Trial reportJournal of medical Internet research2025
Machine Learning-Based Cognitive Assessment With The Autonomous Cognitive Examination: Randomized Controlled Trial.
Trial report in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The rising prevalence of dementia necessitates a scalable solution to cognitive assessments. The Autonomous Cognitive Examination (ACoE) is a foundational cognitive test for the phenotyping of cognitive symptoms across the primary cognitive domains. However, while the ACoE has been internally validated, it has not been externally validated in a clinical population, and its ability to render accurate appraisals of cognition is unknown. Further, it is unclear if these phenotypic assessments are useful in clinical tasks such as screening patients with and those without impairments. Objective: The objective of this study is to validate the ability of the ACoE to reliably phenotype cognition and to act as a screening examination relative to standard paper-based tests. Methods: To compare the evaluations of the ACoE to established paper-based tests, 46 patients with neurological disorders were enrolled in a randomized crossover study and received either the ACoE or a standard paper-based cognitive test. Patients received either the Addenbrooke Cognitive Examination-3 (ACE-3; n=35) or the Montreal Cognitive Examination (MoCA; n=11). We evaluated 3 primary metrics of the ACoE's performance relative to paper-based tests: (1) interrater reliability of overall cognitive scores, (2) interrater reliability of cognitive domain scores, and (3) ability to classify patients similarly to paper-based tests. Results: The ACoE's overall cognitive assessments were significantly reliable (ICC [intraclass correlation coefficient]=0.89; P<.001). Each cognitive domain's assessments were also significantly reliable, including attention (ICC=0.74; PFWE<.001), language (ICC=0.89; PFWE<.001), memory (ICC=0.91; PFWE<.001), fluency (ICC=0.74; PFWE<.001), and visuospatial function (ICC=0.78; PFWE<.001). The ACoE was also able to successfully diagnose patients similarly to both paper-based tests (area under the receiver operating characteristic curve=0.96; PFWE<.001). Conclusions: In this study, we evaluated if the ACoE could reliably phenotype cognitive symptoms relative to the assessments of established standard paper-based cognitive assessments. We found that the ACoE reliably phenotypes patient cognition, which can be used to screen patients. In the future, these cognitive phenotypes may be used to diagnose specific etiologies.
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