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. 2019 Sep 2;103(11):1553–1560. doi: 10.1136/bjophthalmol-2019-314729

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© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

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Receiver operating characteristic curves and areas under the curve (AUCs) of the deep learning system for referable cataracts regarding disease severity and aetiology. (A) The deep learning system for adult cataract severity evaluation. according to the Emery nuclear grading system in current practice, mild cataract (non-referable) is defined as nuclear I–II, and severe cataract (referable) is defined as nuclear III–V. (B) The deep learning system for the detection of referable cataracts based on different aetiologies and diagnoses. Referable cataracts were defined as significant subcapsular opacification (PCO/ACO) in mild adult cataracts, VAO in paediatric cataracts or VAO) in postoperative eyes. ACO, anterior capsular opacification; PCO, posterior capsular opacification; VAO, visual axis opacification.