Machine learning for detecting intracranial dural arteriovenous fistula on susceptibility weighted image using a convolutional neural network
Bejoy Thomas1, Jithin S S1, Ajimi mol Anzar1, and Santhosh Kannath2
1Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, India, 2Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, India
Artificial intelligence techniques are widely used in medical imaging and diagnostics. In this retrospective study, a convolutional neural network (CNN) architecture was developed to classify intracranial dural arteriovenous fistula (DAVF) on Susceptibility Weighted Images (SWI). The dataset used was a total of 3965 SWI image slices of DAVF patients and 4380 images of controls. The proposed classifier showed significant accuracy in the diagnosis of DAVF and it could be developed as a computer assisted diagnosis tool to identify unsuspected DAVF in routine MR imaging.
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