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SmartPLR: a digital solution for AI-powered smartphone pupillometry.

Kyu Lim Kim1, Dong Kyu Kim2,3, Jeong Hoon Lee1

  • 1iDynamics Research Institute, Seoul, Republic of Korea.

BMC Ophthalmology
|November 12, 2025
PubMed
Summary

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This summary is machine-generated.

A novel smartphone application uses deep learning to accurately measure pupillary light reflex (PLR) without extra devices. This innovative approach offers a commercializable alternative to existing pupillometry methods.

Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Smartphone-based pupillometry aims to provide a portable and accessible method for assessing pupillary light reflex (PLR).
  • Existing smartphone applications often require additional hardware, limiting their commercial viability.

Purpose of the Study:

  • To develop a deep learning-based smartphone application for pupillometry.
  • To evaluate the accuracy of this application against a commercial pupillometer (NPi-300).

Main Methods:

  • Deep learning models (Mask R-CNN with ConvNeXt V2 backbone) were trained and validated on 336 PLR exams.
  • Image quality filtering was applied based on eyelid opening and blurriness.
  • Pupil size difference, constriction velocity (CV), and percentage change (CP) were compared with the NPi-300, leading to the SmartPLR scoring system.
Keywords:
Deep learningPupil dynamicsPupillary light reflexPupillometrySmartphone application

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Main Results:

  • The Mask R-CNN model achieved high segmentation and detection accuracy (mAP 0.8670).
  • Strong Pearson correlations were found between the smartphone app and NPi-300 for pupil size difference (0.77), CV (0.77), and CP (0.74).
  • The SmartPLR system was defined to classify pupil reactivity.

Conclusions:

  • A novel smartphone application utilizing deep learning for pupillometry was successfully developed.
  • The application demonstrated high accuracy comparable to a commercial device, without needing infrared light or add-ons.
  • This technology presents a fully commercializable solution for remote and accessible PLR assessment.