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Classification of optic disc shape in glaucoma using machine learning based on quantified ocular parameters.

Abstract This study aimed to develop a machine learning-based algorithm for objective classification of the optic disc in patients with open-angle glaucoma (OAG), using quantitative parameters obtained from ophthalmic examination instruments.
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PMID- 29261773
OWN - NLM
STAT- In-Process
LR  - 20171224
IS  - 1932-6203 (Electronic)
IS  - 1932-6203 (Linking)
VI  - 12
IP  - 12
DP  - 2017
TI  - Classification of optic disc shape in glaucoma using machine learning based on
      quantified ocular parameters.
PG  - e0190012
LID - 10.1371/journal.pone.0190012 [doi]
AB  - PURPOSE: This study aimed to develop a machine learning-based algorithm for
      objective classification of the optic disc in patients with open-angle glaucoma
      (OAG), using quantitative parameters obtained from ophthalmic examination
      instruments. METHODS: This study enrolled 163 eyes of 105 OAG patients (age: 62.3
      +/- 12.6, mean deviation of Humphrey field analyzer: -8.9 +/- 7.5 dB). The eyes
      were classified into Nicolela's 4 optic disc types by 3 glaucoma specialists.
      Randomly, 114 eyes were selected for training data and 49 for test data. A neural
      network (NN) was trained with the training data and evaluated with the test data.
      We used 91 types of quantitative data, including 7 patient background
      characteristics, 48 quantified OCT (swept-source OCT; DRI OCT Atlantis, Topcon)
      values, including optic disc topography and circumpapillary retinal nerve fiber
      layer thickness (cpRNFLT), and 36 blood flow parameters from laser speckle
      flowgraphy, to build the machine learning classification model. To extract the
      important features among 91 parameters, minimum redundancy maximum relevance and 
      a genetic feature selection were used. RESULTS: The validated accuracy against
      test data for the NN was 87.8% (Cohen's Kappa = 0.83). The important features in 
      the NN were horizontal disc angle, spherical equivalent, cup area, age, 6-sector 
      superotemporal cpRNFLT, average cup depth, average nasal rim disc ratio, maximum 
      cup depth, and superior-quadrant cpRNFLT. CONCLUSION: The proposed machine
      learning system has proved to be good identifiers for different disc types with
      high accuracy. Additionally, the calculated confidence levels reported here
      should be very helpful for OAG care.
FAU - Omodaka, Kazuko
AU  - Omodaka K
AD  - Department of Ophthalmology, Graduate School of Medicine, Tohoku University
      Graduate School of Medicine, Sendai, Japan.
AD  - Department of Ophthalmic Imaging and Information Analytics, Tohoku University
      Graduate School of Medicine, Sendai, Japan.
FAU - An, Guangzhou
AU  - An G
AD  - R&D Division, TOPCON Corporation, Tokyo, Japan.
AD  - Cloud-Based Eye Disease Diagnosis Joint Research Team, RIKEN, Wako, Japan.
FAU - Tsuda, Satoru
AU  - Tsuda S
AD  - Department of Ophthalmology, Graduate School of Medicine, Tohoku University
      Graduate School of Medicine, Sendai, Japan.
FAU - Shiga, Yukihiro
AU  - Shiga Y
AD  - Department of Ophthalmology, Graduate School of Medicine, Tohoku University
      Graduate School of Medicine, Sendai, Japan.
FAU - Takada, Naoko
AU  - Takada N
AD  - Department of Ophthalmology, Graduate School of Medicine, Tohoku University
      Graduate School of Medicine, Sendai, Japan.
FAU - Kikawa, Tsutomu
AU  - Kikawa T
AD  - R&D Division, TOPCON Corporation, Tokyo, Japan.
FAU - Takahashi, Hidetoshi
AU  - Takahashi H
AD  - Division of Ophthalmology, Tohoku Medical and Pharmaceutical University,
      Department of Medicine, Sendai, Japan.
FAU - Yokota, Hideo
AU  - Yokota H
AD  - Cloud-Based Eye Disease Diagnosis Joint Research Team, RIKEN, Wako, Japan.
AD  - Image Processing Research Team, RIKEN, Wako, Japan.
FAU - Akiba, Masahiro
AU  - Akiba M
AD  - R&D Division, TOPCON Corporation, Tokyo, Japan.
AD  - Cloud-Based Eye Disease Diagnosis Joint Research Team, RIKEN, Wako, Japan.
FAU - Nakazawa, Toru
AU  - Nakazawa T
AUID- ORCID: http://orcid.org/0000-0002-5591-4155
AD  - Department of Ophthalmology, Graduate School of Medicine, Tohoku University
      Graduate School of Medicine, Sendai, Japan.
AD  - Department of Ophthalmic Imaging and Information Analytics, Tohoku University
      Graduate School of Medicine, Sendai, Japan.
AD  - Image Processing Research Team, RIKEN, Wako, Japan.
AD  - Department of Retinal Disease Control, Ophthalmology, Tohoku University Graduate 
      School of Medicine, Sendai, Japan.
AD  - Department of Advanced Ophthalmic Medicine, Tohoku University Graduate School of 
      Medicine, Sendai, Japan.
LA  - eng
PT  - Journal Article
DEP - 20171219
PL  - United States
TA  - PLoS One
JT  - PloS one
JID - 101285081
PMC - PMC5736185
EDAT- 2017/12/21 06:00
MHDA- 2017/12/21 06:00
CRDT- 2017/12/21 06:00
PHST- 2017/09/14 00:00 [received]
PHST- 2017/12/06 00:00 [accepted]
PHST- 2017/12/21 06:00 [entrez]
PHST- 2017/12/21 06:00 [pubmed]
PHST- 2017/12/21 06:00 [medline]
AID - 10.1371/journal.pone.0190012 [doi]
AID - PONE-D-17-33534 [pii]
PST - epublish
SO  - PLoS One. 2017 Dec 19;12(12):e0190012. doi: 10.1371/journal.pone.0190012.
      eCollection 2017.