Automated photographic analysis of inferior oblique overaction based on deep learning.

Background: Inferior oblique overaction (IOOA) is a common ocular motility disorder. This study aimed to propose a novel deep learning-based approach to automatically evaluate the amount of IOOA.

Methods: This prospective study included 106 eyes of 72 consecutive patients attending the strabismus clinic in a tertiary referral hospital. Patients were eligible for inclusion if they were diagnosed with IOOA. IOOA was clinically graded from +1 to +4. Based on photograph in the adducted position, the height difference between the inferior corneal limbus of both eyes was manually measured using ImageJ and automatically measured by our deep learning-based image analysis system with human supervision. Correlation coefficients, Bland-Altman plots and mean absolute deviation (MAD) were analyzed between two different measurements of evaluating IOOA.

Results: There were significant correlations between automated photographic measurements and clinical gradings (Kendall's tau: 0.721; 95% confidence interval: 0.652 to 0.779; P<0.001), between automated and manual photographic measurements [intraclass correlation coefficients (ICCs): 0.975; 95% confidence interval: 0.963 to 0.983; P<0.001], and between two-repeated automated photographic measurements (ICCs: 0.998; 95% confidence interval: 0.997 to 0.999; P<0.001). The biases and MADs were 0.10 [95% limits of agreement (LoA): -0.45 to 0.64] mm and 0.26±0.14 mm between automated and manual photographic measurements, and 0.01 (95% LoA: -0.14 to 0.16) mm and 0.07±0.04 mm between two-repeated automated photographic measurements, respectively.

Conclusions: The automated photographic measurements of IOOA using deep learning technique were in excellent agreement with manual photographic measurements and clinical gradings. This new approach allows objective, accurate and repeatable measurement of IOOA and could be easily implemented in clinical practice using only photographs.