A Tablet-based Quantitative Analysis: Diagnostic Value of Digital vs. Conventional Geriatric Complex Figure for Discriminating Patients with aMCI from Healthy Individuals

Abstract Background The population with dementia is expected to rise to 152 million in 2050 due to the aging population worldwide. Therefore, it is of great significance to identify and intervene in the early stage of dementia. The Rey-Osterreth complex figure (ROCF)test is a visuospatial test scale. Its scoring methods are numerous, time-consuming, and have poor consistency, which is unsuitable for wide application as required by the high number of people at risk. Therefore, there is an urgent need for a rapid, objective, and sensitive digital scoring method to accurately detect cognitive dysfunction in the early stage. Objective This study aims to clarify the organizational strategy of aMCI patients to draw complex figures through a multi-dimensional digital evaluation system. At the same time, a rapid, objective, and sensitive digital scoring method is established to replace traditional scoring. Methods the data of 64 subjects (38 aMCI patients and 26 NC individuals) were analyzed in this study. All subjects completed the tablet's Geriatric Complex Figure (GCF) test, including copying, 3-min recall, and 20-min delayed recall, and also underwent a standardized neuropsychological test battery and classic ROCF test. Digital GCF(dGCF) variables and conventional GCF(cGCF) scores were input into the forward stepwise logistic regression model to construct classification models. Finally, ROC curves were made to visualize the difference in the diagnostic value of dGCF variables vs. cGCF scores in categorizing the diagnostic groups. Results In 20 min delayed recall, the time in air and pause time of aMCI patients were longer than NC individuals. And patients with aMCI had more short strokes and poorer ability of detail integration (all p < 0.05). The diagnostic sensitivity of dGCF variables for aMCI patients was 89.47%, slightly higher than cGCF scores (sensitivity: 84.21%). The diagnostic accuracy of both was comparable (dGCF: 70.3%; cGCF: 73.4%). Moreover, the combination of dGCF variables and cGCF scores could significantly improve the diagnostic accuracy and specificity (accuracy:78.1%, specificity: 84.62%). At the same time, we construct the regression equations of the two models. Conclusions Our study shows that dGCF equipment can quantitatively evaluate drawing performance, and its performance is comparable to the time-consuming cGCF score. The regression equation of the model we constructed can well identify patients with aMCI in clinical application. We believe that this new approach has the potential to become a digital biomarker for MCI patients.