Artificial intelligence approaches to predicting and detecting cognitive decline in older adults: A conceptual review

Abstract

Preserving cognition and mental capacity is critical to aging with autonomy. Early detection of pathological cognitive decline facilitates the greatest impact of restorative or preventative treatments. Artificial Intelligence (AI) in healthcare is the use of computational algorithms that mimic human cognitive functions to analyze complex medical data. AI technologies like machine learning (ML) support the integration of biological, psychological, and social factors when approaching diagnosis, prognosis, and treatment of disease. This paper serves to acquaint clinicians and other stakeholders with the use, benefits, and limitations of AI for predicting, diagnosing, and classifying mild and major neurocognitive impairments, by providing a conceptual overview of this topic with emphasis on the features explored and AI techniques employed. We present studies that fell into six categories of features used for these purposes: (1) sociodemographics; (2) clinical and psychometric assessments; (3) neuroimaging and neurophysiology; (4) electronic health records and claims; (5) novel assessments (e.g., sensors for digital data); and (6) genomics/other omics. For each category we provide examples of AI approaches, including supervised and unsupervised ML, deep learning, and natural language processing. AI technology, still nascent in healthcare, has great potential to transform the way we diagnose and treat patients with neurocognitive disorders.

Digital Biomarkers for the Early Detection of Mild Cognitive Impairment: Artificial Intelligence Meets Virtual Reality

“…The application of ML for healthcare purposes has been further developed into two main sub-classes, supervised (SL) and unsupervised (UL) techniques. SL jointly employs pre-labeled data, e.g., MCI versus healthy subjects, and additional features derived from clinical or neuroimaging sources to determine which feature predicts the pre-labeled data the most (Dwyer et al, 2018;Graham et al, 2020). SL operates with probabilistic and non-probabilistic classifiers (Naïve Bayes and Support Vector Machine, respectively), as well as with decision tree, linear, and logistic regression (Dhall and Kaur, 2020).…”

“…SL operates with probabilistic and non-probabilistic classifiers (Naïve Bayes and Support Vector Machine, respectively), as well as with decision tree, linear, and logistic regression (Dhall and Kaur, 2020). UL techniques, instead, sets unlabeled and unstructured data, e.g., clinical notes, as a starting point to seek relationships or patterns and to learn general representations that enable the automatic extraction of information when building predictors (Miotto et al, 2017;Dwyer et al, 2018;Graham et al, 2020). The algorithms employed by UL include K-means clustering, PCA, and Artificial Neural Networks (ANN) (Dhall and Kaur, 2020).…”

“…Including an evaluation where the elderly person him/herself performs IADL might be essential for establishing more precise criteria ( Díaz-Mardomingo et al, 2017 ; Seo et al, 2017 ). Several authors have tried to refine early MCI detection by combining two out of the three variables considered in this paper: either behavioral alteration (IADL, gait) within a VR environment ( Lee et al, 2003 ; Seo et al, 2017 ; Kim et al, 2019 ; Eraslan Boz et al, 2019 ), gait kinematics extracted and analyzed by means of ML, which will be discussed further ( Begg and Kamruzzaman, 2005 ; Pogorelc et al, 2012 ; Zhang and Wang, 2012 ; Eskofier et al, 2013 ; Akl et al, 2015 ; Costa et al, 2016 ; Mannini et al, 2016 ; Caldas et al, 2017 ; Farah et al, 2017 ; Ur Rehman et al, 2019 ), or ML techniques for predicting MCI evolution ( Filipovych and Davatzikos, 2011 ; Williams and Weakley, 2013 ; Moradi et al, 2014 , 2015 ; Bratić et al, 2018 ; Grassi et al, 2018 , 2019 ; Graham et al, 2020 ). Thus, to our best knowledge, this is the first paper proposing an integration of VR, gait kinematics, and ML in order to refine early detection of MCI following a dimensional approach in line with the most recent diagnostic systems and possibly providing information on disease progression.…”

“…The employment of kinematic measures and VR within a healthcare setting, i.e., a hospital, inevitably involves the use of a large amount of electronic health records (EHR) of patients’ evolution over time. Despite the challenges related to the use of EHRs, several prediction algorithms and models have been developed from their use ( Häyrinen et al, 2008 ; Miotto et al, 2016 , 2017 ; Goldstein et al, 2017 ; Graham et al, 2020 ). Among other advantages, EHR-based predictors consider various metrics of multiple individuals, observed at different time points: this makes use of a higher frequency of data recording, facilitating the prediction of possible near-term evolution; they also reflect real life more closely than cohort studies ( Goldstein et al, 2017 ).…”

Beyond artificial intelligence: exploring artificial wisdom

“…However, clinical decisionmaking requires more than intelligent thinking-it requires wise thinking that incorporates ethical and moral considerations. Therefore, we are still far from routine adoption of AI in mental healthcare, especially psychogeriatrics, in view of its limitations and potential risks (Graham et al, 2019(Graham et al, , 2020. The incredibly fast advances in computer science and related technologies that have outpaced the development of societal guidelines have raised serious questions about the ethics and morality of AI, and called for international oversight and regulations to ensure safety.…”