A Survey of Machine Learning Approaches for Age Related Macular Degeneration Diagnosis and Prediction

Age Related Macular Degeneration (AMD) is a complex disease caused by the interaction of multiple genes and environmental factors. AMD is the leading cause of visual dysfunction and blindness in developed countries, and a rising cause in underdeveloped countries. Currently, retinal images are studied in order to identify drusen in the retina. The classification of these images allows to support the medical diagnosis. Likewise, genetic variants and risk factors are studied in order to make predictive studies of the disease, which are carried out with the support of statistical tools and, recently, with Machine Learning (ML) methods. In this paper, we present a survey of studies performed in complex diseases under both approaches, especially for the case of AMD. We emphasize the approach based on the genetic variants of individuals, as it is a support tool for the prevention of AMD. According to the vision of personalized medicine, disease prevention is a priority to improve the quality of life of people and their families, as well as to avoid the inherent health burden.

[1]  Abhijit Dasgupta,et al.  Brief review of regression‐based and machine learning methods in genetic epidemiology: the Genetic Analysis Workshop 17 experience , 2011, Genetic epidemiology.

[2]  R. T. Smith,et al.  Variation in factor B (BF) and complement component 2 (C2) genes is associated with age-related macular degeneration , 2006, Nature Genetics.

[3]  Kevin Noronha,et al.  Novel risk index for the identification of age-related macular degeneration using radon transform and DWT features , 2016, Comput. Biol. Medicine.

[4]  Kevin Noronha,et al.  Decision support system for age-related macular degeneration using discrete wavelet transform , 2014, Medical & Biological Engineering & Computing.

[5]  Rui Jiang,et al.  A random forest approach to the detection of epistatic interactions in case-control studies , 2009, BMC Bioinformatics.

[6]  David G. Stork,et al.  Pattern Classification (2nd ed.) , 1999 .

[7]  J. Keeffe,et al.  Modeling the risk of age-related macular degeneration and its predictive comparisons in a population in South India , 2015 .

[8]  Frans Coenen,et al.  Data mining techniques for the screening of age-related macular degeneration , 2012, Knowl. Based Syst..

[9]  V. Sheffield,et al.  Recommendations for genetic testing of inherited eye diseases: report of the American Academy of Ophthalmology task force on genetic testing. , 2012, Ophthalmology.

[10]  R. Priya,et al.  Automated diagnosis of age-related macular degeneration using machine learning techniques , 2014, Int. J. Comput. Appl. Technol..

[11]  Tien Yin Wong,et al.  Early age-related macular degeneration detection by focal biologically inspired feature , 2012, 2012 19th IEEE International Conference on Image Processing.

[12]  Huseyin Seker,et al.  Discovery of the connection among age-related macular degeneration, MTHFR C677T and PAI 1 4G/5G gene polymorphisms, and body mass index by means of Bayesian inference methods , 2013 .

[13]  Concha Bielza,et al.  Machine Learning in Bioinformatics , 2008, Encyclopedia of Database Systems.

[14]  Jiang Liu,et al.  Growcut-based drusen segmentation for age-related macular degeneration detection , 2014, 2014 IEEE Visual Communications and Image Processing Conference.

[15]  Margaret A. Pericak-Vance,et al.  Using Genetic Variation and Environmental Risk Factor Data to Identify Individuals at High Risk for Age-Related Macular Degeneration , 2011, PloS one.

[16]  Rajiv Raman,et al.  A 32 kb Critical Region Excluding Y402H in CFH Mediates Risk for Age-Related Macular Degeneration , 2011, PloS one.

[17]  Bart De Moor,et al.  Candidate gene prioritization by network analysis of differential expression using machine learning approaches , 2010, BMC Bioinformatics.

[18]  U. Rajendra Acharya,et al.  Automated screening tool for dry and wet age-related macular degeneration (ARMD) using pyramid of histogram of oriented gradients (PHOG) and nonlinear features , 2017, J. Comput. Sci..

[19]  G. Abecasis,et al.  Genetic susceptibility to age-related macular degeneration: a paradigm for dissecting complex disease traits. , 2007, Human molecular genetics.

[20]  Damon Wing Kee Wong,et al.  Effective Drusen Localization for Early AMD Screening using Sparse Multiple Instance Learning , 2015 .

[21]  Chia-Ling Tsai,et al.  Automatic characterization of classic choroidal neovascularization by using AdaBoost for supervised learning. , 2011, Investigative ophthalmology & visual science.

[22]  Eleazar Eskin,et al.  Postassociation cleaning using linkage disequilibrium information , 2011, Genetic epidemiology.

[23]  F. Collins,et al.  Potential etiologic and functional implications of genome-wide association loci for human diseases and traits , 2009, Proceedings of the National Academy of Sciences.

[24]  S. R. Krishnadas,et al.  Clinical and genetic characterization of a large primary open angle glaucoma pedigree , 2017, Ophthalmic genetics.

[25]  Miguel Caixinha,et al.  Machine Learning Techniques in Clinical Vision Sciences , 2017, Current eye research.

[26]  Mauro Giacomini,et al.  Combining macula clinical signs and patient characteristics for age-related macular degeneration diagnosis: a machine learning approach , 2015, BMC Ophthalmology.

[27]  V. Moreno,et al.  Análisis estadístico de polimorfismos genéticos en estudios epidemiológicos , 2005 .

[28]  Heping Zhang,et al.  A forest-based approach to identifying gene and gene–gene interactions , 2007, Proceedings of the National Academy of Sciences.

[29]  S.S. Parvathi,et al.  Automatic Drusen Detection from Colour Retinal Images , 2007, International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007).

[30]  R. Klein,et al.  Global prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: a systematic review and meta-analysis. , 2014, The Lancet. Global health.