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Convolutional neural network accurately identifies “mass effect” lesions in more than 50 disease entities.
An artificial intelligence model based on MRI images can accurately identify which brain lesions cause mass effect and which do not for a wide variety of brain diseases, according to findings presented at SIIM2020.
Brain lesions have a number of diagnostically and prognostically relevant features – a key one being whether it exerts a “mass effect” – a distortion or compression of ventricles and sulci. To make this determination, investigators from the University of Pennsylvania, University of California San Francisco, and University of Texas Austin developed a convolutional neural network (CNN) that can be used across many underlying pathologies.
To create the CNN and achieve a high level of accuracy, they extracted T1 and T2-FLAIR images from 384 MRI studies (298 negative, 88 positive) of patients who had 60 different disease entities. Of those images 189 were used for training, 54 for validation, and 142 for a held-out test, ensuring that two-to-three samples were in the test set. In addition, they extracted cerebral spinal fluid masks from the T1 images, and the brain was extracted from the FLAIR image.
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Overall, researchers determined their mass effect detection model was 84.5 percent accurate. The network, they said, performed well across 53 disease entities.
As next steps, the team plans to: classify negative mass effect, use multiple channels to improve tissue segmentation, and produce saliency maps to determine why the classifier is making its decisions.
For more coverage of SIIM2020, click here.