Background
Retinal diseases are a significant global health concern affecting an estimated 2.2 billion individuals worldwide. Artificial intelligence (AI) and deep learning models are increasingly being utilized in the clinic within the field of ophthalmology. Over the last 5-year, most of the various AI diagnostic systems developed are based on fundus photography. Although fundus photography is one of the most common clinical imaging modalities in evaluating retinal abnormalities, however, it does not provide layer information of the retina. The high-resolution imaging capability of optical coherence tomography (OCT) allows for detailed visualization of the various layers of the retina, making it a valuable noninvasive diagnostic method widely used for diagnosing, characterizing, and monitoring ocular pathologies OCT’s sensitivity and specificity are comparable, if not superior, to those of fundus photography. Consequently, the application of artificial intelligence (AI) to OCT imaging holds great promise for screening and monitoring retinal diseases. Global and individual retinal layer thickness changes from OCT images have been observed in age-related macular degeneration (AMD), diabetic retinopathy (Dr), and glaucoma as well as systemic disorders.
Poor renal function as assessed by Cystatin C and estimated glomerular filtration rate was associated with a thinner circumpapillary retinal nerve fiber layer thickness (cpRNFLT). An adverse lipid profile (i.e., low high-density lipoprotein (HDL) cholesterol, as well as high total, high non-HDL, high low-density lipoprotein cholesterol. Further studies showed that a thinner RNFL is associated with an increased risk of dementia, including Alzheimer disease. Smoking and Alcohol have also been proven to cause a thinning of the retinal layers.
These studies underline how the retina is not only affected by eye-specific pathologies but could be a window to identify systemic disorders. Most of the underlying studies, however, employ a linear model to assess the correlation between retinal layer thickness and diseases, which may not accurately capture the potential non-linear nature of the relationship between retinal microstructure and ocular and systemic disorders. In contrast, deep learning is naturally capable to quantify both linear and non-linear relationships.
What We Do
Our team aims to design a deep learning model to quantify the retinal layer importance and regional importance of each retinal layer in predicting various ocular and systematic diseases.
In particular, we exacted the retinal layer thickness maps for the 10 retinal layers segmented by the Heidelberg Engineering software including retinal nerve fiber layer (RNFL), ganglion cell layer (GCL), inner plexiform layer (IPL), inner nuclear layer (INL), outer plexiform layer (OPL), outer nuclear layer (ONL), myoid zone (MZ), ellipsoid zone (EZ) and outer-photoreceptor segment (OS) combined (EZ+OS), interdigitation zone (IZ) and retinal pigment epithelium (RPE). We used each of the 10 layers individually and 10 layers together to predict diabetes diagnosis. For each layer, we quantify the regional importance on predicting the eye and systemic diseases. We use the area under the receiver operating characteristic curve (AUC) to measure the layer importance and regional importance for each layer.
The new knowledge of the layer importance and regional importance for each layer can shed light on how retina is affected by different eye and systemic diseases distinctly, which can be potentially used to better preserve vision.
Selected Publications
- Luo, Y., Rauscher, F.G., Elze, T., Tian, Y., Shi, M., Hashemabad, S.K., Eslami, M., Wirkner, K., Peschel, T., Stumvoll, M. and Isermann, B., 2023. Assessing Retinal Layers’ Association with Diabetes using a Deep Learning Framework. Investigative Ophthalmology & Visual Science, 64(9), pp.PB0033-PB0033.
- Tian, Y., Rauscher, F.G., Elze, T., Luo, Y., Shi, M., Hashemabad, S.K., Eslami, M., Wirkner, K., Peschel, T., Loeffler, M. and Engel, C., 2023. The Impact of Age-Related Macular Degeneration on Retinal Layers Quantified by Deep Learning. Investigative Ophthalmology & Visual Science, 64(9), pp.PP0014-PP0014.