Advancing automated pupillometry: a practical deep learning model utilizing infrared pupil images

AIM:To establish pupil diameter measurement algorithms based on infrared images that can be used in real-world clinical settings.METHODS:A total of 188 patients from outpatient clinic at He Eye Specialist Shenyang Hospital from Spetember to December 2022 were included, and 13 470 infrared pupil images were collected for the study. All infrared images for pupil segmentation were labeled using the Labelme software. The computation of pupil diameter is divided into four steps: image pre-processing, pupil identification and localization, pupil segmentation, and diameter calculation. Two major models are used in the computation process: the modified YoloV3 and Deeplabv3+ models, which must be trained beforehand.RESULTS:The test dataset included 1 348 infrared pupil images. On the test dataset, the modified YoloV3 model had a detection rate of 99.98% and an average precision(AP)of 0.80 for pupils. The DeeplabV3+ model achieved a background intersection over union(IOU)of 99.23%, a pupil IOU of 93.81%, and a mean IOU of 96.52%. The pupil diameters in the test dataset ranged from 20 to 56 pixels, with a mean of 36.06±6.85 pixels. The absolute error in pupil diameters between predicted and actual values ranged from 0 to 7 pixels, with a mean absolute error(MAE)of 1.06±0.96 pixels.CONCLUSION:This study successfully demonstrates a robust infrared image-based pupil diameter measurement algorithm, proven to be highly accurate and reliable for clinical application.