Recently, the research team led by Prof. Kong Dexing, Dean of ZJNU College of Mathematical Medicine, and visiting professor Dai Qi, has published a research paper titled Uneven Index: A Digital Biomarker to Prompt Demodex Blepharitis Based on Deep Learning in the prestigious journal Frontiers in Physiology.
Demodex mites are the most common permanent ectoparasites in humans. It has been identified as a vector for different bacteria, fungi, and viruses in disease development and is associated with chalazia, blepharitis, conjunctivitis, corneal manifestations, meibomian gland dysfunction (MGD) and dry eye. However, studies on the morphological changes of meibomian glands (MGs) caused by Demodex infestation are scarce, and the relationship between morphological changes of MGs and Demodex infestation has not been quantitatively examined. In this study, based on a deep learning model, combined with the total variation and other mathematical methods, we compared the differences in clinical features and morphological changes of MGs between outpatients with Demodex blepharitis and control group, and proposed a novel digital biomarker for Demodex blepharitis to fill the gap in the research field. The research results successfully applied a series of mathematical theories and methods to the intelligent diagnosis and treatment of ophthalmic diseases and artificial intelligence-assisted diagnosis, which is an important progress made by the College of Mathematical Medicine in the field of mathematical medicine.
Frontiers in Physiology is a leading journal in its field, publishing rigorously peer-reviewed research on the physiology of living systems, from the subcellular and molecular domains to the intact organism, and its interaction with the environment.