Machine learning models for diagnosing Alzheimer's disease using brain cortical complexity.

Objective: This study aimed to develop and validate machine learning models (MLMs) to diagnose Alzheimer's disease (AD) using cortical complexity indicated by fractal dimension (FD).

Methods: A total of 296 participants with normal cognitive (NC) function and 182 with AD from the AD Neuroimaging Initiative database were randomly divided into training and internal validation cohorts. Then, FDs, demographic characteristics, baseline global cognitive function scales [Montreal Cognitive Assessment (MoCA), Functional Activities Questionnaire (FAQ), Global Deterioration Scale (GDS), Neuropsychiatric Inventory (NPI)], phospho-tau (p-tau 181), amyloidβ-42/40, apolipoprotein E (APOE) and polygenic hazard score (PHS) were collected to establish multiple MLMs. Receiver operating characteristic curves were used to evaluate model performance. Participants from our institution ( = 66; 33 with NC and 33 with AD) served as external validation cohorts to validate the MLMs. Decision curve analysis was used to estimate the models' clinical values.

Results: The FDs from 30 out of 69 regions showed significant alteration. All MLMs were conducted based on the 30 significantly different FDs. The FD model had good accuracy in predicting AD in three cohorts [area under the receiver operating characteristic (ROC) curve (AUC) = 0.842, 0.808, and 0.803]. There were no statistically significant differences in AUC values between the FD model and the other combined models in the training and internal validation cohorts except MoCA + FD and FAQ + FD models. Among MLMs, the MoCA + FD model showed the best predictive efficiency in three cohorts (AUC = 0.951, 0.931, and 0.955) and had the highest clinical net benefit.

Conclusion: The FD model showed favorable diagnostic performance for AD. Among MLMs, the MoCA + FD model can predict AD with the highest efficiency and could be used as a non-invasive diagnostic method.