Deep Learning of Knee Joint MRI Intelligent Detection - Trial NCT04958408

Timeline & Enrollment

Primary Outcome

Marking system design based on Magnetic Resonance Imaging（MRI）,Data export and annotation,Build a deep learning model

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.

ICD-10 Classifications