Dry eye disease is one of the most common ophthalmological complaints and is defined by a loss of tear film homeostasis. Establishing a diagnosis can be time-consuming, resource demanding and unpleasant for the patient. In this pilot study, we retrospectively included clinical data from 431 patients with dry eye disease examined in the Norwegian Dry Eye Clinic to evaluate how artificial intelligence algorithms perform on clinical data related to dry eye disease. The data was processed and subjected to numerous machine learning classification algorithms with the aim to predict decreased tear film break-up time. Moreover, feature selection techniques (information gain and information gain ratio) were applied to determine which clinical factors contribute most to an unstable tear film. The applied machine learning algorithms outperformed baseline classifications performed with ZeroR according to included evaluation metrics. Clinical features such as ocular surface staining, meibomian gland expressibility and dropout, blink frequency, osmolarity, meibum quality and symptom score were recognized as important predictors for tear film instability. We identify and discuss potential limitations and pitfalls.

干眼病是最常见的眼科疾病之一，其定义为泪膜稳态丧失。做出诊断可能既费时又费资源，而且对患者来说是不愉快的。在这项试点研究中，我们回顾性地纳入了在挪威干眼诊所接受检查的 431 名干眼症患者的临床数据，以评估人工智能算法如何处理与干眼症相关的临床数据。数据经过处理并采用多种机器学习分类算法，旨在预测泪膜破裂时间的减少。此外，应用特征选择技术（信息增益和信息增益比）来确定哪些临床因素对不稳定泪膜的贡献最大。ZeroR根据包含的评估指标。眼表染色、睑板腺表达能力和脱落、眨眼频率、渗透压、睑脂质量和症状评分等临床特征被认为是泪膜不稳定的重要预测因子。我们识别并讨论潜在的限制和陷阱。