Aims/Purpose: This study aimed to explore the feasibility of predicting intraocular pressure (IOP) using a neural network model incorporating both image data and patient metadata. The objective was to evaluate the potential of this approach in improving the understanding and prediction of IOP levels in individuals, utilizing data from the PAPILA public database.Methods: We employed a dataset containing 331 images from the PAPILA database, with 298 images classified as normal and 33 diagnosed with glaucoma. Additionally, patient metadata such as glaucoma diagnosis status, age, sex, refractive defect, pachymetry, and axial length were incorporated. The dataset was divided into 70% for training the neural network model, 15% for validation, and 15% for testing.Results: The analysis of the neural network model's predictive performance produced the following metrics: Mean Absolute Error (MAE) of 2.515, Root Mean Square Error (RMSE) of 3.131, and Mean Absolute Percentage Error (MAPE) of 16.611%. These results indicate a moderate level of predictive capability for IOP based on the input data.Conclusions: In conclusion, our study demonstrates promising potential in utilizing artificial intelligence to predict intraocular pressure (IOP) using image data and patient metadata. While our model's predictive performance is moderate, it sets the stage for further advancements. With continued refinement and exploration of additional factors, AI‐driven approaches hold promise for revolutionizing glaucoma management.References Kovalyk O, Morales‐Sánchez J, Verdú‐Monedero R, Sellés‐Navarro I, Palazón‐Cabanes A, Sancho‐Gómez JL. PAPILA: Dataset with fundus images and clinical data of both eyes of the same patient for glaucoma assessment. Sci Data. 2022 Jun 9;9(1):291. doi: 10.1038/s41597‐022‐01388‐1. PMID: 35680965; PMCID: PMC9184612.
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