From (A) concept through to the (B) design of the EyeD with a 3D-printed...
From (A) concept through to the (B) design of the EyeD with a 3D-printed phantom eye-model (with the synthetic eye model incorporated into the housing design) mimicking the optical properties of the eye, allowing Raman spectra through an eye-like lens to be acquired on model samples and optimizing of the resolution and optical throughput and onto the (C) lab prototype, which uses the eye to focus a collimated beam onto the retina, enabling measuring the spectroscopic fingerprint bands while concurrently performing fundus imaging to verify that the correct area is imaged [(a) and (b)] created using BioRender. Subsequently, SKiNET generates (c) feature extraction and identification of the most important Raman bands by separating the high-dimensionality data from different classes, identifying the underlying chemical differences, and classifying the data using the network with peaks in SOMDI corresponding to cm−1 and modes that contribute most to the clustering observed in SOM. Its implementation using open-source libraries enables data analysis to be performed online and run from any browser or smartphone (d), reducing the hardware footprint and increasing portability. (D) The engineered EyeD incorporates a (a) disposable eye guard, (b) SKiNET software for rapid data classification, and visually clear on-site readout for clinicians or paramedics, enabling automated interpretation of Raman data and thus markedly improving the speed and cost of diagnosis at the point of care using an (c) ergonomic handheld device for simultaneous fundus photography and high–wave number Raman spectroscopy.

Image source: Banbury et al., Science Advances 2023 (CC BY 4.0)

News • Point-of-care neurodiagnosis

Eye-safe laser technology to diagnose traumatic brain injury

The technology is expected to be developed into a hand-held device for use in the critical ‘golden hour’ after traumatic brain injury

Described in Science Advances, the technique is dramatically different from other diagnostic methods and is expected to be developed into a hand-held device for use in the critical ‘golden hour’ after traumatic brain injury, when life critical decisions on treatment must be made. The device incorporates a class 1, CE marked, eye-safe laser and a unique Raman spectroscopy system, which uses light to reveal the biochemical and structural properties of molecules by detecting how they scatter light, to detect the presence and levels of known biomarkers for brain injury. 

There is an urgent need for new technologies to improve the timeliness of TBI diagnosis, which is a leading cause of death worldwide. TBI is caused by sudden shock or impact to the head, which can cause mild to severe injury to the brain, and although it needs diagnosis and treatment as soon as possible to prevent further irreversible damage, it is hard to diagnose at the point of injury. Moreover, radiological investigations such as X-ray or MRI are very expensive and slow to show results.

"Early diagnosis of TBI is crucial, as life-critical decisions on treatment must be made with the first ‘golden hour’ after injury

Pola Goldberg Oppenheimer

Birmingham researchers, led by Professor Pola Goldberg Oppenheimer from the School of Chemical Engineering, designed and developed the novel diagnostic hand-held device to assess patients as soon as injury occurs. It is fast, precise and non-invasive for the patient, causing no additional discomfort, can provide information on the severity of the trauma, and will be suitable to be used on-site - at the roadside, on the battlefield or on the sports pitch - to assess TBI. "Early diagnosis of TBI is crucial, as life-critical decisions on treatment must be made with the first ‘golden hour’ after injury. However, current diagnostic procedure relies on observation by ambulance crews, and MRI or CT scans at a hospital – which may be some distance away," said Professor Goldberg Oppenheimer. 

The device works by scanning the back of the eye where the optic nerve sits. Because the optic nerve is so closely linked to the brain, it carries the same biological information in the form of protein and lipid biomarkers. These biomarkers exist in a very tightly regulated balance, meaning even the slightest change may have serious effects on the ‘brain-health’. TBI causes these biomarkers to change, indicating that something is wrong. 

Previous research has demonstrated the technology can accurately detect the changes in animal brain and eye tissues with different levels of brain injuries - picking up the slightest changes.1,2,3 The device detailed in the current paper detects and analyses the composition and balance of these biomarkers to create ‘molecular fingerprints’. The current study details the development, manufacture, and optimisation of a proof-of-concept prototype, and its use in reading biochemical fingerprints of brain injury on the optic nerve, to see whether it is a viable and effective approach for initial ‘on the scene’ diagnosis of TBI. 

The researchers constructed a phantom eye to test its alignment and ability to focus on the back of the eye, used animal tissue to test whether it could discern between TBI and non-TBI states, and also developed decision support tools for the device, using AI, to rapidly classify TBIs. 

The device is now ready for further evaluation including clinical feasibility and efficacy studies, and patient acceptability. The researchers expect the diagnostic device to be developed into a portable technology which is suitable for use in point-of-care conditions capable to rapidly determine whether TBI occurs as well as classify whether it is mild, moderate or severe, and therefore, direct triage appropriately and in timely manner. 


References:

  1. Banbury, I. Styles, N. Eisenstein, E. R. Zanier, G. Vegliante, A. Belli, A. Logan, P. Goldberg Oppenheimer, Spectroscopic detection of traumatic brain injury severity and biochemistry from the retina. Biomed. Opt. Express 11, 6249–6261 (2020).
  2. Banbury, R. Mason, I. Styles, N. Eisenstein, M. Clancy, A. Belli, A. Logan, P. Goldberg Oppenheimer, Development of the self optimising kohonen index network (SKiNET) for Raman spectroscopy based detection of anatomical eye tissue. Sci. Rep. 9, 10812 (2019).
  3. Marro, A. Taubes, A. Abernathy, S. Balint, B. Moreno, B. Sanchez-Dalmau, E. H. Martínez-Lapiscina, I. Amat-Roldan, D. Petrov, P. Villoslada, Dynamic molecular monitoring of retina inflammation by in vivo Raman spectroscopy coupled with multivariate analysis. J. Biophotonics 7, 724–734 (2014).


Source: University of Birmingham

02.12.2023

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