1. Multi-modal Machine Learning using Visual Fields and Peripapillary Circular OCT Scans in Detection of Glaucomatous Optic Neuropathy

    Multi-modal Machine Learning using Visual Fields and Peripapillary Circular OCT Scans in Detection of Glaucomatous Optic Neuropathy

    Purpose: To develop and test a multi-modal artificial intelligence (AI) algorithm, FusionNet, using the pattern deviation probability plots (PDPs) from visual field (VF) reports and circular peripapillary optical coherence tomography (OCT) to detect glaucomatous optic neuropathy (GON). Design: Cross-sectional study. Subjects: A total of 2463 pairs of VF and OCT images from 1083 patients. Methods: A novel deep learning algorithm (FusionNet) based on bimodal input of VF-OCT paired data was developed to detect GON. VF data were collected using Humphrey Field Analyzer (HFA). OCT images were collected from three types of devices (DRI-OCT, Cirrus OCT and Spectralis OCT). A total ...

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