Magnetic resonance image diagnosis of femoral head necrosis based on ResNet18 network

Abstract

Purpose: In order to enhance the practicability of the application of Magnetic Resonance Imaging (MRI) in the diagnosis of femoral head necrosis, combined with the convolutional neural network (CNN), we propose an automatic identification of femoral head necrosis model based on the ResNet18 network.

Methods: In order to verify that MRI has a higher detection rate for early femoral head necrosis, we collected 360 cases of femoral MRI and the same number of femoral CT. Combining this method with ResNet18, AlexNet, and VGG16, compare the clinical staging and typical signs of femoral head necrosis with 8 diagnostic methods.

Results: The total detection rate of MRI combined with ResNet18 is as high as 99.27%, which is much higher than the other three comparison methods. The sensitivity is 97%, the specificity is 98.99%, and the accuracy is 98.23%. The difference is statistically significant.

Conclusion: The automatic recognition femoral MRI model based on the ResNet18 network has a high detection rate for early femoral head necrosis, and can effectively detect bone marrow edema, line-like signs and other signs, providing a reliable reference for early treatment.

Keywords: Computed Tomography; Convolutional neural network; Early treatment; Magnetic Resonance Imaging; ResNet18.