Abstract
OBJECTIVE: Isolated dystonia is characterized by abnormal, often painful, postures and repetitive movements due to sustained or intermittent involuntary muscle contractions. Botulinum toxin (BoTX) injections into the affected muscles are the first line of therapy. However, there are no objective predictive markers or standardized tests of BoTX efficacy that can be utilized for appropriate candidate selection prior to treatment initiation. METHODS: We developed a deep learning algorithm, DystoniaBoTXNet, which uses a 3D convolutional neural network architecture and raw structural brain magnetic resonance images (MRIs) to automatically discover and test a neural network biomarker of BoTX efficacy in 284 patients with 4 different forms of focal dystonia, including laryngeal dystonia, blepharospasm, cervical dystonia, and writer's cramp. RESULTS: DystoniaBoTXNet identified clusters in superior parietal lobule, inferior and middle frontal gyri, middle orbital gyrus, inferior temporal gyrus, corpus callosum, inferior fronto-occipital fasciculus, and anterior thalamic radiation as components of the treatment biomarker. These regions are known to contribute to both dystonia pathophysiology across a broad clinical spectrum of disorder and the central effects of botulinum toxin treatment. Based on its biomarker, DystoniaBoTXNet achieved an overall accuracy of 96.3%, with 100% sensitivity and 86.1% specificity, in predicting BoTX efficacy in patients with isolated dystonia. The algorithmic decision was computed in 19.2 seconds per case. INTERPRETATION: DystoniaBoTXNet and its treatment biomarker have a high translational potential as an objective, accurate, generalizable, fast, and cost-effective algorithmic platform for enhancing clinical decision making for BoTX treatment in patients with isolated dystonia. ANN NEUROL 2023;93:460-471.