Deep learning combining FDG-PET and neurocognitive data accurately predicts MCI conversion to Alzheimer's dementia 3-year post MCI diagnosis

Introduction: This study reports a novel deep learning approach to predict mild cognitive impairment (MCI) conversion to Alzheimer's dementia (AD) within three years using whole-brain fluorodeoxyglucose (FDG) positron emission tomography (PET) and cognitive scores (CS).Methods: This analysis co...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Cao, Eric [verfasserIn] Ma, Da [verfasserIn] Nayak, Siddharth [verfasserIn] Duong, Tim Q. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Positron emission tomography Mild cognitive impairment Machine learning Artificial intelligence Dementia MRI

Übergeordnetes Werk:	Enthalten in: Neurobiology of disease - [Amsterdam] : Elsevier, 1994, 187
Übergeordnetes Werk:	volume:187

DOI / URN:	10.1016/j.nbd.2023.106310

Katalog-ID:	ELV065222393

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245	1	0	\|a Deep learning combining FDG-PET and neurocognitive data accurately predicts MCI conversion to Alzheimer's dementia 3-year post MCI diagnosis
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520			\|a Introduction: This study reports a novel deep learning approach to predict mild cognitive impairment (MCI) conversion to Alzheimer's dementia (AD) within three years using whole-brain fluorodeoxyglucose (FDG) positron emission tomography (PET) and cognitive scores (CS).Methods: This analysis consisted of 150 normal controls (CN), 257 MCI, and 205 AD subjects from ADNI. FDG-PET and CS were obtained at MCI diagnosis to predict AD conversion within three years of MCI diagnosis using convolutional neural networks.Results: Neurocognitive scores predicted better than FDG-PET per se, but the best model was a combination of FDG-PET, age, and neurocognitive data, yielding an AUC of 0.785 ± 0.096 and a balanced accuracy of 0.733 ± 0.098. Saliency maps highlighted putamen, thalamus, inferior frontal gyrus, parietal operculum, precuneus cortices, calcarine cortices, temporal gyrus, and planum temporale to be important for prediction.Discussion: Deep learning accurately predicts MCI conversion to AD and provides neural correlates of brain regions associated with AD conversion.
650		4	\|a Positron emission tomography
650		4	\|a Mild cognitive impairment
650		4	\|a Machine learning
650		4	\|a Artificial intelligence
650		4	\|a Dementia
650		4	\|a MRI
700	1		\|a Ma, Da \|e verfasserin \|4 aut
700	1		\|a Nayak, Siddharth \|e verfasserin \|4 aut
700	1		\|a Duong, Tim Q. \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Neurobiology of disease \|d [Amsterdam] : Elsevier, 1994 \|g 187 \|h Online-Ressource \|w (DE-627)268125414 \|w (DE-600)1471408-5 \|w (DE-576)27234947X \|x 1095-953X \|7 nnns
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allfields	10.1016/j.nbd.2023.106310 doi (DE-627)ELV065222393 (ELSEVIER)S0969-9961(23)00326-1 DE-627 ger DE-627 rda eng 610 570 VZ 44.90 bkl Cao, Eric verfasserin aut Deep learning combining FDG-PET and neurocognitive data accurately predicts MCI conversion to Alzheimer's dementia 3-year post MCI diagnosis 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Introduction: This study reports a novel deep learning approach to predict mild cognitive impairment (MCI) conversion to Alzheimer's dementia (AD) within three years using whole-brain fluorodeoxyglucose (FDG) positron emission tomography (PET) and cognitive scores (CS).Methods: This analysis consisted of 150 normal controls (CN), 257 MCI, and 205 AD subjects from ADNI. FDG-PET and CS were obtained at MCI diagnosis to predict AD conversion within three years of MCI diagnosis using convolutional neural networks.Results: Neurocognitive scores predicted better than FDG-PET per se, but the best model was a combination of FDG-PET, age, and neurocognitive data, yielding an AUC of 0.785 ± 0.096 and a balanced accuracy of 0.733 ± 0.098. Saliency maps highlighted putamen, thalamus, inferior frontal gyrus, parietal operculum, precuneus cortices, calcarine cortices, temporal gyrus, and planum temporale to be important for prediction.Discussion: Deep learning accurately predicts MCI conversion to AD and provides neural correlates of brain regions associated with AD conversion. Positron emission tomography Mild cognitive impairment Machine learning Artificial intelligence Dementia MRI Ma, Da verfasserin aut Nayak, Siddharth verfasserin aut Duong, Tim Q. verfasserin aut Enthalten in Neurobiology of disease [Amsterdam] : Elsevier, 1994 187 Online-Ressource (DE-627)268125414 (DE-600)1471408-5 (DE-576)27234947X 1095-953X nnns volume:187 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 44.90 Neurologie VZ AR 187
spelling	10.1016/j.nbd.2023.106310 doi (DE-627)ELV065222393 (ELSEVIER)S0969-9961(23)00326-1 DE-627 ger DE-627 rda eng 610 570 VZ 44.90 bkl Cao, Eric verfasserin aut Deep learning combining FDG-PET and neurocognitive data accurately predicts MCI conversion to Alzheimer's dementia 3-year post MCI diagnosis 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Introduction: This study reports a novel deep learning approach to predict mild cognitive impairment (MCI) conversion to Alzheimer's dementia (AD) within three years using whole-brain fluorodeoxyglucose (FDG) positron emission tomography (PET) and cognitive scores (CS).Methods: This analysis consisted of 150 normal controls (CN), 257 MCI, and 205 AD subjects from ADNI. FDG-PET and CS were obtained at MCI diagnosis to predict AD conversion within three years of MCI diagnosis using convolutional neural networks.Results: Neurocognitive scores predicted better than FDG-PET per se, but the best model was a combination of FDG-PET, age, and neurocognitive data, yielding an AUC of 0.785 ± 0.096 and a balanced accuracy of 0.733 ± 0.098. Saliency maps highlighted putamen, thalamus, inferior frontal gyrus, parietal operculum, precuneus cortices, calcarine cortices, temporal gyrus, and planum temporale to be important for prediction.Discussion: Deep learning accurately predicts MCI conversion to AD and provides neural correlates of brain regions associated with AD conversion. Positron emission tomography Mild cognitive impairment Machine learning Artificial intelligence Dementia MRI Ma, Da verfasserin aut Nayak, Siddharth verfasserin aut Duong, Tim Q. verfasserin aut Enthalten in Neurobiology of disease [Amsterdam] : Elsevier, 1994 187 Online-Ressource (DE-627)268125414 (DE-600)1471408-5 (DE-576)27234947X 1095-953X nnns volume:187 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 44.90 Neurologie VZ AR 187
allfields_unstemmed	10.1016/j.nbd.2023.106310 doi (DE-627)ELV065222393 (ELSEVIER)S0969-9961(23)00326-1 DE-627 ger DE-627 rda eng 610 570 VZ 44.90 bkl Cao, Eric verfasserin aut Deep learning combining FDG-PET and neurocognitive data accurately predicts MCI conversion to Alzheimer's dementia 3-year post MCI diagnosis 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Introduction: This study reports a novel deep learning approach to predict mild cognitive impairment (MCI) conversion to Alzheimer's dementia (AD) within three years using whole-brain fluorodeoxyglucose (FDG) positron emission tomography (PET) and cognitive scores (CS).Methods: This analysis consisted of 150 normal controls (CN), 257 MCI, and 205 AD subjects from ADNI. FDG-PET and CS were obtained at MCI diagnosis to predict AD conversion within three years of MCI diagnosis using convolutional neural networks.Results: Neurocognitive scores predicted better than FDG-PET per se, but the best model was a combination of FDG-PET, age, and neurocognitive data, yielding an AUC of 0.785 ± 0.096 and a balanced accuracy of 0.733 ± 0.098. Saliency maps highlighted putamen, thalamus, inferior frontal gyrus, parietal operculum, precuneus cortices, calcarine cortices, temporal gyrus, and planum temporale to be important for prediction.Discussion: Deep learning accurately predicts MCI conversion to AD and provides neural correlates of brain regions associated with AD conversion. Positron emission tomography Mild cognitive impairment Machine learning Artificial intelligence Dementia MRI Ma, Da verfasserin aut Nayak, Siddharth verfasserin aut Duong, Tim Q. verfasserin aut Enthalten in Neurobiology of disease [Amsterdam] : Elsevier, 1994 187 Online-Ressource (DE-627)268125414 (DE-600)1471408-5 (DE-576)27234947X 1095-953X nnns volume:187 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 44.90 Neurologie VZ AR 187
allfieldsGer	10.1016/j.nbd.2023.106310 doi (DE-627)ELV065222393 (ELSEVIER)S0969-9961(23)00326-1 DE-627 ger DE-627 rda eng 610 570 VZ 44.90 bkl Cao, Eric verfasserin aut Deep learning combining FDG-PET and neurocognitive data accurately predicts MCI conversion to Alzheimer's dementia 3-year post MCI diagnosis 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Introduction: This study reports a novel deep learning approach to predict mild cognitive impairment (MCI) conversion to Alzheimer's dementia (AD) within three years using whole-brain fluorodeoxyglucose (FDG) positron emission tomography (PET) and cognitive scores (CS).Methods: This analysis consisted of 150 normal controls (CN), 257 MCI, and 205 AD subjects from ADNI. FDG-PET and CS were obtained at MCI diagnosis to predict AD conversion within three years of MCI diagnosis using convolutional neural networks.Results: Neurocognitive scores predicted better than FDG-PET per se, but the best model was a combination of FDG-PET, age, and neurocognitive data, yielding an AUC of 0.785 ± 0.096 and a balanced accuracy of 0.733 ± 0.098. Saliency maps highlighted putamen, thalamus, inferior frontal gyrus, parietal operculum, precuneus cortices, calcarine cortices, temporal gyrus, and planum temporale to be important for prediction.Discussion: Deep learning accurately predicts MCI conversion to AD and provides neural correlates of brain regions associated with AD conversion. Positron emission tomography Mild cognitive impairment Machine learning Artificial intelligence Dementia MRI Ma, Da verfasserin aut Nayak, Siddharth verfasserin aut Duong, Tim Q. verfasserin aut Enthalten in Neurobiology of disease [Amsterdam] : Elsevier, 1994 187 Online-Ressource (DE-627)268125414 (DE-600)1471408-5 (DE-576)27234947X 1095-953X nnns volume:187 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 44.90 Neurologie VZ AR 187
allfieldsSound	10.1016/j.nbd.2023.106310 doi (DE-627)ELV065222393 (ELSEVIER)S0969-9961(23)00326-1 DE-627 ger DE-627 rda eng 610 570 VZ 44.90 bkl Cao, Eric verfasserin aut Deep learning combining FDG-PET and neurocognitive data accurately predicts MCI conversion to Alzheimer's dementia 3-year post MCI diagnosis 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Introduction: This study reports a novel deep learning approach to predict mild cognitive impairment (MCI) conversion to Alzheimer's dementia (AD) within three years using whole-brain fluorodeoxyglucose (FDG) positron emission tomography (PET) and cognitive scores (CS).Methods: This analysis consisted of 150 normal controls (CN), 257 MCI, and 205 AD subjects from ADNI. FDG-PET and CS were obtained at MCI diagnosis to predict AD conversion within three years of MCI diagnosis using convolutional neural networks.Results: Neurocognitive scores predicted better than FDG-PET per se, but the best model was a combination of FDG-PET, age, and neurocognitive data, yielding an AUC of 0.785 ± 0.096 and a balanced accuracy of 0.733 ± 0.098. Saliency maps highlighted putamen, thalamus, inferior frontal gyrus, parietal operculum, precuneus cortices, calcarine cortices, temporal gyrus, and planum temporale to be important for prediction.Discussion: Deep learning accurately predicts MCI conversion to AD and provides neural correlates of brain regions associated with AD conversion. Positron emission tomography Mild cognitive impairment Machine learning Artificial intelligence Dementia MRI Ma, Da verfasserin aut Nayak, Siddharth verfasserin aut Duong, Tim Q. verfasserin aut Enthalten in Neurobiology of disease [Amsterdam] : Elsevier, 1994 187 Online-Ressource (DE-627)268125414 (DE-600)1471408-5 (DE-576)27234947X 1095-953X nnns volume:187 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 44.90 Neurologie VZ AR 187
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topic_facet	Positron emission tomography Mild cognitive impairment Machine learning Artificial intelligence Dementia MRI
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authorswithroles_txt_mv	Cao, Eric @@aut@@ Ma, Da @@aut@@ Nayak, Siddharth @@aut@@ Duong, Tim Q. @@aut@@
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author	Cao, Eric
spellingShingle	Cao, Eric ddc 610 bkl 44.90 misc Positron emission tomography misc Mild cognitive impairment misc Machine learning misc Artificial intelligence misc Dementia misc MRI Deep learning combining FDG-PET and neurocognitive data accurately predicts MCI conversion to Alzheimer's dementia 3-year post MCI diagnosis
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topic_title	610 570 VZ 44.90 bkl Deep learning combining FDG-PET and neurocognitive data accurately predicts MCI conversion to Alzheimer's dementia 3-year post MCI diagnosis Positron emission tomography Mild cognitive impairment Machine learning Artificial intelligence Dementia MRI
topic	ddc 610 bkl 44.90 misc Positron emission tomography misc Mild cognitive impairment misc Machine learning misc Artificial intelligence misc Dementia misc MRI
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title	Deep learning combining FDG-PET and neurocognitive data accurately predicts MCI conversion to Alzheimer's dementia 3-year post MCI diagnosis
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title_full	Deep learning combining FDG-PET and neurocognitive data accurately predicts MCI conversion to Alzheimer's dementia 3-year post MCI diagnosis
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title_sort	deep learning combining fdg-pet and neurocognitive data accurately predicts mci conversion to alzheimer's dementia 3-year post mci diagnosis
title_auth	Deep learning combining FDG-PET and neurocognitive data accurately predicts MCI conversion to Alzheimer's dementia 3-year post MCI diagnosis
abstract	Introduction: This study reports a novel deep learning approach to predict mild cognitive impairment (MCI) conversion to Alzheimer's dementia (AD) within three years using whole-brain fluorodeoxyglucose (FDG) positron emission tomography (PET) and cognitive scores (CS).Methods: This analysis consisted of 150 normal controls (CN), 257 MCI, and 205 AD subjects from ADNI. FDG-PET and CS were obtained at MCI diagnosis to predict AD conversion within three years of MCI diagnosis using convolutional neural networks.Results: Neurocognitive scores predicted better than FDG-PET per se, but the best model was a combination of FDG-PET, age, and neurocognitive data, yielding an AUC of 0.785 ± 0.096 and a balanced accuracy of 0.733 ± 0.098. Saliency maps highlighted putamen, thalamus, inferior frontal gyrus, parietal operculum, precuneus cortices, calcarine cortices, temporal gyrus, and planum temporale to be important for prediction.Discussion: Deep learning accurately predicts MCI conversion to AD and provides neural correlates of brain regions associated with AD conversion.
abstractGer	Introduction: This study reports a novel deep learning approach to predict mild cognitive impairment (MCI) conversion to Alzheimer's dementia (AD) within three years using whole-brain fluorodeoxyglucose (FDG) positron emission tomography (PET) and cognitive scores (CS).Methods: This analysis consisted of 150 normal controls (CN), 257 MCI, and 205 AD subjects from ADNI. FDG-PET and CS were obtained at MCI diagnosis to predict AD conversion within three years of MCI diagnosis using convolutional neural networks.Results: Neurocognitive scores predicted better than FDG-PET per se, but the best model was a combination of FDG-PET, age, and neurocognitive data, yielding an AUC of 0.785 ± 0.096 and a balanced accuracy of 0.733 ± 0.098. Saliency maps highlighted putamen, thalamus, inferior frontal gyrus, parietal operculum, precuneus cortices, calcarine cortices, temporal gyrus, and planum temporale to be important for prediction.Discussion: Deep learning accurately predicts MCI conversion to AD and provides neural correlates of brain regions associated with AD conversion.
abstract_unstemmed	Introduction: This study reports a novel deep learning approach to predict mild cognitive impairment (MCI) conversion to Alzheimer's dementia (AD) within three years using whole-brain fluorodeoxyglucose (FDG) positron emission tomography (PET) and cognitive scores (CS).Methods: This analysis consisted of 150 normal controls (CN), 257 MCI, and 205 AD subjects from ADNI. FDG-PET and CS were obtained at MCI diagnosis to predict AD conversion within three years of MCI diagnosis using convolutional neural networks.Results: Neurocognitive scores predicted better than FDG-PET per se, but the best model was a combination of FDG-PET, age, and neurocognitive data, yielding an AUC of 0.785 ± 0.096 and a balanced accuracy of 0.733 ± 0.098. Saliency maps highlighted putamen, thalamus, inferior frontal gyrus, parietal operculum, precuneus cortices, calcarine cortices, temporal gyrus, and planum temporale to be important for prediction.Discussion: Deep learning accurately predicts MCI conversion to AD and provides neural correlates of brain regions associated with AD conversion.
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title_short	Deep learning combining FDG-PET and neurocognitive data accurately predicts MCI conversion to Alzheimer's dementia 3-year post MCI diagnosis
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author2	Ma, Da Nayak, Siddharth Duong, Tim Q.
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doi_str	10.1016/j.nbd.2023.106310
up_date	2024-07-06T22:16:14.770Z
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Deep learning combining FDG-PET and neurocognitive data accurately predicts MCI conversion to Alzheimer's dementia 3-year post MCI diagnosis

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