ArticleArtificial intelligence in medicine2022
Construction of a confounder-free clinical MRI dataset in the Mass General Brigham system for classification of Alzheimer's disease.
Article in Artificial intelligence in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- ACross-Paradigm CNN-Swin Transformer Ensemble with Super-Resolution Enhancement for Multi-Class Alzheimer's Disease Classification.Bioengineering (Basel, Switzerland) · 2026Article
- Decision Strategies in AI-Based Ensemble Models in Opportunistic Alzheimer's Detection from Structural MRI.Journal of imaging informatics in medicine · 2026Article
- Closed-form feedback-free learning with forward projection.Nature communications · 2026Article
- Differential dementia detection from multimodal brain images in a real-world dataset.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Real-world brain imaging in a population-based cohort enables accurate markers for dementia.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Functional magnetic resonance imaging of depression: a bibliometrics and meta-analysis.Annals of general psychiatry · 2024Article
- Transparent medical image AI via an image-text foundation model grounded in medical literature.Nature medicine · 2024Article
- Multimodal classification of Alzheimer's disease and mild cognitive impairment using custom MKSCDDL kernel over CNN with transparent decision-making for explainable diagnosis.Scientific reports · 2024Article
- Challenges of implementing computer-aided diagnostic models for neuroimages in a clinical setting.NPJ digital medicine · 2023Review
- Fostering transparent medical image AI via an image-text foundation model grounded in medical literature.medRxiv : the preprint server for health sciences · 2023Article
- Adversarial confound regression and uncertainty measurements to classify heterogeneous clinical MRI in Mass General Brigham.PloS one · 2023Article
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Abstract
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.
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