AI Detects Dangerous Blood Cells Doctors May Miss
- A new artificial intelligence system called CytoDiffusion analyzes the shape and structure of blood cells, possibly improving the diagnosis of diseases like leukemia.
- cytodiffusion uses generative AI - the same technology behind image generators like DALL-E - to analyze blood cell appearance.
- Unlike many existing medical AI tools that categorize images, CytoDiffusion recognizes the full range of normal blood cell appearances.
AI System Improves Leukemia Diagnosis with Detailed Blood Cell Analysis
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A new artificial intelligence system called CytoDiffusion analyzes the shape and structure of blood cells, possibly improving the diagnosis of diseases like leukemia. Researchers at the University of Cambridge, University College London, and Queen Mary University of London report the tool identifies abnormal cells with greater accuracy and consistency than human specialists, reducing diagnostic errors.
cytodiffusion uses generative AI – the same technology behind image generators like DALL-E – to analyze blood cell appearance. It doesn’t just look for obvious patterns; it studies subtle variations visible under a microscope.
Moving Beyond Pattern Recognition
Unlike many existing medical AI tools that categorize images, CytoDiffusion recognizes the full range of normal blood cell appearances. This allows it to reliably identify rare or unusual cells that may indicate disease. The findings were published in Nature Machine Intelligence.
Diagnosing blood disorders relies on identifying small differences in cell size, shape, and structure. However, mastering this skill requires years of experience, and even experts can disagree on complex cases.
“We’ve all got many different types of blood cells that have different properties and different roles within our body,” said Simon Deltadahl from Cambridge’s department of Applied Mathematics and Theoretical Physics,the study’s first author.”White blood cells specialize in fighting infection, such as. But knowing what an unusual or diseased blood cell looks like under a microscope is an important part of diagnosing many diseases.”
Handling the Scale of blood Analysis
A single blood smear can contain thousands of cells, too many for a person to examine individually. “Humans can’t look at all the cells in a smear – it’s just not possible,” Deltadahl said. “Our model can automate that process, triage the routine cases, and highlight anything unusual for human review.”
Dr. Suthesh Sivapalaratnam from Queen mary University of London, a co-senior author, experienced this challenge firsthand. “The clinical challenge I faced as a junior hematology doctor was that after a day of work, I would face a lot of blood films to analyze,” he said.”As I was analyzing them in the late hours, I became convinced AI would do a better job than me.”
Training on an Unprecedented Dataset
The researchers trained CytoDiffusion on over 500,000 blood smear images collected at Addenbrooke’s Hospital in Cambridge.