Brett C. Meyer | HXI - Human-centered eXtended Intelligence

HoloStroke: Holographic Telestroke and Remote Assessment

Thu, 01 Jan 2026 00:00:00 +0000

Overview

Stroke treatment runs against a clock. Thrombolysis reaches fewer than 5% of acute strokes, because the decision has to be made within hours of symptom onset, and it needs both a stroke specialist and adjunctive imaging. Neither is reliably present where and when the patient arrives, and rural and community hospitals carry most of that shortfall.

HoloStroke moves the expertise rather than the patient. Holograms of a patient, or of their imaging, are rendered in three-dimensional space on a HoloLens and transmitted to a distant site, so that a remote stroke neurologist assesses and interacts as if present at the bedside.

Four studies have tested the approach. Holo-Stroke established that patients found remote holographic visits with a stroke provider satisfying and immersive. Holo-Stroke-CTA compared providers reading computed tomographic angiography for large vessel occlusion in holographic space against a conventional DICOM viewer, and found higher reliability and markedly higher satisfaction. Holo-Stroke-ii extended the approach to inpatient immersion. Holo-PACS widened it from stroke to radiographic scans more generally, testing whether holograms improve provider acumen when reading a scan.

Funding and External Collaborations

HoloStroke is a collaboration with the UCSD Stroke Center and HomniHealth.

UbiStroke: Multimodal Sensing of Stroke-Related Deficits

Wed, 01 Jan 2020 00:00:00 +0000

Overview

Stroke assessment rests on human observation. Diagnosing a stroke quickly enough to treat it requires a specialist, and the deficit scales in clinical use record symptoms categorically, which makes outcome prediction crude: existing scales are generally unable to say whether a patient will do well or very poorly.

UbiStroke develops techniques to identify and quantify stroke-related deficits from multiple sensors at once, producing a per-patient stroke signature from body posture, facial droop, pupil tracking, speech patterns, and other symptoms. Machine learning models trained on this data detect stroke-associated hemiparesis and support the clinician rather than replacing the exam.

The work evaluated UbiStroke alongside the clinician-performed NIHSS exam, and studied how clinicians rely on a computational aid during an acute assessment.

Funding and External Collaborations

UbiStroke is a collaboration with the UCSD Stroke Center, the UCSD Bioengineering Department, the Institute of Neural Computation, and HomniHealth. Research leading to UbiStroke has been funded by the National Science Foundation, the NSF I-Corps, the National Library of Medicine at NIH, IBM Research, and UCSD’s Office of Research Affairs.

(Holo-PACS): use of holograms to improve provider acumen when reading clinical radiographic scans

Thu, 01 Jan 2026 00:00:00 +0000

Holo-Stroke-ii: Stroke care through stroke hologram teleportation- inpatient immersion

Thu, 01 Jan 2026 00:00:00 +0000