Optimal transport- and kernel-based early detection of mild cognitive impairment patients based on magnetic resonance and positron emission tomography images.
Optimal transport- and kernel-based early detection of mild cognitive impairment patients based on magnetic resonance and positron emission tomography images.
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DOI：
10.1186/s13195-021-00915-3
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发表时间：
2022-01-07
期刊：
影响因子：
--
通讯作者：
Huang K
中科院分区：
文献类型：
--
作者：
Liu Z;Johnson TS;Shao W;Zhang M;Zhang J;Huang K
为了帮助临床医生提供及时的治疗和延缓疾病的进展，在轻度认知障碍(MCI)阶段识别痴呆患者，并在他们发展为阿尔茨海默病(AD)之前将这些MCI患者分为早期和晚期MCI阶段是至关重要的。在活体患者诊断MCI和AD的过程中，脑扫描是使用神经成像技术收集的，例如计算机断层扫描(CT)、磁共振成像(MRI)或正电子发射断层扫描(PET)。这些脑扫描测量大脑内的体积和分子活动，从而为以微创方式早期诊断患者提供了一种非常有希望的途径。我们开发了一个基于最优传输的迁移学习模型来区分早期和晚期的MCI。将该转移学习模型与Bootstrap聚合策略相结合，克服了过拟合问题，提高了模型的稳定性和预测精度。利用我们开发的迁移学习方法，我们的表现优于当前最先进的MCI阶段分类框架，并表明利用阿尔茨海默病和正常对照受试者准确预测早期和晚期认知障碍是至关重要的。我们的方法是基于基准比较的最新技术。该方法为基于MRI的AD早期检测在临床上的广泛应用奠定了必要的技术基础。网上版载有补充材料，可在(10.1186/s13195-021-00915-3)查阅。
To help clinicians provide timely treatment and delay disease progression, it is crucial to identify dementia patients during the mild cognitive impairment (MCI) stage and stratify these MCI patients into early and late MCI stages before they progress to Alzheimer’s disease (AD). In the process of diagnosing MCI and AD in living patients, brain scans are collected using neuroimaging technologies such as computed tomography (CT), magnetic resonance imaging (MRI), or positron emission tomography (PET). These brain scans measure the volume and molecular activity within the brain resulting in a very promising avenue to diagnose patients early in a minimally invasive manner. We have developed an optimal transport based transfer learning model to discriminate between early and late MCI. Combing this transfer learning model with bootstrap aggregation strategy, we overcome the overfitting problem and improve model stability and prediction accuracy. With the transfer learning methods that we have developed, we outperform the current state of the art MCI stage classification frameworks and show that it is crucial to leverage Alzheimer’s disease and normal control subjects to accurately predict early and late stage cognitive impairment. Our method is the current state of the art based on benchmark comparisons. This method is a necessary technological stepping stone to widespread clinical usage of MRI-based early detection of AD. The online version contains supplementary material available at (10.1186/s13195-021-00915-3).
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