A deep learning-based image analysis for assessing the extent of abduction in abducens nerve palsy patients before and after strabismus surgery.

Purpose: 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.

Methods: 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.

Results: 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).

Conclusions: 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.