Abstract
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.