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Abstract #4047

Automation of Quantifying Axonal Loss in Patients with Peripheral Neuropathies through Deep Learning Derived Muscle Fat Fraction

Yongsheng Chen1, Daniel Moiseev1, Wan Yee Kong1, Alexandar Bezanovski1, and Jun Li1,2
1Department of Neurology, Wayne State University School of Medicine, Detroit, MI, United States, 2John D. Dingell VA Medical Center, Detroit, MI, United States

Axonal loss determines the final disability in patients with peripheral neuropathies. Consequently, axonal loss results in intramuscular fat accumulation. Therefore, measuring muscle fat fraction through Dixon MRI has been a promising biomarker for monitoring disease progression. However, the responsiveness is yet to be improved, particularly in the early phase of the disease. In this study, we developed a deep learning-based method to automate the quantification of individual muscle fat fraction, which mitigates the laborious manual segmentations and enables the use of individual muscle fat fraction as outcome measures to track axonal loss in patients with neuropathies.

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