Construction of a confounder-free clinical MRI dataset in the Mass General Brigham system for classification of Alzheimer's disease.

Deep learning has the potential to standardize and automate diagnostics for complex medical imaging data, but real-world clinical images are plagued by a high degree of heterogeneity and confounding factors that may introduce imbalances and biases to such processes. To address this, we developed and applied a data matching algorithm to 467,464 clinical brain magnetic resonance imaging (MRI) data from the Mass General Brigham (MGB) healthcare system for Alzheimer's disease (AD) classification. We identified 18 technical and demographic confounding factors that can be readily distinguished by MRI or have significant correlations with AD status and isolated a training set free from these confounds. We then applied an ensemble of 3D ResNet-50 deep learning models to classify brain MRIs between groups of AD, mild cognitive impairment (MCI), and healthy controls. From a confounder-free matched dataset of 287,367 MRI files, we achieved an area under the receiver operating characteristic (AUROC) of 0.82 in distinguishing healthy controls from patients with AD or MCI. We also showed that confounding factors in heterogeneous clinical data could lead to artificial gains in model performance for disease classification, which our data matching approach could correct. This approach could accelerate using deep learning models for clinical diagnosis and find broad applications in medical image analysis.