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

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. C 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

Blooms of the cyanobacterium Lyngbya majuscula in coastal Queensland, Australia: disparate sites, common factors

During the last decade there has been a significant rise in observations of blooms of the toxic cyanobacterium, Lyngbya majuscula along the east coast of Queensland, Australia. Whether the increase in cyanobacterial abundance is a biological indicator of widespread water quality degradation or also a function of other environmental change is unknown. A bioassay approach was used to assesses the potential for runoff from various land uses to stimulate productivity of L. majuscula. In Moreton Bay, L. majuscula productivity was significantly (p < 0.05) stimulated by soil extracts, which were high in phosphorus, iron and organic carbon. Productivity of L. majuscula from the Great Barrier Reef was also significantly (p < 0.05) elevated by iron and phosphorus rich extracts, in this case seabird guano adjacent to the bloom site. Hence, it is possible that other L. majuscula blooms are a result of similar stimulating factors (iron, phosphorus and organic carbon), delivered through different mechanisms. (c) 2004 Elsevier Ltd. All rights reserved