Attentive pairwise interaction network for AI-assisted clock drawing test assessment of early visuospatial deficits

Abstract

Dementia is a debilitating neurological condition which impairs the cognitive function and the ability to take care of oneself. The Clock Drawing Test (CDT) is widely used to detect dementia but differentiating normal from borderline cases requires years of clinical experience. Misclassifying mild abnormal as normal will delay the chance to investigate for potential reversible causes or slow down the progression. To help address this issue we propose an automatic CDT scoring system that adopts Attentive Pairwise Interaction Network (API-Net) a fine-grained deep learning model that is designed to distinguish visually similar images. Inspired by how humans often learn to recognize different objects by looking at two images side-by-side API-Net is optimized using image pairs in a contrastive manner as opposed to standard supervised learning which optimizes a model using individual images. In this study we extend API-Net to infer Shulman CDT scores from a dataset of 3108 subjects. We compare the performance of API-Net to that of convolutional neural networks: VGG16 ResNet-152 and DenseNet-121. The best API-Net achieves an F1-score of 0.79 which is a 3% absolute improvement over ResNet-152抯 F1-score of 0.76. The code for API-Net and the dataset used have been made available at https://github.com/cccnlab/CDT-API-Network . ? 2023 Springer Nature Limited.