Development of an automated system for the detection of genotype in polypoidal choroidal vasculopathy using retinal image phenotype. (August 2020) - Alagappan, Lakshmi Priyankka; Koh, Joel En Wei; V, Jahmunah; Ramesh, Adhithi; Bhende, Muna; Raman, Rajiv; Acharya, U. Rajendra; Mathavan, Sinnakaruppan
Highlights: PCV is a retinal disorder characterized by the presence of aneurismal polypoidal lesions in the choroidal vasculature. Single nucleotide polymorphism (SNP) is a common genetic variant associated with the disease. This study investigated the association of the HERPUD1 (rs2217332) gene with PCV in the Indian population An automated system for genotype and phenotype correlation using fundus images and machine learning methods is proposed Textural features coupled with SVM classifier is used 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 Indian population. High F1 and precision values of 85.71%, 86.84% and 85.71%, 93.75% and were achieved in the pre and post-treatment phases respectively. Based on our analysis it may be possible to predict the genotype and disease status of PCV patients using fundus images and machine learning algorithm. Abstract: 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 naïve 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. Graphical abstract: Image, graphical abstract