HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text Full Text (PDF) Submit a response Alert me when this article is cited Alert me when eLetters are posted Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in ISI Web of Science Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Thomas, G. Articles by Williams, A. B. Search for Related Content PubMed PubMed Citation Articles by Thomas, G. Articles by Williams, A. B.

IDOCS: Intelligent Distributed Ontology Consensus System—The Use of Machine Learning in Retinal Drusen Phenotyping

¹From the Department of Computer Science, the ⁸Center for Bioinformatics and Computational Biology, and the ⁹Departments of Ophthalmology and Visual Sciences and ¹⁰Biomedical Engineering, University of Iowa, Iowa City, Iowa; the ³Department of Ophthalmology, University of Chicago, Chicago, Illinois; ⁴Assistive Intelligence, Inc., Iowa City, Iowa; the ⁵Department of Ophthalmology, Mayo Clinic, Rochester, Minnesota; the ⁶Department of Ophthalmology, David Geffen School of Medicine—UCLA, Jules Stein Eye Institute, Los Angeles, California; the ⁷Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, Wisconsin; the ¹¹Howard Hughes Medical Institute, Chevy Chase, Maryland; and the ¹²Department of Computer and Information Sciences, Spelman College, Atlanta, Georgia.

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.