Abstract
The burst of new optical technologies for the past several decades has had a big impact in medicine, and in particular ophthalmology. Let us consider some of the current innovations:
Optical coherence tomography (OCT) has markedly improved in speed and spatial resolution over time, enabling the visualization of corneal and retinal layers and measurement of their thicknesses, and glaucoma to name just a few.1–3
The future developments to OCT will meet the needs to (i) achieve ultrawide coverage of the fundus so that the pathological features appearing in the peripheral and equatorial regions can be visualized and quantified to aid the diagnosis of early stage of diseases, for example, diabetic retinopathy; (ii) assess the functional properties of photoreceptors in the outer retina that affect the visual acuity; and (iii) quantify blood flow velocity within retinal capillary vessels that may play a role in the mechanistic investigations of diabetic retinopathy and age related macular degeneration. Polarized light imaging offers a method of gating the reflectance to create an image based either on the initially and superficially scattered subdiffusive photons, or on the deeper multiply scattered diffusive photons. Several structures in the eye (e.g., cornea, retinal nerve fiber layer, and retinal pigment epithelium) alter the polarization state of the light and show, therefore, a tissue-specific contrast in the imaging. Recent development of polarization sensitive OCT has demonstrated improvement of tissue contrast over the traditional OCT imaging that has advantages of assisting more accurate segmentation of retinal layers and lesions based on their intrinsic tissue-specific contrast.
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Future effort includes translating this imaging into the clinic and establishing the correlation of polarization features as measured by OCT with the ocular diseases. Spectral imaging measures the broad-band reflectance spectrum using cost-effective array sensors, including dedicated cameras and smartphones. Camera chips now allow simultaneous acquisition of up to 25 wavelengths, that is, 25 images each at a different wavelength. Hence, each pixel is a 25-wavelength spectrum, which can be analyzed to specify blood content, oxygenation, water content, and tissue scattering. Although such spectroscopy is not new, the combination of spectral imaging with artificial intelligence (AI) (AI-trained analysis) can rapidly analyze an image and discriminate the mentioned parameters as well as specify the width and depth of blood vessels.
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Fundus autofluorescence (FAF) is a relatively new noninvasive imaging modality in ophthalmology that has in fact been developed for the past 40 years. FAF uses the fluorescent properties of lipofuscin to generate images that provide information beyond what can be offered by more conventional imaging techniques such as fluorescein angiography, fundus photography, and regular OCT. It has proved to be useful in enhancing our understanding of the pathophysiological mechanisms, diagnostics, and identification of predictive markers for disease progression, and for monitoring of novel therapies.
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