Leveraging archetypal analysis for classifying glaucomatous visual field defects

摘要: Purpose: Visual field (VF) interpretation in glaucoma management relies traditionally on subjective assessment which lacks consistency and efficiency. Archetypal Analysis (AA), an unsupervised learning approach, has demonstrated effectiveness in quantifying distinct VF patterns in glaucomatous loss [1]. We explored if using AA as a feature extraction layer could improve deep learning classification of glaucomatous VF defects.Methods: We applied AA to total deviation (TD) values of 3814 24-2 VFs from 1692 eyes (mean age 63 years, SD= 16; average mean deviation-6.5 dB, SD= 7.5), with augmentation via vertical flipping. AA weights were extracted from a subset of 5612 VFs (90% training, 10% test), including healthy VFs and those marked with one or more common glaucomatous defects (N= 10) by two glaucoma specialists, with an adjudication process for resolving discrepancies. The AA weights were then …