Leveraging Artificial Intelligence to Quantify Meibomian Gland Morphology
Meng C. Lin, Professor
Optometry
Closed. This professor is continuing with Fall 2024 apprentices on this project; no new apprentices needed for Spring 2025.
This research investigates the fascinating impact of aging on the morphology of the Meibomian gland (MG), which plays a pivotal role in Ocular Surface Disease. Traditionally, clinicians have employed subjective methods to identify and grade MG features such as atrophy, tortuosity, length, width, and ghosting. At the exciting crossroads of technology and healthcare, our innovative approach harnesses the power of Artificial Intelligence (AI) to precisely quantify MG morphological characteristics from meibography images. This method aims to establish a more standardized and objective evaluation process for MG. Our study aims to uncover the natural changes in MG from childhood through adulthood, providing new insights and potential advancements in eye health.
Role: The undergraduate student will be responsible for the following:
- Understanding meibomian gland physiology
- Understanding ocular surface physiology
- Understanding tear film physical properties
- Understanding current clinical study design and study aims
- Understanding study protocol to effectively recruiting subjects based on a list of exclusion and inclusion criteria specific to the study
- Communicating details of the study with interested participants
- Scheduling and keeping track of participants’ appointments for the study
- Data management
- Help drafting a manuscript for publication upon study completion
This study will be a good opportunity for the student to take part in the full process of a clinical research study from research development and planning to data collection and analysis. The student will gain knowledge in dry eyes, ocular surface physiology, clinical design and scientific methodology.
Qualifications: It is important that the student has good communication skills and be comfortable interacting with participants. The student should be familiar with basic data management such as Microsoft Excel, Google Workspace, or equivalent. It is a plus if the student has had experience with using Qualtrics, Adobe Lightroom, Adobe Premiere Pro, and Adobe Photoshop. It is important for the student to be able to work independently and as a team member. The ideal student would also be curious, proactive, and detail-oriented. A background in computer science, biology, and interest in research would be beneficial in this role.
Hours: 9-11 hrs
Biological & Health Sciences