The aim of this analysis was to evaluate the diagnostic usefulness of frequency doubling technology (FDT) perimetry and short-wavelength perimetry (SWAP). Moreover, to study a combination of both methods using the machine-learning technique double-bagging, which was recently established in glaucoma research.
Forty-three patients with "preperimetric" open-angle glaucoma (glaucomatous optic disc atrophy and no visual field defect in standard perimetry), 26 patients with "perimetric" open angle glaucoma (glaucomatous optic disc atrophy and visual field defect in standard perimetry), and 40 control subjects had FDT screening (protocol: C-20-5) and SWAP (Octopus 101, G2). Criteria for exclusion were color vision abnormalities, media opacities, and an age below 31 years or above 63 years. Data of 1 eye of each patient and control subject entered the statistical evaluation. A point wise evaluation of the diagnostic power of SWAP values was performed to derive spatial patterns of visual field loss. A double-bagging machine-learning algorithm was used to train classification rules on the basis of a combination of FDT scores and nerve fiber related visual field losses in SWAP. The diagnostic power of the classifiers was compared regarding their misclassification error rates and area under the receiver-operating characteristic curve.
The combination of FDT perimetry and SWAP yielded better diagnostic results compared with FDT or SWAP separately. The overall estimated misclassification error rate of the combined classifier was 24% compared with 28% for both SWAP and FDT perimetry. Regarding the estimated performance of classifier at high specificities (>80%) in control eyes as measured by the partial area under the receiver-operating characteristic curve, the combination of both instruments is also superior to the individual instruments.
A combination of SWAP and FDT perimetry, each targeting different neuronal pathways, may improve early glaucoma detection.