Transfer learning for predicting conversion from mild cognitive impairment to dementia of Alzheimer's type based on a three-dimensional convolutional neural network.
Transfer learning for predicting conversion from mild cognitive impairment to dementia of Alzheimer's type based on a three-dimensional convolutional neural network.
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基于三维卷积神经网络的预测轻度认知障碍向阿尔茨海默型痴呆转化的迁移学习。
DOI：
10.1016/j.neurobiolaging.2020.12.005
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发表时间：
2021-03
影响因子：
4.2
通讯作者：
Alzheimer's Disease Neuroimaging Initiative
中科院分区：
文献类型：
--
作者：
Bae J;Stocks J;Heywood A;Jung Y;Jenkins L;Hill V;Katsaggelos A;Popuri K;Rosen H;Beg MF;Wang L;Alzheimer's Disease Neuroimaging Initiative
阿尔茨海默型痴呆症（DAT）与破坏性和不可逆转的认知能力下降有关。预测哪些轻度认知障碍（MCI）患者将进展为DAT是该领域的一个持续挑战。我们开发了一个深度学习模型来预测从MCI到DAT的转换。结构磁共振成像扫描被用作三维卷积神经网络（3D-CNN）的输入。3D-CNN使用迁移学习进行训练;在源任务中，正常对照和DAT扫描用于预训练模型。然后，这个预训练的模型在分类哪些MCI患者转换为DAT的目标任务上进行了重新训练。我们的模型在目标任务上的分类准确率为82.4%，优于该领域的当前模型。接下来，我们使用遮挡图方法可视化了显著有助于预测MCI转换的大脑区域。贡献区域包括脑桥、杏仁核和海马体。最后，我们发现该模型的预测值与临床评估评分的变化率显著相关，表明该模型能够预测个体患者未来的认知能力下降。这些信息，结合所确定的解剖特征，将有助于为MCI患者建立个性化的治疗策略。
Dementia of Alzheimer’s Type (DAT) is associated with devastating and irreversible cognitive decline. Predicting which patients with mild cognitive impairment (MCI) will progress to DAT is an ongoing challenge in the field. We developed a deep learning model to predict conversion from MCI to DAT. Structural magnetic resonance imaging scans were used as input to a three-dimensional convolutional neural network (3D-CNN). The 3D-CNN was trained using transfer learning; in the source task, normal control and DAT scans were used to pre-train the model. This pre-trained model was then re-trained on the target task of classifying which MCI patients converted to DAT. Our model resulted in 82.4% classification accuracy at the target task, outperforming current models in the field. Next, we visualized brain regions that significantly contribute to the prediction of MCI conversion using an occlusion map approach. Contributory regions included the pons, amygdala, and hippocampus. Finally, we showed that the model’s prediction value is significantly correlated with rates of change in clinical assessment scores, indicating that the model is able to predict an individual patient’s future cognitive decline. This information, in conjunction with the identified anatomical features, will aid in building a personalized therapeutic strategy for individuals with MCI.
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影响因子：
4.2
作者：
Da, Xiao;Toledo, Jon B.;Zee, Jarcy;Wolk, David A.;Xie, Sharon X.;Ou, Yangming;Shacklett, Amanda;Parmpi, Paraskevi;Shaw, Leslie;Trojanowski, John Q.;Davatzikos, Christos
通讯作者：
Davatzikos, Christos
DOI：
10.1109/tbme.2015.2404809
发表时间：
2015-07
期刊：
IEEE transactions on bio-medical engineering
影响因子：
--
作者：
Cheng B;Liu M;Zhang D;Munsell BC;Shen D
通讯作者：
Shen D
影响因子：
4.2
作者：
Coupe, Pierrick;Eskildsen, Simon F.;Manjon, Jose V.;Fonov, Vladimir S.;Pruessner, Jens C.;Allard, Michele;Collins, D. Louis
通讯作者：
Collins, D. Louis
影响因子：
4
作者：
Ferrarini, Luca;Frisoni, Giovanni B.;Milles, Julien
通讯作者：
Milles, Julien
影响因子：
120.7
作者：
Jack, Clifford R., Jr.;Wiste, Heather J.;Petersen, Ronald C.
通讯作者：
Petersen, Ronald C.