Objective:To develop a deep learning model based on multimodal data for glaucoma diagnosis and severity assessment.Methods:A diagnostic test was conducted.A total of 145 normal eyes from 86 participants and 507 eyes with primary open-angle glaucoma from 314 participants were collected at the First Affiliated Hospital of Kunming Medical University from June to December in 2023.Fundus photographs and visual field data were obtained, and glaucoma eyes were divided into three groups based on the mean deviation value of the visual field, namely mild group (154 eyes), moderate group (113 eyes), and severe group (240 eyes).Three convolutional neural network (CNN) models, including DenseNet 121, ResNet 50 and VGG 19, were used to build an artificial intelligence (AI) model.The impact of single-modal and multimodal data on the classification results was evaluated, and the most appropriate CNN network architecture for multimodal data was identified.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of The First Affiliated Hospital of Kunming Medical University (No.2023L93).Written informed consent was obtained from each subject.Results:A total of 652 eyes had both fundus photographs and visual field test results.Images were randomly assigned to training and test datasets in a 4∶1 ratio by using computer random number method.AI models built with different CNN models showed high accuracy, with DenseNet 121 outperforming ResNet 50 and VGG 19 on various effectiveness measures.In the single-modal algorithm using fundus photographs, single-modal algorithm using visual field tests, and multimodal algorithm combining fundus photographs and visual field data, the area under the curve for early glaucoma detection was 0.87, 0.93 and 0.95, respectively.Conclusions:The use of multimodal data enables the development of a highly accurate tool for the glaucoma diagnosis and severity grading.