{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T06:57:05Z","timestamp":1783407425324,"version":"3.54.6"},"reference-count":35,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2024,1,23]],"date-time":"2024-01-23T00:00:00Z","timestamp":1705968000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Applied Clin Med Phys"],"published-print":{"date-parts":[[2024,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>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\u2010learning methodology to characterize and validate enhancements applied to the grey\u2010level co\u2010occurrence matrix (GLCM) while assessing its validity in comparison to clinical evaluations for evaluating PCO.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>One hundred patients diagnosed with age\u2010related cataracts who were scheduled for phacoemulsification surgery were included in the study. Following mydriasis, anterior segment photographs were captured using a high\u2010resolution 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\u2013Altman plot, while agreement between them was assessed through the Bland\u2013Altman plot.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>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 (<jats:italic>p<\/jats:italic>\u00a0&lt;\u00a00.001). Furthermore, the GLCM+V method demonstrated competitive performance comparable to that of two experienced clinicians (<jats:italic>r<\/jats:italic>\u00a0=\u00a00.825, 0.843) and superior to that of two junior clinicians (<jats:italic>r<\/jats:italic>\u00a0=\u00a00.786, 0.756). Notably, a high level of agreement was observed between predictions and the ground truth, without significant evidence of proportional bias (<jats:italic>p<\/jats:italic>\u00a0&gt;\u00a00.05).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Overall, our findings suggest that a machine\u2010learning 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.<\/jats:p><\/jats:sec>","DOI":"10.1002\/acm2.14268","type":"journal-article","created":{"date-parts":[[2024,1,23]],"date-time":"2024-01-23T14:20:21Z","timestamp":1706019621000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Improvements to a GLCM\u2010based machine\u2010learning approach for quantifying posterior capsule opacification"],"prefix":"10.1002","volume":"25","author":[{"given":"Chang","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Ophthalmology Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology Shanghai Sixth People's Hospital 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