Researchers from the National Eye Institute (NEI) and the National Institute of Standards and Technology (NIST) recently used AI to evaluate stem cell-derived “patches” of retinal pigment epithelium tissue for implanting into the eyes of patients with age-related macular degeneration (AMD).
According to a statement from the National Institutes of Health (NIH), the study demonstrates the potential for AI to perform quality control of therapeutic cells and tissues.
“This AI-based method of validating stem cell-derived tissues is a significant improvement over conventional assays, which are low-yield, expensive, and require a trained user,” said Kapil Bharti, PhD, a senior investigator in the NEI Ocular and Stem Cell Translational Research Section.
Cells of the retinal pigment epithelium (RPE) tissue support the light-sensoring photoreceptors in the eye and are among the first to die from geographic atrophy, known as dry AMD. Without the RPE, photoreceptors die, leading to blindness and vision loss.
“Our approach will help scale up manufacturing and will speed delivery of tissues to the clinic,” added Bharti, who led the research along with Carl Simon Jr., PhD, and Peter Bajcsy, PhD, of NIST.
The researchers’ AI-based validation method used deep neural networks, an AI technique that performs mathematical computations aimed at detecting patterns in unlabeled and unstructured data, and the algorithm operated on images of the RPE obtained using quantitative bright-field absorbance microscopy. The networks were trained to identify visual indications of RPE maturation that correlated with positive RPE function.
The method was validated using stem cell-derived RPE from a healthy donor, and its effectiveness was then tested, with the AI-based image analysis method accurately detecting known markers of RPE maturity and function. Moving forward, the team will work to develop a technique for making RPE replacement patches from AMD patients’ cells.
“Multiple AI-methods and advanced hardware allowed us to analyze terabytes and terabytes of imaging data for each individual patient, and do it more accurately and much faster than in the past,” Bajcsy said.
“This work demonstrates how a garden variety microscope, if used carefully, can make a precise, reproducible measurement of tissue quality,” Simon said.
The work was supported by the NEI Intramural Research Program and the Common Fund Therapeutics Challenge Award, along with the National Heart, Lung and Blood Institute.