Abstract 4369051: Deep Learning-Guided CT Image Analysis Quantifies 18-Month Changes in Regional Muscle Atrophy in Patients with Peripheral Artery Disease

Introduction: Peripheral artery disease (PAD) is characterized by atherosclerosis of lower extremity arteries that promotes reduced muscle perfusion and skeletal muscle atrophy, which contributes to functional impairment in PAD patients. Computed tomography (CT) imaging offers a non-invasive method for quantifying peripheral muscle characteristics; however, manual image segmentation of muscles is time-consuming, and regional muscle analysis remains understudied in PAD. Therefore, we sought to develop and validate a deep learning approach for calf muscle segmentation to assess serial regional changes in muscle density of PAD patients over an 18-month study period. Methods: Patients with PAD (n=89) were prospectively enrolled for lower extremity non-contrast CT imaging. A subset of patients (n=45) was recruited for an additional follow-up CT scan 18 months later. Calf muscle groups, including the gastrocnemius, soleus, and tibialis anterior, were manually segmented from axial CT images of each patient’s symptomatic limb, and mean muscle densities for each muscle were quantified based on CT Hounsfield units. Manual segmentations were used to train an nnU-Net deep learning model. Data augmentation techniques were performed to enhance generalization. The dataset was split into an 80/20 ratio for the training and test sets. Dice coefficients were calculated to evaluate the overlap/agreement between manual and deep learning segmentations. Paired t-tests were performed to assess the differences in muscle densities between the baseline and 18-month follow-up measures. Results: The deep learning model achieved high segmentation performance, with dice scores of 0.90 ± 0.02 for the gastrocnemius and soleus, and 0.89 ± 0.02 for the tibialis anterior. Deep learning-guided serial CT image analysis detected a significant reduction in muscle densities across all muscle groups at 18-month follow-up when compared to baseline measurements (p<0.05). Conclusion: Deep learning analysis can enable rapid, regional analysis of lower extremity skeletal muscle characteristics, reducing the time for muscle-by-muscle analysis in PAD patients from hours to seconds. Future implementation of regional muscle analysis in PAD may assist vascular medicine specialists with identifying regional muscle wasting that is related to patient walking impairment and/or symptoms as well as aid in serial monitoring of PAD patients following revascularization or supervised exercise for claudication.