ArticleScientific reports2019
Deep-learning-based imaging-classification identified cingulate island sign in dementia with Lewy bodies.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it, 82 citations in OpenAlex.
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- Machine learning classification and regional differentiation of neuropathologically-confirmed Alzheimer's disease and comorbid Lewy body disease.Communications medicine · 2026Article
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- Clinical validity and reproducibility of a visual rating scale for cingulate island sign in a real-world memory clinic: An FDG-PET/MRI study.European journal of neurology · 2025Article
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- A Reproducible Deep-Learning-Based Computer-Aided Diagnosis Tool for Frontotemporal Dementia Using MONAI and Clinica Frameworks.Life (Basel, Switzerland) · 2022Article
- Differentiating dementia with Lewy bodies from Alzheimer's disease and Parkinson's disease dementia: an update on imaging modalities.Journal of neurology · 2022Review
- Deep learning to infer visual acuity from optical coherence tomography in diabetic macular edema.Frontiers in medicine · 2022Article
- The Feasibility of Differentiating Lewy Body Dementia and Alzheimer's Disease by Deep Learning Using ECD SPECT Images.Diagnostics (Basel, Switzerland) · 2021Article
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Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The differentiation of dementia with Lewy bodies (DLB) from Alzheimer's disease (AD) using brain perfusion single photon emission tomography is important but is challenging because these conditions exhibit typical features. The cingulate island sign (CIS) is the most recently identified specific feature of DLB for a differential diagnosis. The current study aimed to examine the usefulness of deep-learning-based imaging classification for the diagnoses of DLB and AD. Furthermore, we investigated whether CIS was emphasized by a deep convolutional neural network (CNN) during differentiation. Brain perfusion single photon emission tomography images from 80 patients, each with DLB and AD, and 80 individuals with normal cognition (NL) were used for training and 20 each for final testing. The CNN was trained on brain surface perfusion images. Gradient-weighted class activation mapping (Grad-CAM) was applied to the CNN to visualize the features that was emphasized by the trained CNN. The binary classifications between DLB and NL, DLB and AD, and AD and NL were 93.1%, 89.3%, and 92.4% accurate, respectively. The CIS ratios closely correlated with the output scores before softmax for DLB-AD discrimination (DLB/AD scores). The Grad-CAM highlighted CIS in the DLB discrimination. Visualization of learning process by guided Grad-CAM revealed that CIS became more focused by the CNN as the training progressed. The DLB/AD score was significantly associated with the three core features of DLB. Deep-learning-based imaging classification was useful for an objective and accurate differentiation of DLB from AD and for predicting clinical features of DLB. The CIS was identified as a specific feature during DLB classification. The visualization of specific features and learning processes could be critical in deep learning to discover new imaging features.
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