Spasmodic Dysphonia (SD) is a neurological disorder of unknown pathophysiology, characterized by involuntary spasms in laryngeal muscles predominantly during speaking. Neural markers of SD are non-existent, resulting in significant delays in diagnosis and treatment of this disorder. Background – Diagnosing spasmodic dysphonia is a challenging task Automatic Diagnosis of Spasmodic Dysphonia with Structural MRI and Machine Learning Davide Valeriani, Ph.D., and Kristina Simonyan, M.D., Ph.D., Dr. med. [email protected] – [email protected] Methods – Two alternative pipelines for selecting classification features Machine learning Diagnosis Structural MRI SD Healthy o Supervised pipeline Gray matter volume (GMV) Meta-analysis (GingerALE) on six SD studies for feature selection mean GMV and CT in each cluster Ensemble of convolutional neural networks (CNNs) $ Funding Aim – Identify objective markers of SD with structural MRI and machine learning • 104 high-resolution T1-weighted images (52 SD patients and 52 controls) • Two pipelines for the identification of diagnostic markers • Four machine-learning (ML) algorithms for diagnostic classification Cortical thickness (CT) T1-weighted image Semi-supervised pipeline Conv3D (5x5x5) Conv3D (3x3x3) Conv3D (3x3x3) Conv3D (3x3x3) Dense Dense Dense Dense MaxPooling Dropout 0.3 MaxPooling Dropout 0.3 Dropout 0.5 Dropout 0.5 Dropout 0.5 Conv3D (5x5x5) 64@25x25x25 Conv3D (3x3x3) 128@25x25x25 Conv3D (3x3x3) 128@9x9x9 Conv3D (3x3x3) 256@9x9x9 Dense 512 Dense 256 Dense 128 Dense 1 MaxPooling Dropout 0.3 MaxPooling Dropout 0.3 Dropout 0.5 Dropout 0.5 Dropout 0.5 + 13-fold cross-validation Conclusions and Future Work • High accuracy of objective diagnosis based on the use of both supervised and semi-supervised ML and neural alterations in spasmodic dysphonia • Accuracy of ML-based diagnosis outperforms more than two-fold the diagnostic agreement between physicians: 72% (this study) vs. 34% (Ludlow et al., 2018) • Future: Larger sample size may improve the accuracy of CNNs Input and preprocessing AUC = 72.2% AUC = 70.2% AUC = 66.4% AUC = 68.0% Support vector machines (SVM) linear kernel and C=100 Neural network (NN) one hidden layer, 12 neurons Linear discriminant analysis (LDA) Ensemble of CNNs Results – Objective markers of SD diagnosis #1 y=12 Primary motor cortex #2 y=-2 Premotor cortex #5 y=4 #6 y=10 Inferior frontal gyrus Primary motor cortex #3 y=10 Putamen #4 y=40 Inferior parietal cortex LDA SVM NN test set: 25% Volumetric patches Poster number: W277 Generous gift by Mr. and Mrs. Richardson