A 3D deep learning model to predict the diagnosis of dementia with Lewy bodies, Alzheimer’s disease, and mild cognitive impairment using brain 18F-FDG PET

Kobra Etminani, Amira Soliman, Anette Davidsson, Jose R. Chang, Begoña Martínez-Sanchis, Stefan Byttner, Valle Camacho, Matteo Bauckneht, Roxana Stegeran, Marcus Ressner, Marc Agudelo-Cifuentes, Andrea Chincarini, Matthias Brendel, Axel Rominger, Rose Bruffaerts, Rik Vandenberghe, Milica G. Kramberger, Maja Trost, Nicolas Nicastro, Giovanni B. FrisoniAfina W. Lemstra, Bart N. M. van Berckel, Andrea Pilotto, Alessandro Padovani, Silvia Morbelli, Dag Aarsland, Flavio Nobili, Valentina Garibotto, Miguel Ochoa-Figueroa

Research output: Contribution to journalArticleAcademicpeer-review

32 Citations (Scopus)


Purpose: The purpose of this study is to develop and validate a 3D deep learning model that predicts the final clinical diagnosis of Alzheimer’s disease (AD), dementia with Lewy bodies (DLB), mild cognitive impairment due to Alzheimer’s disease (MCI-AD), and cognitively normal (CN) using fluorine 18 fluorodeoxyglucose PET (18F-FDG PET) and compare model’s performance to that of multiple expert nuclear medicine physicians’ readers. Materials and methods: Retrospective 18F-FDG PET scans for AD, MCI-AD, and CN were collected from Alzheimer’s disease neuroimaging initiative (556 patients from 2005 to 2020), and CN and DLB cases were from European DLB Consortium (201 patients from 2005 to 2018). The introduced 3D convolutional neural network was trained using 90% of the data and externally tested using 10% as well as comparison to human readers on the same independent test set. The model’s performance was analyzed with sensitivity, specificity, precision, F1 score, receiver operating characteristic (ROC). The regional metabolic changes driving classification were visualized using uniform manifold approximation and projection (UMAP) and network attention. Results: The proposed model achieved area under the ROC curve of 96.2% (95% confidence interval: 90.6–100) on predicting the final diagnosis of DLB in the independent test set, 96.4% (92.7–100) in AD, 71.4% (51.6–91.2) in MCI-AD, and 94.7% (90–99.5) in CN, which in ROC space outperformed human readers performance. The network attention depicted the posterior cingulate cortex is important for each neurodegenerative disease, and the UMAP visualization of the extracted features by the proposed model demonstrates the reality of development of the given disorders. Conclusion: Using only 18F-FDG PET of the brain, a 3D deep learning model could predict the final diagnosis of the most common neurodegenerative disorders which achieved a competitive performance compared to the human readers as well as their consensus.
Original languageEnglish
JournalEuropean journal of nuclear medicine and molecular imaging
Early online date2021
Publication statusE-pub ahead of print - 2021


  • Alzheimer’s disease
  • Artificial intelligence
  • Deep learning
  • Dementia with Lewy bodies
  • Mild cognitive impairment

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