Abstract
Purpose:
To investigate the extent of lens opacity image features measured by anterior segment optical coherence tomography (AS-OCT) and their association with disease severity based on Lens Opacities Classification System III (LOCS III) in eyes with nuclear cataract (NC) and to determine the diagnostic performance of relative features for grading of nuclear lens opacity.
Setting:
Multicenter study at 2 sites.
Design:
Clinical validation.
Methods:
A total of 127 individuals with different severity of NC were recruited from 2 different clinical centers: Thailand (Thai, n = 81) and Shenzhen, China (SZRM, n = 46). All patients underwent AS-OCT examination, images were graded under the LOCS III standard. Automated machine learning models were developed to extract the nuclear region annotation, and feature-based quantifiers were then analyzed and evaluated through classifying cataract severity based on disease severity according to LOCS III.
Results:
AS-OCT pixel-based features such as mean, variance, root mean square, interquartile range, and percentiles significantly correlate with NC grading (
P
Conclusions:
Automated AS-OCT image features have strong consistency in lens opacity grading. Potentials are also shown in supportive diagnosis and surgical planning in NC.