Remote diagnosis of dementia using AI methods on clock drawing images

Abstract

AbstractBackgroundDetection of any form of cognitive impairment is challenging and the subjects have to undergo numerous evaluations and clinical tests. Hence, it would be of great importance to design a reliable and accessible procedure by which patients may get diagnosed for dementia remotely. The capability of the Clock Drawing Test (CDT)as an effective cognitive assessment tool has motivated us to develop an online diagnostic tool by leveraging artificial intelligence techniques.MethodDigital pen recordings of 3,263 normal subjects and 160 with dementia in the Framingham Heart Study (FHS) were collected, where all subjects have completed two analog clock drawings, one drawn on command and the other by copying. Using the idea of transfer learning, we first modified and trained a Convolutional Neural Network (CNN) pre‐trained on the ImageNet dataset to extract high level features of the CDT images, which generated a score associated with the likelihood of dementia for each patient. The proposed method integrates the scores of the CDT images and other demographic information. Therefore, the generated scores for both command and copy CDTs along with age were used to train a logistic regression model to classify individuals as demented or normal.ResultWe have evaluated the performance of the developed models by applying 5‐fold cross validation on the FHS dataset. On the test dataset, the model (modified pre‐trained CNN) based on command CDT images yielded an AUC of 0.81±0.043. The logistic regression model using age and the generated scores of command and copy CDTs, yielded an average AUC and average F1 score of 0.92±0.008 and 0.94±0.008, respectively.ConclusionOur method need not necessarily have access to digital biomarkers or clinical tests since the CDT can be completed using pen and paper, capturing the image using a smartphone. Hence, our method offers a cost‐effective and accurate screening tool to diagnose dementia and related diseases remotely.