# Relative Afferent Pupillary Defect Screening through Transfer Learning

arXiv: https://arxiv.org/abs/1908.02300 · HTML: https://mlsanity.com/paper/1908.02300/

## Key facts

- Cited by 8 papers in the map (data through 13 Oct 2026).
- First posted on arXiv 6 Aug 2019.

## Built on by (most cited first, 8 of 8)

- [OLIVES Dataset: Ophthalmic Labels for Investigating Visual Eye Semantics](https://mlsanity.com/paper/2209.11195/) (2022)
- [Extracting Causal Visual Features for Limited label Classification](https://mlsanity.com/paper/2103.12322/) (2021)
- [Clinically Labeled Contrastive Learning for OCT Biomarker Classification](https://mlsanity.com/paper/2305.15154/) (2023)
- [Clinical Trial Active Learning](https://mlsanity.com/paper/2307.11209/) (2023)
- [Explainable Machine Learning for Hydrocarbon Prospect Risking](https://mlsanity.com/paper/2212.07563/) (2022)
- [VR-SFT: Reproducing Swinging Flashlight Test in Virtual Reality to Detect Relative Afferent Pupillary Defect](https://mlsanity.com/paper/2210.06474/) (2022)
- [Learning Trajectory-Conditioned Relations to Predict Pedestrian Crossing Behavior](https://mlsanity.com/paper/2301.05796/) (2023)
- [Gradient based Severity Labeling for Biomarker Classification in OCT](https://mlsanity.com/paper/2602.19907/) (2026)

## Abstract (arXiv)

Abnormalities in pupillary light reflex can indicate optic nerve disorders that may lead to permanent visual loss if not diagnosed in an early stage. In this study, we focus on relative afferent pupillary defect (RAPD), which is based on the difference between the reactions of the eyes when they are exposed to light stimuli. Incumbent RAPD assessment methods are based on subjective practices that can lead to unreliable measurements. To eliminate subjectivity and obtain reliable measurements, we introduced an automated framework to detect RAPD. For validation, we conducted a clinical study with lab-on-a-headset, which can perform automated light reflex test. In addition to benchmarking handcrafted algorithms, we proposed a transfer learning-based approach that transformed a deep learning-based generic object recognition algorithm into a pupil detector. Based on the conducted experiments, proposed algorithm RAPDNet can achieve a sensitivity and a specificity of 90.6% over 64 test cases in a balanced set, which corresponds to an AUC of 0.929 in ROC analysis. According to our benchmark with three handcrafted algorithms and nine performance metrics, RAPDNet outperforms all other algorithms in every performance category.