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Artificial Intelligence-Driven Telehealth Framework for Detecting Nystagmus.

Harshal Sanghvi1, Ali A Danesh2, Jillene Moxam3

  • 1Department of Information Technology and Operations Management, College of Business, Florida Atlantic University, Boca Raton, USA.

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Summary

An AI system for nystagmus detection shows promise for remote diagnosis. This artificial intelligence tool analyzes eye movements, potentially supplementing traditional videonystagmography (VNG) methods.

Keywords:
diagnostic testingevaluation for telehealthnystagmusophthalmic researchophthalmology

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Area of Science:

  • Ophthalmology
  • Artificial Intelligence in Medicine
  • Medical Diagnostics

Background:

  • Nystagmus detection often requires specialized equipment like videonystagmography (VNG).
  • Telemedicine offers potential for remote patient care, but requires effective diagnostic tools.
  • Integrating AI into clinical workflows can enhance diagnostic capabilities.

Purpose of the Study:

  • To implement and evaluate a proof-of-concept AI-driven clinical decision support system for nystagmus detection.
  • To assess the system's potential for real-time analysis of clinical data and integration into telemedicine platforms.
  • To explore patient benefits including convenience and reduced healthcare costs.

Main Methods:

  • A cloud-based deep learning framework was developed to track eye movements and detect facial landmarks in real time.
  • The system was trained to analyze data from a bedside clinical test and videonystagmography (VNG).
  • Ten subjects participated in this pilot study.

Main Results:

  • The AI system's slow-phase velocity (SPV) calculations showed statistical significance (p < 0.05).
  • Mean squared error was 0.00459, with a correction error of ±4.8% compared to VNG.
  • The system demonstrated accurate real-time eye movement analysis.

Conclusions:

  • The deep learning model shows potential for remote diagnostic consultation, possibly supplementing or replacing traditional methods like VNG.
  • Advancements in medical AI can improve patient diagnosis, specialist referrals, and physician support.
  • Further research with larger sample sizes is warranted for this proof-of-concept study.