability. These entities could be precursors of mechanical ankle instability and at the origin of frequently associated ankle injuries, such as peroneus tendon lesions, impingement syndromes, or osteochondral lesions.Imaging methods, especially magnetic resonance (MR) imaging and MR arthrography, are key in precisely diagnosing ligament lesions and associated injuries, facilitating an adequate therapeutic approach.

Computer-Aided Ankle Ligament Injury Diagnosis from Magnetic Resonance Images Using Machine Learning Techniques

Ankle injuries caused by the Anterior Talofibular Ligament (ATFL) are the most common type of injury. Thus, finding new ways to analyze these injuries through novel technologies is critical for assisting medical diagnosis and, as a result, reducing the subjectivity of this process. As a result, the purpose of this study is to compare the ability of specialists to diagnose lateral tibial tuberosity advancement (LTTA) injury using computer vision analysis on magnetic resonance imaging (MRI). The experiments were carried out on a database obtained from the Vue PACS-Carestream software, which contained 132 images of ATFL and normal (healthy) ankles. Because there were only a few images, image augmentation techniques was used to increase the number of images in the database. Following that, various feature extraction algorithms (GLCM, LBP, and HU invariant moments) and classifiers such as Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), k-Nearest Neighbors (kNN), and Random Forest (RF) were used. Based on the results from this analysis, for cases that lack clear morphologies, the method delivers a hit rate of 85.03% with an increase of 22% over the human expert-based analysis.