Deep Learning of Knee Joint MRI Intelligent Detection

Summary

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.

Conditions

• Knee Injuries

Sponsors & Collaborators

• Huashan Hospital

  collaborator OTHER

• Shanghai Jiao Tong University Affiliated Sixth People's Hospital

  collaborator OTHER

• Chinese PLA General Hospital

  collaborator OTHER

• Inner Mongolia People's Hospital

  collaborator OTHER

• The First Affiliated Hospital of BaoTou Medical College

  collaborator OTHER

• Fourth Medical Center of PLA General Hospital

  collaborator OTHER

• The 8th medical center of chinese PLA general hospital

  collaborator UNKNOWN

• Hebei Medical University Third Hospital

  collaborator OTHER

• Tianjin Hospital

  collaborator OTHER

• Peking University Third Hospital

  lead OTHER

Principal Investigators

• Lin Lin · Peking University Third Hospital

Eligibility

Sex

ALL

Healthy Volunteers

No

Timeline & Regulatory

Start

2021-01-01

Primary Completion

2021-12-31

Completion

2022-05-15

Countries

• China

Study Locations