Uncertainty-aware regression model to predict post-operative visual acuity in patients with macular holes

Abstract

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.