Clinical trial NCT06083441: SeeMe: An Automated Tool to Detect Early Recovery After Brain Injury
Official title: SeeMe: A Pilot Study Developing Predictive, Real-Time Consciousness Assessment Metrics Based on Facial Expression Changes
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Disorder of Consciousness
Consciousness, Loss of
Trauma, Brain
Traumatic Brain Injury
Acute Brain Injury
Keywords: Consciousness
Acute brain injury
Consciousness Recovery
Consciousness Measurement
Facial Motion Analysis
Microscopic movements
Traumatic Brain Injury
Lead sponsor: Stony Brook University
Sponsor class: OTHER
First posted: 2023-10-16
Enrollment: 200
Sex: ALL
Minimum age (years): 18
Maximum age (years): 85
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Healthy volunteers: Adults 18-85 years old with no history of a neurologically debilitating disease (i.e., dementia, glioblastoma, Alzheimer's, multiple sclerosis, major vessel stroke, previous severe TBI, etc.) that are recruited via flyer and word of mouth

Patients in a coma: Adults 18-85 years old with no history of a neurologically debilitating disease that are comatose (GCS \< 9) due to an acute brain injury. Patients in a comatose state will be identified during neurosurgical consultation in the hospital by the study physicians

Brief summary: Early prediction of outcomes after acute brain injury (ABI) remains a major unsolved problem. Presently, physicians make predictions using clinical examination, traditional scoring systems, and statistical models. In this study, we will use a novel technique, "SeeMe," to objectively assess the level of consciousness in patients suffering from comas following ABI. SeeMe is a program that quantifies total facial motion over time and compares the response after a spoken command (i.e. "open your eyes") to a pre-stimulus baseline.

Detailed description: Acute brain injury (ABI) recovers at a variable rate. While some progress has been made in predicting long-term outcomes in traumatic brain injury (TBI) and intracranial hemorrhage, there is a critical need for short-term prediction of outcomes, in the first days and weeks after injury. With advances in machine learning and artificial intelligence, there is a growing interest in facial analysis and its application in neurological and psychiatric disorders. Here we describe "SeeMe," a novel automated objective measure of consciousness based on microexpression analyses in response to auditory commands. In measuring the smallest muscular movements undetectable by clinical observation, this technique has the high spatial resolution needed to detect hidden signs of recovery and the high temporal resolution needed to study neural circuits.

Eligibility criteria: Inclusion Criteria:

* 18 years old or older
* Healthy Volunteers
* Comatose patients (patients with a GCS \< 9) due to an acute brain injury (traumatic brain injury, spontaneous subarachnoid hemorrhage, severe meningoencephalitis, etc.)

Exclusion Criteria:

* A history of a neurologically debilitating disease (i.e., dementia, glioblastoma, Alzheimer's, multiple sclerosis, major vessel stroke, previous severe TBI, etc.)
* Any other medical condition that, in the judgment of the investigator, makes participation in the study unsafe.
* Pregnant subjects
* Comatose patients without a legal authorized representative (LAR)
* Prisoners or wards of the state
* Persons who have not attained the legal age for consent to treatments or procedures

Primary outcomes:
measure: Detection of Command Following
timeFrame: Measured Daily from enrollment until subject is following commands or date of death of any cause, whichever comes first, up to 60 days.
Secondary outcomes:
Other outcomes:
Locations:
city: Stony Brook
country: United States
facility: Stony Brook University Hospital
state: New York
Study officials:
affiliation: Stony Brook Medicine
name: Sima Mofakham, PhD
role: PRINCIPAL_INVESTIGATOR
References:
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citation: Zhao Y, Xu J. A Convolutional Neural Network for Compound Micro-Expression Recognition. Sensors (Basel). 2019 Dec 16;19(24):5553. doi: 10.3390/s19245553.
citation: Valstar MF, Pantic M. Fully automatic recognition of the temporal phases of facial actions. IEEE Trans Syst Man Cybern B Cybern. 2012 Feb;42(1):28-43. doi: 10.1109/TSMCB.2011.2163710. Epub 2011 Sep 15.
citation: Chouinard B, Scott K, Cusack R. Using automatic face analysis to score infant behaviour from video collected online. Infant Behav Dev. 2019 Feb;54:1-12. doi: 10.1016/j.infbeh.2018.11.004. Epub 2018 Nov 30.
citation: Saadon JR, Yang F, Burgert R, Mohammad S, Gammel T, Sepe M, Rafailovich M, Mikell CB, Polak P, Mofakham S. Real-time emotion detection by quantitative facial motion analysis. PLoS One. 2023 Mar 10;18(3):e0282730. doi: 10.1371/journal.pone.0282730. eCollection 2023.
citation: Kalmar K, Giacino JT. The JFK Coma Recovery Scale--Revised. Neuropsychol Rehabil. 2005 Jul-Sep;15(3-4):454-60. doi: 10.1080/09602010443000425.