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Abstract #26075 Published in IGR 12-2
Combining Functional and Structural Tests Improves the Diagnostic Accuracy of Relevance Vector Machine Classifiers
Racette L; Chiou CY; Hao J; Bowd C; Goldbaum MH; Zangwill LM; Lee TW; Weinreb RN; Sample PAJournal of Glaucoma 2010; 19: 167-175
PURPOSE: To investigate whether combining optic disc topography and short-wavelength automated perimetry (SWAP) data improves the diagnostic accuracy of relevance vector machine (RVM) classifiers for detecting glaucomatous eyes compared with using each test alone. METHODS: One eye of 144 glaucoma patients and 68 healthy controls from the Diagnostic Innovations in Glaucoma Study were included. RVM were trained and tested with cross-validation on optimized (backward elimination) SWAP features (thresholds plus age; pattern deviation; and total deviation) and on Heidelberg retina tomograph II (HRT) optic disc topography features, independently and in combination. RVM performance was also compared with 2 HRT linear discriminant functions and to SWAP mean deviation and pattern standard deviation. Classifier performance was measured by the area under the receiver operating characteristic curves (AUROCs) generated for each feature set and by the sensitivities at set specificities of 75%, 90%, and 96%. RESULTS: RVM trained on combined HRT and SWAP thresholds plus age had significantly higher AUROC (0.93) than RVM trained on HRT (0.88) and SWAP (0.76) alone. AUROCs for the SWAP global indices (mean deviation: 0.68; pattern standard deviation: 0.72) offered no advantage over SWAP thresholds plus age, whereas the linear discriminant functions AUROCs were significantly lower than RVM trained on the combined SWAP and HRT feature set and on HRT alone feature set. CONCLUSIONS: Training RVM on combined optimized HRT and SWAP data improved diagnostic accuracy compared with training on SWAP and HRT parameters alone. Future research may identify other combinations of tests and classifiers that can also improve diagnostic accuracy.
Department of Ophthalmology, Hamilton Glaucoma Center daggerInstitute of Neural Computation double daggerVA San Diego Health Services, University of California, San Diego, CA.
Classification:
6.9.1.1 Confocal Scanning Laser Ophthalmoscopy (Part of: 6 Clinical examination methods > 6.9 Computerized image analysis > 6.9.1 Laser scanning)
6.6.3 Special methods (e.g. color, contrast, SWAP etc.) (Part of: 6 Clinical examination methods > 6.6 Visual field examination and other visual function tests)
6.30 Other (Part of: 6 Clinical examination methods)
