Accurate Prediction of Humphrey 10-2 Visual Fields in Glaucoma from a Single Rapid IMOvifa 24plus(1-2).

Purpose: Predicting the Humphrey Field Analyzer (HFA) 10-2 visual field (VF) using machine learning (ML) based on IMOvifa 24plus(1-2) VF data. Design: Retrospective cross-sectional study. Participants: Seventy actual IMOvifa 24plus(1-2) tests from 25 patients (The Jikei University School of Medicine) and 3472 synthesized IMOvifa 24plus(1-2) tests from 884 patients who underwent HFA 24-2 and HFA 10-2 VF measurements at 4 affiliated hospitals. Methods: Synthesized 24plus(1-2) data were created by merging 54 points from HFA 24-2 and 24 points from HFA 10-2 tests. An XGBoost model, trained on the synthetic data set, predicted thresholds at the 68 HFA 10-2 test locations. Model performance was assessed on the actual data set using leave-one-out cross-validation. Stratified patient-level bootstrap analyses accounting for multiple tests per patient were used to compare results across 3 glaucoma severity groups (mild, moderate, and advanced) based on HFA 10-2 mean deviation. Four models utilizing different input subsets were evaluated: model 1 (all 78 IMOvifa 24plus[1-2] points), model 2 (54 points of 24-2), model 3 (central 40 points of 24plus[1-2]), and model 4 (central 16 points of 24-2). Test durations were also compared. Main Outcome Measures: Mean absolute error (MAE), root mean squared error (RMSE), and coefficient of determination (R Results: Model 1 (all 24plus[1-2] points) achieved the highest overall accuracy (MAE 3.59 dB, RMSE 5.71 dB, R Conclusions: Incorporating the additional central test points from the IMOvifa 24plus(1-2) significantly enhances the accuracy of ML based HFA 10-2 VF prediction. This approach offers an efficient strategy for obtaining detailed central VF information from a single, rapid test, potentially improving glaucoma management. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.