Publications
Proceedings, refereed
Identifying Important Proteins in Meibomian Gland Dysfunction with Explainable Artificial Intelligence
In IEEE International Symposium on Computer-Based Medical Systems (IEEE CBMS2023), 2023.Status: Accepted
Identifying Important Proteins in Meibomian Gland Dysfunction with Explainable Artificial Intelligence
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.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2023 |
| Conference Name | IEEE International Symposium on Computer-Based Medical Systems (IEEE CBMS2023) |
| Keywords | Dry eye disease, Explainable artificial intelligence, Machine learning, meibomian gland dysfunction, proteomics |
Predicting Meibomian Gland Dropout and Feature Importance Analysis with Explainable Artificial Intelligence
In IEEE International Symposium on Computer-Based Medical Systems (IEEE CBMS2023), 2023.Status: Accepted
Predicting Meibomian Gland Dropout and Feature Importance Analysis with Explainable Artificial Intelligence
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 and LightGBM 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.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2023 |
| Conference Name | IEEE International Symposium on Computer-Based Medical Systems (IEEE CBMS2023) |
| Keywords | Dry eye disease, Explainable artificial intelligence, Machine learning, meibography, meibomian gland dysfunction |
Predicting the degree of meibomian gland dropout with artificial intelligence
In ARVO Annual Meeting, 2023.Status: Published
Predicting the degree of meibomian gland dropout with artificial intelligence
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2023 |
| Conference Name | ARVO Annual Meeting |
Using explainable artificial intelligence (XAI) to explore factors affecting meibomian gland (MG) dropout
In ARVO Annual Meeting, 2023.Status: Published
Using explainable artificial intelligence (XAI) to explore factors affecting meibomian gland (MG) dropout
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2023 |
| Conference Name | ARVO Annual Meeting |
Journal Article
Predicting an unstable tear film through artificial intelligence
Scientific Reports 12, no. 1 (2022): 21416.Status: Published
Predicting an unstable tear film through artificial intelligence
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.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2022 |
| Journal | Scientific Reports |
| Volume | 12 |
| Issue | 1 |
| Pagination | 21416 |
| Date Published | 12/2022 |
| Publisher | Springer Nature |
| URL | https://www.nature.com/articles/s41598-022-25821-y |
| DOI | 10.1038/s41598-022-25821-y |
