(untitled) (Vietnam Journal of Community Medicine, 2022)
Using Darknet convolutional neural network technology, YOLO version 3 develops an artificial intelligence system called EYEDR, that can screen glaucoma by disc imaging automatically. Purposes: Determining the validity and reliability of the artificial intelligence algorithm EYEDR in the screening of adult glaucoma by color images of the optic disc. Methods: Cross-sectional study. The 2470 color images of the optic disc were selected to train machine learning with Darknet YOLO convolutional neural network to determine the shape of the optic disc and optic cup, thereby an artificial intelligence algorithm was developed to detect the glaucoma and non-glaucoma optic nerve. A test dataset of 1028 disc images was used to evaluate the value of this AI software based on the sensitivity, specificity, area under the ROC curve (AUC), likelihood ratio, accuracy, and Cohen’s Kappa coefficient. Results: The area under the curve (AUC) was 0.93 ± 0.01 (95% CI; 0.92 - 0.95), with the sensitivity 90.03%, specificity 95.06%, the accuracy 86.8% and the Kappa coefficient 0.86 with p<0.001. Conclusions: EYEDR artificial intelligence software developed by CNN Darknet YOLO vesion 3 technology with high sensitivity could screen glaucoma by color fundus images in the community.