Narrative review of artificial intelligence in diabetic macular edema: Diagnosis and predicting treatment response using optical coherence tomography

Abstract

Diabetic macular edema (DME), being a frequent manifestation of DR, disrupts the retinal symmetry. This event is particularly triggered by vascular endothelial growth factors (VEGF). Intravitreal injections of anti-VEGFs have been the most practiced treatment but an expensive option. A major challenge associated with this treatment is determining an optimal treatment regimen and differentiating patients who do not respond to anti-VEGF. As it has a significant burden for both the patient and the health care providers if the patient is not responding, any clinically acceptable method to predict the treatment outcomes holds huge value in the efficient management of DME. In such situations, artificial intelligence (AI) or machine learning (ML)-based algorithms come useful as they can analyze past clinical details of the patients and help clinicians to predict the patient's response to an anti-VEGF agent. The work presented here attempts to review the literature that is available from the peer research community to discuss solutions provided by AI/ML methodologies to tackle challenges in DME management. Lastly, a possibility for using two different types of data has been proposed, which is believed to be the key differentiators as compared to the similar and recent contributions from the peer research community.

Innovations in Diabetic Macular Edema Management: A Comprehensive Review of Automated Quantification and Anti-vascular Endothelial Growth Factor Intervention

“…Advancements in the quantification of retinal ischemia have been made with the imaging modalities of fluorescein angiography (FA), ultra-widefield imaging (UWF), and optical coherence tomography angiography (OCTA), with each imaging modality offering certain benefits over the others [ 24 ]. FA remains the gold standard in assessing the extent of ischemia.…”

“…UWF imaging has allowed for the assessment of peripheral ischemia via FA. It is, however, OCTA that offers the best visualization of retinal vasculature with its noninvasive depth-resolved imaging and therefore has the potential to become a mainstay in the assessment of retinal ischemia [ 24 ].…”

“…OCT plays a crucial role in the assessment of DME by providing detailed information about retinal thickness and fluid distribution [ 24 ]. Integrating AI algorithms has significantly advanced the field, automating the segmentation and analysis of OCT data.…”

“…Predicting treatment responses in patients with DME, particularly in the context of anti-VEGF therapy, has been a focus of exploration for AI techniques. These predictive models offer the potential for more informed decision-making in treatment planning and ongoing management [ 24 ]. The proposal of an end-to-end deep fusion model represents a cutting-edge approach to DME detection.…”

Validation of an Automated Artificial Intelligence Algorithm for the Quantification of Major OCT Parameters in Diabetic Macular Edema

“…Over the last several years, significant advances in telecommunications, artificial intelligence (AI), and deep learning (DL)-based systems have opened new horizons for creating efficient tools for the quantification of major parameters in macular disorders [ 20 , 21 , 22 ]. The literature data suggest that AI may achieve high performance in detecting retinal fluid [ 23 , 24 ] and in assessing anatomic changes over the course of the disease [ 25 ].…”