News | Magnetic Resonance Imaging (MRI) | January 23, 2018

Brain Imaging Predicts Language Learning in Deaf Children

Study shows that early cochlear implantation is critical to language development in deaf children 

Brain Imaging Predicts Language Learning in Deaf Children

January 23, 2018 – In a new international collaborative study between The Chinese University of Hong Kong and Ann & Robert H. Lurie Children's Hospital of Chicago, researchers created a machine learning algorithm that uses brain scans to predict language ability in deaf children after they receive a cochlear implant. This study's novel use of artificial intelligence to understand brain structure underlying language development has broad-reaching implications for children with developmental challenges. It was published in the Proceedings of the National Academy of Sciences of the United States of America.

"The ability to predict language development is important because it allows clinicians and educators to intervene with therapy to maximize language learning for the child," said co-senior author Patrick C. M. Wong, Ph.D., a cognitive neuroscientist, professor and director of the Brain and Mind Institute at The Chinese University of Hong Kong. "Since the brain underlies all human ability, the methods we have applied to children with hearing loss could have widespread use in predicting function and improving the lives of children with a broad range of disabilities," said Wong.

A cochlear implant is the most effective treatment for children born with significant hearing loss when hearing aids are not enough for the child to develop age-appropriate listening and language ability. Decades of research have shown that early cochlear implantation is critical. Although a cochlear implant enables many children with hearing loss to understand and develop speech, some children lag behind their normal-hearing peers despite receiving an implant as an infant or toddler. Helping these children achieve the language and literacy of hearing children is important and the focus of much research, as these skills are critical to academic success, social and emotional well-being and employment opportunities.

"So far, we have not had a reliable way to predict which children are at risk to develop poorer language. Our study is the first to provide clinicians and caregivers with concrete information about how much language improvement can be expected given the child's brain development immediately before surgery," said co-senior author Nancy M. Young, M.D., Medical Director, Audiology and Cochlear Implant Programs at Lurie Children's, a surgeon and professor at Northwestern University Feinberg School of Medicine. "The ability to forecast children at risk is the critical first step to improving their outcome. It will lay the groundwork for future development and testing of customized therapies."

"A one-size-fits-all intensive therapy approach is impractical and may not adequately address the needs of those children most at risk to fall behind," added Wong.

Erin Ingvalson, assistant professor at Florida State University who began work on the project as a post-doctoral student at Northwestern University, said "our goal is to eliminate the gap in language outcomes often found when children with hearing loss are compared to those with normal hearing."

"The ability to optimize therapy for each child with hearing loss will transform many lives," said Ingvalson.

Successful hearing and spoken language development depends on both the ear and the brain. Hearing loss early in life deprives the auditory areas of the brain of stimulation, which causes abnormal patterns of brain development.

"We used MRI [magnetic resonance imaging] to capture these abnormal patterns before cochlear implant surgery and constructed a machine-learning algorithm for predicting language development with a relatively high degree of accuracy, specificity and sensitivity," Wong explained. "Although the current algorithm is built for children with hearing impairment, research is being conducted to also predict language development in other pediatric populations."

For more information: www.pnas.org

 

Related Content

Artificial Intelligence Used in Clinical Practice to Measure Breast Density
News | Artificial Intelligence | January 15, 2019
An artificial intelligence (AI) algorithm measures breast density at the level of an experienced mammographer,...
Sponsored Content | Videos | Artificial Intelligence | January 15, 2019
ITN Contributing Editor Greg Freiherr offers an overview of...
Machine Learning Uncovers New Insights Into Human Brain Through fMRI
News | Neuro Imaging | January 11, 2019
An interdisciplinary research team led by scientists from the National University of Singapore (NUS) has successfully...
Videos | Interventional Radiology | January 11, 2019
Julius Chapiro, M.D., research faculty member and an...
AI Approach Outperformed Human Experts in Identifying Cervical Precancer
News | Digital Pathology | January 10, 2019
January 10, 2019 — A research team led by investigators from the National Institutes of Health and Global Good has de
Mobile App Data Collection Shows Promise for Population Health Surveys
News | Population Health | January 10, 2019
Mobile app data collection can bring access to more potential clinical study participants, reduce clinical study...
Artificial intelligence, also called deep learning and machine learning, was the hottest topic at the 2018 Radiological Society of North America (RSNA)) meeting.

Artificial intelligence was the hottest topic at the 2018 Radiological Society of North America (RSNA)) meeting, which included a large area with its own presentation therater set asside for AI vendors.

Feature | Artificial Intelligence | January 10, 2019 | Dave Fornell, Editor
Hands down, the hottest topic in radiology the past two years has been the implementation of...
Hypertension With Progressive Cerebral Small Vessel Disease Increases Cognitive Impairment Risk
News | Magnetic Resonance Imaging (MRI) | January 08, 2019
Patients with high blood pressure and progression of periventricular white matter hyperintensities showed signs of...
Artificial Intelligence Pinpoints Nine Different Abnormalities in Head Scans

A brain scan (left) showing an intraparenchymal hemorrhage in left frontal region and a scan (right) of a subarachnoid hemorrhage in the left parietal region. Both conditions were accurately detected by the Qure.ai tool. Image courtesy of Nature Medicine.

News | Artificial Intelligence | January 07, 2019
The rise in the use of computed tomography (CT) scans in U.S. emergency rooms has been a well-documented trend1 in...
Electronic Brachytherapy Effective in Long-Term Study of 1,000 Early-Stage Breast Cancers
News | Brachytherapy Systems, Women's Healthcare | January 07, 2019
Breast cancer recurrence rates of patients treated with intraoperative radiation therapy (IORT) using the Xoft Axxent...