Improvements to a GLCM‐based machine‐learning approach for quantifying posterior capsule opacification

• Published in: Journal of applied clinical medical physics Vol. 25; no. 2; pp. e14268 - n/a
• Main Authors: Liu, Chang, Hu, Ying, Chen, Yan, Fang, Jian, Liu, Ruhan, Bi, Lei, Tan, Xunan, Sheng, Bin, Wu, Qiang
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.02.2024; John Wiley and Sons Inc
• Subjects: Algorithms; cataract surgery; Cataracts; Edema; Eye surgery; grey level co‐occurrence matrix; Intraocular lenses; machine learning; Medical Imaging; Methods; posterior capsular opacification quantification; machine learning; cataract surgery; grey level co-occurrence matrix; posterior capsular opacification quantification
• ISSN: 1526-9914; 1526-9914
• DOI: 10.1002/acm2.14268

Background Posterior capsular opacification (PCO) is a common complication following cataract surgery that leads to visual disturbances and decreased quality of vision. The aim of our study was to employ a machine‐learning methodology to characterize and validate enhancements applied to the grey‐level co‐occurrence matrix (GLCM) while assessing its validity in comparison to clinical evaluations for evaluating PCO. Methods One hundred patients diagnosed with age‐related cataracts who were scheduled for phacoemulsification surgery were included in the study. Following mydriasis, anterior segment photographs were captured using a high‐resolution photographic system. The GLCM was utilized as the feature extractor, and a supported vector machine as the regressor. Three variations, namely, GLCM, GLCM+C (+axial information), and GLCM+V (+regional voting), were analyzed. The reference value for regression was determined by averaging clinical scores obtained through subjective analysis. The relationships between the predicted PCO outcome scores and the ground truth were assessed using Pearson correlation analysis and a Bland–Altman plot, while agreement between them was assessed through the Bland–Altman plot. Results Relative to the ground truth, the GLCM, GLCM+C, and GLCM+V methods exhibited correlation coefficients of 0.706, 0.768, and 0.829, respectively. The relationship between the PCO score predicted by the GLCM+V method and the ground truth was statistically significant (p < 0.001). Furthermore, the GLCM+V method demonstrated competitive performance comparable to that of two experienced clinicians (r = 0.825, 0.843) and superior to that of two junior clinicians (r = 0.786, 0.756). Notably, a high level of agreement was observed between predictions and the ground truth, without significant evidence of proportional bias (p > 0.05). Conclusions Overall, our findings suggest that a machine‐learning approach incorporating the GLCM, specifically the GLCM+V method, holds promise as an objective and reliable tool for assessing PCO progression. Further studies in larger patient cohorts are warranted to validate these findings and explore their potential clinical applications.