Browse by author
Lookup NU author(s): Burak Kucukgoz, Dr Declan Murphy, Professor David SteelORCiD, Professor Boguslaw ObaraORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2024 The Authors. Full-thickness macular holes are a relatively common and visually disabling condition with a prevalence of approximately 0.5% in the over-40-year-old age group. If left untreated, the hole typically enlarges, reducing visual acuity (VA) below the definition of blindness in the eye affected. They are now routinely treated with surgery, which can close the hole and improve vision in most cases. The extent of improvement, however, is variable and dependent on the size of the hole and other features which can be discerned in spectral-domain optical coherence tomography imaging, which is now routinely available in eye clinics globally. Artificial intelligence (AI) models have been developed to enable surgical decision-making and have achieved relatively high predictive performance. However, their black-box behavior is opaque to users and uncertainty associated with their predictions is not typically stated, leading to a lack of trust among clinicians and patients. In this paper, we describe an uncertainty-aware regression model (U-ARM) for predicting VA for people undergoing macular hole surgery using preoperative spectral-domain optical coherence tomography images, achieving an MAE of 6.07, RMSE of 9.11 and R2 of 0.47 in internal tests, and an MAE of 6.49, RMSE of 9.49, and R2 of 0.42 in external tests. In addition to predicting VA following surgery, U-ARM displays its associated uncertainty, a p-value of <0.005 in internal and external tests, showing the predictions are not due to random chance. We then qualitatively evaluated the performance of U-ARM. Lastly, we demonstrate out-of-sample data performance, generalizing well to data outside the training distribution, low-quality images, and unseen instances not encountered during training. The results show that U-ARM outperforms commonly used methods in terms of prediction and reliability. U-ARM is thus a promising approach for clinical settings and can improve the reliability of AI models in predicting VA.
Author(s): Kucukgoz B, Zou K, Murphy DC, Steel DH, Obara B, Fu H
Publication type: Article
Publication status: Published
Journal: Computerized Medical Imaging and Graphics
Year: 2025
Volume: 119
Print publication date: 01/01/2025
Online publication date: 26/11/2024
Acceptance date: 03/11/2024
Date deposited: 08/11/2024
ISSN (print): 0895-6111
ISSN (electronic): 1879-0771
Publisher: Elsevier Ltd
URL: https://doi.org/10.1016/j.compmedimag.2024.102461
DOI: 10.1016/j.compmedimag.2024.102461
Data Access Statement: The benchmark OCT dataset is available on request from the corresponding author for non-commercial use. Data from HD-OCT of MH is available at ( ). The source code is publicly available at: TBC.
Altmetrics provided by Altmetric
