ArticleEuropean journal of nuclear medicine and molecular imaging2022
Explainable AI to improve acceptance of convolutional neural networks for automatic classification of dopamine transporter SPECT in the diagnosis of clinically uncertain parkinsonian syndromes.
Article in European journal of nuclear medicine and molecular imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed.
- AI-enabled multimodal neuroimaging for neurotransmitter mapping in normal aging and age-related disease.Frontiers in aging neuroscience · 2026Review
- AGMNet: an explainable gated multimodal framework integrating MRI heterogeneity and dopaminergic asymmetry for Parkinson's disease classification.Frontiers in artificial intelligence · 2026Article
- Easy-to-use and easy-to-interpret quality control of 3D gradient echo T1-weighted MR acquisition sequences for improved test-retest stability of MRI-based hippocampus volumetry.Journal of Alzheimer's disease : JAD · 2025Article
- Fully automatic categorical analysis of striatal subregions in dopamine transporter SPECT using a convolutional neural network.Annals of nuclear medicine · 2025Article
- Prediction of 123I-FP-CIT SPECT Results from First Acquired Projections Using Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2025Article
- Incorporating label uncertainty during the training of convolutional neural networks improves performance for the discrimination between certain and inconclusive cases in dopamine transporter SPECT.European journal of nuclear medicine and molecular imaging · 2025Article
- Explainable AI in Diagnostic Radiology for Neurological Disorders: A Systematic Review, and What Doctors Think About It.Diagnostics (Basel, Switzerland) · 2025Review
- Explainable Machine Learning Models for Brain Diseases: Insights from a Systematic Review.Neurology international · 2024Review
- Automated identification of uncertain cases in deep learning-based classification of dopamine transporter SPECT to improve clinical utility and acceptance.European journal of nuclear medicine and molecular imaging · 2024Article
- Article
- Diagnosis of thyroid disease using deep convolutional neural network models applied to thyroid scintigraphy images: a multicenter study.Frontiers in endocrinology · 2023Article
- Real-World Testing of a Machine Learning-Derived Visual Scale for Tc99m TRODAT-1 for Diagnosing Lewy Body Disease: Comparison with a Traditional Approach Using Semiquantification.Journal of personalized medicine · 2022Article
- Deep Learning-Based Automated Diagnosis for Coronary Artery Disease Using SPECT-MPI Images.Journal of clinical medicine · 2022Article
- Decoding the dopamine transporter imaging for the differential diagnosis of parkinsonism using deep learning.European journal of nuclear medicine and molecular imaging · 2022Article
- Soft Attention Based DenseNet Model for Parkinson's Disease Classification Using SPECT Images.Frontiers in aging neuroscience · 2022Article
- Deep learning interpretability in neuroimaging: A comprehensive survey and methodological recommendations.Imaging neuroscience (Cambridge, Mass.)Article
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7 authors.
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Abstract
purposeDeep convolutional neural networks (CNN) provide high accuracy for automatic classification of dopamine transporter (DAT) SPECT images. However, CNN are inherently black-box in nature lacking any kind of explanation for their decisions. This limits their acceptance for clinical use. This study tested layer-wise relevance propagation (LRP) to explain CNN-based classification of DAT-SPECT in patients with clinically uncertain parkinsonian syndromes.
methodsThe study retrospectively included 1296 clinical DAT-SPECT with visual binary interpretation as "normal" or "reduced" by two experienced readers as standard-of-truth. A custom-made CNN was trained with 1008 randomly selected DAT-SPECT. The remaining 288 DAT-SPECT were used to assess classification performance of the CNN and to test LRP for explanation of the CNN-based classification.
resultsOverall accuracy, sensitivity, and specificity of the CNN were 95.8%, 92.8%, and 98.7%, respectively. LRP provided relevance maps that were easy to interpret in each individual DAT-SPECT. In particular, the putamen in the hemisphere most affected by nigrostriatal degeneration was the most relevant brain region for CNN-based classification in all reduced DAT-SPECT. Some misclassified DAT-SPECT showed an "inconsistent" relevance map more typical for the true class label.
conclusionLRP is useful to provide explanation of CNN-based decisions in individual DAT-SPECT and, therefore, can be recommended to support CNN-based classification of DAT-SPECT in clinical routine. Total computation time of 3 s is compatible with busy clinical workflow. The utility of "inconsistent" relevance maps to identify misclassified cases requires further investigation.
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