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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3A10505184" target="_blank" >RIV/00216208:11320/25:10505184 - isvavai.cz</a>

Result on the web

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=Wt_S_2Uhl_" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=Wt_S_2Uhl_</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.jbi.2025.104929" target="_blank" >10.1016/j.jbi.2025.104929</a>

Result language

angličtina

Original language name

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

Original language description

Objective: Early diagnosis of Alzheimer&apos;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&apos;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.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Journal of Biomedical Informatics

ISSN

1532-0464

e-ISSN

1532-0480

Volume of the periodical

171

Issue of the periodical within the volume

November 2025

Country of publishing house

US - UNITED STATES

Number of pages

14

Pages from-to

104929

UT code for WoS article

001605598000001

EID of the result in the Scopus database

2-s2.0-105020852822