Zhou Ziying, Shi Shengqiang, Tang Xiajing, Xu Zhaoyang, Ye Juan, Huang Xingru, Lou Lixia
Eye Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, China.
Hangzhou Dianzi University, Hangzhou, China.
Adv Ophthalmol Pract Res. 2024 Jun 25;4(4):202-208. doi: 10.1016/j.aopr.2024.06.004. eCollection 2024 Nov-Dec.
This study aimed to propose a novel deep learning-based approach to assess the extent of abduction in patients with abducens nerve palsy before and after strabismus surgery.
This study included 13 patients who were diagnosed with abducens nerve palsy and underwent strabismus surgery in a tertiary hospital. Photographs of primary, dextroversion and levoversion position were collected before and after strabismus surgery. The eye location and eye segmentation network were trained via recurrent residual convolutional neural networks with attention gate connection based on U-Net (R2AU-Net). Facial images of abducens nerve palsy patients were used as the test set and parameters were measured automatically based on the masked images. Absolute abduction also was measured manually, and relative abduction was calculated. Agreements between manual and automatic measurements, as well as repeated automatic measurements were analyzed. Preoperative and postoperative results were compared.
The intraclass correlation coefficients (ICCs) between manual and automatic measurements of absolute abduction ranged from 0.985 to 0.992 (＜0.001), and the bias ranged from -0.25 mm to -0.05 mm. The ICCs between two repeated automatic measurements ranged from 0.994 to 0.997 (＜0.001), and the bias ranged from -0.11 mm to 0.05 mm. After strabismus surgery, absolute abduction of affected eye increased from 2.18 ± 1.40 mm to 3.36 ± 1.93 mm (＜0.05). The relative abduction was improved in 76.9% patients (10/13) after surgery (＜0.01).
This image analysis technique demonstrated excellent accuracy and repeatability for automatic measurements of ocular abduction, which has promising application prospects in objectively assessing surgical outcomes in patients with abducens nerve palsy.
本研究旨在提出一种基于深度学习的新方法，用于评估外展神经麻痹患者斜视手术前后的外展程度。
本研究纳入了13例在三级医院被诊断为外展神经麻痹并接受斜视手术的患者。在斜视手术前后收集了第一眼位、右眼外转位和左眼外转位的照片。通过基于U-Net的带有注意力门控连接的循环残差卷积神经网络（R2AU-Net）训练眼睛定位和眼睛分割网络。将外展神经麻痹患者的面部图像用作测试集，并基于掩码图像自动测量参数。还手动测量绝对外展，并计算相对外展。分析了手动测量与自动测量之间以及重复自动测量之间的一致性。比较了术前和术后结果。
绝对外展手动测量与自动测量之间的组内相关系数（ICC）范围为0.985至0.992（＜0.001），偏差范围为-0.25毫米至-0.05毫米。两次重复自动测量之间的ICC范围为0.994至0.997（＜0.001），偏差范围为-0.11毫米至0.05毫米。斜视手术后，患眼的绝对外展从2.18±1.40毫米增加到3.36±1.93毫米（＜0.05）。术后76.9%的患者（10/13）相对外展得到改善（＜0.01）。
这种图像分析技术在自动测量眼球外展方面显示出优异的准确性和可重复性，在客观评估外展神经麻痹患者的手术效果方面具有广阔的应用前景。
