Work

Automated measurement of horizontal strabismus in children’s primary gaze photographs using deep learning and computer vision

Year: 2026

Type: article

Abstract: PurposeTo develop and validate algorithms that automatically measure horizontal ocular alignment in children’s primary gaze photographs using deep learning and computer vision.Materials and MethodsWe proposed two-stage artificial intelligence (AI) systems, including deep learning (DL) algorithms for automatic segmentation of eye structure and computer vision (CV) algorithms to measure horizontal ocular alignment. DL algorithms were trained using a public ocular images dataset. The measurements of CV algorithms were tested in primary gaze photographs recruited from a tertiary hospital.ResultsThe DL training dataset involved 11,018 ocular and corresponding ground truth images. We then used 147 primary gaze photographs to validate the CV algorithms. The strabismus angles measured by the AI systems closely followed the Hirschberg test (HT) using linear regression analysis (slope = 0.919, p < 0.001). There has an excellent agreement between the two methods using intra-class correlation coefficients (ICC = 0.98). Compared to the angle measured by HT, the proposed algorithms achieved a limits of agreement (LoA) of -7.1 to 6.3 prism diopters (PD) and coefficients of variation (CoV) of 13.5%.ConclusionIn conclusion, we proposed an automated DL and CV system to measure horizontal ocular alignment using gaze photographs. Our system produced predictions similar to the measurement by Hirschberg test. In addition, the two-stage AI systems provided an explanation of the rationale for clinical decisions.

Source: Photodiagnosis and Photodynamic Therapy

DOI: 10.1016/j.pdpdt.2026.105438