OCTDL: Optical Coherence Tomography Dataset for Image-Based Deep Learning Methods.

Optical coherence tomography (OCT) is a non-invasive imaging technique with extensive clinical applications in ophthalmology. OCT enables the visualization of the retinal layers, playing a vital role in the early detection and monitoring of retinal diseases. OCT uses the principle of light wave interference to create detailed images of the retinal microstructures, making it a valuable tool for diagnosing ocular conditions. This work presents an open-access OCT dataset (OCTDL) comprising over 2000 OCT images labeled according to disease group and retinal pathology. The dataset consists of OCT records of patients with Age-related Macular Degeneration (AMD), Diabetic Macular Edema (DME), Epiretinal Membrane (ERM), Retinal Artery Occlusion (RAO), Retinal Vein Occlusion (RVO), and Vitreomacular Interface Disease (VID). The images were acquired with an Optovue Avanti RTVue XR using raster scanning protocols with dynamic scan length and image resolution. Each retinal b-scan was acquired by centering on the fovea and interpreted and cataloged by an experienced retinal specialist. In this work, we applied Deep Learning classification techniques to this new open-access dataset.

Enhancing Electroretinogram Classification with Multi-Wavelet Analysis and Visual Transformer.

The electroretinogram (ERG) is a clinical test that records the retina's electrical response to light. Analysis of the ERG signal offers a promising way to study different retinal diseases and disorders. Machine learning-based methods are expected to play a pivotal role in achieving the goals of retinal diagnostics and treatment control. This study aims to improve the classification accuracy of the previous work using the combination of three optimal mother wavelet functions. We apply Continuous Wavelet Transform (CWT) on a dataset of mixed pediatric and adult ERG signals and show the possibility of simultaneous analysis of the signals. The modern Visual Transformer-based architectures are tested on a time-frequency representation of the signals. The method provides 88% classification accuracy for Maximum 2.0 ERG, 85% for Scotopic 2.0, and 91% for Photopic 2.0 protocols, which on average improves the result by 7.6% compared to previous work.

Simulation the distribution of thermodynamic temperatures and microwave radiation of the human head.

BACKGROUND AND OBJECTIVE: The influence of biophysical parameters on the formation of microwave radiation of the human head is poorly studied. Existing approaches to modeling microwave radiation of the human head have limitations associated with simplifying the geometry of human anatomy. The article proposes methodological solutions for numerical modeling of microwave radiation of the brain biological tissues using the geometry obtained from MRI data. METHODS: The geometrical characteristics of biological tissues in model are determined using an MRI image of the head. The methodology proposed in the article allows simulation of a human body voxel models performed the Pennes bio-heat transfer equation using the Fenix software package. RESULTS: Modeling evaluations have shown that anatomical tissues heterogeneities on the surface of the head form temperature gradient of up to 2.0 K, and changes of the microwave radiation up to 0.3 K. CONCLUSIONS: Verification data made by IR thermograph practically coincide with the results of numerical modeling. The fluctuations of the brain microwave radiation are not only the result of thermal processes in its tissues, but are determined by the dynamics of its thermoregulation processes and are an indicator of changes in the physiological processes occurring in it.