Alexandria Mulligan

ROP-StuDIO is a research project centered around medical applications of machine learning regarding retinopathy of prematurity (ROP). At present, ROP is the leading cause of preventable childhood blindness and impacts premature newborns. The more premature a birth, the greater the risk of ROP and the increased risk medical scanning induces on the health of the child. While sometimes ROP is corrected naturally other times it is severe and treatment is required. Current ROP treatment is able to save a part vision in a child that would otherwise go blind. However, it is critical for ROP screening methods to predict the risk level of each newborn to reduce limiting sight on a child that may fully recover or missing treating another child who could have had some of their vision saved end up with no vision. In addition ROP screening methods require a specialised medical practitioner who is not always available in remote locations and the methods themselves are invasive for critical state premature births.

Research Aim:

This project aims to explore and address two research questions. The first, “can machine learning applications use clinical data of premature babies and their mothers to map risk assessment for infants developing ROP?”

Furthermore, the second question for exploration is, “can deep learning approaches be used to track ROP development using retinal fundus images?” The exploration of this question includes developing a set of guidelines around how to acquire optimal retinal scans for analysis. This secondary outcome intends to decrease the number of images and scans necessary for each baby.

Research Scope and Objectives:

The scope of this project is agile with seven main sprints or deliverable packages. The first is achieving domain understanding using a literature review and interacting with domain experts. One of these experts is the internal advisory supervisor for this project who has access to a substantial image dataset at Cork University Hospital. The second sprint focuses on this dataset and requires more domain training to understand the rental scans in the context of ROP. The third focus of the research project is to clean the data and perform necessary image pre-processing for the fourth sprint which seeks to develop a deep learning model for image evaluation. This fourth work package’s discoveries will be prepared for publication.

The fifth sprint is model development and testing for clinical data of the baby and mothers. This data provides a possible challenge in acquiring and will need to be applied for. The outcome of this model should identify premature babies as high risk or low risk for ROP due to the clinical data building off the current practice of using gestational age and weight as risk identifiers. The feature behaviours, and model developed in this stage will also be prepared for publication.

Work package six for this project evaluates the models from package four and five alongside clinical experts. The outcome of this is to determine if the models hold results that are acceptable in clinical practice. The final package of this project is thesis write up and submission combining the results and discoveries from all prior work packages.