The Aged Mind Observed with a Digital Filter: Detecting Mild Cognitive Impairment through Virtual Reality and Machine Learning

virtual reality (VR) has been used in recent years to detect MCI and other forms of cognitive decline, with comparable or better results than commonly used paper-and-pencil tools. This technology, regardless of the level of immersion proposed in previous studies (i.e., non-immersive, semi-immersive, and full-immersive VR), creates a sense of agency and comfort in the elderly while increasing the degree of ecological validity. The VR assessments typically use scenarios that correspond to everyday activities (e.g., virtual supermarket tasks, spatial orientation tasks, etc.). As the elderly user interacts with these scenarios, the clinician can assess different cognitive domains (e.g., memory, spatial memory, executive functions) that are typically affected by MCI. In this case, VR makes it possible to consider and extract new types of behavioral data useful for early detection of cognitive decline, such as average performance time, distance traveled in the VR environment (VRE), and movement patterns performed in the scenario. In addition, VR tools can be used quickly (5–20 minutes for a complete assessment), allowing older adults to use VR and maintain motivation. Furthermore, there is evidence that these tools are well accepted by the elderly population, as they do not often experience complications such as cybersickness.6 However, VR assessment suffers from several limitations, the most important of which is the lack of automated tools for extracting and evaluating the data collected in the virtual environment, resulting in less efficient classification and diagnosis of cognitive decline. In this context, artificial intelligence (AI) and especially machine learning (ML) have been widely applied in this medical field. ML is a branch of AI that consists of developing computer programs that, instead of being programmed to perform specific tasks, are able to learn from different types of data and patterns to make predictions, identify patterns, and solve problems.7 ML has been extensively used in the field of medical diagnosis, also moving toward the detection of conditions such as MCI and AD, achieving good results in predicting and assisting medical diagnoses.