CelloType produces more accurate results when analyzing high content tissue images.
The Children’s Hospital of Philadelphia (CHOP) announced that researchers working at the organization developed a new AI tool that can identify and classify cells in high content tissue images with more accuracy than traditional methods.1 The technology is named CelloType and used spatial omics to achieve its results.
Kai Tan, PhD, is the lead author of a study conducted on the effectiveness of CelloType. In a press release from CHOP, Tan said, “We are just beginning to unlock the potential of this technology. This approach could redefine how we understand complex tissues at the cellular level, paving the way for transformative breakthroughs in healthcare."
Tan, who is also a professor in the Department of Pediatrics at CHOP, continued, “Our findings underscore the increasingly pivotal role technology plays in today's biomedical research. CelloType advances spatial omics by providing a robust, scalable tool for analyzing complex tissue architectures, thereby expediting discoveries in cellular interactions, tissue function and disease mechanisms."
Earlier this month, CHOP announced a partnership with the Brazilian National Cancer Institute (INCA).2 Together, the two organizations will work to develop a state-of-the-art CAR-T cell production center at INCA.
In a press release issued at the time, CHOP’s associate director of the cell based therapy laboratory Stephan Kadauke, MD, PhD, said, “While immunotherapy is now generally available for pediatric patients with relapsed or refractory B-cell acute lymphoblastic leukemia (B-ALL) in Brazil, the public health system struggles to pay for its steep price. Traditional CAR-T cell manufacturing is costly, in part due to its complex, lengthy process that requires highly trained staff to manage every step of the manufacturing operations."
In the same press release, CHOP’s inaugural director of the Kelly Center for Cancer Immunotherapy Stephan Grupp said, “If you want to do the kind of true outreach that helps a large number of people instead of the few who can travel to specialized centers abroad, you need to do it at the point of care where people need the therapies. Our goal is to make this life-saving treatment more affordable and accessible to patients around the world."
CHOP also recently announced that it developed a supplemental questions on a routine healthcare questionnaire that had increased accuracy when predicting suicidal behavior in teens.3
In a press release, senior study author David Brent, MD, distinguished professor of psychiatry and endowed chair in Suicide Studies at the University of Pittsburgh, said, “Our study highlights that screening for depression is necessary but not sufficient for identifying youth at risk for suicide. Screening for depression in addition to past suicidal behavior, chronicity of depression and current suicidal ideation improve the accuracy of prediction of suicide attempts over the PHQ-9."
In the same press release, first study author Fuchiang (Rich) Tsui, PhD, endowed chair in Biomedical Informatics and Entrepreneurial Science at Children's Hospital of Philadelphia.
"By integrating the results of a patient's PHQ-9M with their electronic health records, we believe there is an opportunity to further improve predictive modeling for suicidal behavior. The more we can improve detection accuracy, the earlier we can design personalized intervention strategies."
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