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

Abstract

BackgroundTo 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).MethodsData 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 trees as well as less interpreitable techniques known as black box methods, such as support vector machines (SVM), random forests and AdaBoost, were used to develop models (trained and validated on unseen data) to diagnose AMD. The gold standard was confirmed diagnosis of AMD by physicians. Sensitivity, specificity and area under the receiver operating characteristic (AUC) were used to assess performance.ResultsStudy population included 487 patients (912 eyes). In terms of AUC, random forests, logistic regression and adaboost showed a mean performance of (0.92), followed by SVM and decision trees (0.90). All machine learning models identified soft drusen and age as the most discriminating variables in clinicians’ decision pathways to diagnose AMD.ConclusionsBoth black-box and white box methods performed well in identifying diagnoses of AMD and their decision pathways. Machine learning models developed through the proposed approach, relying on clinical signs identified by retinal specialists, could be embedded into EHR to provide physicians with real time (interpretable) support.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2415-15-10) contains supplementary material, which is available to authorized users.

Inflammation and its role in age-related macular degeneration

“…RPE cells are the origin of numerous components found in drusen deposits; in conjunction with pigment mottling, these are the first clinical signs detectable in the AMD [ 25 , 203 – 205 ]. Subretinal drusen resemble the extracellular deposits found in Alzheimer’s disease, amyloidosis, atherosclerosis, elastosis, and dense deposit disease [ 38 , 206 , 207 ].…”

Healthcare Associated Infections: An Interoperable Infrastructure for Multidrug Resistant Organism Surveillance

“…Similar systems have been set up and are being used in another region in Italy for a clinical network for human immunodeficiency virus [63] and for ophthalmology [35,64].…”

A Modular Multipurpose, Parameter Centered Electronic Health Record Architecture

“…The strong modularity of the system allows to use the general structure described above for different diseases. The system has so far been used for the implementation of the EHR for the Ligurian HIV network [30][31][32], for ophthalmology [33,34] and for infectious diseases [35]. For each of these cases, different clinical events, parameter categories and parameters have been implemented in order to provide a tool which is very suited to the specific needs of physicians and caregivers.…”