Multi-stream deep learning framework to predict mild cognitive impairment with Rey Complex Figure Test

TL;DR

This study introduces a multi-stream deep learning framework combining raw RCFT images and automated scoring to improve early detection of mild cognitive impairment, demonstrating high accuracy and robustness across datasets.

Contribution

The paper presents a novel multi-stream deep learning model integrating spatial image analysis and scoring data for MCI prediction, validated on large Korean cohorts.

Findings

Superior performance with AUC=0.872 and accuracy=0.781

Effective integration of visual and scoring data enhances detection

Model demonstrates robustness across external datasets

Abstract

Drawing tests like the Rey Complex Figure Test (RCFT) are widely used to assess cognitive functions such as visuospatial skills and memory, making them valuable tools for detecting mild cognitive impairment (MCI). Despite their utility, existing predictive models based on these tests often suffer from limitations like small sample sizes and lack of external validation, which undermine their reliability. We developed a multi-stream deep learning framework that integrates two distinct processing streams: a multi-head self-attention based spatial stream using raw RCFT images and a scoring stream employing a previously developed automated scoring system. Our model was trained on data from 1,740 subjects in the Korean cohort and validated on an external hospital dataset of 222 subjects from Korea. The proposed multi-stream model demonstrated superior performance over baseline models (AUC = 0.872, Accuracy = 0.781) in external validation. The integration of both spatial and scoring streams enables the model to capture intricate visual details from the raw images while also incorporating structured scoring data, which together enhance its ability to detect subtle cognitive impairments. This dual approach not only improves predictive accuracy but also increases the robustness of the model, making it more reliable in diverse clinical settings. Our model has practical implications for clinical settings, where it could serve as a cost-effective tool for early MCI screening.