Knee joint is the most common part of sports injury. MRI is a powerful tool to diagnose knee joint injury. However, it takes a long time to read the film, needs a lot, and some hidden injuries have a high rate of missed diagnosis. The emerging deep learning technology can establish automatic recognition model through large samples. A large sample of knee joint MRI was collected retrospectively to train the deep learning model of knee joint MRI, and the sensitivity and specificity of the deep learning model were verified in multi center. Depending on the clinical needs, the deep learning model annotation system is established. A large number of knee MRI were obtained and labeled. According to the knee joint MRI training depth learning model, and iterative optimization, the final version is formed. Multi center validation was carried out. Continuous operation records and corresponding preoperative knee MRI were obtained from multiple hospitals. The sensitivity and specificity of the model were calculated with operation records as the gold standard. At the same time, an expert team composed of senior radiologists and sports medicine doctors was organized to read the films. The sensitivity and specificity of manual reading and AI reading were compared to prove the superiority of AI reading. This study can improve the efficiency of clinical MRI film reading, reduce the workload of doctors, improve the film reading level of grass-roots hospitals, promote the development of the discipline, and has good social benefits and market prospects.

The knee joint is the most common sports injury site in the human body, including ligament rupture, meniscus tear, cartilage lesions, and free body formation. Knee MRI has extremely high sensitivity and specificity in diagnosing knee diseases, especially its negative predictive value is close to 100%, and it is an effective means to assist clinicians in diagnosing knee diseases. However, there are many MRI sequences of the knee joint, and different diseases have different imaging effects on various sequences, and the types of knee joint diseases are complicated, so it takes a long time to evaluate the knee joint MRI. Due to the huge clinical demand for knee MRI, it has caused a great burden on radiology and sports medicine orthopedics. At the same time, for some special injuries of the knee joint, such as hidden meniscus tear, rupture of the anterior cross part and adhesion in place after rupture, local ligament injury, etc., the conclusions given by different readers are very different, and it is easy to miss the diagnosis. And the missed diagnosis seriously affects the prognosis of the knee joint, leading to the progression of arthritis. In addition, professional musculoskeletal system imaging experts have a long training cycle, and a large number of orthopedic doctors and radiologists in basic hospitals have limited reading skills for knee MRI, which limits the development of local sports medicine disciplines and the development of related diagnosis and treatment. The purpose of our research is to train the deep learning model of knee MRI through multi-center and large sample of knee MRI; Multi-center verification of the sensitivity and specificity of the knee MRI deep learning model, and compare the accuracy of the deep learning model and manual image reading.

Inclusion Criteria:

1. ACL-injured patients;
2. Follow-up of patients after ACL injury;
3. patients with genetic predisposition to ACL injury;

Exclusion Criteria:

1. Patients with joint injury caused by clear external forces;
2. Definitely have stroke, heart disease, epilepsy, cranial neurosurgery, migraine;
3. Have had a concussion or head injury in the past 6 months.

All patients related to sports injuries