Loss values of style transfer from UBM to AS-OCT images for plateau iris classification.

Ultrasound biomicroscopy (UBM) is the standard for diagnosing plateau iris, but its limited accessibility in routine clinical settings presents challenges. While anterior segment optical coherence tomography (AS-OCT) is more convenient, its effectiveness in detecting plateau iris is limited. Previous research has demonstrated that combining UBM and AS-OCT image pairs through neural style transfer has improved classification accuracy. However, obtaining paired images is impractical in everyday practice. In this study, we propose a novel semi-supervised approach that eliminates the need for paired images. A generative model learns to distinguish plateau and non-plateau features from UBM images. AS-OCT images are input into the generator, which attempts to transform them into corresponding UBM images. The model's performance is measured by loss values, representing the difficulty of transforming AS-OCT images, which are then used to predict plateau iris. The classification baseline, which applies AS-OCT solely without the style-transfer of UBM information, obtained 52.72% sensitivity, 60.82% specificity, and 57.89% accuracy for external validation; in contrast, the classification with neural style transfer of the image pairs respectively obtained 94.54%, 100.00%, and 98.03%, whereas the semi-supervised approach using loss values classification obtained 93.10%, 93.13%, and 93.12%, respectively. This semi-supervised transfer learning model presents a novel technique for detecting plateau iris with AS-OCT.