Meibomian gland dysfunction is the most common cause of dry eye disease, which is a prevalent condition that can damage the ocular surface and cause reduced vision and substantial pain. Meibum secreted from the meibomian glands makes up the majority of the outer, protective lipid layer of the tear film. Changes in the secreted meibum and markers of glandular damage can be detected through tear sampling.
Several studies have investigated the tear film protein expression in meibomian gland dysfunction, but less work apply machine learning to analyze the protein patterns. We use machine learning and methods from explainable artificial intelligence to detect potential clinically relevant proteins in meibomian gland dysfunction. Two different explainable artificial intelligence methods are compared. Several of the proteins found important in the models have been linked to dry eye disease in the past, while some are novel. Consequently, explainable artificial intelligence methods serve as a promising tool for screening for proteins that are relevant for meibomian gland dysfunction. By doing so, one may be able to discover new biomarkers and treatments, and gain a better understanding of how diseases develop.

Dry eye disease (DED) has a prevalence of between 5 and 50%, depending on the diagnostic criteria used and population under study. However, it remains one of the most underdiagnosed and undertreated conditions in ophthalmology. Many tests used in the diagnosis of DED rely on an experienced observer for image interpretation, which may be considered subjective and result in variation in diagnosis. Since artificial intelligence (AI) systems are capable of advanced problem solving, use of such techniques could lead to more objective diagnosis. Although the term ‘AI’ is commonly used, recent success in its applications to medicine is mainly due to advancements in the sub-field of machine learning, which has been used to automatically classify images and predict medical outcomes. Powerful machine learning techniques have been harnessed to understand nuances in patient data and medical images, aiming for consistent diagnosis and stratification of disease severity. This is the first literature review on the use of AI in DED. We provide a brief introduction to AI, report its current use in DED research and its potential for application in the clinic. Our review found that AI has been employed in a wide range of DED clinical tests and research applications, primarily for interpretation of interferometry, slit-lamp and meibography images. While initial results are promising, much work is still needed on model development, clinical testing and standardisation.

The in vitro fertilization procedure called intracytoplasmic sperm injection can be used to help fertilize an egg by injecting a single sperm cell directly into the cytoplasm of the egg. In order to evaluate, refine and improve the method in the fertility clinic, the procedure is usually observed at the clinic. Alternatively, a video of the procedure can be examined and labeled in a time-consuming process. To reduce the time required for the assessment, we propose an unsupervised method that automatically clusters video frames of the intracytoplasmic sperm injection procedure. Deep features are extracted from the video frames and form the basis for a clustering method. The method provides meaningful clusters representing different stages of the intracytoplasmic sperm injection procedure. The clusters can lead to more efficient examinations and possible new insights that can improve clinical practice. Further on, it may also contribute to improved clinical outcomes due to increased understanding about the technical aspects and better results of the procedure. Despite promising results, the proposed method can be further improved by increasing the amount of data and exploring other types of features.