Honeywell Introduces Digital Holographic Microscopy for Faster Healthcare Testing

The company stated that this technology could help reduce the time it takes to diagnose infections, particularly for patients undergoing at-home peritoneal dialysis who are at risk of abdominal lining infections.

Honeywell has unveiled Digital Holographic Microscopy (DHM), an AI-powered technology designed to accelerate healthcare testing by simplifying cell and particle analysis. The company stated that this technology could help reduce diagnosis time for infections, particularly benefiting patients undergoing at-home peritoneal dialysis who are at risk of abdominal lining infections.

Currently, diagnosing such infections takes 1-2 days, as samples must be sent to a lab equipped with specialized microscopes. DHM aims to address this delay by allowing point-of-care imaging using a portable device with a laser, imager sensor, and disposable microscope slide. AI algorithms then analyze the images to detect white blood cell count and type, providing faster results for timely medical intervention.

“We are seeing a growing need for solutions that can provide both rapid and precise results, empowering healthcare professionals to make informed decisions quickly,” said Sarah Martin, president of Honeywell Sensing Solutions. “We have invested in innovations like Digital Holographic Microscopy because of their power to make testing and analysis more accessible to patients while also improving patient care and efficiency across our healthcare system.”

Portable Microscopy for Point-of-Care and Environmental Analysis

DHM generates high-resolution images without requiring complex optical components found in traditional microscopes. Instead, the technology processes light passing through a sample to produce hologram images, which are then analyzed using computational algorithms. This method eliminates the need for staining, simplifying biological sample preparation.

Honeywell highlighted that DHM could also be applied in environmental monitoring beyond healthcare, such as analyzing air pollutants to assess indoor air quality and monitoring cells and microorganisms in liquid samples for safety testing.

By integrating AI and machine learning, this technology has the potential to replace or supplement traditional lab analysis, offering a simplified workflow and faster test results.