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George Thomas, Michael A. Grassi, John R. Lee, Albert O. Edwards, Michael B. Gorin, Ronald Klein, Thomas L. Casavant, Todd E. Scheetz, Edwin M. Stone, Andrew B. Williams; IDOCS: Intelligent Distributed Ontology Consensus System—The Use of Machine Learning in Retinal Drusen Phenotyping. Invest. Ophthalmol. Vis. Sci. 2007;48(5):2278-2284. doi: 10.1167/iovs.06-1022.
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purpose. To use the power of knowledge acquisition and machine learning in the development of a collaborative computer classification system based on the features of age-related macular degeneration (AMD).
methods. A vocabulary was acquired from four AMD experts who examined 100 ophthalmoscopic images. The vocabulary was analyzed, hierarchically structured, and incorporated into a collaborative computer classification system called IDOCS. Using this system, three of the experts examined images from a second set of digital images compiled from more than 1000 patients with AMD. Images were annotated, and features were identified and defined. Decision trees, a machine learning method, were trained on the data collected and used to extract patterns. Interrelationships between the data from the different clinicians were investigated.
results. Six drusen classes in the structured vocabulary were largely sufficient to describe all the identified features. The decision trees classified the data with 76.86% to 88.5% accuracy and distilled patterns in the form of hierarchical trees composed of 5 to 15 nodes. Experts were largely consistent in their characterization of soft, and to a lesser extent, hard drusen, but diverge in definition of other drusen. Size and crystalline morphology were the main determinants of drusen type across all experts.
conclusions. Machine learning is a powerful tool for the characterization of disease phenotypes. The creation of a defined feature set for AMD will facilitate the development of an IDOCS-based classification system.
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