A novel cascade machine learning pipeline for Alzheimer's disease identification and prediction

Kun Zhou; Sirong Piao; Xiao Liu; Xiao Luo; Hongyi Chen; Rui Xiang; Daoying Geng

doi:10.3389/fnagi.2022.1073909

A novel cascade machine learning pipeline for Alzheimer's disease identification and prediction

Front Aging Neurosci. 2023 Jan 16:14:1073909. doi: 10.3389/fnagi.2022.1073909. eCollection 2022.

Authors

Kun Zhou¹, Sirong Piao², Xiao Liu³, Xiao Luo¹, Hongyi Chen¹, Rui Xiang¹, Daoying Geng^{1

2}

Affiliations

¹ Academy for Engineering and Technology, Fudan University, Shanghai, China.
² Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China.
³ School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China.

Abstract

Introduction: Alzheimer's disease (AD) is a progressive and irreversible brain degenerative disorder early. Among all diagnostic strategies, hippocampal atrophy is considered a promising diagnostic method. In order to proactively detect patients with early Alzheimer's disease, we built an Alzheimer's segmentation and classification (AL-SCF) pipeline based on machine learning.

Methods: In our study, we collected coronal T1 weighted images that include 187 patients with AD and 230 normal controls (NCs). Our pipeline began with the segmentation of the hippocampus by using a modified U2-net. Subsequently, we extracted 851 radiomics features and selected 37 features most relevant to AD by the Hierarchical clustering method and Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. At last, four classifiers were implemented to distinguish AD from NCs, and the performance of the models was evaluated by accuracy, specificity, sensitivity, and area under the curve.

Results: Our proposed pipeline showed excellent discriminative performance of classification with AD vs NC in the training set (AUC=0.97, 95% CI: (0.96-0.98)). The model was also verified in the validation set with Dice=0.93 for segmentation and accuracy=0.95 for classification.

Discussion: The AL-SCF pipeline can automate the process from segmentation to classification, which may assist doctors with AD diagnosis and develop individualized medical plans for AD in clinical practice.

Keywords: Alzheimer’s disease; automatic segmentation; coronal T1 weighted images; machine learning; radiomics classification.