Optimizing dry eye disease diagnosis: integrating deep learning insights from OSDI questionnaires and ocular blinking videos

Mini Han Wang; Zhiyuan Lin; Peijin Zeng; Xiaoxiao Fang; Yang Yu; Wenhan Hu; Yunxiao Liu; Haoyang Liu; Xinyue Li; Xudong Jiang; Guangshun Chen; Ruoyu Zhou; Guanghui Hou; Kelvin KL Chong; Junbin Fang

doi:10.1117/12.3026333

8 April 2024 Optimizing dry eye disease diagnosis: integrating deep learning insights from OSDI questionnaires and ocular blinking videos

Mini Han Wang, Zhiyuan Lin, Peijin Zeng, Xiaoxiao Fang, Yang Yu, Wenhan Hu, Yunxiao Liu, Haoyang Liu, Xinyue Li, Xudong Jiang, Guangshun Chen, Ruoyu Zhou, Guanghui Hou, Kelvin KL Chong, Junbin Fang

Author Affiliations +

Proceedings Volume 13090, International Conference on Computer Application and Information Security (ICCAIS 2023); 130901H (2024) https://doi.org/10.1117/12.3026333
Event: International Conference on Computer Application and Information Security (ICCAIS 2023), 2023, Wuhan, China

Abstract

This study introduces a novel and comprehensive diagnostic approach for Dry Eye Disease (DED) by combining a dedicated Ocular Surface Disease Index (OSDI) questionnaire and a measurement system tailored for Chinese citizens with the implementation of the YOLOv8 deep learning model. The research involves the analysis of 52 real-world ophthalmic videos to detect eye blinking conditions, with the model trained to identify abnormal blinking patterns through feature extraction such as blink frequency, duration, and irregularities. Performance metrics, including mean Average Precision (mAP), specificity, recall, f1-score, and Frame Per Second (FPS), are measured on a PC (CPU, Core i5-10500H) with an input size of 640*640. The integration of these deep learning methods, utilizing both subjective OSDI questionnaires and objective ocular blinking videos, signifies a groundbreaking approach that enhances diagnostic accuracy for DED. The study anticipates transformative effects on DED diagnosis and improved patient outcomes as technology advances. Additionally, the research team introduces a user-friendly system for dry eye detection, named the “AI Dry Eye Analytic System,” accessible at the URL “mini.ac.cn,” demonstrating the practical implementation of the developed methodologies.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Mini Han Wang, Zhiyuan Lin, Peijin Zeng, Xiaoxiao Fang, Yang Yu, Wenhan Hu, Yunxiao Liu, Haoyang Liu, Xinyue Li, Xudong Jiang, Guangshun Chen, Ruoyu Zhou, Guanghui Hou, Kelvin KL Chong, and Junbin Fang "Optimizing dry eye disease diagnosis: integrating deep learning insights from OSDI questionnaires and ocular blinking videos", Proc. SPIE 13090, International Conference on Computer Application and Information Security (ICCAIS 2023), 130901H (8 April 2024); https://doi.org/10.1117/12.3026333

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KEYWORDS

Video

Eye

Object detection

Deep learning

Diseases and disorders

Eye models

Artificial intelligence

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