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Data-driven identification of diagnostically useful extrastriatal signal in dopamine transporter SPECT using explainable AI

This study used explainable artificial intelligence for data-driven identification of extrastriatal brain regions that can contribute to the interpretation of dopamine transporter SPECT with (123)I-FP-CIT in parkinsonian syndromes. A total of 1306 (123)I-FP-CIT-SPECT were included retrospectively. B...

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Autores principales: Nazari, Mahmood, Kluge, Andreas, Apostolova, Ivayla, Klutmann, Susanne, Kimiaei, Sharok, Schroeder, Michael, Buchert, Ralph
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8617288/
https://www.ncbi.nlm.nih.gov/pubmed/34824352
http://dx.doi.org/10.1038/s41598-021-02385-x
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author Nazari, Mahmood
Kluge, Andreas
Apostolova, Ivayla
Klutmann, Susanne
Kimiaei, Sharok
Schroeder, Michael
Buchert, Ralph
author_facet Nazari, Mahmood
Kluge, Andreas
Apostolova, Ivayla
Klutmann, Susanne
Kimiaei, Sharok
Schroeder, Michael
Buchert, Ralph
author_sort Nazari, Mahmood
collection PubMed
description This study used explainable artificial intelligence for data-driven identification of extrastriatal brain regions that can contribute to the interpretation of dopamine transporter SPECT with (123)I-FP-CIT in parkinsonian syndromes. A total of 1306 (123)I-FP-CIT-SPECT were included retrospectively. Binary classification as ‘reduced’ or ‘normal’ striatal (123)I-FP-CIT uptake by an experienced reader served as standard-of-truth. A custom-made 3-dimensional convolutional neural network (CNN) was trained for classification of the SPECT images with 1006 randomly selected images in three different settings: “full image”, “striatum only” (3-dimensional region covering the striata cropped from the full image), “without striatum” (full image with striatal region removed). The remaining 300 SPECT images were used to test the CNN classification performance. Layer-wise relevance propagation (LRP) was used for voxelwise quantification of the relevance for the CNN-based classification in this test set. Overall accuracy of CNN-based classification was 97.0%, 95.7%, and 69.3% in the “full image”, “striatum only”, and “without striatum” setting. Prominent contributions in the LRP-based relevance maps beyond the striatal signal were detected in insula, amygdala, ventromedial prefrontal cortex, thalamus, anterior temporal cortex, superior frontal lobe, and pons, suggesting that (123)I-FP-CIT uptake in these brain regions provides clinically useful information for the differentiation of neurodegenerative and non-neurodegenerative parkinsonian syndromes.
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spelling pubmed-86172882021-11-29 Data-driven identification of diagnostically useful extrastriatal signal in dopamine transporter SPECT using explainable AI Nazari, Mahmood Kluge, Andreas Apostolova, Ivayla Klutmann, Susanne Kimiaei, Sharok Schroeder, Michael Buchert, Ralph Sci Rep Article This study used explainable artificial intelligence for data-driven identification of extrastriatal brain regions that can contribute to the interpretation of dopamine transporter SPECT with (123)I-FP-CIT in parkinsonian syndromes. A total of 1306 (123)I-FP-CIT-SPECT were included retrospectively. Binary classification as ‘reduced’ or ‘normal’ striatal (123)I-FP-CIT uptake by an experienced reader served as standard-of-truth. A custom-made 3-dimensional convolutional neural network (CNN) was trained for classification of the SPECT images with 1006 randomly selected images in three different settings: “full image”, “striatum only” (3-dimensional region covering the striata cropped from the full image), “without striatum” (full image with striatal region removed). The remaining 300 SPECT images were used to test the CNN classification performance. Layer-wise relevance propagation (LRP) was used for voxelwise quantification of the relevance for the CNN-based classification in this test set. Overall accuracy of CNN-based classification was 97.0%, 95.7%, and 69.3% in the “full image”, “striatum only”, and “without striatum” setting. Prominent contributions in the LRP-based relevance maps beyond the striatal signal were detected in insula, amygdala, ventromedial prefrontal cortex, thalamus, anterior temporal cortex, superior frontal lobe, and pons, suggesting that (123)I-FP-CIT uptake in these brain regions provides clinically useful information for the differentiation of neurodegenerative and non-neurodegenerative parkinsonian syndromes. Nature Publishing Group UK 2021-11-25 /pmc/articles/PMC8617288/ /pubmed/34824352 http://dx.doi.org/10.1038/s41598-021-02385-x Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Nazari, Mahmood
Kluge, Andreas
Apostolova, Ivayla
Klutmann, Susanne
Kimiaei, Sharok
Schroeder, Michael
Buchert, Ralph
Data-driven identification of diagnostically useful extrastriatal signal in dopamine transporter SPECT using explainable AI
title Data-driven identification of diagnostically useful extrastriatal signal in dopamine transporter SPECT using explainable AI
title_full Data-driven identification of diagnostically useful extrastriatal signal in dopamine transporter SPECT using explainable AI
title_fullStr Data-driven identification of diagnostically useful extrastriatal signal in dopamine transporter SPECT using explainable AI
title_full_unstemmed Data-driven identification of diagnostically useful extrastriatal signal in dopamine transporter SPECT using explainable AI
title_short Data-driven identification of diagnostically useful extrastriatal signal in dopamine transporter SPECT using explainable AI
title_sort data-driven identification of diagnostically useful extrastriatal signal in dopamine transporter spect using explainable ai
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8617288/
https://www.ncbi.nlm.nih.gov/pubmed/34824352
http://dx.doi.org/10.1038/s41598-021-02385-x
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