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

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.

Real-time arrhythmia classification for large databases.

In this paper we introduce a coarse-to-fine arrhythmia classification technique that can be used for efficient processing of large Electrocardiogram (ECG) records. This technique reduces time-complexity of arrhythmia classification by reducing size of the beats as well as by quantizing the number of beats using Multi-Section Vector Quantization (MSVQ) without compromising on the accuracy of the classification. The proposed solution is tested on MIT-BIH arrhythmia database.

Accurate arrhythmia classification using auto-associative neural network.

Currently about one in eighteen of the American population suffer from cardiac Arrhythmias that lead to Coronary Heart Diseases and this rate is steadily increasing. An early monitoring and diagnosis of Arrhythmia based on Electrocardiogram signals can help in reducing mortality. This paper primarily focuses on the application of Auto Associative Neural Network as a new classification approach, which does not require feature extraction task.