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
| Autor: | Giovanni B. Frisoni, Alessandro Padovani, Andrea Chincarini, Begoña Martínez-Sanchis, Afina W. Lemstra, Marc Agudelo-Cifuentes, Marcus Ressner, Valle Camacho, Valentina Garibotto, Andrea Pilotto, Dag Aarsland, Jose R. Chang, Anette Davidsson, Amira Soliman, Miguel A. Ochoa-Figueroa, Silvia Morbelli, Matteo Bauckneht, Rose Bruffaerts, Stefan Byttner, Bart N.M. van Berckel, Milica G. Kramberger, Matthias Brendel, Roxana Stegeran, Nicolas Nicastro, Axel Rominger, Flavio Nobili, Rik Vandenberghe, Kobra Etminani, Maja Trošt |
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| Přispěvatelé: | Byttner, Stefan/0000-0002-0293-040X, Etminani, Kobra, Soliman, Amira, Davidsson, Anette, Chang, Jose R., Martinez-Sanchis, Begona, Byttner, Stefan, Camacho, Valle, Bauckneht, Matteo, Stegeran, Roxana, Ressner, Marcus, Agudelo-Cifuentes, Marc, Chincarini, Andrea, Brendel, Matthias, Rominger, Axel, BRUFFAERTS, Rose, Vandenberghe, Rik, Kramberger, Milica G., Trost, Maja, Nicastro, Nicolas, Frisoni, Giovanni B., Lemstra, Afina W., van Berckel, Bart N. M., Pilotto, Andrea, Padovani, Alessandro, Morbelli, Silvia, Aarsland, Dag, Nobili, Flavio, Garibotto, Valentina, Ochoa-Figueroa, Miguel, Neurology, Amsterdam Neuroscience - Neurodegeneration, Radiology and nuclear medicine, Amsterdam Neuroscience - Brain Imaging |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Artificial intelligence
Deep learning FDG PET Alzheimers disease Mild cognitive impairment Dementia with Lewy bodies Neurologi Alzheimer’s disease Audiology 030218 nuclear medicine & medical imaging ddc:616.89 0302 clinical medicine Alzheimer's disease 610 Medicine & health Radiology Nuclear Medicine & Medical Imaging Alzheimer’s disease Artificial intelligence Deep learning Dementia with Lewy bodies FDG PET Mild cognitive impairment Brain Neurodegenerative Diseases General Medicine PREVALENCE Neurology Original Article F1 score Life Sciences & Biomedicine Lewy Body Disease medicine.medical_specialty behavioral disciplines and activities ddc:616.0757 03 medical and health sciences Text mining Neuroimaging Alzheimer Disease Fluorodeoxyglucose F18 mental disorders medicine Humans Cognitive Dysfunction Radiology Nuclear Medicine and imaging Retrospective Studies Science & Technology Receiver operating characteristic business.industry medicine.disease Confidence interval ddc:616.8 Positron-Emission Tomography Posterior cingulate business 030217 neurology & neurosurgery |
| Zdroj: | European journal of nuclear medicine and molecular imaging, Vol. 49, No 2 (2022) pp. 563-584 Etminani, K, Soliman, A, Davidsson, A, Chang, J R, Martínez-Sanchis, B, Byttner, S, Camacho, V, Bauckneht, M, Stegeran, R, Ressner, M, Agudelo-Cifuentes, M, Chincarini, A, Brendel, M, Rominger, A, Bruffaerts, R, Vandenberghe, R, Kramberger, M G, Trost, M, Nicastro, N, Frisoni, G B, Lemstra, A W, van Berckel, B N M, Pilotto, A, Padovani, A, Morbelli, S, Aarsland, D, Nobili, F, Garibotto, V & Ochoa-Figueroa, M 2021, ' 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 ', European Journal of Nuclear Medicine and Molecular Imaging . https://doi.org/10.1007/s00259-021-05483-0 European Journal of Nuclear Medicine and Molecular Imaging. Springer Verlag EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING r-IIS La Fe. Repositorio Institucional de Producción Científica del Instituto de Investigación Sanitaria La Fe instname r-IIB SANT PAU. Repositorio Institucional de Producción Científica del Instituto de Investigación Biomédica Sant Pau European Journal of Nuclear Medicine and Molecular Imaging Etminani, Kobra; Soliman, Amira; Davidsson, Anette; Chang, Jose R; Martínez-Sanchis, Begoña; Byttner, Stefan; Camacho, Valle; Bauckneht, Matteo; Stegeran, Roxana; Ressner, Marcus; Agudelo-Cifuentes, Marc; Chincarini, Andrea; Brendel, Matthias; Rominger, Axel; Bruffaerts, Rose; Vandenberghe, Rik; Kramberger, Milica G; Trost, Maja; Nicastro, Nicolas; Frisoni, Giovanni B; ... (2022). 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. European journal of nuclear medicine and molecular imaging, 49(2), pp. 563-584. Springer 10.1007/s00259-021-05483-0 |
| ISSN: | 1619-7070 |
| DOI: | 10.1007/s00259-021-05483-0 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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