Introduction
Researchers
from the Mass General Brigham Center collaborated
with the Dana-Farber/Boston Childrens Cencer and
Boold Disorders Center and Boston Children's Hospital to create an
artificial intelligence system that can predict the return of brain tumors to
children with great accuracy. The system sequentially analyzes MRI images based
on a technique called temporal learning, which allows
the processing of several medical images taken after treatment.
In order to
rarer people with brain cancer in children, researchers have difficulty
collecting the necessary data. In order to solve this challenge, the team of researchers
with a group of American medical institutions compiled a database containing a
large number of MRI images of 715 children.
The
researchers, led by lead researcher Dr. Benjamin Kann of the Mass General
Brigham and Other Centers, used data collected in the database with temporal
learning technology, in order to train the system to analyze the MRI images of the child's brain over time after surgery in
order to predict the return of the child's brain tumor.
The
researchers initially trained the model to arrange the postoperative images
chronologically, so that it could capture subtle changes over
time, and then modified the model to link the changes
to the possibility of tumor relapse and recurrence.
A new development in the medical field, researchers have created an artificial intelligence system that can predict the return of brain tumors in children with high accuracy
An AI system that can predict the return of brain tumors in children with high accuracy
In order to
predict the children who are more likely to return the tumor, so children
undergo repeated MRI examinations for many years and this is a psychological
and physical burden on the affected children and their families, so this system
came in order to identify patients at risk of infection early.
What is the benefit of an artificial intelligence system that predicts the return of brain tumors in children with high accuracy?
In order to
predict children who are more likely to relapse, children
undergo frequent MRI examinations for many years, and this is a psychological
and physical burden on the affected children and their families, so this system
came in order to identify patients prone to tumor recurrence early.
How does the system predict the return of brain tumors in children?
The artificial intelligence system performs
sequential analysis of MRI images, based on a technique called temporal
learning. This technology allows the processing of
several medical images taken after treatment. The system analyzes changes in
the child's brain images long after surgery, which improves the system's
ability to predict the return of the child's brain tumor.
The
researchers trained the model as a start on arranging postoperative images
chronologically so that it could capture subtle changes over time, and then modified
the model to link the changes to the possibility of tumor relapse and
recurrence.
What is the difference between traditional medical AI models and 'a time learning' AI system?
Traditional AI
models for image analysis analyze a single image in order to make a specific
decision, but an AI system powered by "time
learning" technology analyzes several successive images in order to
determine the likelihood of a brain tumor returning to children.
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