A Deep Learning Approach for Nerve Injury Classification in Brachial Plexopathies Using Magnetic Resonance Neurography with Modified Hiking Optimization Algorithm.

Journal: Academic radiology
Published Date: Apr 28, 2025

Abstract

RATIONALE AND OBJECTIVES: Brachial plexopathies (BPs) encompass a complex spectrum of nerve injuries affecting motor and sensory function in the upper extremities. Diagnosis is challenging due to the intricate anatomy and symptom overlap with other neuropathies. Magnetic Resonance Neurography (MRN) provides advanced imaging but requires specialized interpretation. This study proposes an AI-based framework that combines deep learning (DL) with the modified Hiking Optimization Algorithm (MHOA) enhanced by a Comprehensive Learning (CL) technique to improve the classification of nerve injuries (neuropraxia, axonotmesis, neurotmesis) using MRN data.

Authors

  • Abdelghani Dahou
    School of Computer Science and Technology, Wuhan University of Technology, 122 Luoshi Road, Wuhan, Hubei 430070, China.
  • Mohamed Abd Elaziz
    Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, Egypt.
  • Mohamed G Khattap
    Technology of Radiology and Medical Imaging Program, Faculty of Applied Health Sciences Technology, Galala University, Suez435611, Egypt (M.G.K., H.G.E.M.A.H.). Electronic address: mohamed.khattap.2016@gmail.com.
  • Hend Galal Eldeen Mohamed Ali Hassan
    Department of Diagnostic, Interventional Radiology and Molecular Imaging, Faculty of Medicine, Ain Shams University, Cairo11591, Egypt (H.G.E.M.A.H.); Technology of Radiology and Medical Imaging Program, Faculty of Applied Health Sciences Technology, Galala University, Suez435611, Egypt (M.G.K., H.G.E.M.A.H.). Electronic address: doctor_hendgalal@gu.edu.eg.

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