Etiology of Macular Edema Defined by Deep Learning in Optical Coherence Tomography Scans

A new texture-based labeling framework for hyper-reflective foci identification in retinal optical coherence tomography images

An important abnormality in Optical Coherence Tomography (OCT) images is Hyper-Reflective Foci (HRF). This anomaly can be interpreted as a biomarker of serious retinal diseases such as Age-related Macular Degeneration (AMD) and Diabetic Macular Edema (DME) or the progression of disease from an early stage to a late one. In this paper, a new method is proposed for the identification of HRFs. The new method divides the OCT B-scan into patches and separately verifies each patch to determine whether or not the patch contains an HRF. The procedure of patch verification contains a texture-based framework which assigns appropriate labels according to intensity changes to each column and row. Then, a feature vector is extracted for each patch based on the assigned labels. The feature vectors are utilized in the training step of well-known classifiers like Support Vector Machine (SVM). Then, the classifiers are used to produce the labels for the test OCT images. The new method is evaluated on a public dataset including HRF labels. The experimental results show that the new method is capable of providing outstanding results in terms of speed and accuracy.

Application and progress of artificial intelligence technology in the segmentation of hyperreflective foci in OCT images for ophthalmic disease research

With the advancement of retinal imaging, hyperreflective foci (HRF) on optical coherence tomography (OCT) images have gained significant attention as potential biological biomarkers for retinal neuroinflammation. However, these biomarkers, represented by HRF, present pose challenges in terms of localization, quantification, and require substantial time and resources. In recent years, the progress and utilization of artificial intelligence (AI) have provided powerful tools for the analysis of biological markers. AI technology enables use machine learning (ML), deep learning (DL) and other technologies to precise characterization of changes in biological biomarkers during disease progression and facilitates quantitative assessments. Based on ophthalmic images, AI has significant implications for early screening, diagnostic grading, treatment efficacy evaluation, treatment recommendations, and prognosis development in common ophthalmic diseases. Moreover, it will help reduce the reliance of the healthcare system on human labor, which has the potential to simplify and expedite clinical trials, enhance the reliability and professionalism of disease management, and improve the prediction of adverse events. This article offers a comprehensive review of the application of AI in combination with HRF on OCT images in ophthalmic diseases including age-related macular degeneration (AMD), diabetic macular edema (DME), retinal vein occlusion (RVO) and other retinal diseases and presents prospects for their utilization.

RD-OCT net: hybrid learning system for automated diagnosis of macular diseases from OCT retinal images

Macular Edema is a leading cause of visual impairment and blindness in patients with ocular fundus diseases. Due to its non-invasive and high-resolution characteristics, optical coherence tomography (OCT) has been extensively utilized for the diagnosis of macular diseases. The manual detection of retinal diseases by clinicians is a laborious process, further complicated by the challenging identification of macular diseases. This difficulty arises from the significant pathological alterations occurring within the retinal layers, as well as the accumulation of fluid in the retina. Deep Learning neural networks are utilized for automatic detection of retinal diseases. This paper aims to propose a lightweight hybrid learning Retinal Disease OCT Net with a reduced number of trainable parameters and enable automatic classification of retinal diseases. A Hybrid Learning Retinal Disease OCT Net (RD-OCT) is utilized for the multiclass classification of major retinal diseases, namely neovascular age-related macular degeneration (nAMD), diabetic macular edema (DME), retinal vein occlusion (RVO), and normal retinal conditions. The diagnosis of retinal diseases is facilitated by the use of hybrid learning models and pre-trained deep learning models in the field of artificial intelligence. The Hybrid Learning RD-OCT Net provides better accuracy of 97.6% for nAMD, 98.08% for DME, 98% for RVO, and 97% for the Normal group. The respective area under the curve values were 0.99, 0.97, 1.0, and 0.99. The utilization of the RD-OCT model will be useful for ophthalmologists in the diagnosis of prevalent retinal diseases, due to the simplicity of the system and reduced number of trainable parameters.

Artificial intelligence for diabetic retinopathy in low-income and middle-income countries: a scoping review

Diabetic retinopathy (DR) is a leading cause of blindness globally. There is growing evidence to support the use of artificial intelligence (AI) in diabetic eye care, particularly for screening populations at risk of sight loss from DR in low-income and middle-income countries (LMICs) where resources are most stretched. However, implementation into clinical practice remains limited. We conducted a scoping review to identify what AI tools have been used for DR in LMICs and to report their performance and relevant characteristics. 81 articles were included. The reported sensitivities and specificities were generally high providing evidence to support use in clinical practice. However, the majority of studies focused on sensitivity and specificity only and there was limited information on cost, regulatory approvals and whether the use of AI improved health outcomes. Further research that goes beyond reporting sensitivities and specificities is needed prior to wider implementation.

Retinal Hydration Assessment With Optical Coherence Tomography: Unraveling Its Significance in Retinal Fluid Dynamics, Macular Edema and Cell Viability

PURPOSE

The purpose of this study was to quantify retinal hydration (RH) levels with optical coherence tomography (OCT) and determine the extent of cellular damage resulting from intraretinal fluid alterations.

METHODS

We took 6.0 mm sections of the human sensory retina that were excised from 18 fresh (<24 hours) donor eyes. They were either exposed to various osmotic stresses between 90 and 305 mOsm or dehydrated under a laminar flow hood. Change in tissue weight was used to calculate the retinal water content (RWC). Image analyses were conducted on OCT between 0 and 180 minutes to assess retinal thickness (RT) and "optically empty areas" (OEAs) representing intraretinal fluid. Correlations were sought among RWC, OEA, RWC, and RT. The effect of RH on retinal cell viability (RCV) was assessed with the Live-Dead Assay.

RESULTS

RH demonstrated a stronger correlation with the OEA than plain RT measurements (r = 0.99, P < 0.001). RH-RCV interaction fits well to a bell-shaped curve. A significant proportion of retinal cells (>80%) remained viable despite the change in RH ranging between 0.87 and 1.42 times. This "safe zone" was found to be associated with a 22% increase in OEA (r = 0.99, P < 0.01).

CONCLUSIONS

OCT has been demonstrated as a valuable tool for assessing RH and can be used for intraretinal fluid content analysis. RH is a better indicator of RCV compared with RT. Computing RH may improve the determination of functional outcome of intravitreal pharmacotherapeutics used for diabetic macular edema and exudative age-related macular degeneration.

TRANSLATIONAL RELEVANCE

We link basic research and clinical care by assessing retinal hydration's impact on retinal fluid dynamics, macular edema, and cell viability.