Longitudinal Modeling of Glaucoma Progression Using 2-Dimensional Continuous-Time Hidden Markov Model

Yu-Ying Liu¹, Hiroshi Ishikawa^{2, 3}, Mei Chen⁴, Gadi Wollstein², Joel S. Schuman^{2, 3}, and James M. Rehg¹

¹College of Computing, Georgia Institute of Technology, Atlanta, U.S.

²UPMC Eye Center, University of Pittsburgh School of Medicine, Pittsburgh, U.S.

³Department of Bioengineering, University of Pittsburgh, Pittsburgh, U.S.

⁴Intel Science and Technology Center on Embedded Computing, Pittsburgh, U.S.

Abstract. We propose a 2D continuous-time Hidden Markov Model (2D CT-HMM) for glaucoma progression modeling given longitudinal structural and functional measurements. CT-HMM is suitable for modeling longitudinal medical data consisting of visits at arbitrary times, and 2D state structure is more appropriate for glaucoma since the time courses of functional and structural degeneration are usually different. The learned model not only corroborates the clinical findings that structural degeneration is more evident than functional degeneration in early glaucoma and the opposite is observed in more advanced stages, but also reveals the exact stages where the trend reverses. A method to detect time segments of fast progression is also proposed. Our results show that this detector can effectively identify patients with rapid degeneration. The model and the derived detector can be of clinical value for glaucoma monitoring.

LNCS 8150, p. 444 ff.

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