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. 2023 Nov 15;13:19960. doi: 10.1038/s41598-023-46253-2

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© The Author(s) 2023

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Top panel shows the prediction AUCs obtained when baseline demographic and clinical data is used with only OCT images (blue), only VF MD values (orange), combined OCT and VF MD inputs (green), and OCT images combined with VF MD and longitudinal IOP values (red). Statistical annotations are as follows: **P value < 0.01, *P value < 0.05 and ns denotes “not statistically significant”. Confusion matrices shown in the bottom panel for three input combinations to the progression model suggest that the “OCT + VF + Baseline” input combination performs well for both Class-1 and Class-2 patients, correctly predicting 73% of the fast progressing cases (Class-2).