Ren Yueqi, Shahbaba Babak, Stark Craig E L
Medical Scientist Training Program, School of Medicine, University of California Irvine, Irvine, California, USA.
Department of Statistics, Donald Bren School of Information and Computer Sciences, University of California Irvine, Irvine, California, USA.
Alzheimers Dement. 2025 Aug;21(8):e70539. doi: 10.1002/alz.70539.
Identifying dementia neuropathology is critical for guiding effective therapies and clinical trials. To tackle this, we developed semi-supervised models for identifying neuropathology using low-burden data to improve generalizability.
We defined low-burden data as being reasonably obtainable at a primary care setting. By using a semi-supervised learning paradigm, we can amplify the utility of low-burden data. We trained a clustering and a semi-supervised prediction model to yield clustering and prediction results for different neuropathology lesion types.
Our clustering model identified two clinically meaningful outlier groups that were either neuropathology-enriched or -scarce. We predicted neuropathology burden across different pathology types and found that using low-burden data over multiple clinical visits can predict neuropathology on par with using higher-burden data.
This work fills a critical gap in the field by using low-burden clinical data to predict neuropathology, thereby improving dementia screening, therapy, and targeted clinical trials.
Clinical data are useful for neuropathology screening in future clinical trials. Novel application of semi-supervised learning for identifying neuropathology. Clustering model found groups with highly different neuropathology prevalence. Low-burden data can provide relatively accurate predictions of pathology load. Higher-burden, longitudinal data are most helpful for predicting vascular lesions.
识别痴呆症神经病理学对于指导有效治疗和临床试验至关重要。为了解决这一问题，我们开发了半监督模型，利用低负荷数据识别神经病理学，以提高通用性。
我们将低负荷数据定义为在初级保健机构中能够合理获取的数据。通过使用半监督学习范式，我们可以扩大低负荷数据的效用。我们训练了一个聚类模型和一个半监督预测模型，以生成不同神经病理学病变类型的聚类和预测结果。
我们的聚类模型识别出两个具有临床意义的异常组，一组富含神经病理学特征，另一组则较少。我们预测了不同病理类型的神经病理学负担，发现通过多次临床就诊使用低负荷数据能够与使用高负荷数据一样预测神经病理学情况。
这项工作通过使用低负荷临床数据预测神经病理学，填补了该领域的一个关键空白，从而改善痴呆症筛查、治疗和靶向临床试验。
临床数据在未来临床试验的神经病理学筛查中很有用。半监督学习在识别神经病理学方面的新应用。聚类模型发现了神经病理学患病率差异很大的组。低负荷数据可以提供相对准确的病理负荷预测。高负荷的纵向数据对预测血管病变最有帮助。
