Machine-learning models for Alzheimer’s disease diagnosis using neuroimaging data: survey, reproducibility, and generalizability evaluation
Year: 2025
Type: review
Abstract: Clinical diagnosis of Alzheimer's disease (AD) is usually made after symptoms such as short-term memory loss are exhibited, which minimizes the intervention and treatment options. The existing screening techniques cannot distinguish between stable MCI (sMCI) cases (i.e., patients who do not convert to AD for at least three years) and progressive MCI (pMCI) cases (i.e., patients who convert to AD in three years or sooner). Delayed diagnosis of AD also disproportionately affects underrepresented and socioeconomically disadvantaged populations. The significant positive impact of an early diagnosis solution for AD across diverse ethno-racial and demographic groups is well-known and recognized. While advancements in high-throughput technologies have enabled the generation of vast amounts of multimodal clinical, and neuroimaging datasets related to AD, most methods utilizing these data sets for diagnostic purposes have not found their way in clinical settings. To better understand the landscape, we surveyed the major preprocessing, data management, traditional machine-learning (ML), and deep learning (DL) techniques used for diagnosing AD using neuroimaging data such as structural magnetic resonance imaging (sMRI), functional magnetic resonance imaging (fMRI), and positron emission tomography (PET). Once we had a good understanding of the methods available, we conducted a study to assess the reproducibility and generalizability of open-source ML models. Our evaluation shows that existing models show reduced generalizability when different cohorts of the data modality are used while controlling other computational factors. The paper concludes with a discussion of major challenges that plague ML models for AD diagnosis and biomarker discovery.
Source: Brain Informatics
Institutions: Florida International University, University of North Texas
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $1,090
Awards: G104586243, G1578372831, DRIVING PERFORMANCE IN PRECLINICAL ALZHEIMER'S DISEASE, Alzheimer's Disease Neuroimaging Initiative, Healthy Aging and Senile Dementia, Washington University Institute of Clinical and Translational Sciences, Machine-Learning Models for big data omics, Alzheimer's Disease Research Center, Washington University Institute of Clinical and Translational Sciences, THE XNAT IMAGING INFORMATICS PLATFORM, Antecedent Biomarkers for Alzheimer Disease: The Adult Children Study, G904343237
OpenAlex ID: W4408715817