Open AccessPredicting Clinician Fixations on Glaucoma OCT Reports via CNN-Based Saliency Prediction Methods
Author(s) -
Mingyang Zang,
Pooja Mukund,
Britney Forsyth,
Andrew F. Laine,
Kaveri A. Thakoor
Publication year - 2024
Publication title -
ieee open journal of engineering in medicine and biology
Language(s) - English
Resource type - Journals
ISSN - 2644-1276
DOI - 10.1109/ojemb.2024.3367492
Subject(s) - bioengineering , components, circuits, devices and systems , computing and processing
Goal: To predict physician fixations specifically on ophthalmology optical coherence tomography (OCT) reports from eye tracking data using CNN based saliency prediction methods in order to aid in the education of ophthalmologists and ophthalmologists-in-training. Methods: Fifteen ophthalmologists were recruited to each examine 20 randomly selected OCT reports and evaluate the likelihood of glaucoma for each report on a scale of 0-100. Eye movements were collected using a Pupil Labs Core eye-tracker. Fixation heat maps were generated using fixation data. Results: A model trained with traditional saliency mapping resulted in a correlation coefficient (CC) value of 0.208, a Normalized Scanpath Saliency (NSS) value of 0.8172, a Kullback–Leibler (KLD) value of 2.573, and a Structural Similarity Index (SSIM) of 0.169. Conclusions: The TranSalNet model was able to predict fixations within certain regions of the OCT report with reasonable accuracy, but more data is needed to improve model accuracy. Future steps include increasing data collection, improving quality of data, and modifying the model architecture.