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Virtual Reality-Based Infrared Pupillometry (VIP) for Long-COVID.

Chen Hui Tang1, Yi Fei Yang1, Ken Chun Fung Poon2

  • 1Department of Biomedical Engineering, Faculty of Engineering, The Chinese University of Hong Kong, Hong Kong, SAR.

Ophthalmology
|December 4, 2024
PubMed
Summary

Virtual reality infrared pupillometry (VIP) shows distinct pupillary light responses in individuals with long coronavirus disease (LCVD). This technology offers a non-intrusive, objective method for detecting LCVD by analyzing specific pupillometric signatures.

Keywords:
Long-COVIDMachine learningMedical data analyticsPupillary light responseVirtual reality head mount display

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Area of Science:

  • Ophthalmology
  • Neurology
  • Medical Technology

Background:

  • Long coronavirus disease (LCVD) presents a significant health challenge with diverse symptoms.
  • Objective diagnostic tools for LCVD are needed to aid in patient identification and management.
  • Pupillary light responses (PLRs) can reflect autonomic nervous system function, potentially impacted by COVID-19.

Purpose of the Study:

  • To evaluate the efficacy of virtual reality-based infrared pupillometry (VIP) in detecting individuals with long coronavirus disease (LCVD).
  • To identify specific pupillometric signatures associated with LCVD.
  • To assess the performance of machine learning models in classifying participants into LCVD, post-COVID (PCVD), or control groups based on pupillary responses.

Main Methods:

  • A prospective, case-control cross-sectional study involving 185 participants (20-60 years old).
  • Pupillary light responses (PLRs) were recorded using a virtual reality head-mount display (VRHMD) with three light intensities.
  • Nine PLR waveform features were extracted, and machine learning models analyzed these features and the entire pupillometric waveform for classification.

Main Results:

  • Constriction time (CT) after the brightest stimulus (L8) was significantly associated with LCVD status.
  • The accuracy and AUC for CT after L8 alone were 0.7808 and 0.8711 (LCVD vs. control/PCVD).
  • Machine learning models, particularly a long short-term memory model analyzing the whole waveform, achieved high accuracy (up to 0.9375) in differentiating LCVD from PCVD.

Conclusions:

  • Specific pupillometric signatures can differentiate LCVD from PCVD and control groups using VIP.
  • Combining statistical feature selection with machine learning analysis enhances VIP's performance for LCVD detection.
  • VIP offers a non-intrusive, low-cost, portable, and objective method for detecting LCVD.