{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["15(2)"],"submitter":["Handzlik D"],"pubmed_abstract":["<h4>Introduction</h4>The clock drawing task (CDT) is frequently used to aid in detecting cognitive impairment, but current scoring techniques are time-consuming and miss relevant features, justifying the creation of an automated quantitative scoring approach.<h4>Methods</h4>We used computer vision methods to analyze the stored scanned images (<i>N</i> = 7,109), and an intelligent system was created to examine these files in a study of aging World Trade Center responders. Outcomes were CDT, Montreal Cognitive Assessment (MoCA) score, and incidence of mild cognitive impairment (MCI).<h4>Results</h4>The system accurately distinguished between previously scored CDTs in three CDT scoring categories: contour (accuracy = 92.2%), digits (accuracy = 89.1%), and clock hands (accuracy = 69.1%). The system reliably predicted MoCA score with CDT scores removed. Predictive analyses of the incidence of MCI at follow-up outperformed human-assigned CDT scores.<h4>Discussion</h4>We created an automated scoring method using scanned and stored CDTs that provided additional information that might not be considered in human scoring."],"journal":["Alzheimer's & dementia (Amsterdam, Netherlands)"],"pagination":["e12441"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10201210"],"repository":["biostudies-literature"],"pubmed_title":["Explainable automated evaluation of the clock drawing task for memory impairment screening."],"pmcid":["PMC10201210"],"pubmed_authors":["Skiena S","Luft BJ","Richmond LL","Carr MA","Clouston SAP","Handzlik D"],"additional_accession":[]},"is_claimable":false,"name":"Explainable automated evaluation of the clock drawing task for memory impairment screening.","description":"<h4>Introduction</h4>The clock drawing task (CDT) is frequently used to aid in detecting cognitive impairment, but current scoring techniques are time-consuming and miss relevant features, justifying the creation of an automated quantitative scoring approach.<h4>Methods</h4>We used computer vision methods to analyze the stored scanned images (<i>N</i> = 7,109), and an intelligent system was created to examine these files in a study of aging World Trade Center responders. Outcomes were CDT, Montreal Cognitive Assessment (MoCA) score, and incidence of mild cognitive impairment (MCI).<h4>Results</h4>The system accurately distinguished between previously scored CDTs in three CDT scoring categories: contour (accuracy = 92.2%), digits (accuracy = 89.1%), and clock hands (accuracy = 69.1%). The system reliably predicted MoCA score with CDT scores removed. Predictive analyses of the incidence of MCI at follow-up outperformed human-assigned CDT scores.<h4>Discussion</h4>We created an automated scoring method using scanned and stored CDTs that provided additional information that might not be considered in human scoring.","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Apr-Jun","modification":"2025-04-04T10:27:42.253Z","creation":"2025-04-04T10:27:42.253Z"},"accession":"S-EPMC10201210","cross_references":{"pubmed":["37223333"],"doi":["10.1002/dad2.12441"]}}