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The classification of flash visual evoked potential based on deep learning.

Na Liang1, Chengliang Wang2, Shiying Li3,4

  • 1College of Computer Science, Chongqing University, Chongqing, China.

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Summary

This study introduces an AI algorithm to improve the accuracy of flash visual evoked potential (FVEP) analysis for diagnosing retinitis pigmentosa (RP). The deep learning model enhances objective visual function assessment and lesion localization.

Keywords:
Convolutional neural networksDeep learningFVEPOut-of-distribution detection

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Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Visual electrophysiology, including flash visual evoked potential (FVEP), objectively evaluates visual function but suffers from significant inter-individual waveform variability.
  • Current FVEP labeling relies on automated systems with manual correction, introducing potential biases from subject variations, data limitations, and technician expertise.
  • Retrospective big data analysis is employed to develop an AI algorithm for improved FVEP analysis in complex clinical scenarios.

Purpose of the Study:

  • To develop and validate a novel artificial intelligence algorithm for accurate classification and out-of-distribution detection of FVEP signals.
  • To enhance the objective assessment of visual function and improve the screening of retinal diseases like retinitis pigmentosa (RP).

Main Methods:

  • A multi-input neural network incorporating convolution and confidence branching (MCAC-Net) was designed for FVEP signal analysis.
  • The MCAC-Net integrates global and local feature extraction tailored for FVEP characteristics and includes a confidence branch for detecting out-of-distribution samples.
  • A new input layer was incorporated to accommodate proposed manual features.

Main Results:

  • The MCAC-Net model achieved 90.7% accuracy in the FVEP classification task.
  • The model demonstrated 93.3% accuracy in out-of-distribution detection, indicating robustness in identifying atypical signals.
  • Performance was validated on a clinically collected FVEP dataset.

Conclusions:

  • A deep learning-based algorithm for FVEP classification has been successfully developed.
  • This AI tool shows significant promise for improving the screening and diagnosis of retinitis pigmentosa (RP) using FVEP signals.
  • The approach offers a more objective and potentially more accurate method for visual function assessment.