Objective:To investigate the efficacy of internal fixation of Pipkin types I and II femoral head fractures through the modified Smith-Petersen (S-P) approach.Methods:A retrospective case control study was conducted to analyze the clinical data of 33 patients with Pipkin types I and II femoral head fractures admitted to Union Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology from June 2015 to September 2019. There were 22 males and 11 females, aged 20-40 years (mean, 29.5 years). There were 15 patients with Pipkin type I fractures and 18 with Pipkin type II fractures. A total of 22 patients were treated using the modified S-P approach via the sartorius and tensor fascia lata space (modified S-P group) and 11 patients were treated using the modified K-L approach via the posterior superior iliac spine and gluteus maximus (modified K-L group). The operation duration, intraoperative blood loss, postoperative drainage volume, length of hospital stay, numeric rating scales (NRS) for pain assessment at postoperative 15 days, bone healing time, Harris hip joint score at postoperative one month, and complication rate were compared between the two groups.Results:All patients were followed up for 1-24 months (mean, 6.5 months). The operation duration, blood loss, drainage rate and length of hospital stay in modified S-P group were better than those in modified K-L group [(71.7±7.3)minutes vs. (112.1±6.7)minutes, (55.9±6.2)ml vs. (99.4±8.7)ml, (91.2±5.9)ml vs. (121.3±7.0)ml, (6.0±1.5)days vs. (10.5±1.6)days] ( P<0.01). There were no significant differences between two groups in terms of NRS, bone healing time and Harris score ( P>0.05). The incidence of complications was similar between the two groups, including femoral head ischemia necrosis, traumatic arthritis, and heterogenous ossification ( P>0.05). Conclusion:For Pipkin types I and II femoral head fractures, the modified modified S-P approach is superior to modified K-L approach in aspects of operative time, intraoperative blood loss, postoperative drainage and length of hospital stay.

Multiple injuries caused by trauma have high rates of disability and mortality and are difficult to treat, which have a negative impact on the patients, their families and the society. At present, the medical model of trauma treatment is still inadequate, and the treatment of trauma patients faces great challenges. Artificial intelligence (AI) is an intelligent technology based on machine learning, reinforcement learning and deep learning algorithm, and it has been applied to the treatment of patients with trauma. Its efficient and accurate computer vision, planning and decision-making, and big data statistical analysis not only improve the safety and efficiency in the treatment of trauma, but also reduce the workload of clinicians, which makes up for the deficiency of the traditional model of trauma care. After screening the recent studies of AI in trauma care, the authors review its application in emergency triage, diagnosis, treatment and prevention of war trauma, in order to introduce the latest research progress of AI in trauma care and provide references for future developments.

Orthopaedic surgery is complex and delicate. The Orthopaedic Navigation System is developed to provide an augmented reality three-dimensional (3D) visualization environment to improve treatment outcomes by analyzing preoperative, intraoperative and postoperative data. With the rapid development and clinical application of digital technology, artificial intelligence technology has been introduced into orthopaedic intraoperative navigation system. Artificial intelligence, combined with instrumentation devices and imaging technology, enhances the visualization capabilities of orthopaedic surgeons, allowing them to receive real-time feedback and guidance during surgery, which in turn provides optimal clinical decision-making. The application of artificial intelligence to intraoperative orthopaedic navigation also improves the repeatability of procedures and reduces the incidence of human error. This paper reviews the current status of the application of artificial intelligence in orthopaedic intraoperative navigation, and introduces the basic concepts of artificial and the development of image alignment, real-time tracking, and 3D visualization techniques based on artificial intelligence, as well as discusses the current limitations and shortcomings.