Purpose This study aimed to evaluate the efficacy of a new automated method for the evaluation of histopathological images of pterygium using artificial intelligence.
Methods An in-house software for automated grading of histopathological images was developed. Histopathological images of pterygium (400 images from 40 patients) were analysed using our newly developed software. Manual grading (I–IV), labelled 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-maximisation and k-nearest neighbours. Fifty-five radiomic features extracted from each image were analysed with feature selection methods to examine the significant features. Five classifiers were evaluated for their ability to predict quantitative grading.
Results 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.
Conclusions 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.
Data availability statement
Data are available upon reasonable request. Not applicable.
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Contributors (1) Substantial contributions to the conception (JYH, YJL and SBH) or design (JHK, YJK and KGK) of the work, or the acquisition (JYH, YJL and SBH), analysis (JHK, YJK, JYH, YJL, SBH and KGK) or interpretation (JHK, YJK, SBH and KGK) of data. (2) Drafting the work (JHK and SBH) or revising it critically (JHK, SBH and KGK) for important intellectual content. (3) Final approval of the version published (JHK, YJK, YJL, JYH, SBH and KGK). (4) Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved (JHK, YJK, YJL, JYH, SBH and KGK). (5) Guarantor (SBH and KGK).
Funding This paper was supported by the Kangwon National University Hospital Grant, Basic Science Research Programme through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (Grant No. NRF-2021R1F1A1048448) and the GRRC programme of Gyeonggi province (GRRC-Gachon2020(B01)), AI-based Medical Image Analysis.
Competing interests None declared.
Provenance and peer review Not commissioned; externally peer reviewed.
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