Early detection of Alzheimer's disease based on leveraging multimodal features of the clock drawing test.

BackgroundAlthough multi-task handwriting analysis has the potential to improve early detection of Alzheimer's disease (AD), the educational bias inherent in its text-based tasks poses a significant obstacle to its widespread adoption across different regions.ObjectiveUsing the clock drawing test, we aim to design a deep neural network to extract features from static images and process signals to achieve high-precision recognition of early AD.MethodsEarly Detection of Alzheimer's Disease based on Leveraging Multimodal Features of the clock-drawing test (EDADLMF) is proposed. Firstly, to utilize the behavioral features inherent in the clock drawing test task, we propose a Dual Stream Clock Drawing Feature Extraction module,which employs a convolutional neural networks to capture the spatial features of static clock face images, while concurrently employing a multi-layer perceptron to map low-dimensional process signal into a high-dimensional feature space. Furthermore, we propose a Feature Fusion module with the Squeeze-and-Excitation attention mechanism to adaptively enhance key features and fuses complementary information from different modalities. Thirdly, to enhance the model's focus on hard-to-classify samples, a PolyLoss function is introduced to assign greater weights to difficult samples.ResultsComparative experiments on benchmark demonstrated that EDADLMF outperforms the compaired methods on accuracy (92.59%), precision (93.65%), recall (92.65%), and F1-score (92.59%), and the case study indicates that the developed prototype system has well effectiveness.ConclusionsThe clock drawing test, combined with process signals and image data, exhibits better screening accuracy and could serve as a practical alternative to initial MRI scans.