Deep learning-based anterior segment identification and parameter assessment of primary angle closure disease in ultrasound biomicroscopy images.

Purpose: To develop an artificial intelligence algorithm to automatically identify the anterior segment structures and assess multiple parameters of primary angle closure disease (PACD) in ultrasound biomicroscopy (UBM) images.

Design: Development and validation of an artificial intelligence algorithm for UBM images.

Methods: 2339 UBM images from 592 subjects were collected for algorithm development. A multitissue segmentation model based on deep learning was developed for automatic identification of anterior segments and localisation of scleral spur. Then, measurement of the typical angle parameters was performed from the predicted results, including angle-opening distance at 500 µm (AOD 500), trabecular-ciliary angle (TCA) and iris area. We then collected 222 UBM images from 45 subjects in two centres for model validation.

Results: The multitissue identification model established in this study reached mean Intersection over Union (IoU) of 0.98, 0.98 and 0.98 on cornea segmentation, iris segmentation and ciliary body segmentation and a mean error distance of 1.07 pixels on scleral spur localisation. Our model got a mean IoU of 0.98, 0.98 and 0.99 on cornea segmentation, iris segmentation and ciliary body segmentation and a mean error distance of 0.49 pixels on scleral spur localisation in open-angle images and received 0.98, 0.98, 0.978 and 1.42 pixels respectively in angle-closure images. The mean differences between automatic and manual measurement of the angle parameters were 3.07 μm of AOD, 3.34 degrees of TCA and 0.05 mm² of iris area.

Conclusions: The automatic method of multitissue identification for PACD eyes developed was feasible, and the automatic measurement of angle parameters was reliable.