Explainable AI to improve acceptance of convolutional neural networks for automatic classification of dopamine transporter SPECT in the diagnosis of clinically uncertain parkinsonian syndromes
Publikation: Beitrag in Fachzeitschrift › Forschungsartikel › Beigetragen › Begutachtung
Beitragende
Abstract
PURPOSE: Deep 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.
METHODS: The 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.
RESULTS: Overall 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.
CONCLUSION: LRP 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.
Details
Originalsprache | Englisch |
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Seiten (von - bis) | 1176-1186 |
Seitenumfang | 11 |
Fachzeitschrift | European journal of nuclear medicine and molecular imaging |
Jahrgang | 49 |
Ausgabenummer | 4 |
Publikationsstatus | Veröffentlicht - März 2022 |
Peer-Review-Status | Ja |
Externe IDs
PubMedCentral | PMC8921148 |
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Scopus | 85117020918 |
unpaywall | 10.1007/s00259-021-05569-9 |
Mendeley | 9d10a0f6-0ea5-33bd-acc2-003e2971abce |
ORCID | /0000-0003-2848-6949/work/141543352 |
Schlagworte
Forschungsprofillinien der TU Dresden
ASJC Scopus Sachgebiete
Schlagwörter
- Dopamine Plasma Membrane Transport Proteins, Humans, Neural Networks, Computer, Parkinsonian Disorders/diagnostic imaging, Retrospective Studies, Tomography, Emission-Computed, Single-Photon, Explainable AI, Convolutional neural network, Dopamine transporter, Parkinson’s disease, Relevance propagation, SPECT