Biomedical engineers at Duke University have developed a novel imaging system that harnesses the power of artificial intelligence to visualize individual retinal cells better than more expensive technologies. This advance could greatly enhance modern medicine’s diagnostic and monitoring capabilities for a range of diseases.The retina, a thin layer of light-sensitive cells at the back of the eye, plays a vital role in converting and transmitting visual information to the brain. As an extension of the central nervous system, it offers researchers a unique, non-invasive means to visualize neurons at the cellular level.

The most commonly used method for imaging retinal cells is adaptive optics scanning light ophthalmoscopy (AOSLO). Traditional AOSLO images are formed only from directly reflected light off the retina. However, these images often contain misleading artifacts, prompting modern AOSLO systems to incorporate non-confocal information from indirectly reflected light to resolve retinal cells. Non-confocal AOSLO methods typically employ only two sensors to capture scattered light.

To overcome these limitations, Farsiu and his team developed a novel approach called Deep-Compressed AOSLO (DCAOSLO). This method uses compressed sensing, a signal processing technique that requires only a few projections of the imaged tissue to rapidly reconstruct images. By employing an array of tiny mirrors tilted via software to collect retinal light reflections, DCAOSLO captures essential features without the need for the time-consuming sensor-by-sensor scanning used in standard AOSLO. The data is then processed by an AI algorithm to create images as if generated by multiple sensors.

Source: https://pratt.duke.edu/news/ai-integrated-imaging-reveals-retinal-cellular-structures-with-precision-and-speed/