AI-based Tissue Staining to Detect Amyloid Deposits Without Chemical Stains or Microscopy
The Californer/10291398

LOS ANGELES - Californer -- Researchers at the University of California, Los Angeles (UCLA) have pioneered a groundbreaking approach in the imaging and detection of amyloid deposits in tissue samples. The innovative method leverages deep learning and autofluorescence microscopy to achieve virtual birefringence imaging and histological staining, eliminating the need for polarization imaging and traditional chemical stains like Congo red.

Systemic amyloidosis, a condition characterized by the accumulation of misfolded proteins in organs and tissues, poses significant diagnostic challenges. Amyloidosis affects several million people every year, often leading to severe organ damage, heart failure, and high mortality rates if not diagnosed and treated early. Traditionally, Congo red staining under polarized light microscopy has been the gold standard for visualizing amyloid deposits. However, this method is labor-intensive, costly, and subject to variability that can lead to false diagnoses.

More on The Californer

• Heritage at South Brunswick Offers Immediate Townhome Appointments and Special Mortgage Incentive Fast-Moving Sales
• Sam Sammane wins Literary Titan Award for Republic of Mars, a haunting sci-fi debut on memory
• Fair Trade LA, The Tote Project, and Fair Trade USA™ Distribute 100 "Fair Care Packages" to Families Affected by Los Angeles Fires
• For Third Consecutive Year, ELEVATE Recognized as Star Performer & Major Contender by Everest Group
• NASA Collaborative Agreement for Supply of Thin-Film Solar Tech for Orbital Application to Advance Development of Thin-Film PV Power Beaming: $ASTI

The new technique utilizes a single neural network to transform autofluorescence images of label-free tissue into high-fidelity brightfield and polarized microscopy images that mirror those obtained through traditional histochemical staining and polarization microscopy. The technique was tested on cardiac tissue samples, showing that virtually stained images provided consistent and reliable identification of amyloid patterns compared to traditional methods, also eliminating the need for chemical staining and specialized polarization microscopes, potentially speeding up diagnosis and reducing costs. This virtual staining process not only matches but, in some cases, surpasses the quality of conventional methods, as validated by multiple board-certified pathologists from UCLA as well as USC and the Hadassah Hebrew University Medical Center.

Dr. Aydogan Ozcan, the senior author of the study and the Volgenau Chair for Engineering Innovation at UCLA, explains, "Our deep learning model can perform both autofluorescence-to-birefringence and autofluorescence-to-brightfield image transformations, offering a reliable, consistent, and cost-effective alternative to traditional histology methods. This breakthrough could greatly enhance the speed and accuracy of amyloidosis diagnosis, reducing the risk of false negatives and improving patient outcomes."

More on The Californer

• Shoot 'Em Up Classic Undeadline Coming to America & Europe for the First Time in New Collector's Set
• Exciting New Era of Sports, Entertainment & Gaming Innovation Spotlighted by Rebrand of Expanding AI Driven, Online Fan Engagement Company: SEGG Media
• FANATICS AND COMPLEX PRESENT <BLACKPINK IN YOUR AREA> LEAGUE COLLECTION TO CELEBRATE THEIR COMEBACK
• Service Ninjas Debuts First-of-Its-Kind "Membership" Platform for Home Service Pros
• Project Management Institute San Francisco and TalentCheetah Inc. Announce New Strategic Partnership

The study's findings suggest that this virtual staining approach could be seamlessly integrated into existing clinical workflows, facilitating the broader adoption of digital pathology. The method requires no specialized optical components and can be implemented on standard digital pathology scanners, making it accessible to a wide range of healthcare settings.

The researchers plan to expand their evaluations to other tissue types, such as kidney, liver, and spleen, to further validate the model's clinical utility across different amyloidosis manifestations. They also aim to explore the development of automated detection systems to assist pathologists in identifying problematic areas, potentially improving diagnostic accuracy and reducing false negatives.

Funding: This research was supported by the U.S. National Science Foundation (NSF) and the National Institutes of Health (NIH).

Original publication: https://doi.org/10.1038/s41467-024-52263-z