Can Eye Tracking Help Assess the State of Consciousness in Non-Verbal Brain Injury Patients?

<b>Background/Objectives:</b> Developments in eye-tracking technology are opening up new possibilities for diagnosing patients in a state of minimal consciousness because they can provide information on visual behavior, and the movements of the eyeballs are correlated with the patients' level of consciousness. The purpose of this study was to provide validation of a tool, based on eye tracking by comparing the results obtained with the assessment obtained using the Coma Recovery Scale-Revised (CRS-R). <b>Methods:</b> The mul-ti-center clinical trial was conducted in Poland in 2022-2023. The results of 46 patients who were not able to communicate verbally due to severe brain injury were analyzed in this study. The state of consciousness of patients was assessed using the Minimally Conscious State Detection test (MCSD), installed on an eye tracker and compared to CRS-R. The examinations consisted of performing the MCSD test on patients five times (T1-T5) within 14 days. Collected data were processed based on the FDA and GCP's regulatory requirements. Depending on the nature of the data, the mean and standard deviation, median and lower and upper quartiles, and maximum and minimum values were calculated. Passing-Bablok regression analysis was used to assess the measurement equiva-lence of the methods used. <b>Results:</b> There was no difference between the MCSD and CRS-R in the raw change between T5 and T1 time points, as well as in the total % of points from all time points. The MCSD results from each time point show that at least the first two measurements serve to famil-iarize and adapt the patient to the measurement process, and the third and next measurement should be considered reliable. <b>Conclusions:</b> The results indicated a significant relationship be-tween the scores obtained with MCSD and CRS-R. The results suggest that it seems reasonable to introduce an assessment of the patient's state of consciousness based on eye-tracking technology. The use of modern technology to assess a patient's state of consciousness opens up the opportunity for greater objectivity, as well as a reduction in the workload of qualified personnel.

Authors

Institutions

Publication Details

Journal
Journal of Clinical Medicine
Published
2024-10-18
DOI
https://doi.org/10.3390/jcm13206227
Citations
4
Primary Topic
Traumatic Brain Injury Research
Type
article
Field-Weighted Citation Impact
1.55

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Can Eye Tracking Help Assess the State of Consciousness in Non-Verbal Brain Injury Patients?

Katarzyna Kujawa, Marek Binder, Bartosz Kunka, Robert Kosikowski et al.
4 citations
Journal of Clinical Medicine
Traumatic Brain Injury Research
1.55
article

Can Eye Tracking Help Assess the State of Consciousness in Non-Verbal Brain Injury Patients?

Katarzyna Kujawa, Marek Binder, Bartosz Kunka, Robert Kosikowski, Agata Gorączko, Roman Olejniczak, Grzegorz Żurek, Jarosław Szczygieł, Anna Stachowicz, Karolina Kryś-Noszczyk, Joanna Dryjska, Marcin Dryjski, Małgorzata Rodzeń, Danuta Karaś, Gabriela Mucha
article en
4 citations

Abstract

<b>Background/Objectives:</b> Developments in eye-tracking technology are opening up new possibilities for diagnosing patients in a state of minimal consciousness because they can provide information on visual behavior, and the movements of the eyeballs are correlated with the patients' level of consciousness. The purpose of this study was to provide validation of a tool, based on eye tracking by comparing the results obtained with the assessment obtained using the Coma Recovery Scale-Revised (CRS-R). <b>Methods:</b> The mul-ti-center clinical trial was conducted in Poland in 2022-2023. The results of 46 patients who were not able to communicate verbally due to severe brain injury were analyzed in this study. The state of consciousness of patients was assessed using the Minimally Conscious State Detection test (MCSD), installed on an eye tracker and compared to CRS-R. The examinations consisted of performing the MCSD test on patients five times (T1-T5) within 14 days. Collected data were processed based on the FDA and GCP's regulatory requirements. Depending on the nature of the data, the mean and standard deviation, median and lower and upper quartiles, and maximum and minimum values were calculated. Passing-Bablok regression analysis was used to assess the measurement equiva-lence of the methods used. <b>Results:</b> There was no difference between the MCSD and CRS-R in the raw change between T5 and T1 time points, as well as in the total % of points from all time points. The MCSD results from each time point show that at least the first two measurements serve to famil-iarize and adapt the patient to the measurement process, and the third and next measurement should be considered reliable. <b>Conclusions:</b> The results indicated a significant relationship be-tween the scores obtained with MCSD and CRS-R. The results suggest that it seems reasonable to introduce an assessment of the patient's state of consciousness based on eye-tracking technology. The use of modern technology to assess a patient's state of consciousness opens up the opportunity for greater objectivity, as well as a reduction in the workload of qualified personnel.

Journal of Clinical MedicineVol. 13(20)
Jagiellonian University (PL), Institute of Psychology (PL), Wroclaw University of Health and Sport Sciences (PL), Neuro Device (Poland) (PL), Krakowskie Centrum Rehabilitacji (PL)
Narodowe Centrum Badań i Rozwoju
Openalex Percentile: Top 16%
Traumatic Brain Injury Research
1.55
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.