| Literature DB >> 29392245 |
Shikha Chaganti1, Jamie R Robinson2, Camilo Bermudez3, Thomas Lasko4, Louise A Mawn5, Bennett A Landman6.
Abstract
Multi-modal analyses of diseases of the optic nerve, that combine radiological imaging with other electronic medical records (EMR), improve understanding of visual function. We conducted a study of 55 patients with glaucoma and 32 patients with thyroid eye disease (TED). We collected their visual assessments, orbital CT imaging, and EMR data. We developed an image-processing pipeline that segmented and extracted structural metrics from CT images. We derive EMR phenotype vectors with the help of PheWAS (from diagnostic codes) and ProWAS (from treatment codes). Next, we performed a principal component analysis and multiple-correspondence analysis to identify their association with visual function scores. We find that structural metrics derived from CT imaging are significantly associated with functional visual score for both glaucoma (R2=0.32) and TED (R2=0.4). Addition of EMR phenotype vectors to the model significantly improved (p<1E-04) the R2 to 0.4 for glaucoma and 0.54 for TED.Entities:
Keywords: CT imaging; EMR; MCA; Optic nerve; PCA; Regression
Year: 2017 PMID: 29392245 PMCID: PMC5790176 DOI: 10.1007/978-3-319-67558-9_43
Source DB: PubMed Journal: Deep Learn Med Image Anal Multimodal Learn Clin Decis Support (2017)