Combining macula clinical signs and patient characteristics for age-related macular degeneration diagnosis: a machine learning approach.

ABSTRACT:

Background

To investigate machine learning methods, ranging from simpler interpretable techniques to complex (non-linear) "black-box" approaches, for automated diagnosis of Age-related Macular Degeneration (AMD).

Methods

Data from healthy subjects and patients diagnosed with AMD or other retinal diseases were collected during routine visits via an Electronic Health Record (EHR) system. Patients' attributes included demographics and, for each eye, presence/absence of major AMD-related clinical signs (soft drusen, retinal pigment epitelium, defects/pigment mottling, depigmentation area, subretinal haemorrhage, subretinal fluid, macula thickness, macular scar, subretinal fibrosis). Interpretable techniques known as white box methods including logistic regression and decision tr

SUBMITTER: Fraccaro P 

PROVIDER: S-EPMC4417241 | biostudies-literature | 2015 Jan

REPOSITORIES: biostudies-literature