Dry eye disease is a common disorder of the ocular surface, leading patients
to seek eye care. Clinical signs and symptoms are currently used to diagnose
dry eye disease. Metabolomics, a method for analyzing biological systems, has
been found helpful in identifying distinct metabolites in patients and in
detecting metabolic profiles that may indicate dry eye disease at early stages.
In this study, we explored using machine learning and metabolomics information
to identify which cataract patients suffered from dry eye disease. As there is
no one-size-fits-all machine learning model for metabolomics data, choosing the
most suitable model can significantly affect the quality of predictions and
subsequent metabolomics analyses. To address this challenge, we conducted a
comparative analysis of nine machine learning models on three metabolomics data
sets from cataract patients with and without dry eye disease. The models were
evaluated and optimized using nested k-fold cross-validation. To assess the
performance of these models, we selected a set of suitable evaluation metrics
tailored to the data set's challenges. The logistic regression model overall
performed the best, achieving the highest area under the curve score of 0.8378,
balanced accuracy of 0.735, Matthew's correlation coefficient of 0.5147, an
F1-score of 0.8513, and a specificity of 0.5667. Additionally, following the
logistic regression, the XGBoost and Random Forest models also demonstrated
good performance.

通过代谢组学数据，使用 9 种机器学习模型对白内障患者进行干眼病的诊断与预测，结果显示逻辑回归模型表现最佳，达到了最高的 0.8378 的曲线下面积得分、0.735 的平衡准确率、0.5147 的 Matthew 相关系数、0.8513 的 F1 得分和 0.5667 的特异性，其次是 XGBoost 和随机森林模型。

利用机器学习和代谢组学数据对干眼病患者和健康对照组进行分类

Classifying Dry Eye Disease Patients from Healthy Controls Using Machine  Learning and Metabolomics Data

With a prevalence of 5 to 50%, Dry Eye Disease (DED) is one of the leading
reasons for ophthalmologist consultations. The diagnosis and quantification of
DED usually rely on ocular surface analysis through slit-lamp examinations.
However, evaluations are subjective and non-reproducible. To improve the
diagnosis, we propose to 1) track the ocular surface in 3-D using video
recordings acquired during examinations, and 2) grade the severity using
registered frames. Our registration method uses unsupervised image-to-depth
learning. These methods learn depth from lights and shadows and estimate pose
based on depth maps. However, DED examinations undergo unresolved challenges
including a moving light source, transparent ocular tissues, etc. To overcome
these and estimate the ego-motion, we implement joint CNN architectures with
multiple losses incorporating prior known information, namely the shape of the
eye, through semantic segmentation as well as sphere fitting. The achieved
tracking errors outperform the state-of-the-art, with a mean Euclidean distance
as low as 0.48% of the image width on our test set. This registration improves
the DED severity classification by a 0.20 AUC difference. The proposed approach
is the first to address DED diagnosis with supervision from monocular videos

本篇研究通过运用三维摄像来追踪眼表面，并利用 CNN 架构估算眼球运动，解决了眼表面分析在光源移动和透明眼组织等问题上的难点，从而提高了对干眼症的有效诊断。