Predicting Age-related Macular Degeneration Progression from Retinal Optical Coherence Tomography with Intra-Subject Temporal Consistency

Arunava Chakravarty, Taha Emre, Dmitrii Lachinov, Antoine Rivail, Ursula Schmidt-Erfurth, Hrvoje Bogunović
Proceedings of The 7nd International Conference on Medical Imaging with Deep Learning, PMLR 250:184-198, 2024.

Abstract

The wide variability in the progression rates of Age-Related Macular Degeneration (AMD) and the absence of well-established clinical biomarkers make it difficult to predict an individualś risk of AMD progression from intermediate stage (iAMD) to late dry stage (dAMD) using Optical Coherence Tomography (OCT) scans.To address this challenge, we propose to jointly train an AMD stage classifier to discriminate between iAMD and dAMD with a Neural-ODE that models the future trajectory of the disease progression in the learned embedding space. A temporal ordering is imposed such that the distance of a scan from the decision hyperplane of the AMD stage classifier is inversely related to its time-to-conversion. In addition, an intra-subject temporal consistency in the predicted conversion risk scores is ensured by incorporating a pair of longitudinal scans from the same eye during training. We evaluated our proposed method on a longitudinal dataset comprising 235 eyes (3,534 OCT scans) with 40 converters. The results demonstrate the effectiveness of our approach, achieving an average area under the ROC of 0.84 for predicting conversion within the next 6, 12, 18 and 24 months. Additionally, the Concordance Index of 0.78 surpasses the performance of several popular methods for survival analysis.

Cite this Paper

BibTeX
@InProceedings{pmlr-v250-chakravarty24a, title = {Predicting Age-related Macular Degeneration Progression from Retinal Optical Coherence Tomography with Intra-Subject Temporal Consistency}, author = {Chakravarty, Arunava and Emre, Taha and Lachinov, Dmitrii and Rivail, Antoine and Schmidt-Erfurth, Ursula and Bogunovi\'c, Hrvoje}, booktitle = {Proceedings of The 7nd International Conference on Medical Imaging with Deep Learning}, pages = {184--198}, year = {2024}, editor = {Burgos, Ninon and Petitjean, Caroline and Vakalopoulou, Maria and Christodoulidis, Stergios and Coupe, Pierrick and Delingette, Hervé and Lartizien, Carole and Mateus, Diana}, volume = {250}, series = {Proceedings of Machine Learning Research}, month = {03--05 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v250/main/assets/chakravarty24a/chakravarty24a.pdf}, url = {https://proceedings.mlr.press/v250/chakravarty24a.html}, abstract = {The wide variability in the progression rates of Age-Related Macular Degeneration (AMD) and the absence of well-established clinical biomarkers make it difficult to predict an individualś risk of AMD progression from intermediate stage (iAMD) to late dry stage (dAMD) using Optical Coherence Tomography (OCT) scans.To address this challenge, we propose to jointly train an AMD stage classifier to discriminate between iAMD and dAMD with a Neural-ODE that models the future trajectory of the disease progression in the learned embedding space. A temporal ordering is imposed such that the distance of a scan from the decision hyperplane of the AMD stage classifier is inversely related to its time-to-conversion. In addition, an intra-subject temporal consistency in the predicted conversion risk scores is ensured by incorporating a pair of longitudinal scans from the same eye during training. We evaluated our proposed method on a longitudinal dataset comprising 235 eyes (3,534 OCT scans) with 40 converters. The results demonstrate the effectiveness of our approach, achieving an average area under the ROC of 0.84 for predicting conversion within the next 6, 12, 18 and 24 months. Additionally, the Concordance Index of 0.78 surpasses the performance of several popular methods for survival analysis.} }
Endnote
%0 Conference Paper %T Predicting Age-related Macular Degeneration Progression from Retinal Optical Coherence Tomography with Intra-Subject Temporal Consistency %A Arunava Chakravarty %A Taha Emre %A Dmitrii Lachinov %A Antoine Rivail %A Ursula Schmidt-Erfurth %A Hrvoje Bogunović %B Proceedings of The 7nd International Conference on Medical Imaging with Deep Learning %C Proceedings of Machine Learning Research %D 2024 %E Ninon Burgos %E Caroline Petitjean %E Maria Vakalopoulou %E Stergios Christodoulidis %E Pierrick Coupe %E Hervé Delingette %E Carole Lartizien %E Diana Mateus %F pmlr-v250-chakravarty24a %I PMLR %P 184--198 %U https://proceedings.mlr.press/v250/chakravarty24a.html %V 250 %X The wide variability in the progression rates of Age-Related Macular Degeneration (AMD) and the absence of well-established clinical biomarkers make it difficult to predict an individualś risk of AMD progression from intermediate stage (iAMD) to late dry stage (dAMD) using Optical Coherence Tomography (OCT) scans.To address this challenge, we propose to jointly train an AMD stage classifier to discriminate between iAMD and dAMD with a Neural-ODE that models the future trajectory of the disease progression in the learned embedding space. A temporal ordering is imposed such that the distance of a scan from the decision hyperplane of the AMD stage classifier is inversely related to its time-to-conversion. In addition, an intra-subject temporal consistency in the predicted conversion risk scores is ensured by incorporating a pair of longitudinal scans from the same eye during training. We evaluated our proposed method on a longitudinal dataset comprising 235 eyes (3,534 OCT scans) with 40 converters. The results demonstrate the effectiveness of our approach, achieving an average area under the ROC of 0.84 for predicting conversion within the next 6, 12, 18 and 24 months. Additionally, the Concordance Index of 0.78 surpasses the performance of several popular methods for survival analysis.
APA
Chakravarty, A., Emre, T., Lachinov, D., Rivail, A., Schmidt-Erfurth, U. & Bogunović, H.. (2024). Predicting Age-related Macular Degeneration Progression from Retinal Optical Coherence Tomography with Intra-Subject Temporal Consistency. Proceedings of The 7nd International Conference on Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 250:184-198 Available from https://proceedings.mlr.press/v250/chakravarty24a.html.

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