Now, researchers at Nagoya University have developed an AI tool that uses data from patient follow-up studies to estimate the speed of disease progression and identify patterns of muscle decline.
The study is published in npj Digital Medicine.
The system identified six distinct patterns of disease progression among the patients. Some patients showed slow decline in motor function, with little effect on speech or breathing, while others experienced rapid deterioration.
One of the most important findings was that DiSPAH could, to some extent, predict a patient's progression speed and broad progression pattern from information available at the first medical visit.
These early predictions have important potential for patient care.
Doctors could use them to plan treatment, prepare patients and families, and design better clinical trials by grouping participants according to how their disease advances.
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