Abstract
To develop and test a deep learning algorithm to automatically detect cortical tubers in magnetic resonance imaging (MRI), to explore the utility of deep learning in rare disorders with limited data, and to generate an open-access deep learning standalone application. T2 and FLAIR axial images with and without tubers were extracted from MRIs of patients with tuberous sclerosis complex (TSC) and controls, respectively. We trained three different convolutional neural network (CNN) architectures on a training dataset and selected the one with the lowest binary cross-entropy loss in the validation dataset, which was evaluated on the testing dataset. We visualized image regions most relevant for classification with gradient-weighted class activation maps (Grad-CAM) and saliency maps. 114 patients with TSC and 114 controls were divided into a training set, a validation set, and a testing set. The InceptionV3 CNN architecture performed best in the validation set and was evaluated in the testing set with the following results: sensitivity: 0.95, specificity: 0.95, positive predictive value: 0.94, negative predictive value: 0.95, F1-score: 0.95, accuracy: 0.95, and area under the curve: 0.99. Grad-CAM and saliency maps showed that tubers resided in regions most relevant for image classification within each image. A stand-alone trained deep learning App was able to classify images using local computers with various operating systems. This study shows that deep learning algorithms are able to detect tubers in selected MRI images, and deep learning can be prudently applied clinically to manually selected data in a rare neurological disorder.
Highlights
Tuberous sclerosis complex (TSC) is a genetic neurocutaneous syndrome with an incidence of 1/6,000 to 1/10,000 live births and a population prevalence of 1/12,000 to 1/25,000 [1,2,3]
This study shows that deep learning algorithms are able to detect tubers in selected magnetic resonance imaging (MRI) images, and deep learning can be prudently applied clinically to manually selected data in a rare neurological disorder
We tried three different CNN architectures: 1) Tuberous sclerosis complex convolutional neural network (TSCCNN), a relatively simple architecture that we developed with 4 blocks, each of them consisting of several convolutional layers followed by a pooling layer, and a final block of fully-connected layers, 2) InceptionV3, a popular architecture within the family of CNNs that parallelize computations in a split-transformmerge approach to increase depth and improve accuracy while keeping computations efficient [16, 17], and 3) ResNet50, a popular architecture within the family of residual CNNs that allow very deep CNNs by using blocks of layers that behave like relatively shallow classifiers and work together as an ensemble to produce a very good classifier [18,19,20]
Summary
Tuberous sclerosis complex (TSC) is a genetic neurocutaneous syndrome with an incidence of 1/6,000 to 1/10,000 live births and a population prevalence of 1/12,000 to 1/25,000 [1,2,3]. Convolutional neural networks (CNNs) automatically detect patterns of interest in images and have demonstrated image-classification performance at or above the level of humans [7], including detection of diabetic retinopathy [8], skin cancer [9], echocardiography findings [10], and acute neuroimaging findings [11] at the level of specialist physicians. These studies required many thousands of images to train the CNNs, which are challenging to obtain in rare neurological disorders like TSC and make computerized support of rare disorders difficult to develop. The application of CNNs in clinical practice is frequently limited because of privacy concerns
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