Multicenter analysis of nuclear cataract population parameters and their clinical correlations based on AS-OCT

To investigate the extent of lens opacity image features measured by 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.

Multicenter study at 2 sites.

Clinical validation.

A total of 127 individuals with different severity of nuclear cataract were recruited from two 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, feature-based quantifiers were then analyzed and evaluated through classifying cataract severity based on disease severity according to LOCS III.

AS-OCT pixel based features such as mean, variance, Root Mean Square (RMS), interquartile range, and percentiles significantly correlate with nuclear cataract grading (p < 0.01). Features such as variance, standard deviation, and median showed high consistency, while kurtosis and skewness were negatively correlated. Prediction model achieved 0.81 accuracy on SZRM center (F1-score 0.82), and 0.87 accuracy on Thai center (F1-score 0.83).

Automated AS-OCT image features has strong consistency in lens opacity grading. Potentials are also shown in supportive diagnosis and surgical planning in nuclear cataract.