Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026
Automatic measurement and evaluation of anterior segment anatomical structures via UBM images using a deep learning-based approach.
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Chen Sijing, Chu Xiaoran, Pazo Emmanuel Eric, Huang Yue, Liu Hui, Zhang Chen, Yang Ruibo, Zhao Shaozhen
Аннотация
PURPOSE: To develop a deep-learning model capable of measuring essential anterior segment (AS) parameters derived from preoperative ultrasound biomicroscopy (UBM) images of candidates for implantable collamer lens (ICL) surgery.
SETTING: Tianjin Medical University Eye Hospital, Tianjin, China.
DESIGN: Cross-sectional retrospective study.
METHODS: A dataset comprising 1,480 preoperative panoramic UBM images taken from 638 eyes of 320 subjects was collected and was divided into training and testing subsets at a proportion of 7:3. Using the YOLOv8-pose algorithm, the model identified ten anatomical key point coordinates and computed six relevant AS parameters. Both manual and anterior segment optical coherence tomography-based non-contact measurements served as reference standards for evaluating the model's accuracy. The relationship between postoperative vault and preoperative parameters measured by the model was analyzed using multiple linear regression.
RESULTS: On the test dataset, the model achieved an intraclass correlation coefficient (ICC) exceeding 0.978, with a mean Euclidean distance of 67.65 ± 54.25 μm across all point locations. The ICC values for anterior chamber depth (ACD), pupil diameter, and sulcus-to-sulcus distance were above 0.980 (95% CI: 0.975 to 0.985), with average relative error below 1.7%. Additionally, postoperative vault at one month was significantly correlated with model-measured parameters, including crystalline lens rise, iris concavity, and ACD (P < 0.001).
CONCLUSION: This study introduces a robust program capable of quantitatively measuring AS parameters with accuracy comparable to that of experienced ophthalmologists. The findings provide valuable guidance for ICL sizing and vault prediction.
SETTING: Tianjin Medical University Eye Hospital, Tianjin, China.
DESIGN: Cross-sectional retrospective study.
METHODS: A dataset comprising 1,480 preoperative panoramic UBM images taken from 638 eyes of 320 subjects was collected and was divided into training and testing subsets at a proportion of 7:3. Using the YOLOv8-pose algorithm, the model identified ten anatomical key point coordinates and computed six relevant AS parameters. Both manual and anterior segment optical coherence tomography-based non-contact measurements served as reference standards for evaluating the model's accuracy. The relationship between postoperative vault and preoperative parameters measured by the model was analyzed using multiple linear regression.
RESULTS: On the test dataset, the model achieved an intraclass correlation coefficient (ICC) exceeding 0.978, with a mean Euclidean distance of 67.65 ± 54.25 μm across all point locations. The ICC values for anterior chamber depth (ACD), pupil diameter, and sulcus-to-sulcus distance were above 0.980 (95% CI: 0.975 to 0.985), with average relative error below 1.7%. Additionally, postoperative vault at one month was significantly correlated with model-measured parameters, including crystalline lens rise, iris concavity, and ACD (P < 0.001).
CONCLUSION: This study introduces a robust program capable of quantitatively measuring AS parameters with accuracy comparable to that of experienced ophthalmologists. The findings provide valuable guidance for ICL sizing and vault prediction.