Deep-learning estimation of choroidal thickness from color fundus photographs: a validation study

被引：0
|
作者：
Takahashi, Hidenori [1 ]
Arai, Yusuke [1 ]
Yamashita, Takehiro [2 ]
Hasegawa, Tetsuya [3 ]
Ohgami, Tomohiro [4 ]
Sonoda, Shozo [2 ]
Tanaka, Yoshiaki [3 ]
Tampo, Hironobu [1 ]
Inoda, Satoru [1 ]
Sakamoto, Shinichi [1 ]
Kakehashi, Akihiro [3 ]
Kawashima, Hidetoshi [1 ]
Yanagi, Yasuo [5 ,6 ]
机构：
[1] Jichi Med Univ, Ophthalmol, Shimotsuke, Tochigi, Japan
[2] Kagoshima Univ, Kagoshima, Japan
[3] Saitama Med Ctr, Saitama, Japan
[4] Ibaraki Seinan Med Ctr, Sashima, Japan
[5] Singapore Eye Res Inst, Singapore, Singapore
[6] Singapore Natl Eye Ctr, Singapore, Singapore
来源：
INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE | 2018年 / 59卷 / 09期
关键词：
D O I：
暂无
中图分类号：
R77 [眼科学];
学科分类号：
100212 ;
摘要：
1730
引用
收藏
页数：3
相关论文
共 50 条
  • [1] Deep-learning estimation of choroidal thickness from color fundus photographs
    Tampo, Hironobu
    Takahashi, Hidenori
    Yanagi, Yasuo
    Sakamoto, Shin-ichi
    Inoda, Satoru
    Kawashima, Hidetoshi
    Inoue, Yuji
    Arai, Yusuke
    Takahashi, Ryota
    Soeta, Megumi
    INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE, 2017, 58 (08)
  • [2] Deep Learning-Based Estimation of Axial Length and Subfoveal Choroidal Thickness From Color Fundus Photographs
    Dong, Li
    Hu, Xin Yue
    Yan, Yan Ni
    Zhang, Qi
    Zhou, Nan
    Shao, Lei
    Wang, Ya Xing
    Xu, Jie
    Lan, Yin Jun
    Li, Yang
    Xiong, Jian Hao
    Liu, Cong Xin
    Ge, Zong Yuan
    Jonas, Jost B.
    Bin Wei, Wen
    FRONTIERS IN CELL AND DEVELOPMENTAL BIOLOGY, 2021, 9
  • [3] Predicting central choroidal thickness from colour fundus photographs using deep learning
    Arai, Yusuke
    Takahashi, Hidenori
    Takayama, Takuya
    Yousefi, Siamak
    Tampo, Hironobu
    Yamashita, Takehiro
    Hasegawa, Tetsuya
    Ohgami, Tomohiro
    Sonoda, Shozo
    Tanaka, Yoshiaki
    Inoda, Satoru
    Sakamoto, Shinichi
    Kawashima, Hidetoshi
    Yanagi, Yasuo
    PLOS ONE, 2024, 19 (03): : e0301467
  • [4] Choroidal thickness estimation from colour fundus photographs by adaptive binarisation and deep learning, according to central serous chorioretinopathy status
    Komuku, Yuki
    Ide, Atsuya
    Fukuyama, Hisashi
    Masumoto, Hiroki
    Tabuchi, Hitoshi
    Okadome, Takeshi
    Gomi, Fumi
    SCIENTIFIC REPORTS, 2020, 10 (01)
  • [5] Choroidal thickness estimation from colour fundus photographs by adaptive binarisation and deep learning, according to central serous chorioretinopathy status
    Yuki Komuku
    Atsuya Ide
    Hisashi Fukuyama
    Hiroki Masumoto
    Hitoshi Tabuchi
    Takeshi Okadome
    Fumi Gomi
    Scientific Reports, 10
  • [6] Development and Validation of a Deep Learning Algorithm for Differentiation of Choroidal Nevi from Small Melanoma in Fundus Photographs
    Sabazade, Shiva
    Michalski, Marco A. Lumia
    Bartoszek, Jakub
    Fili, Maria
    Holmstrom, Mats
    Stalhammar, Gustav
    OPHTHALMOLOGY SCIENCE, 2025, 5 (01):
  • [7] Central choroidal thickness prediction with color tone and phase analysis on fundus photographs
    Azuma, Keiko
    Yanagi, Yasuo
    INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE, 2015, 56 (07)
  • [8] Detecting Cataract from Color Fundus Photographs Using Explainable Deep Learning
    Elsawy, Amr
    Keenan, Tiarnan D. L.
    Chen, Qingyu
    Thavikulwat, Alisa T.
    Bhandari, Sanjeeb
    Chew, Emily Y.
    Lu, Zhiyong
    INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE, 2022, 63 (07)
  • [9] Finding Glaucoma in Color Fundus Photographs Using Deep Learning
    Bojikian, Karine D.
    Lee, Cecilia S.
    Lee, Aaron Y.
    JAMA OPHTHALMOLOGY, 2019, 137 (12) : 1361 - 1362
  • [10] Development and validation of a deep-learning algorithm for the detection of neovascular age-related macular degeneration from colour fundus photographs
    Keel, Stuart
    Li, Zhixi
    Scheetz, Jane
    Robman, Liubov
    Phung, James
    Makeyeva, Galina
    Aung, KhinZaw
    Liu, Chi
    Yan, Xixi
    Meng, Wei
    Guymer, Robyn
    Chang, Robert
    He, Mingguang
    CLINICAL AND EXPERIMENTAL OPHTHALMOLOGY, 2019, 47 (08): : 1009 - 1018
12345