Automatic deep-learning AI tool measures volume of cerebral ventricles on MR scans of children

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Automatic deep-learning AI tool measures volume of cerebral ventricles on MR scans of children

Press releases may be edited for formatting or style | December 01, 2020 MRI Pediatrics
CHARLOTTESVILLE, VA (DECEMBER 1, 2020). Researchers from multiple institutions in North America have developed a fully automated, deep-learning (DL), artificial-intelligence clinical tool that can measure the volume of cerebral ventricles on magnetic resonance images (MRIs) in children within about 25 minutes. The ability to track ventricular volume over time in a clinical setting will prove invaluable in the treatment of children and adults with hydrocephalus. Details on the development of the tool and its validation are reported today in a new article, "Artificial intelligence for automatic cerebral ventricle segmentation and volume calculation: a clinical tool for the evaluation of pediatric hydrocephalus," by Jennifer L. Quon, MD, and colleagues, in the Journal of Neurosurgery: Pediatrics.

Hydrocephalus is a pathological condition caused by an excessive amount of cerebrospinal fluid (CSF) in chambers of the brain known as ventricles. The condition results from an imbalance between the production and absorption of CSF. Hydrocephalus is called "communicating" when CSF can pass from one ventricle to another and "obstructive" when passage from one ventricle to another is blocked. The prevalence of pediatric hydrocephalus is approximately six in 10,000 live births. It has been called "the most common surgically correctable neurological problem in infants, children, and adolescents."

Diagnosis of hydrocephalus is based on clinical signs and symptoms as well as on findings of enlarged ventricles on neuroimaging studies. Placement of a shunt (an internal draining system that drains excess CSF away from the brain) is the most common surgical procedure performed to reduce hydrocephalus. Following surgery, patients must be monitored periodically to ensure that the shunt continues to work properly. Changes in ventricular volume can guide clinical decision-making. However, to date, accurate assessments of ventricular volume can be time consuming or require research-level automated tools that are not easily adapted to the patient's clinical visit.

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The authors of this study sought to develop an automated deep-learning (DL)-based model that could be used to evaluate changes in the volume of brain ventricles over time in children with hydrocephalus during their clinic visits. Deep learning is an advanced form of artificial intelligence that mimics the workings of the human brain; it is capable of processing large quantities of data and creating patterns used in decision making. The authors' goal was to create a DL tool that would work efficiently in multiple institutions with various clinical MRI machines from different manufacturers.

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