Importance of neural network complexity for the automatic segmentation of individual thigh muscles in MRI images from patients with neuromuscular diseases.

Objective: Segmentation of individual thigh muscles in MRI images is essential for monitoring neuromuscular diseases and quantifying relevant biomarkers such as fat fraction (FF). Deep learning approaches such as U-Net have demonstrated effectiveness in this field. However, the impact of reducing neural network complexity remains unexplored in the FF quantification in individual muscles.

Electrophysiological Monitoring of Asymptomatic Transthyretin Mutation Carriers.

Introduction/aims: It is imperative to screen asymptomatic carriers of transthyretin (TTR) mutations to initiate treatment early. The protocol for repeated electrodiagnostic (EDX) assessments over time lacks standardization. Our aim was to report the electrophysiological evolution of a cohort of asymptomatic carriers and to determine which biomarkers were most sensitive to change.

Real-life experience with disease-modifying drugs in hereditary transthyretin amyloid polyneuropathy: A clinical and electrophysiological appraisal.

Introduction: New treatments have dramatically improved the prognosis for Hereditary Transthyretin Amyloid Polyneuropathy (ATTRv-PN). However, there is a lack of routine follow-up studies outside of therapeutic trials. Our aim was to report the long-term clinical and electrophysiological evolution of a cohort of ATTRv-PN patients and to determine which biomarkers are most sensitive to change.

Quantitative muscle MRI combined with AI-based segmentation as a follow-up biomarker for ATTRv patients: A longitudinal pilot study.

Background And Objectives: Intramuscular fat fraction (FF), assessed using quantitative MRI (qMRI), has emerged as a promising biomarker for hereditary transthyretin amyloidosis (ATTRv) patients. Currently, the main drawbacks to its use in future therapeutic trials are its sensitivity to change over a short period of time and the time-consuming manual segmentation step to extract quantitative data. This pilot study aimed to demonstrate the suitability of an Artificial Intelligence-based (AI) segmentation technique to assess disease progression in a real-life cohort of ATTRv patients over 1 year.