A new AI program can accurately and efficiently detect cognitive impairment from voice recordings. Scientists develop an artificial intelligence program that detects cognitive impairment accurately and efficiently from voice recordings. A lot of time--and money--is required to diagnose Alzheimer's disease. After running lengthy in-person neuropsychological exams, clinicians have to transcribe, review, and analyze every response in detail. However, researchers at Boston University (BU) have developed a new tool that could automate the process and eventually allow it to move online.

Dementia can make it hard for a person to focus and remain alert -- two things that are really important for road safety. Being able to identify when someone is experiencing early signs of cognitive impairment could be key to saving lives on the road -- unfortunately, it's not always easy to notice these early signs. Now, machine learning could make it easier to detect dementia. The innovation: A team led by researchers at Columbia University has developed machine learning models that detect early and mild cognitive impairment in older drivers with 88% accuracy. The opportunity: By analyzing driving behavior, these machine learning algorithms can help identify when a driver might be exhibiting early indicators of dementia and mild cognitive impairment.

Detection of cognitive impairment, especially at the early stages, is critical. Such detection has traditionally been performed manually by one or more clinicians based on reports and test results. Machine learning algorithms offer an alternative method of detection that may provide an automated process and valuable insights into diagnosis and classification. In this paper, we explore the use of neuropsychological and demographic data to predict Clinical Dementia Rating (CDR) scores (no dementia, very mild dementia, dementia) and clinical diagnoses (cognitively healthy, mild cognitive impairment, dementia) through the implementation of four machine learning algorithms, naïve Bayes (NB), C4.5 decision tree (DT), back-propagation neural network (NN), and support vector machine (SVM). Additionally, a feature selection method for reducing the number of neuropsychological and demographic data needed to make an accurate diagnosis was investigated. The NB classifier provided the best accuracies, while the SVM classifier proved to offer some of the lowest accuracies. We also illustrate that with the use of feature selection, accuracies can be improved. The experiments reported in this paper indicate that artificial intelligence techniques can be used to automate aspects of clinical diagnosis of individuals with cognitive impairment.