Abstract
Alzheimer’s disease (AD) poses a significant global
challenge, with a notable absence of accessible and cost-effective
diagnostic tools for widespread AD detection. The retina, mirroring
the brain in anatomy and physiology, has emerged as
a potential avenue for rapid AD identification through retinal
imaging. The current retinal image-based AD detection methods
usually focus primarily on the macular area, but ignore the
potential value that the optic disc region may have for the
detection task. In this study, we leverage both macular- and
disc-centered OCTA images and propose a multi-region fusion
framework for AD detection. Based on clinical evidence, we
integrate handcrafted features into the framework to improve
model performance and interpretability. Specifically, vascular
morphological parameters extracted from the macular and disc
regions are used as input to a revalued KNN model to improve
predictive capabilities. Furthermore, recognizing the significance
of extracting and utilizing complementary information from the
macular and optic disc regions, we propose an uncertaintyguided
strategy based on Dempster-Shefer Theory (DST) to
fuse knowledge from different regions. This approach considers
each region’s forecast quality and significantly improves the
effectiveness and robustness of the model. Through comparative
analysis with existing methods, we have demonstrated that our
method outperforms the state-of-the-art ones and provides more
valuable pathological evidence for the association between retinal
vascular changes and AD.
challenge, with a notable absence of accessible and cost-effective
diagnostic tools for widespread AD detection. The retina, mirroring
the brain in anatomy and physiology, has emerged as
a potential avenue for rapid AD identification through retinal
imaging. The current retinal image-based AD detection methods
usually focus primarily on the macular area, but ignore the
potential value that the optic disc region may have for the
detection task. In this study, we leverage both macular- and
disc-centered OCTA images and propose a multi-region fusion
framework for AD detection. Based on clinical evidence, we
integrate handcrafted features into the framework to improve
model performance and interpretability. Specifically, vascular
morphological parameters extracted from the macular and disc
regions are used as input to a revalued KNN model to improve
predictive capabilities. Furthermore, recognizing the significance
of extracting and utilizing complementary information from the
macular and optic disc regions, we propose an uncertaintyguided
strategy based on Dempster-Shefer Theory (DST) to
fuse knowledge from different regions. This approach considers
each region’s forecast quality and significantly improves the
effectiveness and robustness of the model. Through comparative
analysis with existing methods, we have demonstrated that our
method outperforms the state-of-the-art ones and provides more
valuable pathological evidence for the association between retinal
vascular changes and AD.
Original language | English |
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Title of host publication | Proceedings of IEEE International Conference on Bioinformatics and Biomedicine 2024 (IEEE BIBM 2024) |
Publisher | IEEE Explore |
Publication status | Accepted/In press - 14 Oct 2024 |
Event | IEEE International Conference on Bioinformatics and Biomedicine 2024 (IEEE BIBM 2024) - Lisbon, Portugal Duration: 3 Dec 2024 → 6 Dec 2024 |
Conference
Conference | IEEE International Conference on Bioinformatics and Biomedicine 2024 (IEEE BIBM 2024) |
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Country/Territory | Portugal |
City | Lisbon |
Period | 3/12/24 → 6/12/24 |
Keywords
- Alzheimer’s disease
- OCTA
- Uncertainty
- Macular
- Optic disc