Literature DB >> 27066307

THink: Inferring Cognitive Status from Subtle Behaviors.

Randall Davis1, David J Libon2, Rhoda Au3, David Pitman1, Dana L Penney4.   

Abstract

The Digital Clock Drawing Test is a fielded application that provides a major advance over existing neuropsychological testing technology. It captures and analyzes high precision information about both outcome and process, opening up the possibility of detecting subtle cognitive impairment even when test results appear superficially normal. We describe the design and development of the test, document the role of AI in its capabilities, and report on its use over the past seven years. We outline its potential implications for earlier detection and treatment of neurological disorders. We also set the work in the larger context of the THink project, which is exploring multiple approaches to determining cognitive status through the detection and analysis of subtle behaviors.

Entities:  

Year:  2014        PMID: 27066307      PMCID: PMC4825804     

Source DB:  PubMed          Journal:  Proc Conf AAAI Artif Intell        ISSN: 2159-5399


  3 in total

1.  The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment.

Authors:  Ziad S Nasreddine; Natalie A Phillips; Valérie Bédirian; Simon Charbonneau; Victor Whitehead; Isabelle Collin; Jeffrey L Cummings; Howard Chertkow
Journal:  J Am Geriatr Soc       Date:  2005-04       Impact factor: 5.562

2.  Clock drawing test ratings by dementia specialists: interrater reliability and diagnostic accuracy.

Authors:  Anil K Nair; Brandon E Gavett; Moniek Damman; Welmoed Dekker; Robert C Green; Alan Mandel; Sanford Auerbach; Eric Steinberg; Emily J Hubbard; Angela Jefferson; Robert A Stern
Journal:  J Neuropsychiatry Clin Neurosci       Date:  2010       Impact factor: 2.198

3.  The Framingham Heart Study clock drawing performance: normative data from the offspring cohort.

Authors:  Justin A Nyborn; Jayandra J Himali; Alexa S Beiser; Sherral A Devine; Yangchun Du; Edith Kaplan; Maureen K O'Connor; William E Rinn; Helen S Denison; Sudha Seshadri; Philip A Wolf; Rhoda Au
Journal:  Exp Aging Res       Date:  2013       Impact factor: 1.645

  3 in total
  11 in total

1.  Learning Classification Models of Cognitive Conditions from Subtle Behaviors in the Digital Clock Drawing Test.

Authors:  William Souillard-Mandar; Randall Davis; Cynthia Rudin; Rhoda Au; David J Libon; Rodney Swenson; Catherine C Price; Melissa Lamar; Dana L Penney
Journal:  Mach Learn       Date:  2015-10-20       Impact factor: 2.940

2.  An analysis of a digital variant of the Trail Making Test using machine learning techniques.

Authors:  Jessamyn Dahmen; Diane Cook; Robert Fellows; Maureen Schmitter-Edgecombe
Journal:  Technol Health Care       Date:  2017       Impact factor: 1.285

3.  Variational autoencoder provides proof of concept that compressing CDT to extremely low-dimensional space retains its ability of distinguishing dementia.

Authors:  Catherine Price; Patrick Tighe; Sabyasachi Bandyopadhyay; Catherine Dion; David J Libon; Parisa Rashidi
Journal:  Sci Rep       Date:  2022-05-14       Impact factor: 4.996

4.  Modeling Users' Cognitive Performance Using Digital Pen Features.

Authors:  Alexander Prange; Daniel Sonntag
Journal:  Front Artif Intell       Date:  2022-05-03

5.  Phenotyping Cognitive Impairment using Graphomotor and Latency Features in Digital Clock Drawing Test.

Authors:  Anis Davoudi; Catherine Dion; Shawna Amini; David J Libon; Patrick J Tighe; Catherine C Price; Parisa Rashidi
Journal:  Annu Int Conf IEEE Eng Med Biol Soc       Date:  2020-07

6.  Association of Digital Clock Drawing With PET Amyloid and Tau Pathology in Normal Older Adults.

Authors:  Dorene M Rentz; Kathryn V Papp; Danielle V Mayblyum; Justin S Sanchez; Hannah Klein; William Souillard-Mandar; Reisa A Sperling; Keith A Johnson
Journal:  Neurology       Date:  2021-02-15       Impact factor: 9.910

7.  Automatic dementia screening and scoring by applying deep learning on clock-drawing tests.

Authors:  Shuqing Chen; Daniel Stromer; Harb Alnasser Alabdalrahim; Stefan Schwab; Markus Weih; Andreas Maier
Journal:  Sci Rep       Date:  2020-11-30       Impact factor: 4.379

8.  Use of the Clock Drawing Test and the Rey-Osterrieth Complex Figure Test-copy with convolutional neural networks to predict cognitive impairment.

Authors:  Young Chul Youn; Jung-Min Pyun; Nayoung Ryu; Min Jae Baek; Jae-Won Jang; Young Ho Park; Suk-Won Ahn; Hae-Won Shin; Kwang-Yeol Park; Sang Yun Kim
Journal:  Alzheimers Res Ther       Date:  2021-04-20       Impact factor: 6.982

9.  Digitally generated Trail Making Test data: Analysis using hidden Markov modeling.

Authors:  Mengtian Du; Stacy L Andersen; Stephanie Cosentino; Robert M Boudreau; Thomas T Perls; Paola Sebastiani
Journal:  Alzheimers Dement (Amst)       Date:  2022-03-08

10.  An explainable self-attention deep neural network for detecting mild cognitive impairment using multi-input digital drawing tasks.

Authors:  Natthanan Ruengchaijatuporn; Itthi Chatnuntawech; Thiparat Chotibut; Chaipat Chunharas; Surat Teerapittayanon; Sira Sriswasdi; Sirawaj Itthipuripat; Solaphat Hemrungrojn; Prodpran Bunyabukkana; Aisawan Petchlorlian; Sedthapong Chunamchai
Journal:  Alzheimers Res Ther       Date:  2022-08-09       Impact factor: 8.823

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