Evidence map›Paper›PMID 40835724›Full record

ArticleCommunications medicine2025

Computer vision detects covert voluntary facial movements in unresponsive brain injury patients.

Xi Cheng, Sujith Swarna, Jermaine Robertson, Nathaniel A Cleri, Jordan R Saadon, Chiemeka Uwakwe, Yindong Hua, Seyed Morsal Mosallami Aghili, Cassie Wang, Robert S Kleyner and 9 more

Abstract read
In one paragraph

Article in Communications medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

19 authors.

Xi ChengDepartment of Neurosurgery, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA.
Sujith SwarnaDepartment of Neurosurgery, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA.
Jermaine RobertsonDepartment of Neurosurgery, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA.
Nathaniel A CleriDepartment of Neurosurgery, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA.
Jordan R SaadonDepartment of Neurosurgery, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA.
Chiemeka UwakweDepartment of Neurosurgery, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA.
Yindong HuaDepartment of Neurosurgery, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA.ORCID http://orcid.org/0009-0006-0938-7071
Seyed Morsal Mosallami AghiliDepartment of Neurosurgery, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA.ORCID http://orcid.org/0000-0002-7372-0143
Cassie WangDepartment of Neurosurgery, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA.
Robert S KleynerDepartment of Neurosurgery, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA.
Xuwen ZhengDepartment of Neurosurgery, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA.
Ariana ForoharDepartment of Neurosurgery, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA.
John ServiderDepartment of Neurosurgery, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA.
Kurt ButlerDepartment of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY, USA.
Chao ChenDepartment of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA.
Jordane DimidschsteinStanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
Petar M DjurićDepartment of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY, USA.
Charles B MikellDepartment of Neurosurgery, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA. charles.mikell@stonybrookmedicine.edu.ORCID http://orcid.org/0000-0002-0701-2325
Sima MofakhamDepartment of Neurosurgery, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA. sima.mofakham@stonybrookmedicine.edu.ORCID http://orcid.org/0000-0002-4509-6080

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMany brain injury patients who appear unresponsive retain subtle, purposeful motor behaviors, signaling capacity for recovery. We hypothesized that low-amplitude movements precede larger-amplitude voluntary movements detectable by clinicians after acute brain injury. To test this hypothesis, we developed a novel, as far as we are aware, computer vision-based tool (SeeMe) that detects and quantifies low-amplitude facial movements in response to auditory commands.

methodsWe enrolled 16 healthy volunteers and 37 comatose acute brain injury patients (Glasgow Coma Scale ≤8) aged 18-85 with no prior neurological diagnoses. We measured facial movements to command assessed using SeeMe and compared them to clinicians' exams. The primary outcome was the detection of facial movement in response to auditory commands. To assess comprehension, we tested whether movements were specific to command type (i.e., eye-opening to open your eyes and not stick out your tongue) with a machine learning-based classifier.

resultsHere we show that SeeMe detects eye-opening in comatose patients 4.1 days earlier than clinicians. SeeMe also detects eye-opening in more comatose patients (30/36, 85.7%) than clinical examination (25/36, 71.4%). In patients without an obscuring endotracheal tube, SeeMe detects mouth movements in 16/17 (94.1%) patients. The amplitude and number of SeeMe-detected responses correlate with clinical outcome at discharge. Using our classifier, eye-opening is specific (81%) to the command open your eyes.

conclusionAcute brain injury patients have low-amplitude movements before overt movements. Thus, many covertly conscious patients may have motor behavior currently undetected by clinicians.

Identifiers

PMID40835724
PMCPMC12368019

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.