Abstract
Early detection of dementia, such as Alzheimer’s disease (AD) or mild cognitive impairment
(MCI), is essential to enable timely intervention and potential treatment. Accurate
detection of AD/MCI is challenging due to the high complexity, cost, and often
invasive nature of current diagnostic techniques, which limit their suitability for largescale
population screening. Given the shared embryological origins and physiological
characteristics of the retina and brain, retinal imaging is emerging as a potentially rapid
and cost-effective alternative for the identification of individuals with or at high risk
of AD. In this paper, we present a novel PolarNet+ that uses retinal optical coherence
tomography angiography (OCTA) to discriminate early-onset AD (EOAD) and MCI
subjects from controls. Our method first maps OCTA images from Cartesian coordinates
to polar coordinates, allowing approximate sub-region calculation to implement
the clinician-friendly early treatment of diabetic retinopathy study (ETDRS) grid analysis.
We then introduce a multi-view module to serialize and analyze the images along
three dimensions for comprehensive, clinically useful information extraction. Finally,
we abstract the sequence embedding into a graph, transforming the detection task into
a general graph classification problem. A regional relationship module is applied after
the multi-view module to explore the relationship between the sub-regions. Such
regional relationship analyses validate known eye-brain links and reveal new discriminative
patterns. The proposed model is trained, tested, and validated on four retinal
OCTA datasets, including 1,671 participants with AD, MCI, and healthy controls. Experimental
results demonstrate the performance of our model in detecting AD and MCI
with an AUC of 88.69% and 88.02%, respectively. Our results provide evidence that
retinal OCTA imaging, coupled with artificial intelligence, may serve as a rapid and
non-invasive approach for large-scale screening of AD and MCI. The code is available
at https://github.com/iMED-Lab/PolarNet-Plus-PyTorch, and the dataset is also available
upon request.
(MCI), is essential to enable timely intervention and potential treatment. Accurate
detection of AD/MCI is challenging due to the high complexity, cost, and often
invasive nature of current diagnostic techniques, which limit their suitability for largescale
population screening. Given the shared embryological origins and physiological
characteristics of the retina and brain, retinal imaging is emerging as a potentially rapid
and cost-effective alternative for the identification of individuals with or at high risk
of AD. In this paper, we present a novel PolarNet+ that uses retinal optical coherence
tomography angiography (OCTA) to discriminate early-onset AD (EOAD) and MCI
subjects from controls. Our method first maps OCTA images from Cartesian coordinates
to polar coordinates, allowing approximate sub-region calculation to implement
the clinician-friendly early treatment of diabetic retinopathy study (ETDRS) grid analysis.
We then introduce a multi-view module to serialize and analyze the images along
three dimensions for comprehensive, clinically useful information extraction. Finally,
we abstract the sequence embedding into a graph, transforming the detection task into
a general graph classification problem. A regional relationship module is applied after
the multi-view module to explore the relationship between the sub-regions. Such
regional relationship analyses validate known eye-brain links and reveal new discriminative
patterns. The proposed model is trained, tested, and validated on four retinal
OCTA datasets, including 1,671 participants with AD, MCI, and healthy controls. Experimental
results demonstrate the performance of our model in detecting AD and MCI
with an AUC of 88.69% and 88.02%, respectively. Our results provide evidence that
retinal OCTA imaging, coupled with artificial intelligence, may serve as a rapid and
non-invasive approach for large-scale screening of AD and MCI. The code is available
at https://github.com/iMED-Lab/PolarNet-Plus-PyTorch, and the dataset is also available
upon request.
| Original language | English |
|---|---|
| Article number | 103513 |
| Journal | Medical Image Analysis |
| Volume | 102 |
| Early online date | 26 Feb 2025 |
| DOIs | |
| Publication status | E-pub ahead of print - 26 Feb 2025 |
Keywords
- Alzheimer’s Disease
- OCTA images
- eep-learning
- Polar Transformation
- Alzheimer’s disease
- Deep-learning
- Polar transformation
- Alzheimer's disease
