Objective:

To present a novel point-of-care diagnostic method for traumatic brain injury (TBI) using EyeD, a laser technology based on Raman spectroscopy.

Approach:

• Development of EyeD: EyeD utilizes a bespoke artificial neural network algorithm (SKiNET) for multivariate analysis, enabling rapid classification of TBI from spectral data.
• Validation and Testing: Feasibility was demonstrated through ex-vivo murine retina studies and experiments on pig eyes, detecting molecular fingerprints of TBI neuromarkers.

Key Findings:

• EyeD can differentiate TBI from healthy controls with high accuracy across various injury severities.
• Raman spectroscopy provides a non-invasive method to assess acute distress changes in neuroretinal or optic nerve tissue.
• The device allows for rapid, point-of-care diagnosis, which is crucial in high-pressure situations.

Interpretation:

Limitations:

• Current validation is based on animal models, necessitating further testing in human subjects.
• The technology's effectiveness in diverse clinical settings and populations remains to be established.

Conclusion:

Sources:

• University of Birmingham