With the arrival of disease-modifying drugs, neurodegenerative diseases will require an accurate diagnosis for optimal treatment. Convolutional neural networks are powerful deep learning techniques that can provide great help to physicians in image analysis. The purpose of this study is to introduce and validate a 3D neural network for classification of Alzheimer's disease (AD), frontotemporal dementia (FTD) or cognitively normal (CN) subjects based on brain glucose metabolism. Retrospective [18F]-FDG-PET scans of 199 AD, 192 FTD and 200 CN subjects were collected from our local database, Alzheimer's disease and frontotemporal lobar degeneration neuroimaging initiatives. Training and test sets were created using randomization on a 90%-10% basis, and training of a 3D VGG16-like neural network was performed using data augmentation and cross-validation. Performance was compared to clinical interpretation by three specialists in the independent test set. Regions determining classification were identified in an occlusion experiment and Gradient-weighted Class Activation Mapping. Test set subjects were age- and sex-matched across categories. The model achieved an overall 89.8% accuracy in predicting the class of test scans. Areas under the ROC curves were 93.3% for AD, 95.3% for FTD, and 99.9% for CN. The physicians' consensus showed a 69.5% accuracy, and there was substantial agreement between them (kappa = 0.61, 95% CI: 0.49-0.73). To our knowledge, this is the first study to introduce a deep learning model able to discriminate AD and FTD based on [18F]-FDG PET scans, and to isolate CN subjects with excellent accuracy. These initial results are promising and hint at the potential for generalization to data from other centers.

中文翻译：

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3D 卷积神经网络使用大脑 18F-FDG PET 将受试者分类为阿尔茨海默病、额颞叶痴呆或健康对照

随着疾病缓解药物的出现，神经退行性疾病将需要准确的诊断才能获得最佳的治疗。卷积神经网络是强大的深度学习技术，可以为医生进行图像分析提供很大的帮助。本研究的目的是引入并验证 3D 神经网络，用于根据大脑葡萄糖代谢对阿尔茨海默病 (AD)、额颞叶痴呆 (FTD) 或认知正常 (CN) 受试者进行分类。从我们的本地数据库、阿尔茨海默病和额颞叶变性神经影像学项目中收集了 199 名 AD、192 名 FTD 和 200 名 CN 受试者的回顾性 [18F]-FDG-PET 扫描。使用 90%-10% 的随机化创建训练和测试集，并使用数据增强和交叉验证来执行 3D VGG16 类神经网络的训练。由三位专家在独立测试集中将性能与临床解释进行比较。在遮挡实验和梯度加权类激活映射中确定了确定分类的区域。测试集受试者的年龄和性别在各个类别中都匹配。该模型在预测测试扫描类别方面的总体准确率达到 89.8%。AD 的 ROC 曲线下面积为 93.3%，FTD 为 95.3%，CN 为 99.9%。医生的共识显示准确度为 69.5%，并且他们之间基本一致（kappa = 0.61，95% CI：0.49-0.73）。据我们所知，这是第一项引入深度学习模型的研究，该模型能够基于 [18F]-FDG PET 扫描来区分 AD 和 FTD，并以极高的准确性隔离 CN 受试者。这些初步结果很有希望，并暗示了推广到其他中心数据的潜力。