Synthetic-to-real attentive deep learning for Alzheimer's assessment: A domain-agnostic framework for ROCF scoring.

Abstract

Objective: Early diagnosis of Alzheimer's disease depends on accessible cognitive assessments, such as the Rey-Osterrieth Complex Figure (ROCF) test. However, manual scoring of this test is labor-intensive and subjective, which introduces experimental biases. Additionally, deep learning models face challenges due to the limited availability of annotated clinical data, particularly for assessments like the ROCF test. This scarcity of data restricts model generalization and exacerbates domain shifts across different populations.

Methods: We propose a novel framework comprising a data synthesis pipeline and ROCF-Net, a deep learning model specifically designed for ROCF scoring. The synthesis pipeline is lightweight and capable of generating realistic, diverse, and annotated ROCF drawings. ROCF-Net, on the other hand, is a cross-domain scoring model engineered to address domain discrepancies in stroke texture and line artifacts. It maintains high scoring accuracy through a novel line-specific attention mechanism tailored to the unique characteristics of ROCF drawings.

Results: Unlike conventional synthetic medical imaging methods, our approach generates ROCF drawings that accurately reflect Alzheimer's-specific abnormalities with minimal computational cost. Our scoring model achieves SOTA performance across differently sourced datasets, with a Mean Absolute Error (MAE) of 3.53 and a Pearson Correlation Coefficient (PCC) of 0.86. This demonstrates both high predictive accuracy and computational efficiency, outperforming existing ROCF scoring methods that rely on Convolutional Neural Networks (CNNs) while avoiding the overhead of parameter-heavy transformer models. We also show that training on our synthetic data generalizes as well as training on real clinical data, where the difference in performance was minimal (MAE differed by 1.43 and PCC by 0.07), indicating no statistically significant performance gap.

Conclusion: Our work introduces four contributions: (1) a cost-effective pipeline for generating synthetic ROCF data, reducing dependency on clinical datasets; (2) a domain-agnostic model for automated ROCF scoring across diverse drawing styles; (3) a lightweight attention mechanism aligning model decisions with clinical scoring for transparency; and (4) a bias-aware framework using synthetic data to reduce demographic disparities, promoting fair cognitive assessment across populations.