A Spatiotemporal Approach to Predicting Glaucoma Progression Using a CT-HMM

Supriya Nagesh, Alexander Moreno, Hiroshi Ishikawa, Gadi Wollstein, Joel S. Shuman, James M. Rehg
Proceedings of the 4th Machine Learning for Healthcare Conference, PMLR 106:140-159, 2019.

Abstract

Glaucoma is the second leading global cause of blindness and its effects are irreversible, making early intervention crucial. The identification of glaucoma progression is therefore a challenging and important task. In this work, we model and predict longitudinal glaucoma measurements using an interpretable, discrete state space model. Two common glaucoma biomarkers are the retinal nerve fibre layer (RNFL) thickness and the visual eld index (VFI). Prior works have frequently used a scalar representation for RNFL, such as the average RNFL thickness, thereby discarding potentially-useful spatial information. We present a technique for incorporating spatiotemporal RNFL thickness measurements obtained from a sequence of OCT images into a longitudinal progression model. While these images capture the details of RNFL thickness, representing them for use in a longitudinal model poses two challenges: First, spatial changes in RNFL thickness must be encoded and organized into a temporal sequence in order to enable state space modeling. Second, a predictive model for forecasting the pattern of changes over time must be developed. We address these challenges through a novel approach to spatiotemporal progression analysis. We jointly model the change in RNFL with VFI using a CT-HMM and predict future measurements. We achieve a decrease in mean absolute error of 74% for spatial RNFL thickness encoding in comparison to prior work using the average RNFL thickness. This work will be useful for accurately predicting the spatial location and intensity of tissue degeneration. Appropriate intervention based on more accurate prediction can potentially help to improve the clinical care of glaucoma.

Cite this Paper

BibTeX
@InProceedings{pmlr-v106-nagesh19a, title = {A Spatiotemporal Approach to Predicting Glaucoma Progression Using a CT-HMM}, author = {Nagesh, Supriya and Moreno, Alexander and Ishikawa, Hiroshi and Wollstein, Gadi and Shuman, Joel S. and Rehg, James M.}, booktitle = {Proceedings of the 4th Machine Learning for Healthcare Conference}, pages = {140--159}, year = {2019}, editor = {Doshi-Velez, Finale and Fackler, Jim and Jung, Ken and Kale, David and Ranganath, Rajesh and Wallace, Byron and Wiens, Jenna}, volume = {106}, series = {Proceedings of Machine Learning Research}, month = {09--10 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v106/nagesh19a/nagesh19a.pdf}, url = {https://proceedings.mlr.press/v106/nagesh19a.html}, abstract = {Glaucoma is the second leading global cause of blindness and its effects are irreversible, making early intervention crucial. The identification of glaucoma progression is therefore a challenging and important task. In this work, we model and predict longitudinal glaucoma measurements using an interpretable, discrete state space model. Two common glaucoma biomarkers are the retinal nerve fibre layer (RNFL) thickness and the visual eld index (VFI). Prior works have frequently used a scalar representation for RNFL, such as the average RNFL thickness, thereby discarding potentially-useful spatial information. We present a technique for incorporating spatiotemporal RNFL thickness measurements obtained from a sequence of OCT images into a longitudinal progression model. While these images capture the details of RNFL thickness, representing them for use in a longitudinal model poses two challenges: First, spatial changes in RNFL thickness must be encoded and organized into a temporal sequence in order to enable state space modeling. Second, a predictive model for forecasting the pattern of changes over time must be developed. We address these challenges through a novel approach to spatiotemporal progression analysis. We jointly model the change in RNFL with VFI using a CT-HMM and predict future measurements. We achieve a decrease in mean absolute error of 74% for spatial RNFL thickness encoding in comparison to prior work using the average RNFL thickness. This work will be useful for accurately predicting the spatial location and intensity of tissue degeneration. Appropriate intervention based on more accurate prediction can potentially help to improve the clinical care of glaucoma.} }
Endnote
%0 Conference Paper %T A Spatiotemporal Approach to Predicting Glaucoma Progression Using a CT-HMM %A Supriya Nagesh %A Alexander Moreno %A Hiroshi Ishikawa %A Gadi Wollstein %A Joel S. Shuman %A James M. Rehg %B Proceedings of the 4th Machine Learning for Healthcare Conference %C Proceedings of Machine Learning Research %D 2019 %E Finale Doshi-Velez %E Jim Fackler %E Ken Jung %E David Kale %E Rajesh Ranganath %E Byron Wallace %E Jenna Wiens %F pmlr-v106-nagesh19a %I PMLR %P 140--159 %U https://proceedings.mlr.press/v106/nagesh19a.html %V 106 %X Glaucoma is the second leading global cause of blindness and its effects are irreversible, making early intervention crucial. The identification of glaucoma progression is therefore a challenging and important task. In this work, we model and predict longitudinal glaucoma measurements using an interpretable, discrete state space model. Two common glaucoma biomarkers are the retinal nerve fibre layer (RNFL) thickness and the visual eld index (VFI). Prior works have frequently used a scalar representation for RNFL, such as the average RNFL thickness, thereby discarding potentially-useful spatial information. We present a technique for incorporating spatiotemporal RNFL thickness measurements obtained from a sequence of OCT images into a longitudinal progression model. While these images capture the details of RNFL thickness, representing them for use in a longitudinal model poses two challenges: First, spatial changes in RNFL thickness must be encoded and organized into a temporal sequence in order to enable state space modeling. Second, a predictive model for forecasting the pattern of changes over time must be developed. We address these challenges through a novel approach to spatiotemporal progression analysis. We jointly model the change in RNFL with VFI using a CT-HMM and predict future measurements. We achieve a decrease in mean absolute error of 74% for spatial RNFL thickness encoding in comparison to prior work using the average RNFL thickness. This work will be useful for accurately predicting the spatial location and intensity of tissue degeneration. Appropriate intervention based on more accurate prediction can potentially help to improve the clinical care of glaucoma.
APA
Nagesh, S., Moreno, A., Ishikawa, H., Wollstein, G., Shuman, J.S. & Rehg, J.M.. (2019). A Spatiotemporal Approach to Predicting Glaucoma Progression Using a CT-HMM. Proceedings of the 4th Machine Learning for Healthcare Conference, in Proceedings of Machine Learning Research 106:140-159 Available from https://proceedings.mlr.press/v106/nagesh19a.html.

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