The Role of Artificial Intelligence in Predicting the Progression of Intraocular Hypertension to Glaucoma.

UNLABELLED

AI systems, especially artificial neural networks (ANNs), are increasingly involved in the diagnosis and personalized management of ophthalmologic disorders.

BACKGROUND

This study shows the practical applications of artificial intelligence for predicting the progression of intraocular hypertension (IOH) to glaucoma.

METHODS

This study involved two groups of patients with IOH and a control group, analyzed using the commercial Neurosolution simulator. The findings were compared with experimental data. The performance of the neural models was evaluated using several metrics: Mean Squared Error (MSE), Normalized Mean Squared Error (NMSE), correlation coefficient (r), and percentage error (Ep).

RESULTS

For all three patient groups, the best performance was achieved with neural networks featuring two hidden layers: MLP(9:18:9:3) for group 1, MLP(10:20:10:3) for group 2, and MLP(10:30:20:3) for group 3. The MSE values during validation were 0.39 for groups 1 and 2, and 0.34 for group 3. For these neural networks, the probability of producing correct outputs during validation was 75% (i.e., 9 correct responses out of a possible 12). The findings in this study are in line with those reported by other researchers in the field.

CONCLUSIONS

The neural network models developed in this study demonstrated their potential for predicting the progression of intraocular hypertension to glaucoma.