A generalised computer vision model for improved glaucoma screening using fundus images
This study demonstrated the high efficacy of our classification model in distinguishing between glaucomatous and healthy discs. However, the model's accuracy slightly dropped when evaluated on unseen data, indicating potential inconsistencies among the datasets-the model needs to be refined and validated on larger, more diverse datasets to ensure reliability and generalisability. Despite this, our model can be utilised for screening glaucoma at the population level.
Authors
- Guei‐Sheung Liu (ORCID: https://orcid.org/0000-0003-3379-724X)
- Puya Gharahkhani (ORCID: https://orcid.org/0000-0002-4203-5952)
- Stuart MacGregor (ORCID: https://orcid.org/0000-0001-6731-8142)
- Alex William Hewitt (ORCID: https://orcid.org/0000-0002-5123-5999)
- Jamie E. Craig (ORCID: https://orcid.org/0000-0001-9955-9696)
- David A. Mackey (ORCID: https://orcid.org/0000-0001-7914-4709)
- Connor J Greatbatch (ORCID: https://orcid.org/0009-0004-7118-2582)
- Abadh K. Chaurasia (ORCID: https://orcid.org/0000-0003-4691-2921)
Institutions
- Lions Eye Institute (AU)
- University of Tasmania (AU)
- Queensland University of Technology (AU)
- The University of Queensland (AU)
- The University of Melbourne (AU)
- Flinders University (AU)
- The University of Western Australia (AU)
- QIMR Berghofer Medical Research Institute (AU)
- Flinders Medical Centre (AU)
- Centre for Eye Research Australia (AU)
Publication Details
- Journal
- Eye
- Published
- 2024-11-05
- DOI
- https://doi.org/10.1038/s41433-024-03388-4
- Citations
- 6
- Primary Topic
- Retinal Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 2.43
Funders
- University of Tasmania
- Medical Research Council
- National Health and Medical Research Council