Using Artificial Intelligence to improve the diagnostic predictions of corneal topography machines for Keratoconus subjects

Summary Keratoconus is a common condition that affects the cornea and despite its increasing prevalence, the cause of keratoconus is largely unknown. There are many clinical gaps regarding keratoconus in terms of subclinical detection, clarifying its disease stage and identifying which features should be used to predict its progression. These gaps impact on a clinician’s decision-making process for keratoconus disease management.

Project Details

Keratoconus is a common condition that affects the cornea and despite its increasing prevalence, the cause of keratoconus is largely unknown. There are many clinical gaps regarding keratoconus in terms of subclinical detection, clarifying its disease stage and identifying which features should be used to predict its progression. These gaps impact on a clinician’s decision-making process for keratoconus disease management. The project aims at developing machine learning algorithms to identify features that define early subclinical keratoconus that are currently refractory as well as identify a series of features that are involved in a) disease staging, as well as b) risk of progression of Keratoconus. It provides an exciting opportunity to conduct big data analysis, generate AI model and manuscript writing.

Students with backgrounds in biomedical and computer science, statistics or optometry and visual science are welcome to apply.