Detection of shallow anterior chamber depth from two-dimensional anterior segment photographs using deep learning

نویسندگان

چکیده

Abstract Background The purpose of this study was to implement and evaluate a deep learning (DL) approach for automatically detecting shallow anterior chamber depth (ACD) from two-dimensional (2D) overview segment photographs. Methods We trained DL model using dataset photographs collected Shanghai Aier Eye Hospital June 2018 December 2019. A Pentacam HR system used capture 2D eye image measure the ACD. Shallow ACD defined as less than 2.4 mm. evaluated by five-fold cross-validation test in hold-out testing dataset. also it against two glaucoma specialists. performance calculated metrics, including accuracy, sensitivity, specificity, area under receiver operating characteristic curve (AUC). Results total 3753 (1720 AC 2033 images) were assigned training dataset, 1302 (509 793 held out internal datasets. In achieved an AUC 0.86 (95% CI, 0.83–0.90) with 80% sensitivity 79% specificity. same better specialists (accuracy vs. accuracy 74 69%). Conclusions proposed high-performing detect Our has potential applications monitoring real world. Trial registration http://clinicaltrials.gov , NCT04340635 retrospectively registered on 29 March 2020.

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ژورنال

عنوان ژورنال: BMC Ophthalmology

سال: 2021

ISSN: ['1471-2415']

DOI: https://doi.org/10.1186/s12886-021-02104-0