Impacts of gender and age on meibomian gland in aged people using artificial intelligence

作者：Binge Huang, Fangrong Fei, Wen Han, Ye Zhu, Zhenzhen Wang, Shuwen Zhang, Liang Hu, Wei Chen, Qinxiang Zheng · 发表于：Frontiers in Cell and Developmental Biology · 年份：2023 · DOI：10.3389/fcell.2023.1199440 · 被引用次数：25 · 研究领域：Ocular Surface and Contact Lens、Glaucoma and retinal disorders、Corneal surgery and disorders

Purpose: To evaluate the effects of age and gender on meibomian gland (MG) parameters and the associations among MG parameters in aged people using a deep-learning based artificial intelligence (AI). Methods: A total of 119 subjects aged ≥60 were enrolled. Subjects completed an ocular surface disease index (OSDI) questionnaire, received ocular surface examinations including Meibography images captured by Keratograph 5M, diagnosis of meibomian gland dysfunction (MGD) and assessment of lid margin and meibum. Images were analyzed using an AI system to evaluate the MG area, density, number, height, width and tortuosity. Results: The mean age of the subjects was 71.61 ± 7.36 years. The prevalence of severe MGD and meibomian gland loss (MGL) increased with age, as well as the lid margin abnormities. Gender differences of MG morphological parameters were most significant in subjects less than 70 years old. The MG morphological parameters detected by AI system had strong relationship with the traditional manual evaluation of MGL and lid margin parameters. Lid margin abnormities were significantly correlated with MG height and MGL. OSDI was related to MGL, MG area, MG height, plugging and lipid extrusion test (LET). Male subjects, especially the ones who smoke or drink, had severe lid margin abnormities, and significantly decreased MG number, height, and area than the females. Conclusion: The AI system is a reliable and high-efficient method for evaluating MG morphology and function. ...