Machine learning technology in the classification of glaucoma severity using fundus photographs.

This study evaluates the performance of a machine learning model in classifying glaucoma severity using color fundus photographs. Glaucoma severity grading was based on the Hodapp-Parrish-Anderson (HPA) criteria incorporating the mean deviation value, defective points in the pattern deviation probability map, and defect proximity to the fixation point. The dataset of 2,940 fundus photographs from 1,789 patients was matched with visual field tests and equally classified into three classes: normal, mild-moderate, and severe glaucoma stages. The EfficientNetB7, a convolutional neural network model, was trained on these images using transfer learning and fine-tuning techniques. The model achieved an overall accuracy of 0.871 (95% CI, 0.822-0.919). For normal, mild-moderate, and severe classes, the area under the curve (AUC) values were 0.988, 0.932, and 0.963; sensitivity 0.903, 0.823, and 0.887; and specificity 0.960, 0.911, and 0.936, respectively. Analysis of the confusion matrix revealed the impact of structural-functional relationships in glaucoma on model performance. In conclusion, the EfficientNetB7 demonstrated high accuracy in classifying glaucoma severity based on the HPA criteria using fundus photographs, offering potential for clinical application in glaucoma diagnosis and management.