title: 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 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.
  
  
  RESULTS
  Study 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.
  
  
  CONCLUSIONS
  Both 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.
authors.affiliation: 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.
authors.forename: Paolo
authors.initials: P
authors.lastname: Fraccaro
authors.affiliation: Di.N.O.G.Mi, University of Genoa, L.go P. Daneo 3, Genoa, 16132, Italy. massimonicolo@gmail.com.
authors.forename: Massimo
authors.initials: M
authors.lastname: Nicolo
authors.affiliation: DIBRIS, University of Genoa, Genoa, Italy.|CEBR, University of Genoa, Genoa, Italy.
authors.forename: Monica
authors.initials: M
authors.lastname: Bonetto
authors.affiliation: DIBRIS, University of Genoa, Genoa, Italy.|CEBR, University of Genoa, Genoa, Italy.
authors.forename: Mauro
authors.initials: M
authors.lastname: Giacomini
authors.affiliation: Centre for Health Informatics, City University London, London, UK.
authors.forename: Peter
authors.initials: P
authors.lastname: Weller
authors.affiliation: Di.N.O.G.Mi, University of Genoa, L.go P. Daneo 3, Genoa, 16132, Italy.
authors.forename: Carlo Enrico
authors.initials: CE
authors.lastname: Traverso
authors.affiliation: Centre for Health Informatics, University of Manchester, Manchester, UK.|Health eResearch Centre, University of Manchester, Manchester, UK.
authors.forename: Mattia
authors.initials: M
authors.lastname: Prosperi
authors.affiliation: Centre for Health Informatics, City University London, London, UK.
authors.forename: Dympna
authors.initials: D
authors.lastname: OSullivan
mesh_terms: D000368:Aged; D019540:Area Under Curve; D003663:Decision Trees; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D000069550:Machine Learning; D008266:Macula Lutea / Q000473:pathology*; D008268:Macular Degeneration / Q000175:diagnosis*; D008297:Male; D012372:ROC Curve; D015593:Retinal Drusen / Q000175:diagnosis; D012166:Retinal Hemorrhage / Q000175:diagnosis; D055213:Retinal Pigment Epithelium / Q000473:pathology; D012680:Sensitivity and Specificity; D058471:Subretinal Fluid