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Correction

Correction: From 2 dimensions to 3rd dimension: Quantitative prediction of anterior chamber depth from anterior segment photographs via deep-learning

  • Zhi Da Soh,
  • Yixing Jiang,
  • Sakthi Selvam S/O Ganesan,
  • Menghan Zhou,
  • Monisha Nongiur,
  • Shivani Majithia,
  • Chung Tham,
  • Tyler Hyungtaek Rim,
  • Chaoxu Qian,
  • Victor Koh,
  • Tin Aung,
  • Tien Yin Wong,
  • Xinxing Xu,
  • Yong Liu,
  • Ching-Yu Cheng
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There is an error in Supplementary Table 2 and the main text. The work of this article included 380 eyes from 380 participants in the testing dataset. In error, these numbers were presented as 257 from the Singapore Chinese Eye Study (SCES) and 163 from the Singapore Indian Eye Study (line 280–281). (SINDI). However, the correct number is comprised of 229 eyes from (SCES) and 151 eyes from SINDI.

Please find the corrected table below;

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Supplementary Table 2.. Demographic and ocular characteristics of participants in SEED and the ISF study.

https://doi.org/10.1371/journal.pdig.0000356.t001

Reference

  1. 1. Soh ZD, Jiang Y, Ganesan SS, Zhou M, Nongiur M, Majithia S, et al. (2023) From 2 dimensions to 3rd dimension: Quantitative prediction of anterior chamber depth from anterior segment photographs via deep-learning. PLOS Digit Health 2(2): e0000193. https://doi.org/10.1371/journal.pdig.0000193 pmid:36812642

Citation: Soh ZD, Jiang Y, Ganesan SSS, Zhou M, Nongiur M, Majithia S, et al. (2023) Correction: From 2 dimensions to 3rd dimension: Quantitative prediction of anterior chamber depth from anterior segment photographs via deep-learning. PLOS Digit Health 2(9): e0000356. https://doi.org/10.1371/journal.pdig.0000356

Published: September 7, 2023

Copyright: © 2023 Soh et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.