Learning From Data: Recognizing Glaucomatous Defect Patterns and Detecting Progression From Visual Field Measurements

• Published in: IEEE transactions on biomedical engineering Vol. 61; no. 7; pp. 2112 - 2124
• Main Authors: Yousefi, Siamak, Goldbaum, Michael H., Balasubramanian, Madhusudhanan, Medeiros, Felipe A., Zangwill, Linda M., Liebmann, Jeffrey M., Girkin, Christopher A., Weinreb, Robert N., Bowd, Christopher
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.2014; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adult; Aged; Aged, 80 and over; Artificial Intelligence; Biomedical optical imaging; Brain; Data models; Defects; Disease Progression; Female; Field tests; Glaucoma; Glaucoma - physiopathology; Humans; Linear Models; Male; Mathematical model; Middle Aged; Pattern Recognition, Automated - methods; Pipelines; Sensitivity; Visual Field Tests - methods; Visual Fields - physiology; Visualization; Data analysis; progression detection; visual field; machine learning; glaucoma

A hierarchical approach to learn from visual field data was adopted to identify glaucomatous visual field defect patterns and to detect glaucomatous progression. The analysis pipeline included three stages, namely, clustering, glaucoma boundary limit detection, and glaucoma progression detection tes...