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The increasing prevalence of glaucomatous optic neuropathy, which can result in permanent blindness (visual impairment), accentuates the importance of screening and early diagnosis for prevention of blindness. GlauCUTU, a novel time until perceived (TUP) visual field (VF) testing, utilizes a portable virtual reality (VR) headset with visual stimulus that progressively increases in intensity to detect VF defects. GlauCUTU was evaluated on participants with normal visual fields and those with early, moderate, and severe glaucoma. Responses were collected in terms of time until response (TUR). TUR was used to calculate TUP and reported in terms of GlauCUTU sensitivity. False positives were detected with pretest and latency analysis using reaction time (RT). In addition, a novel automated transformation was developed to convert GlauCUTU sensitivity into HFA sensitivity using machine learning (ML) and deep learning (DL) algorithms. Visual field index (VFI) was generated from HFA sensitivity to determine severity of glaucoma. The VFI results were evaluated using post-hoc analysis from two-way analysis of variance (ANOVA). Results demonstrate no significant difference (p&#x003D;0.073) between Humphrey visual field analyzer (HFA) and GlauCUTU with machine learning transformation (GlauCUTU-ML) in all glaucoma stages. However, there was a significant difference between HFA and GlauCUTU with deep learning transformation (GlauCUTU-DL) in severe glaucoma (p&#x003C;0.050). GlauCUTU-ML generates the lowest root mean square error (RMSE) of 4.92. Meanwhile, GlauCUTU-DL yields the highest Pearson&#x2019;s <inline-formula> <tex-math notation="LaTeX">$r$ </tex-math></inline-formula> correlation coefficient with HFA of 0.74, but produces the highest RMSE of 6.31. Comparison between three expert ophthalmologists&#x2019; grading of glaucomatous eyes on GlauCUTU-ML and HFA aligns with the majority voting with an average agreement of 0.83, which is highly reliable. All in all, the portable and inexpensive GlauCUTU perimetry system introduces the use of TUP for VF evaluation with results comparable to HFA. GlauCUTU proves to be a promising method to increase accessibility to glaucoma screening, particularly in low-resource setting countries.

GlauCUTU: Time Until Perceived Virtual Reality Perimetry With Humphrey Field Analyzer Prediction-Based Artificial Intelligence