A short digital eye-tracking assessment predicts cognitive status among adults

Gills, Joshua L.; Bott, Nick; Madero, Erica N.; Glenn, Jordan M.; Gray, Michelle

doi:10.1007/s11357-020-00254-5

Cited by 22 publications

(29 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Of the 94 records, 40 studies were not diagnostic accuracy studies or unrelated to CI, 5 studies were published in non-English, and 16 studies were reviews or meta-analyses. In the end, the qualitative descriptive analysis included 11 studies [26][27][28][29][30][31][32][33][34][35][36], 9 comparative studies met all criteria and were included in a quantitative meta-analysis. Fig 1 shows our retrieval process and selection process.…”

Section: Results Of the Searchmentioning

confidence: 99%

“…was published from 2011 to 2020. Regarding the description of the eye movement tracking, 3 studies used visual paired-comparison (VPC) [26][27][28], while most other studies used the regular eye gaze tracking task (such as Saccade, fixation task, and so on). We found that Poletti et al used eye-tracking combined with common tasks of ET-based neuropsychological assessment [29], there are three eye movement parameters (including the 6-word test, RME test, and the MSCT test).…”

Section: Plos Onementioning

confidence: 99%

See 1 more Smart Citation

The effectiveness of eye tracking in the diagnosis of cognitive disorders: A systematic review and meta-analysis

Liu

et al. 2021

PLoS ONE

View full text Add to dashboard Cite

Background Eye tracking (ET) is a viable marker for the recognition of cognitive disorders. We assessed the accuracy and clinical value of ET for the diagnosis of cognitive disorders in patients. Methods We searched the Medline, Embase, Web of Science, Cochrane Library, and Pubmed databases from inception to March 2, 2021, as well as the reference lists of identified primary studies. We included articles written in English that investigated ET for cognitive disorder patients—Mild cognitive impairment (MCI), Alzheimer’s disease (AD), Amyotrophic lateral sclerosis (ALS), and dementia. Two independent researchers extracted the data and the characteristics of each study; We calculated pooled sensitivities and specificities. A hierarchical summary of receiver performance characteristics (HSROC) model was used to test the diagnostic accuracy of ET for cognitive impairment (CI). Findings 11 studies met the inclusion criteria and were included in qualitative comprehensive analysis. Meta-analysis was performed on 9 trials using Neuropsychological Cognitive Testing (NCT) as the reference standard. The comprehensive sensitivity and specificity of ET for detecting cognitive disorders were 0.75 (95% CI 0.72–0.79) and 0.73 (95% CI 0.70 to 0.76), respectively. The combined positive likelihood ratio (LR+) was 2.74 (95%CI 2.32–3.24) and the negative likelihood ratio (LR−) was 0.27 (95%CI 0.18–0.42). Conclusions This review showed that ET technology could be used to detect the decline in CI, clinical use of ET techniques in combination with other tools to assess CI can be encouraged.

show abstract

Section: Results Of the Searchmentioning

confidence: 99%

Section: Plos Onementioning

confidence: 99%

The effectiveness of eye tracking in the diagnosis of cognitive disorders: A systematic review and meta-analysis

Liu

et al. 2021

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…Gills et al ( 95 ) developed and validated a short digital eye-tracking assessment that predicts cognitive status among adults ( 95 ). Fifty-five adults (11 with MCI and 44 cognitively normal) were tested on two occasions.…”

Section: Resultsmentioning

confidence: 99%

“…During the second visit, participants underwent VPC, DSC, NIHTB-CB, and dual-task (DT). VPC reliably predicted cognitive status while demonstrating high test-retest reliability and displayed significant associations with gold standard cognitive assessments ( 95 ). Eye-tracking based VPC may provide a useful, brief, and scalable screening tool for cognitive impairment ( 95 ).…”

Section: Resultsmentioning

confidence: 99%

Affective Computing for Late-Life Mood and Cognitive Disorders

et al. 2021

View full text Add to dashboard Cite

Affective computing (also referred to as artificial emotion intelligence or emotion AI) is the study and development of systems and devices that can recognize, interpret, process, and simulate emotion or other affective phenomena. With the rapid growth in the aging population around the world, affective computing has immense potential to benefit the treatment and care of late-life mood and cognitive disorders. For late-life depression, affective computing ranging from vocal biomarkers to facial expressions to social media behavioral analysis can be used to address inadequacies of current screening and diagnostic approaches, mitigate loneliness and isolation, provide more personalized treatment approaches, and detect risk of suicide. Similarly, for Alzheimer's disease, eye movement analysis, vocal biomarkers, and driving and behavior can provide objective biomarkers for early identification and monitoring, allow more comprehensive understanding of daily life and disease fluctuations, and facilitate an understanding of behavioral and psychological symptoms such as agitation. To optimize the utility of affective computing while mitigating potential risks and ensure responsible development, ethical development of affective computing applications for late-life mood and cognitive disorders is needed.

show abstract

“…Our group has recently validated a short (5-minute) digital cognitive measurement assessing declarative memory [ 5 – 7 ]. This assessment is positively correlated with well-established cognitive screening tools and reliably predicts current cognitive status [ 6 , 7 ]. Remote cognitive screenings provide health care professionals with an additional tool when assessing cognitive decline, are time efficient, and allow for physicians to give information on various treatments to combat further abnormal decline.…”

mentioning

confidence: 99%