@inproceedings {43256,
	title = {Identifying Important Proteins in Meibomian Gland Dysfunction with Explainable Artificial Intelligence},
	journal = {IEEE International Symposium on Computer-Based Medical Systems (IEEE CBMS2023)},
	year = {2023},
	abstract = {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.\&nbsp;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.},
	keywords = {Dry eye disease, Explainable artificial intelligence, Machine learning, meibomian gland dysfunction, proteomics},
	author = {Stor{\r a}s, Andrea and Magn{\o}, Morten and Fineide, Fredrik and Thiede, Bernd and Chen, Xiangjun and Str{\"u}mke, Inga and Halvorsen, P{\r a}l and Utheim, Tor and Riegler, Michael}
}
@inproceedings {43319,
	title = {Predicting Meibomian Gland Dropout and Feature Importance Analysis with Explainable Artificial Intelligence},
	journal = {IEEE International Symposium on Computer-Based Medical Systems (IEEE CBMS2023)},
	year = {2023},
	abstract = {Dry eye disease is a common and potentially debilitating medical condition. Meibum secreted from the meibomian glands is the largest contributor to the outermost, protective lipid layer of the tear film. Dysfunction of the meibomian glands is the most common cause of dry eye disease. As meibomian gland dysfunction progresses, gradual atrophy of the glands is observed. The meibomian glands are commonly visualized through meibography, a technique requiring specialist equipment and knowledge that might not be available to the physician. In the present project we use machine learning on clinical tabular data to predict the degree of meibomian gland dropout. Moreover, we employ explainable artificial intelligence on the best performing algorithms for feature importance evaluation. The best performing algorithms were AdaBoost, multilayer perceptron\&nbsp;and LightGBM\&nbsp;which outperformed the majority vote baseline classifier in every included evaluation metric for both multioutput and binary classification. Through explainable artificial intelligence known associations are validated and novel connections identified and discussed.},
	keywords = {Dry eye disease, Explainable artificial intelligence, Machine learning, meibography, meibomian gland dysfunction},
	author = {Fineide, Fredrik and Stor{\r a}s, Andrea and Riegler, Michael and Utheim, Tor}
}
@inproceedings {43320,
	title = {Predicting the degree of meibomian gland dropout with artificial intelligence},
	journal = {ARVO Annual Meeting},
	year = {2023},
	author = {Fineide, Fredrik and Stor{\r a}s, Andrea and Magn{\o}, Morten and Hugo Lewi {Hammer} and Chen, Xiangjun and Khan, Ayyad and Halvorsen, P{\r a}l and Utheim, Tor and Riegler, Michael}
}
@inproceedings {43255,
	title = {Using explainable artificial intelligence (XAI) to explore factors affecting meibomian gland (MG) dropout},
	journal = {ARVO Annual Meeting},
	year = {2023},
	author = {Stor{\r a}s, Andrea and Fineide, Fredrik and Hugo Lewi {Hammer} and Khan, Ayyad and Magn{\o}, Morten and Xiangjung, Chen and Halvorsen, P{\r a}l and Utheim, Tor and Riegler, Michael}
}
