| Description |
Country affiliation: United States Author Affiliation: Christopher M ( Stephen A. Wynn Institute for Vision Research, University of Iowa, Iowa City, Iowa, United States 2Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa, United States.); Abràmoff MD ( Stephen A. Wynn Institute for Vision Research, University of Iowa, Iowa City, Iowa, United States 2Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa, United States 3Department of Electrical and Computer Engineering, University of I.); Tang L ( Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, Iowa, United States.); Gordon MO ( Division of Biostatistics, Washington University, St. Louis, Missouri, United States 7Department of Ophthalmology and Visual Sciences, Washington University, St. Louis, Missouri, United States.); Kass MA ( Department of Ophthalmology and Visual Sciences, Washington University, St. Louis, Missouri, United States.); Budenz DL ( Department of Ophthalmology, University of North Carolina, Chapel Hill, North Carolina, United States.); Fingert JH ( Stephen A. Wynn Institute for Vision Research, University of Iowa, Iowa City, Iowa, United States 4Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, Iowa, United States.); Scheetz TE ( Stephen A. Wynn Institute for Vision Research, University of Iowa, Iowa City, Iowa, United States 2Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa, United States 4Department of Ophthalmology and Visual Sciences, University of Iow.) |
| Abstract |
PURPOSE: To identify objective, quantitative optic nerve head (ONH) structural features and model the contributions of glaucoma. METHODS: Baseline stereoscopic optic disc images of 1635 glaucoma-free participants at risk for developing primary open-angle glaucoma (POAG) were collected as part of the Ocular Hypertension Treatment Study. A stereo correspondence algorithm designed for fundus images was applied to extract the three-dimensional (3D) information about the ONH. Principal component analysis was used to identify ONH 3D structural features and the contributions of demographic features, clinical variables, and disease were modeled using linear regression and linear component analysis. The computationally identified features were evaluated based on associations with glaucoma and ability to predict which participants would develop POAG. RESULTS: The computationally identified features were significantly associated with future POAG, POAG-related demographics (age, ethnicity), and clinical measurements (horizontal and vertical cup-to-disc ratio, central corneal thickness, and refraction). Models predicting future POAG development using the OHTS baseline data and STEP features achieved an AUC of 0.722 in cross-validation testing. This was a significant improvement over using only demographics (age, sex, and ethnicity), which had an AUC of 0.599. CONCLUSIONS: Methods for identifying objective, quantitative measurements of 3D ONH structure were developed using a large dataset. The identified features were significantly associated with POAG and POAG-related variables. Further, these features increased predictive model accuracy in predicting future POAG. The results indicate that the computationally identified features might be useful in POAG early screening programs or as endophenotypes to investigate POAG genetics. |
| Subject Keyword |
Glaucoma, Open-angle Diagnosis Ocular Hypertension Complications Optic Disk Pathology Optic Nerve Diseases Algorithms Imaging, Three-dimensional Intraocular Pressure Phenotype Visual Fields Research Support, N.i.h., Extramural Research Support, Non-u.s. Gov't Research Support, U.s. Gov't, Non-p.h.s. Discipline Ophthalmology |
