Dual-Mode Imaging System for Early Detection and Monitoring of Ocular Surface Diseases

Yuxing Li;Pak Wing Chiu;Vincent Tam;Allie Lee;Edmund Y. Lam
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Abstract

The global prevalence of ocular surface diseases (OSDs), such as dry eyes, conjunctivitis, and subconjunctival hemorrhage (SCH), is steadily increasing due to factors such as aging populations, environmental influences, and lifestyle changes. These diseases affect millions of individuals worldwide, emphasizing the importance of early diagnosis and continuous monitoring for effective treatment. Therefore, we present a deep learning-enhanced imaging system for the automated, objective, and reliable assessment of these three representative OSDs. Our comprehensive pipeline incorporates processing techniques derived from dual-mode infrared (IR) and visible (RGB) images. It employs a multi-stage deep learning model to enable accurate and consistent measurement of OSDs. This proposed method has achieved a 98.7% accuracy with an F1 score of 0.980 in class classification and a 96.2% accuracy with an F1 score of 0.956 in SCH region identification. Furthermore, our system aims to facilitate early diagnosis of meibomian gland dysfunction (MGD), a primary factor causing dry eyes, by quantitatively analyzing the meibomian gland (MG) area ratio and detecting gland morphological irregularities with an accuracy of 88.1% and an F1 score of 0.781. To enhance convenience and timely OSD management, we are integrating a portable IR camera for obtaining meibography during home inspections. Our system demonstrates notable improvements in expanding dual-mode image-based diagnosis for broader applicability, effectively enhancing patient care efficiency. With its automation, accuracy, and compact design, this system is well-suited for early detection and ongoing assessment of OSDs, contributing to improved eye healthcare in an accessible and comprehensible manner.
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用于早期检测和监控眼表疾病的双模式成像系统。
由于人口老龄化、环境影响和生活方式改变等因素,干眼症、结膜炎和结膜下出血(SCH)等眼表疾病(OSDs)的全球发病率正在稳步上升。这些疾病影响着全球数百万人,因此强调早期诊断和持续监测对有效治疗的重要性。因此,我们提出了一种深度学习增强成像系统,用于自动、客观、可靠地评估这三种具有代表性的 OSD。我们的综合管道采用了源自双模红外(IR)和可见光(RGB)图像的处理技术。它采用了多级深度学习模型,能够对 OSD 进行准确一致的测量。该方法的分类准确率为 98.7%,F1 得分为 0.980;SCH 区域识别准确率为 96.2%,F1 得分为 0.956。此外,我们的系统旨在通过定量分析睑板腺(MG)面积比率和检测腺体形态异常,帮助早期诊断导致干眼症的主要因素--睑板腺功能障碍(MGD),准确率达 88.1%,F1 得分为 0.781。为了提高 OSD 管理的便利性和及时性,我们正在整合便携式红外相机,以便在家庭检查时获取睑板腺造影。我们的系统在扩大双模式图像诊断的适用范围方面取得了显著进步,有效提高了患者护理效率。该系统具有自动化、准确性和紧凑型设计等特点,非常适合早期检测和持续评估 OSD,以方便易懂的方式为改善眼科保健做出贡献。
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