Description

This research aims to achieve early detection of glaucoma by analyzing aqueous humor fluid from the human eye, employing spintronic sensors and machine learning algorithms. Glaucoma is a leading cause of irreversible blindness, and early detection is crucial for effective treatment and prevention of vision loss.

Spintronic sensors offer exceptional sensitivity and precision in detecting biomarkers present in the aqueous humor fluid. These sensors can capture subtle changes in biomolecular concentrations, providing valuable insights into the disease progression at its initial stages.

To harness the full potential of the acquired data, advanced machine learning algorithms are employed to analyze and interpret the sensor outputs. These algorithms can identify complex patterns and correlations, facilitating the development of accurate diagnostic models for early glaucoma detection.

The integration of spintronic sensor technology with machine learning enables non-invasive, cost-effective, and rapid glaucoma screening, significantly improving the chances of timely intervention and preserving patients' vision. This research holds the potential to revolutionize glaucoma diagnostics, reducing the burden of blindness and enhancing healthcare outcomes worldwide.