PMID- 32276189
OWN - NLM
STAT- MEDLINE
DCOM- 20210414
LR  - 20210414
IS  - 1872-7565 (Electronic)
IS  - 0169-2607 (Linking)
VI  - 192
DP  - 2020 Aug
TI  - Development of an automated system for the detection of genotype in polypoidal 
      choroidal vasculopathy using retinal image phenotype.
PG  - 105460
LID - S0169-2607(19)32450-2 [pii]
LID - 10.1016/j.cmpb.2020.105460 [doi]
AB  - BACKGROUND AND OBJECTIVES: Polypoidal choroidal vasculopathy (PCV) is a retinal 
      disorder characterized by the presence of aneurismal polypoidal lesions in the 
      choroidal vasculature. A single nucleotide polymorphism (SNP) is a common genetic 
      variant which may be associated with the disease. This study is to investigate 
      the association of HERPUD1 (rs2217332) gene with PCV in the Indian population and 
      develop an automated system for genotype and phenotype correlation using fundus 
      images and machine learning methods. METHODS: A cohort of 54 PCV patients and 120 
      control subjects were recruited for the study. Genotyping of SNP (HERPUD1, 
      rs2217332) was performed by following polymerase chain reaction and direct 
      sequencing method. Statistical association of SNP to PCV was determined using 
      chi-square analysis. The acquired GG and AG images were preprocessed using an 
      adaptive histogram. 19 and 18 texture features were extracted from the images in 
      the PCV naive cases and PCV patients on treatment, respectively. Student's 
      independent t-test was then employed for the selection of significant features, 
      which were input to the ensemble tree for automated classification. Leave-one-out 
      validation was used to evaluate the system. RESULTS: HERPUD1 rs2217332 SNP is 
      significantly associated in PCV patients compared to control (P = 0.0296, odds 
      ratio [OD] = 2.297, 95% confidence interval [CI] = 1.087-4.856) in the Indian 
      population. High F1 and precision values of 85.71%, 86.84% and 85.71%, 93.75% 
      were achieved in the pre and post- treatment phases, respectively. CONCLUSION: 
      Our results suggest that the HERPUD1 polymorphism is associated in PCV patients. 
      Based on our analysis, it may be possible to predict the genotype and disease 
      status of PCV patients using fundus images in assistance with a machine learning 
      algorithm.
CI  - Copyright (c) 2020 Elsevier B.V. All rights reserved.
FAU - Alagappan, Lakshmi Priyankka
AU  - Alagappan LP
AD  - SN ONGC Department of Genetics and Molecular Biology, Vision Research Foundation, 
      Sankara Nethralaya campus, Chennai-600006, India; School of Chemical and 
      Biotechnology, SASTRA University, Tanjore-613401, India.
FAU - Koh, Joel En Wei
AU  - Koh JEW
AD  - School of Engineering, Ngee Ann Polytechnic, Singapore.
FAU - V, Jahmunah
AU  - V J
AD  - School of Engineering, Ngee Ann Polytechnic, Singapore.
FAU - Ramesh, Adhithi
AU  - Ramesh A
AD  - SN ONGC Department of Genetics and Molecular Biology, Vision Research Foundation, 
      Sankara Nethralaya campus, Chennai-600006, India.
FAU - Bhende, Muna
AU  - Bhende M
AD  - Shri Bhagwan Mahavir Department of Vitreo Retinal Services, Medical Research 
      Foundation, Sankara Nethralaya, Chennai-600006, India.
FAU - Raman, Rajiv
AU  - Raman R
AD  - Shri Bhagwan Mahavir Department of Vitreo Retinal Services, Medical Research 
      Foundation, Sankara Nethralaya, Chennai-600006, India.
FAU - Acharya, U Rajendra
AU  - Acharya UR
AD  - School of Engineering, Ngee Ann Polytechnic, Singapore; Department of Biomedical 
      Engineering, School of Science and Technology, Singapore University of Social 
      Sciences, Singapore; Department of Bioinformatics and Medical Engineering, Asia 
      University, Taiwan. Electronic address: aru@np.edu.sg.
FAU - Mathavan, Sinnakaruppan
AU  - Mathavan S
AD  - SN ONGC Department of Genetics and Molecular Biology, Vision Research Foundation, 
      Sankara Nethralaya campus, Chennai-600006, India.
LA  - eng
PT  - Comparative Study
PT  - Journal Article
DEP - 20200319
PL  - Ireland
TA  - Comput Methods Programs Biomed
JT  - Computer methods and programs in biomedicine
JID - 8506513
SB  - IM
MH  - Choroid Diseases/*diagnosis/*genetics
MH  - Cohort Studies
MH  - *Diagnosis, Computer-Assisted
MH  - *Genotype
MH  - Humans
MH  - *Phenotype
MH  - Retina/*physiopathology
MH  - Vascular Diseases/*diagnosis/*genetics
OTO - NOTNLM
OT  - Adaptive synthetic sampling
OT  - Ensemble classifier
OT  - Fundus images
OT  - Leave-one-out validation
OT  - Machine learning technique
OT  - Polypoidal choroidal vasculopathy
OT  - Single nucleotide polymorphism
OT  - Texture features
OT  - t-test
EDAT- 2020/04/11 06:00
MHDA- 2021/04/15 06:00
CRDT- 2020/04/11 06:00
PHST- 2019/12/27 00:00 [received]
PHST- 2020/02/06 00:00 [revised]
PHST- 2020/03/17 00:00 [accepted]
PHST- 2020/04/11 06:00 [pubmed]
PHST- 2021/04/15 06:00 [medline]
PHST- 2020/04/11 06:00 [entrez]
AID - S0169-2607(19)32450-2 [pii]
AID - 10.1016/j.cmpb.2020.105460 [doi]
PST - ppublish
SO  - Comput Methods Programs Biomed. 2020 Aug;192:105460. doi: 
      10.1016/j.cmpb.2020.105460. Epub 2020 Mar 19.