Convolutional Graph Isomorphism Network to Detect Glaucomatous Visual Field Defects.

Purpose: To evaluate the performance of a deep learning (DL) model based on graph isomorphism networks (GINs) for detecting glaucomatous visual field defects on 24-2 standard automated perimetry (SAP) and to compare it against traditional diagnostic criteria, a dense neural network (NN) model, and a convolutional neural network (CNN) model. Design: A cross-sectional retrospective study. Participants: 1874 reliable SAP tests (Humphrey Field Analyzer, Carl-Zeiss Meditec Inc.) from 1009 eyes of 676 patients. Methods: Standard automated perimetry tests were classified as normal or abnormal due to glaucomatous damage by two glaucoma specialists, with adjudication by a third. A GIN architecture was developed to classify tests using full 54-point spatial SAP data modeled as graphs, with node features comprising sensitivity, total deviation, and pattern deviation values. The dataset was split at the patient level (60% training/validation, 40% testing). The GIN model's diagnostic performance was compared to the Anderson criteria, the glaucoma hemifield test/pattern standard deviation (GHT/PSD) criteria, a fully connected dense NN, and a CNN model. Main Outcome Measures: Area under the receiver operating characteristic curve (AUC), precision-recall curve, sensitivity at 95% specificity, F1-score, repeatability, and model explainability. Results: Among the 1874 SAP tests, 70.0% were graded as abnormal. The GIN model achieved an AUC of 0.982, significantly outperforming the Anderson criteria (AUC: 0.906, Conclusions: By modeling SAP as a graph and incorporating spatial relationships among test points, the GIN model provided superior diagnostic performance and interpretability relative to traditional criteria and standard NNs. This graph-based approach offers a promising tool for accurate and explainable detection of glaucomatous visual field defects in clinical practice. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.