Automated Histopathological Evaluation Of Pterygium Using Artificial Intelligence
Published 2023 - 41st Congress of the ESCRS
Reference: PP02.04 | DOI: 10.82333/rm9p-y617
Authors: Sang Beom Han* 1 , HEE KYUNG YANG 2
1Kangwon National University Hospital,Chuncheon,Korea, Republic Of, 2Seoul National University Bundang Hospital,Seongnam,Korea, Republic Of
This study aimed to evaluate the efficacy of a new automated method for the evaluation of histopathological images of
pterygium using artificial intelligence.
An in-house software for automated grading of histopathological images was developed and tested.
Histopathological images of pterygium (400 images from 40 patients) were analyzed using our newly developed software. Manual grading (I–IV), labeled based on an established scoring system, served as the ground truth for training the four-grade classification models. Region of interest segmentation was performed before the classification of grades, which was achieved by the combination of expectation-maximization and k-nearest neighbors. Fifty-five radiomic features extracted from each image were screened via forwarding feature selection to select only the significant features. Five classifiers were evaluated for their ability to predict quantitative grading.
Among the classifier models applied for automated grading in this study, the bagging tree showed the best performance, with a 75.9% true
positive rate (TPR) and 75.8% positive predictive value (PPV) in internal validation. In external validation, the method also demonstrated reproducibility,
with an 81.3% TPR and 82.0% PPV for the average of four classification grades.
Our newly developed automated method for quantitative grading of histopathological images of pterygium may be a reliable method for
quantitative analysis of histopathological evaluation of pterygium.