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  1. Home
  3. A Deep Learning Approach For Nerve Injury Classification In Brachial Plexopathies Using Magnetic Resonance Neurography With Modified Hiking Optimization Algorithm.
  1. Home
  3. A Deep Learning Approach For Nerve Injury Classification In Brachial Plexopathies Using Magnetic Resonance Neurography With Modified Hiking Optimization Algorithm.

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A Deep Learning Approach for Nerve Injury Classification in Brachial Plexopathies Using Magnetic Resonance

Abdelghani Dahou1, Mohamed Abd Elaziz2, Mohamed G Khattap3

  • 1School of Computer Science and Technology, Zhejiang Normal University, Jinhua321004, China (A.D.); Mathematics and Computer Science department, University of Ahmed DRAIA, 01000, Adrar, Algeria (A.D.).

Academic Radiology
|April 29, 2025

View abstract on PubMed

Summary
This summary is machine-generated.
Keywords:
Brachial Plexopathy (BP)Comprehensive Learning (CL)Deep Learning (DL)Feature Selection (FS)Hiking Optimization Algorithm (HOA)Magnetic Resonance Neurography (MRN)MobileNetV4

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An AI framework using deep learning and optimization significantly improves brachial plexopathy diagnosis from MRN scans, accurately classifying nerve injury severity and aiding clinical decisions.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Neurology

Background:

  • Brachial plexopathies (BPs) present diagnostic challenges due to complex anatomy and overlapping symptoms.
  • Magnetic Resonance Neurography (MRN) offers advanced imaging but requires specialized interpretation.
  • Accurate classification of nerve injury severity (neuropraxia, axonotmesis, neurotmesis) is crucial for effective treatment.

Purpose of the Study:

  • To develop and validate an AI-based framework for improved classification of brachial plexopathies using MRN data.
  • To integrate deep learning (DL) with an optimized feature selection algorithm for enhanced diagnostic accuracy.
  • To differentiate between normal and abnormal nerve conditions and classify injury severity.

Main Methods:

  • A framework combining MobileNetV4 for feature extraction and a modified Hiking Optimization Algorithm (MHOA) with Comprehensive Learning (CL) for feature selection.
  • Utilized MRN data from 39 patients with brachial plexopathies across STIR, T2, T1, and DWI sequences.
  • Classified injuries based on Seddon's criteria, distinguishing normal/abnormal states and injury severity.

    • Main Results:

    • Achieved 1.0000 accuracy in distinguishing normal from abnormal conditions using STIR and T2 sequences.
    • Demonstrated high accuracy (0.9820) in classifying injury severity using STIR, outperforming other metaheuristic algorithms.
    • Reported high classification accuracy (0.9667) on DWI sequences, with overall high sensitivity and specificity.

    Conclusions:

    • The AI framework significantly enhances brachial plexopathy diagnosis by accurately classifying nerve injury types.
    • Integration of DL and optimization techniques reduces diagnostic variability, offering a valuable tool for clinical settings.
    • This framework has the potential to improve clinical decision-making and patient outcomes through precise diagnoses.