ShenZhen People’s Hospital

This two-center clinical validation study recruited 127 individuals with different severities of nuclear cataract from Thailand (n = 81) and Shenzhen, China (n = 46), imaged them with AS-OCT and graded images under the LOCS III standard, developed automated machine learning models to extract nuclear region annotation and analyzed feature-based quantifiers, finding that pixel-based features such as mean, variance, root mean square, interquartile range, and percentiles significantly correlate with NC grading (P < .01), that variance, standard deviation, and median showed high consistency while kurtosis and skewness were negatively correlated, and that the prediction model achieved 0.81 accuracy at the SZRM center (F1 0.82) and 0.87 at the Thai center (F1 0.

This two-center clinical validation study recruited 127 individuals with different severities of nuclear cataract from Thailand (n = 81) and Shenzhen, China (n = 46), imaged them with AS-OCT and graded images under the LOCS III standard, developed automated machine learning models to extract nuclear region annotation and analyzed feature-based quantifiers, finding that pixel-based features such as mean, variance, root mean square, interquartile range, and percentiles significantly correlate with NC grading (P < .01), that variance, standard deviation, and median showed high consistency while kurtosis and skewness were negatively correlated, and that the prediction model achieved 0.81 accuracy at the SZRM center (F1 0.82) and 0.87 at the Thai center (F1 0.