We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

LabMedica

Download Mobile App
Recent News Expo Clinical Chem. Molecular Diagnostics Hematology Immunology Microbiology Pathology Technology Industry Focus

Silgan Unicep

Novel AI-Powered Method for Tissue Analysis Improves Understanding of Disease Pathology

By LabMedica International staff writers
Posted on 11 Jun 2024

Scientists at Brown University (Providence, RI, USA) and the University of Michigan (Ann Arbor, MI, USA) have created a groundbreaking computational technique to examine complex tissue data, potentially revolutionizing our understanding of diseases and their treatment. The method, known as Integrative and Reference-Informed tissue Segmentation (IRIS), utilizes machine learning and artificial intelligence to provide biomedical researchers with accurate insights into tissue development, disease pathology, and tumor structuring. IRIS employs spatially resolved transcriptomics (SRT) data and incorporates single-cell RNA sequencing data as a reference. This approach allows for the simultaneous examination of multiple tissue layers and accurately identifies different regions with exceptional computational speed and precision. In contrast to traditional methods that offer averaged data from tissue samples, SRT delivers a much more detailed perspective, locating thousands of specific points within a single tissue section.

Handling vast and complex datasets has always posed significant challenges, and IRIS addresses these by using algorithms to sift through data, segmenting various functional domains, such as tumor areas, and shedding light on cellular interactions and the dynamics of disease progression. Unlike existing methods, IRIS directly maps the cellular composition of tissues and delineates biologically meaningful spatial domains, enhancing the comprehension of cellular activities that drive tissue functions. The developers of IRIS tested it on six SRT datasets, assessing its effectiveness compared to other spatial domain analysis methods. As SRT technologies gain traction and become more widely used, the creators of IRIS anticipate that it will contribute to identifying new clinical intervention points or pharmaceutical targets, thereby enhancing personalized treatment strategies and ultimately improving patient health outcomes.


Image: The new AI-powered statistics method has the potential to improve tissue and disease research (Photo courtesy of 123RF)
Image: The new AI-powered statistics method has the potential to improve tissue and disease research (Photo courtesy of 123RF)

"The computational approach of IRIS pioneers a novel avenue for biologists to delve into the intricate architecture of complex tissues, offering unparalleled opportunities to explore the dynamic processes shaping tissue structure during development and disease progression," said Xiang Zhou, professor of biostatistics at the University of Michigan School of Public Health. "Through characterizing refined tissue structures and elucidating their alterations during disease states, IRIS holds the potential to unveil mechanistic insights crucial for understanding and combating various diseases." The researchers' findings were published in the journal Nature Methods on June 6, 2024.

Related Links:
Brown University
University of Michigan


New
Platinum Member
Flu SARS-CoV-2 Combo Test
OSOM® Flu SARS-CoV-2 Combo Test
Magnetic Bead Separation Modules
MAG and HEATMAG
POCT Fluorescent Immunoassay Analyzer
FIA Go
New
Gold Member
Helicobacter Pylori Test
HELICOBACTER PYLORI Ag VIRCLIA® MONOTEST

Latest Pathology News

AI Identifies Drug-Resistant Typhoid-Like Infection from Microscopy Images within Hours

Whole-Slide Imaging System Enables Pathologists to Diagnose Patients Using Digital Images

New Highly-Sensitive Test to Help More Easily Diagnose B-Cell Lymphoma