Purpose: To develop and validate an automated lens cortex and nuclear opacity quantification method based on swept-source anterior segment optical coherence tomography (AS-OCT). Methods: This cross-sectional study included 504 cataract surgery candidates. Lens images were captured using swept-source AS-OCT (CASIA-2; Tomey Corporation). Based on nnUNet framework, two artificial intelligence (AI) segmentation models were independently trained to quantify opacity in the lens cortex and nucleus. Data from 275 and 229 individuals were used for lens nucleus model training and external testing, respectively. The corresponding numbers for lens cortex model were 100 and 38. Five-fold cross-validation was employed for model selection. The performance of the auto-segmentation, as well as the mean pixel intensity values within the area of interest, were evaluated against the human-generated labels. Results: The AI models demonstrated good segmentation accuracy for the lens cortex and nucleus (mean intersection over union MIoU = 0.959, 95% CI: 0.957 to 0.961 for cortex; MioU = 0.928, 95% CI: 0.925 to 0.931 for nucleus), and high agreement in the opacity quantification (intraclass correlation coefficient ICC = 0.9933, 95% CI: 0.9872 to 0.9965 for the cortex; ICC = 0.9939, 95% CI: 0.9921 to 0.9953 for the nucleus), compared to manual measurements by ophthalmologists. Conclusions: The AI model is capable of accurately and objectively quantifying the opacity of both the lens cortex and nucleus based on swept-source AS-OCT images, thereby offering a method that is more precise, objective, and rapid for quantification in both clinical practice and research settings.
Han et al. (2025) studied this question.