A Deep Learning-Based Study on Automatic Measurement Method of Biological Parameters in Anterior Segment UBM Images of Angle-Closure Glaucoma.

The DeepLabv3+ model performed excellently in the segmentation tasks, with an mean intersection over union (mIoU) of 85.84%, precision of 92.67%, recall of 91.36%, and Dice coefficient of 92.01%, all representing optimal values.

In the object detection task, the segmented dataset achieved a precision of 88.3%, recall of 89.9%, and mean average precision at 0.50 IoU (mAP50) of 92.9%, showing at least a 19% improvement over the original image dataset.

The average absolute error of the Euclidean distance for four-point localization was 50.41µm, with a root mean square error of 77.85 µm.