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Purpose <p>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.</p> Setting <p>Tianjin Medical University Eye Hospital, Tianjin, China.</p> Design <p>Cross-sectional retrospective study.</p> Methods <p>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.</p> Results <p>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&#xa0;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 (<i>P</i> &lt; 0.001).</p> Conclusion <p>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.</p>

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Automatic measurement and evaluation of anterior segment anatomical structures via UBM images using a deep learning-based approach

  • 期刊论文

  • 发表日期：

    2026年1月8日

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  • Sijing Chen,
  • Xiaoran Chu,
  • Emmanuel Eric Pazo,
  • Yue Huang,
  • Hui Liu,
  • Chen Zhang,
  • Ruibo Yang,
  • Shaozhen Zhao

摘要

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.