Many studies have been published demonstrating neural networks' capability to accurately classify glaucoma and normal patients using retinal fundus images, with AUC values typically around 99%11–14. However, this study represents the first attempt to predict intraocular pressure (IOP) using retinal fundus images in combination with ophthalmology clinical data. Another study by Isshi K et al.15 attempted to predict IOP using machine learning models based solely on systemic variables, but their results were found to be insufficient when compared to predicting IOP using only retinal fundus images.

In our study, we trained a deep learning algorithm using both eye fundus images and eye clinical data, in contrast to Isshi K et al15, who trained machine learning algorithms solely with systemic variables. Our Mean Absolute Error (MAE) of 2.52 is very similar to his MAE of 2.29. This metric is highly clinically interpretable. When the MAE is 2 and the predicted result is 15, it means that, on average, the model's predictions deviate by approximately 2 units from the actual values.

Thus, if the predicted result is 15 with an MAE of 2, we can reasonably expect the actual value to fall within the range of 13 to 17. This interpretation stems from the fact that the MAE represents the average absolute deviation of the model's predictions from the actual values. Therefore, the MAE provides an estimate of the typical magnitude of errors made by the model. With an MAE of 2, it suggests that the majority of actual values would likely fall within ± 2 units of the predicted value of 15.

Our deep learning model achieved an R-squared value of 0.1, which is comparable to the R-squared value of Isshi K et al15, who rounds it to 0.15 depending on the machine learning model employed. However, it's crucial to understand that R-squared is not an ideal metric for evaluating non-linear models like deep learning models16,17. Therefore, even if a deep learning model performs well in terms of prediction accuracy, its R-squared value may not reflect this accurately.

While eye fundus images contain a wealth of information that can be leveraged to predict age18,19, vascular risk factors18,19, sex20, and even neurological diseases such as Alzheimer's21,2, it lacks sufficient information to predict intraocular pressure (IOP)8 on its own. Additional clinical data must be incorporated to accurately predict IOP.

Using deep learning to predict IOP could be useful if employed in conjunction with other neural networks that distinguish between glaucoma and healthy individuals. By doing so, in locations lacking access to ophthalmology resources, a network that distinguishes between healthy and glaucomatous eyes, combined with another network that estimates IOP, could establish protocols and different criteria for referral.