Purpose: To train and evaluate a segmentation-free 3D convolutional neural network (3DCNN) model for estimating visual field (VF) from optical coherence tomography (OCT) images and to compare the residual variability of OCT-based estimated VF (OCT-VF) with that of Humphrey Field Analyzer (HFA) measurements in a diverse clinical population. Design: Retrospective cross-sectional study. Participants: 5,351 patients (9,564 eyes) who underwent macular OCT imaging and Humphrey Field Analyzer (HFA) tests (24-2 or 10-2 test patterns) at a university hospital from 2006 to 2023. The dataset included 47,653 paired OCT-VF data points, including various ocular conditions. Methods: We trained a segmentation-free 3DCNN model based on the EfficientNet3D-b0 architecture on a comprehensive OCT dataset to estimate VF. We evaluated the model's performance using Pearson's correlation coefficient and Bland‒Altman analysis. We assessed residual variability using a jackknife resampling approach and compared OCT-VF and HFA datasets using generalized estimating equations (GEE), adjusting the number of VF tests, follow-up duration, age, and clustering by eye and patient. Main Outcome Measures: Correlations between estimated and measured VF thresholds and mean deviations (MDs), and residual variability of OCT-VF and HFA. Results: We observed strong correlations between the estimated and measured VF parameters (Pearson's r: 24-2 thresholds 0.893, MD 0.932; 10-2 thresholds 0.902, MD 0.945; all p < 0.001). Bland‒Altman analysis showed good agreement between the estimated and measured MD, with a slight proportional bias. GEE analysis demonstrated significantly lower residual variability for OCT-VF than for HFA (24-2 thresholds: 1.10 vs. 2.48 dB; 10-2 thresholds: 1.20 vs. 2.48 dB; all p < 0.001, Bonferroni-corrected), with lower variability across all test points, severities, and ages, thus highlighting the robustness of the segmentation-free 3DCNN approach in a heterogeneous clinical sample. Conclusions: A segmentation-free 3DCNN model objectively estimated VF from OCT images with high accuracy and significantly lower residual variability than subjective HFA measurements in a heterogeneous clinical sample, including patients with glaucoma and individuals with other ocular diseases. The improved reliability, lower variability, and objective nature of OCT-VF highlight its value for enhancing VF assessment and monitoring of various ocular conditions, potentially facilitating earlier detection of progression and more efficient disease management.

Makoto Koyama has potential future royalties from DeepEyeVision Inc. if the product is commercialized, and has purchased stock in the company. He is also engaged in ongoing discussions about potential future product development and commercialization with DeepEyeVision Inc.

Satoru Inoda received a Grant-in-Aid for Scientific Research (21K16903) from the Ministry of Education, Culture, Sports, Science and Technology of Japan. The sponsor or funding organization had no role in the design or conduct of this research.

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The Institutional Review Board of Shimane University Hospital gave ethical approval for this work (IRB No. KS20230719-3, approved on August 10, 2023). Due to the retrospective nature of the study, the requirement for informed consent was waived, and an opt-out approach was implemented. Information about the study was posted on the hospital's website and premises, allowing patients the opportunity to decline participation.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

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I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

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We are unable to make the datasets publicly available due to privacy and ethical considerations related to patient data. We conducted the study on an opt-out basis, without obtaining explicit consent from all participants to release their raw data.