Single-incision fasciotomy leads to long-term improvement in the activity level and QOL of patients with CECS.
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.
Purpose: To determine if boney morphology influences the anatomic location of hip fractures in elderly patients. Methods: All patients with hip fractures between 2008 and 2012 who had hip radiographs taken prior to the fracture were reviewed. Fractures were classified as intracapsular or extracapsular and hip morphology was measured on the pre-fracture x-rays. Hip morphology was determined by alpha angle, lateral central edge angle, acetabular index, neck-shaft angle, hip axis length, femoral neck diameter, Tönnis classification for hip osteoarthritis (OA) and the presence of a crossover sign. Results: 148 subjects (78.4% female, age 83.5 years) with proximal femur fractures were included. 44 patients (29.7%) had intracapsular fractures and 104 (70.3%) had extracapsular fractures. 48% of patients had previous hip fractures on the contralateral side and 74.6% had the same type of fracture bilaterally. The rates of bilateral intracapsular and extracapsular fractures were similar (33.7% vs. 40.9% respectively, p = 0.39). Extracapsular fractures had a statically significant higher neck-shaft angle, a shorter hip axis length, a narrower femoral neck diameter and a higher grade of Tönnis classification of OA ( p = 0.04, 0.046, 0.03, 0.02 respectively). Acetabular coverage and the proximal femoral head-neck junction, which were evaluated by lateral centre-edge angle (LCEA), acetabular index and the presence of a crossover sign, did not correlate with fracture type. The alpha angle > 40° had a statistically significant higher likelihood for extracapsular fractures ( p = 0.013). Conclusions: Acetabular coverage and proximal femoral head-neck junction morphology, were found to partially correlate with the location of hip fractures and do not fully elucidate fracture type susceptibility.
Non-routine preoperative tests prolong time-to-surgery, increased hospitalization time and contribute to 30-day mortality. No postoperative procedure was related to preoperative test findings. The financial cost for these tests does not burden the medical expenses per procedure. Geriatr Gerontol Int 2018; 18: 937-942.
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