Donald C. Wunsch

Neuromuscular diseases are biologically diverse, clinically heterogeneous, and often difficult to diagnose and treat, highlighting the need for computational tools that can help resolve overlapping phenotypes and support timely, mechanism-informed interventions. This narrative review synthesizes recent advances in artificial intelligence (AI) and machine learning applied to neuromuscular diseases across diagnosis, outcome modeling, biomarker development, and therapeutics. AI-based approaches may assist clinical and genetic diagnosis from phenotypic data; however, early phenotype-driven tools have seen limited clinician adoption due to modest accuracy, usability challenges, and poor workflow integration. Electrophysiological studies remain central to diagnosing neuromuscular diseases, and AI shows promise for accurate classification of electrophysiological signals. Predictive models for disease outcome and progression—particularly in amyotrophic lateral sclerosis—are under active investigation, but most remain at an early stage of development and are not yet ready for routine clinical use. Digital biomarkers derived from imaging, gait, voice, and wearable sensors are emerging, with MRI-based quantification of muscle fat replacement representing the most mature and widely accepted application to date. Efforts to apply AI to therapeutic discovery, including drug repurposing and optimization of gene-based therapies, are ongoing but have thus far yielded limited clinical translation. Persistent barriers to broader adoption include disease rarity, data scarcity, heterogeneous acquisition protocols, inconsistent terminology, limited external validation, insufficient model explainability, and lack of seamless integration into clinical workflows. Addressing these challenges is essential to moving AI tools from the laboratory into clinical practice. We conclude with a practical checklist of considerations intended to guide the development and adoption of AI tools in neuromuscular disease care.

This article investigates the problem of prescribed-time Nash equilibrium (NE) seeking for a multicluster pursuit-evasion game (PEG) subject to external disturbances. To mitigate the impact of disturbances and reach the NE within a user-defined prescribed time, a prescribed-time disturbance observer (PTDO) is devised to estimate and compensate for them. Based on this observation, a novel control algorithm is developed, which facilitates collaboration among multiple pursuers to capture multiple evaders within the prescribed time. It is theoretically demonstrated that the designed algorithm ensures prescribed-time convergence to the NE of the multicluster PEG with disturbances. Finally, numerical simulations are conducted to verify the effectiveness of the algorithm under different initial conditions, further demonstrating the flexibility of the user-specified convergence time.

Neuromuscular diseases are biologically diverse, clinically heterogeneous, and often difficult to diagnose and treat, creating an urgent need for computational tools that can resolve overlapping phenotypes and enable timely, mechanism-based therapeutics. This narrative review synthesizes recent advances in artificial intelligence as applied to neuromuscular diseases, drawing from peer-reviewed literature from the past five years. Artificial intelligence can augment diagnosis by extracting disease-relevant patterns from imaging, electrophysiology, and multimodal clinical data, improving discrimination between clinically similar entities such as Duchenne and Becker muscular dystrophy. Artificial intelligence can also enhance early detection of amyotrophic lateral sclerosis. Artificial intelligence can utilize digital biomarkers of disease progression data from gait, voice, and wearable sensors for enhanced modeling of disease outcomes. Deep learning–based multi-omics integration, high-throughput phenotypic screening, and artificial intelligence-based protein structure predictive models are accelerating the path from causative mutation, to molecular mechanism, and on to candidate therapy. Despite these advances, significant challenges remain, including data scarcity, heterogeneous acquisition methods, limited external validation, and the need for interpretable models that can win clinician acceptance. Addressing these constraints is essential to moving high-performance research tools from the laboratory to the neuromuscular clinic. Artificial intelligence has the potential to shorten the diagnostic odyssey and accelerate the historically slow development of targeted therapeutics for rare neuromuscular diseases.

These models can be applied to tasks such as unsupervised clustering, supervised classification, regression, and reinforcement learning (Silva et al., 2019a). This library provides an extensible and modular framework where users can integrate custom models or extend current implementations, allowing for experimentation with existing and novel machine learning techniques.

This paper studies the prescribed-time Nash equilibrium (PTNE) seeking problem of the pursuit-evasion game (PEG) with second-order dynamics under the intermittent control (IC) strategy. To achieve Nash equilibrium (NE) in a user-defined prescribed-time, a time-varying high-gain function is incorporated into the design. The core challenge lies in applying IC to NE seeking, which complicates the convergence analysis and control design. To address this sticking point, we construct an auxiliary function and propose a Lyapunov function considering second-order dynamics to solve the PTNE seeking problem of PEG. Building upon the results for undirected graphs, we further extend our findings to directed graphs, demonstrating that the proposed method can reach the NE of PEG under IC in the prescribed-time. Moreover, we generalize the approach to non-cooperative games, indicating that the prescribed-time intermittent control framework can effectively solve PTNE seeking problem in such competitive scenarios. The numerical simulations validate the practicality of the proposed method.