Predicting an unstable tear film through artificial intelligence

Abstract

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.

Predicting Meibomian Gland Dropout and Feature Importance Analysis with Explainable Artificial Intelligence

“…The fact that TBUT was one of the most important features for all three classifiers substantiates the role of the MGs and meibum in stabilising the tear film. This is in accordance with our previous findings that the degree of MG dropout is an important predictor of an unstable tear film [10]. Interestingly, lid margin abnormalities such as telangiectasias, cicatricial disease, an irregular lid margin and displacement of the mucocutaneous junction stand out as important features.…”

“…However, our group recently used principal components analysis comparing salivary and ocular lipids in DED patients and healthy controls [9]. Also, we employed various ML algorithms on clinical tabular data in DED patients to predict instability of the tear film [10].…”

Potential applications of artificial intelligence in image analysis in cornea diseases: a review

“…A ML model by Fineide et al was able to predict reduced TBUT and differentiate eyes with reduced TBUT with high accuracy [ 51 ]. In addition, other clinical features such as ocular surface staining, meibomian gland dropout, blink frequency, osmolarity, meibum quality and symptom score were found to be important predictors for tear film instability [ 51 ]. This was consistent with what has been established by the Tear Film and Ocular Society Dry Eye Workshop II [ 51 ].…”

“…In addition, other clinical features such as ocular surface staining, meibomian gland dropout, blink frequency, osmolarity, meibum quality and symptom score were found to be important predictors for tear film instability [ 51 ]. This was consistent with what has been established by the Tear Film and Ocular Society Dry Eye Workshop II [ 51 ]. Similarly, Abdelmotaal et al used a CNN algorithm for developing an automated diagnostic tool of DED based on video keratoscopy, with a high accuracy and AUC of 0.98 [ 52 ].…”

Artificial Intelligence Models Utilize Lifestyle Factors to Predict Dry Eye-Related Outcomes

“…These emerging arti cial intelligence models can facilitate the discovery of novel relationships among clinical, lifestyle, and symptom variables, allow examination of previously determined relationships from a new perspective, and generate new hypotheses for further investigation. 7,8 The importance of lifestyle factors in machine learning model predictions of ocular surface disease-related outcomes is the focus of the current work.…”