A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer's disease

过度拟合 认知障碍 机器学习 深度学习 认知 人工神经网络 神经心理学 人工智能 心理学 模式识别(心理学) 计算机科学 神经科学
作者
S Spasov,Luca Passamonti,Andrea Duggento,Píetro Lió,Nicola Toschi
出处
期刊:NeuroImage [Elsevier BV]
卷期号:189: 276-287 被引量:316
标识
DOI:10.1016/j.neuroimage.2019.01.031
摘要

Some forms of mild cognitive impairment (MCI) are the clinical precursors of Alzheimer's disease (AD), while other MCI types tend to remain stable over-time and do not progress to AD. To identify and choose effective and personalized strategies to prevent or slow the progression of AD, we need to develop objective measures that are able to discriminate the MCI patients who are at risk of AD from those MCI patients who have less risk to develop AD. Here, we present a novel deep learning architecture, based on dual learning and an ad hoc layer for 3D separable convolutions, which aims at identifying MCI patients who have a high likelihood of developing AD within 3 years. Our deep learning procedures combine structural magnetic resonance imaging (MRI), demographic, neuropsychological, and APOe4 genetic data as input measures. The most novel characteristics of our machine learning model compared to previous ones are the following: 1) our deep learning model is multi-tasking, in the sense that it jointly learns to simultaneously predict both MCI to AD conversion as well as AD vs. healthy controls classification, which facilitates relevant feature extraction for AD prognostication; 2) the neural network classifier employs fewer parameters than other deep learning architectures which significantly limits data-overfitting (we use ∼550,000 network parameters, which is orders of magnitude lower than other network designs); 3) both structural MRI images and their warp field characteristics, which quantify local volumetric changes in relation to the MRI template, were used as separate input streams to extract as much information as possible from the MRI data. All analyses were performed on a subset of the database made publicly available via the Alzheimer's Disease Neuroimaging Initiative (ADNI), (n = 785 participants, n = 192 AD patients, n = 409 MCI patients (including both MCI patients who convert to AD and MCI patients who do not covert to AD), and n = 184 healthy controls). The most predictive combination of inputs were the structural MRI images and the demographic, neuropsychological, and APOe4 data. In contrast, the warp field metrics were of little added predictive value. The algorithm was able to distinguish the MCI patients developing AD within 3 years from those patients with stable MCI over the same time-period with an area under the curve (AUC) of 0.925 and a 10-fold cross-validated accuracy of 86%, a sensitivity of 87.5%, and specificity of 85%. To our knowledge, this is the highest performance achieved so far using similar datasets. The same network provided an AUC of 1 and 100% accuracy, sensitivity, and specificity when classifying patients with AD from healthy controls. Our classification framework was also robust to the use of different co-registration templates and potentially irrelevant features/image portions. Our approach is flexible and can in principle integrate other imaging modalities, such as PET, and diverse other sets of clinical data. The convolutional framework is potentially applicable to any 3D image dataset and gives the flexibility to design a computer-aided diagnosis system targeting the prediction of several medical conditions and neuropsychiatric disorders via multi-modal imaging and tabular clinical data.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
samchen完成签到,获得积分10
刚刚
星辰大海应助开心小猪采纳,获得10
1秒前
1秒前
科研杂役发布了新的文献求助10
1秒前
CX发布了新的文献求助10
1秒前
3秒前
3秒前
温柔体贴阿尔法完成签到,获得积分10
3秒前
4秒前
xue发布了新的文献求助10
4秒前
ll应助苏苏采纳,获得10
4秒前
秦从露完成签到 ,获得积分10
4秒前
asdfghjkl发布了新的文献求助10
5秒前
5秒前
Fwisme完成签到,获得积分10
6秒前
6秒前
南昌黑人完成签到,获得积分10
7秒前
7秒前
SimonJay发布了新的文献求助10
7秒前
能干雁凡完成签到,获得积分10
7秒前
zjl900111发布了新的文献求助10
9秒前
小余同学发布了新的文献求助10
9秒前
量子星尘发布了新的文献求助10
9秒前
只剩下55发布了新的文献求助10
9秒前
9秒前
鲤鱼幼晴举报旺仔求助涉嫌违规
10秒前
Ace发布了新的文献求助10
10秒前
李健应助li采纳,获得10
11秒前
11秒前
传奇3应助galaxy采纳,获得10
11秒前
12秒前
迷人的冰旋完成签到,获得积分10
12秒前
简单以冬发布了新的文献求助10
12秒前
lmx发布了新的文献求助40
13秒前
13秒前
14秒前
SYLH应助Violet采纳,获得10
14秒前
xue完成签到,获得积分10
14秒前
保持好心情完成签到 ,获得积分10
14秒前
aaaaaa发布了新的文献求助10
15秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
Technical Brochure TB 814: LPIT applications in HV gas insulated switchgear 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Picture Books with Same-sex Parented Families: Unintentional Censorship 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
A Preliminary Study on Correlation Between Independent Components of Facial Thermal Images and Subjective Assessment of Chronic Stress 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3970394
求助须知:如何正确求助?哪些是违规求助? 3515139
关于积分的说明 11177107
捐赠科研通 3250335
什么是DOI,文献DOI怎么找? 1795254
邀请新用户注册赠送积分活动 875732
科研通“疑难数据库(出版商)”最低求助积分说明 805054