An in-silico analysis of retinal electric field distribution induced by different electrode design of trans-corneal electrical stimulation

Improving Spatial Resolution and Selectivity of Transcorneal Electrical Stimulation by Temporal Interference Technology

Transcorneal electrical stimulation (TES) used in a therapeutic device has been demonstrated significant neuroprotective effect for rescuing retinal function. However, the diffuse electric field induced by conventional TES devices reduced their spatial resolution and selectivity, limiting their capability of actively stimulating a severely diseased retina. A cutting-edge neuromodulation approach named temporal interference stimulation (TIS) was reported to induce electric fields focalizing on local neuronal targets. Despite the competent feasibility of application in retinal TIS, the interpretation of characteristics of spatial resolution and selectivity under TIS remains rudimentary. In this study, we conduct in silico investigations to understand the characteristics of spatial selectivity and resolution using a finite element model of a multi-layered eyeball and multiple electrode configuration. By simulating different metrics of electric potentials envelope modulated by TIS, our model supports the possibility of achieving mini-invasive and spatially selective electrical stimulation using retinal TIS. These simulations provide theoretical evidence on the basis of which sophisticated devices for improved spatial selectivity can be designed.Clinical Relevance- This study provides a theoretical basis for understanding how the design of electrode configuration impacts transcorneal TIS performance. This model can guide future development of transcorneal TIS configurations and stimulation strategies that may benefit patients with inherited retinal diseases.

Optimal Combination of Mother Wavelet and AI Model for Precise Classification of Pediatric Electroretinogram Signals

The continuous advancements in healthcare technology have empowered the discovery, diagnosis, and prediction of diseases, revolutionizing the field. Artificial intelligence (AI) is expected to play a pivotal role in achieving the goals of precision medicine, particularly in disease prevention, detection, and personalized treatment. This study aims to determine the optimal combination of the mother wavelet and AI model for the analysis of pediatric electroretinogram (ERG) signals. The dataset, consisting of signals and corresponding diagnoses, undergoes Continuous Wavelet Transform (CWT) using commonly used wavelets to obtain a time-frequency representation. Wavelet images were used for the training of five widely used deep learning models: VGG-11, ResNet-50, DensNet-121, ResNext-50, and Vision Transformer, to evaluate their accuracy in classifying healthy and unhealthy patients. The findings demonstrate that the combination of Ricker Wavelet and Vision Transformer consistently yields the highest median accuracy values for ERG analysis, as evidenced by the upper and lower quartile values. The median balanced accuracy of the obtained combination of the three considered types of ERG signals in the article are 0.83, 0.85, and 0.88. However, other wavelet types also achieved high accuracy levels, indicating the importance of carefully selecting the mother wavelet for accurate classification. The study provides valuable insights into the effectiveness of different combinations of wavelets and models in classifying ERG wavelet scalograms.