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

BMC Ophthalmology

Technical Advance

Peter Weller¹  Dympna OSullivan¹  Paolo Fraccaro²  Mattia Prosperi³  Monica Bonetto⁴  Mauro Giacomini⁴  Carlo Enrico Traverso⁵  Massimo Nicolo⁵ 

[1] Centre for Health Informatics, City University London, London, UK;Centre for Health Informatics, City University London, London, UK;Centre for Health Informatics, University of Manchester, Manchester, UK;NIHR Primary Care Patient Safety Translational Research Centre, University of Manchester, Manchester, UK;Health eResearch Centre, University of Manchester, Manchester, UK;Centre for Health Informatics, University of Manchester, Manchester, UK;Health eResearch Centre, University of Manchester, Manchester, UK;DIBRIS, University of Genoa, Genoa, Italy;CEBR, University of Genoa, Genoa, Italy;Di.N.O.G.Mi, University of Genoa, L.go P. Daneo 3, 16132, Genoa, Italy;

关键词: Age related macular degeneration;    Machine learning;    Automated diagnosis;    Statistical learning;    macula disease;   

DOI  :  10.1186/1471-2415-15-10

 received in 2014-05-06, accepted in 2015-01-03,  发布年份 2015

来源: Springer

【 摘 要 】

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.

【 授权许可】

Unknown   
© Fraccaro et al.; licensee BioMed Central. 2015. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.