Comparing the Drop Vertical Jump Tracking Performance of the Azure Kinect to the Kinect V2.

Traditional motion analysis systems are impractical for widespread screening of non-contact anterior cruciate ligament (ACL) injury risk. The Kinect V2 has been identified as a portable and reliable alternative but was replaced by the Azure Kinect. We hypothesize that the Azure Kinect will assess drop vertical jump (DVJ) parameters associated with ACL injury risk with similar accuracy to its predecessor, the Kinect V2. Sixty-nine participants performed DVJs while being recorded by both the Azure Kinect and the Kinect V2 simultaneously. Our software analyzed the data to identify initial coronal, peak coronal, and peak sagittal knee angles. Agreement between the two systems was evaluated using the intraclass correlation coefficient (ICC). There was poor agreement between the Azure Kinect and the Kinect V2 for initial and peak coronal angles (ICC values ranging from 0.135 to 0.446), and moderate agreement for peak sagittal angles (ICC = 0.608, 0.655 for left and right knees, respectively). At this point in time, the Azure Kinect system is not a reliable successor to the Kinect V2 system for assessment of initial coronal, peak coronal, and peak sagittal angles during a DVJ, despite demonstrating superior tracking of continuous knee angles. Alternative motion analysis systems should be explored.

Should my recommendation letter be written by artificial intelligence?

SummaryLetters of recommendation are increasingly important for the residency match. We assessed whether an artificial intelligence (AI) tool could help in writing letters of recommendation by analyzing recommendation letters written by 3 academic staff and AI duplicate versions for 13 applicants. The preferred letters were selected by 3 blinded orthopedic program directors based on a pre-determined set of criteria. The first orthopedic program director selected the AI letter for 31% of applicants, and the 2 remaining program directors selected the AI letter for 38% of applicants, with the staff-written versions selected more often by all of the program directors (p < 0.05). The first program director recognized only 15% of the AI-written letters, the second was able to identify 92%, and the third director identified 77% of AI-written letters (p < 0.05).

A Prediction Model for Primary Anterior Cruciate Ligament Injury Using Artificial Intelligence.

BACKGROUND: Supervised machine learning models in artificial intelligence (AI) have been increasingly used to predict different types of events. However, their use in orthopaedic surgery has been limited. HYPOTHESIS: It was hypothesized that supervised learning techniques could be used to build a mathematical model to predict primary anterior cruciate ligament (ACL) injuries using a set of morphological features of the knee. STUDY DESIGN: Cross-sectional study; Level of evidence, 3. METHODS: Included were 50 adults who had undergone primary ACL reconstruction between 2008 and 2015. All patients were between 18 and 40 years of age at the time of surgery. Patients with a previous ACL injury, multiligament knee injury, previous ACL reconstruction, history of ACL revision surgery, complete meniscectomy, infection, missing data, and associated fracture were excluded. We also identified 50 sex-matched controls who had not sustained an ACL injury. For all participants, we used the preoperative magnetic resonance images to measure the anteroposterior lengths of the medial and lateral tibial plateaus as well as the lateral and medial bone slope (LBS and MBS), lateral and medial meniscal height (LMH and MMH), and lateral and medial meniscal slope (LMS and MMS). The AI predictor was created using Matlab R2019b. A Gaussian naïve Bayes model was selected to create the predictor. RESULTS: Patients in the ACL injury group had a significantly increased posterior LBS (7.0° ± 4.7° vs 3.9° ± 5.4°; P = .008) and LMS (-1.7° ± 4.8° vs -4.0° ± 4.2°; P = .002) and a lower MMH (5.5 ± 0.1 vs 6.1 ± 0.1 mm; P = .006) and LMH (6.9 ± 0.1 vs 7.6 ± 0.1 mm; P = .001). The AI model selected LBS and MBS as the best possible predictive combination, achieving 70% validation accuracy and 92% testing accuracy. CONCLUSION: A prediction model for primary ACL injury, created using machine learning techniques, achieved a >90% testing accuracy. Compared with patients who did not sustain an ACL injury, patients with torn ACLs had an increased posterior LBS and LMS and a lower MMH and LMH.

Artificial Intelligence in the Management of Anterior Cruciate Ligament Injuries.

BACKGROUND: Technological innovation is a key component of orthopaedic surgery. With the integration of powerful technologies in surgery and clinical practice, artificial intelligence (AI) may become an important tool for orthopaedic surgeons in the future. Through adaptive learning and problem solving that serve to constantly increase accuracy, machine learning algorithms show great promise in orthopaedics. PURPOSE: To investigate the current and potential uses of AI in the management of anterior cruciate ligament (ACL) injury. STUDY DESIGN: Systematic review; Level of evidence, 3. METHODS: A systematic review of the PubMed, MEDLINE, Embase, Web of Science, and SPORTDiscus databases between their start and August 12, 2020, was performed by 2 independent reviewers. Inclusion criteria included application of AI anywhere along the spectrum of predicting, diagnosing, and managing ACL injuries. Exclusion criteria included non-English publications, conference abstracts, review articles, and meta-analyses. Statistical analysis could not be performed because of data heterogeneity; therefore, a descriptive analysis was undertaken. RESULTS: A total of 19 publications were included after screening. Applications were divided based on the different stages of the clinical course in ACL injury: prediction (n = 2), diagnosis (n = 12), intraoperative application (n = 1), and postoperative care and rehabilitation (n = 4). AI-based technologies were used in a wide variety of applications, including image interpretation, automated chart review, assistance in the physical examination via optical tracking using infrared cameras or electromagnetic sensors, generation of predictive models, and optimization of postoperative care and rehabilitation. CONCLUSION: There is an increasing interest in AI among orthopaedic surgeons, as reflected by the applications for ACL injury presented in this review. Although some studies showed similar or better outcomes using AI compared with traditional techniques, many challenges need to be addressed before this technology is ready for widespread use.

Characterization of a Pre-Clinical Mini-Pig Model of Scaphoid Non-Union.

A fractured scaphoid is a common disabling injury that is frequently complicated by non-union. The treatment of non-union remains challenging because of the scaphoid's small size and delicate blood supply. Large animal models are the most reliable method to evaluate the efficacy of new treatment modalities before their translation into clinical practice. The goal of this study was to model a human scaphoid fracture complicated by non-union in Yucatan mini-pigs. Imaging and perfusion studies were used to confirm that the anatomy and blood supply of the radiocarpal bone in mini-pigs were similar to the human scaphoid. A 3 mm osteotomy of the radiocarpal bone was generated and treated with immediate fixation or filled with a dense collagen gel followed by delayed fixation. Bone healing was assessed using quantitative micro computed tomography and histology. With immediate fixation, the osteotomy site was filled with new bone across its whole length resulting in complete bridging. The dense collagen gel, previously shown to impede neo-vascularization, followed by delayed fixation resulted in impaired bridging with less bone of lower quality. This model is an appropriate, easily reproducible model for the evaluation of novel approaches for the repair of human scaphoid fractures.

Isolated axillary artery injury due to blunt trauma in ice hockey.

We report the first case of a purely isolated axillary artery dissection because of focal blunt trauma to the axilla. A 42-year-old man presented to our outpatient orthopedic clinic 7 days after a fall during a hockey game whereby another player's skate blade struck the patient directly in the axilla without disrupting the skin. The patient denied having any symptoms of shoulder dislocation but experienced some pain and numbness, which subsided rapidly. Then he developed a cool hand with exertional claudication. Physical examination revealed absent radial and brachial pulses. Computed tomographic angiography demonstrated dissection of the distal axillary artery extending to the middle two-thirds of the brachial artery. Following urgent consultation with vascular surgery, the patient was treated operatively with reverse saphenous interpositional grafting and embolectomy. This case illustrates the need to have a heightened index of suspicion to all injuries to the axilla and the importance of performing careful soft tissue and neurovascular examinations in hockey players presenting with shoulder complaints, even when bony injury is not present.