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

• Published in: Indian journal of ophthalmology Vol. 69; no. 11; pp. 2999 - 2308
• Main Authors: Chakroborty, Sandipan, Gupta, Mansi, Devishamani, Chitralekha S, Patel, Krunalkumar, Ankit, Chavan, Ganesh Babu, T C, Raman, Rajiv
• Format: Journal Article
• Language: English
• Published: India Wolters Kluwer - Medknow 01-11-2021
• Subjects: Angiogenesis Inhibitors - therapeutic use; Artificial Intelligence; Diabetes Mellitus; Diabetic Retinopathy - complications; Diabetic Retinopathy - diagnosis; Diabetic Retinopathy - drug therapy; Humans; Intravitreal Injections; Macular Edema - diagnosis; Macular Edema - drug therapy; Ranibizumab - therapeutic use; Review; Tomography, Optical Coherence; Vascular Endothelial Growth Factor A; CNN; SVM; diabetic population; machine learning; visual outcomes; ranibizumab; deep learning; RF; regression; DME detection; Anti-VEGF treatment options; Lucentis; diabetic macular edema
• ISSN: 0301-4738; 1998-3689
• DOI: 10.4103/ijo.IJO_1482_21

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.