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.
journal_name
BMC Ophthalmoljournal_title
BMC ophthalmologyauthors
Fraccaro P,Nicolo M,Bonetto M,Giacomini M,Weller P,Traverso CE,Prosperi M,OSullivan Ddoi
10.1186/1471-2415-15-10subject
Has Abstractpub_date
2015-01-27 00:00:00pages
10issn
1471-2415pii
10.1186/1471-2415-15-10journal_volume
15pub_type
杂志文章abstract:BACKGROUND:Bone marrow transplantation retinopathy is a rare condition affecting the posterior pole. The purpose of this case report is to highlight the possible risk factors and clinical features. CASE PRESENTATION:A 19y old male with relapsed and refractory acute lymphoblastic leukaemia was admitted under haematolog...
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