By accelerating the first stage of spatial omics data analysis, a fresh artificial intelligence model was developed in Children’s Hospital of Philadelphia provides detailed insight into the development and progression of the disease at the cellular level, which could lend a hand lead to faster diagnosis and targeted treatment.
The hospital says CHOP’s open-source AI tool is now available in a public repository for non-commercial operate.
WHY IT’S IMPORTANT
Pediatric researchers have developed a deep learning-powered biomedical imaging model called CelloType to speed up the identification and classification of cells in tissue images. They then tested the biomedical imaging AI in a wide range of elaborate diseases, including cancer and chronic kidney disease.
CelloType is programmed to improve the accuracy of cell detection, segmentation and classification, CHOP said, and can effectively handle large-scale tasks such as natural language processing and image analysis.
Although the CHOP model requires training on segmentation and classification tasks, it can learn patterns and make predictions or classifications faster than previous approaches.
The researchers compared CelloType’s performance with multiplexed tissue image segmentation models, including Mesmer and Cellpose2, and detailed the results of the National Institutes of Cancer-funded study in .
“Unlike the traditional two-step approach of segmentation followed by classification, CelloType adopts a multi-task learning strategy that integrates these tasks while improving the performance of both,” they said in their report.
Some cell types are gigantic or irregular in shape, posing a challenge to conventional segmentation methods. CelloType, which uses transformer-based deep learning and automates the analysis of high-dimensional data, better captures elaborate relationships and context in tissue samples, they said.
CelloType uses artificial intelligence to precisely contour objects in an image.
Kai Tan, lead author of the study and professor in CHOP’s Department of Pediatrics, said in a statement that “this approach has the potential to redefine how we understand complex tissues at the cellular level, paving the way for transformative breakthroughs in health care.”
A BIGGER TREND
According to CHOP, in spatial omics – a field that combines genomics, transcriptomics and proteomics with spatial information to map the location of various molecules within cells in elaborate tissues – there is an urgent need to develop more sophisticated computational tools for data analysis.
Recent advances have led to the analysis of intact tissues at the cellular level, allowing for unparalleled insight into the connections between cellular architecture and functionality of various tissues and organs.
Using AI to better understand biomedical images can not only lend a hand clinicians treat patients, but can also enhance patients’ access to advanced imaging and even predict diseases such as cancer, which is why healthcare systems are turning to AI imaging tools.
While researchers in Norway and Denmark are using mammographic images in national breast cancer screening programs to lend a hand predict diagnosis, Stamford Health’s Heart and Vascular Institute announced in October that its patients will automatically be screened for coronary artery disease during each chest computed tomography without contrast. and when their future risk scores enhance.
“This tool enhances our ability to detect early signs of cardiovascular disease and ensures patients receive the follow-up care they need to prevent serious health outcomes,” Dr. David Hsi, chief of the division of cardiology and co-director of the institute, said in his keynote speech. statement.
One medical director and professor of pediatrics said he believes healthcare providers armed with artificial intelligence and machine learning can turn around the fortunes of patients battling elaborate diseases.
“Personalized genetic and epigenetic information can help tailor many drugs to specific patients and specific diseases. “All of these omics involve enormous amounts of data that information technology can now analyze in extraordinary detail and that can be functionally assessed using artificial intelligence and machine learning algorithms,” said Dr. William Hay Jr., chief medical officer of Astarte Medical, a health care company, last year precision medicine.
ON RECORDING
“We are just beginning to unlock the potential of this technology,” Tan said in a statement.