A new artificial intelligence system that examines the shape and structure of blood cells could significantly improve how diseases such as leukemia are diagnosed. Researchers say the tool can identify abnormal cells with greater accuracy and consistency than human specialists, potentially reducing missed or uncertain diagnoses.
The system, known as CytoDiffusion, relies on generative AI, the same type of technology used in image generators such as DALL-E, to analyze blood cell appearance in detail. Rather than focusing only on obvious patterns, it studies subtle variations in how cells look under a microscope.
Moving Beyond Pattern Recognition
Many existing medical AI tools are trained to sort images into predefined categories. In contrast, the team behind CytoDiffusion demonstrated that their approach can recognize the full range of normal blood cell appearances and reliably flag rare or unusual cells that may signal disease. The work was led by researchers from the University of Cambridge, University College London, and Queen Mary University of London, and the findings were published in Nature Machine Intelligence.
Identifying small differences in blood cell size, shape, and structure is central to diagnosing many blood disorders. However, learning to do this well can take years of experience, and even highly trained doctors may disagree when reviewing 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, for example. 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 standard blood smear can contain thousands of individual cells, far more than a person can realistically examine one by one. "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."
This challenge is familiar to clinicians. "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," said co-senior author Dr. Suthesh Sivapalaratnam from Queen Mary University of London. "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
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