What is it about?
The purpose of this research is to build an Artificial Intelligence based tool that will assist the clinician to easily monitor the progression of Cystoid Macula Edema (CME) and its severity level, associated with a macular hole. This method has the prospective to enhance the efficiency of Diabetic Retinopathy (DR) screening.
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Why is it important?
Integration of knowledge from ophthalmology and artificial intelligence (AI) can revolutionize the current diagnostic procedures for eye diseases and is able to modify the conventional clinical procedures. Our AI-based automated method generates the report which shows the details about the localization, detection, and segmentation of IRC fluid. It will also tell us about the severity level or stages of macular hole and monitor its progression.
Perspectives
The performance on the clinically most significant judgment of differentiating patients needing routine or urgent referral (Diabetic patients having CME involving stages of the macular hole) compared to normal patients. The clinician can easily analyze the severity level by determining our automated method results. This early recognition can aid patients in early-stage treatment which is necessary for the prevention of vision loss.
Zeeshan Ahmed
Mehran University of Engineering and Technology
Read the Original
This page is a summary of: Deep learning based automated detection of intraretinal cystoid fluid, International Journal of Imaging Systems and Technology, October 2021, Wiley,
DOI: 10.1002/ima.22662.
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