We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress
Sign In
Advertise with Us
GLOBETECH PUBLISHING LLC

Download Mobile App




Scientists Use Machine Learning and MRI Scans to Predict Learning Difficulties

By MedImaging International staff writers
Posted on 11 Oct 2018
Print article
A team of scientists from the Medical Research Council (MRC) Cognition and Brain Sciences Unit at the University of Cambridge (Cambridge, England, UK) used machine learning - a type of artificial intelligence - with data from hundreds of children who struggle at school to identify clusters of learning difficulties, which did not match their previous diagnosis. According to the researchers, this reinforces the need for children to receive detailed assessments of their cognitive skills to identify the best type of support.

For the study, the researchers recruited 550 children who had been referred to a clinic because they were struggling at school. Previous research on learning difficulties has focused on children who had already been diagnosed with a particular difficulty, such as attention deficit hyperactivity disorder (ADHD), an autism spectrum disorder, or dyslexia. The latest study included children with all difficulties, irrespective of their diagnosis, to better capture the range of difficulties within, and overlap between, the diagnostic categories.

The researchers applied machine learning to a broad spectrum of hundreds of struggling learners by supplying the computer algorithm with lots of cognitive testing data from each child, including measures of listening skills, spatial reasoning, problem solving, vocabulary, and memory. Based on these data, the algorithm suggested that the children best fit into four clusters of difficulties. These clusters aligned closely with other data on the children, such as the parents' reports of their communication difficulties, and educational data on reading and math.

However, there was no correspondence with their previous diagnoses. In order to check if these groupings corresponded to biological differences, the groups were checked against MRI brain scans from 184 children. The groupings mirrored patterns in connectivity within parts of the children's brains, suggesting that that the machine learning was identifying differences that partly reflect underlying biology. Two of the four groupings identified were: difficulties with working memory skills, and difficulties with processing sounds in words. The other two clusters identified were: children with broad cognitive difficulties in many areas, and children with typical cognitive test results for their age. The researchers noted that the children in the grouping that had cognitive test results that were typical for their age might still have had other difficulties that were affecting their schooling, such as behavioral difficulties, which had not been included in the machine learning.

"Our study is the first of its kind to apply machine learning to a broad spectrum of hundreds of struggling learners," said Dr Duncan Astle from the MRC Cognition and Brain Sciences Unit at the University of Cambridge, who led the study.

"These are interesting, early-stage findings which begin to investigate how we can apply new technologies, such as machine learning, to better understand brain function," said Dr Joanna Latimer, Head of Neurosciences and Mental Health at the MRC.

Related Links:
University of Cambridge

Gold Member
Solid State Kv/Dose Multi-Sensor
AGMS-DM+
X-Ray Meter
Cobia SENSE
New
Color Doppler Ultrasound System
DRE Crystal 4PX
New
DR Flat Panel Detector
1500L

Print article

Channels

Ultrasound

view channel
Image: Structure of the proposed transparent ultrasound transducer and its optical transmittance (Photo courtesy of POSTECH)

Ultrasensitive Broadband Transparent Ultrasound Transducer Enhances Medical Diagnosis

The ultrasound-photoacoustic dual-modal imaging system combines molecular imaging contrast with ultrasound imaging. It can display molecular and structural details inside the body in real time without... Read more

Nuclear Medicine

view channel
Image: PET/CT of a 60-year-old male patient with clinical suspicion of lung cancer (Photo courtesy of EJNMMI Physics)

Early 30-Minute Dynamic FDG-PET Acquisition Could Halve Lung Scan Times

F-18 FDG-PET scans are a way to look inside the body using a special dye, and these scans can be either static or dynamic. Static scans happen 60 minutes after the dye is administered into the body, showing... Read more

Imaging IT

view channel
Image: The new Medical Imaging Suite makes healthcare imaging data more accessible, interoperable and useful (Photo courtesy of Google Cloud)

New Google Cloud Medical Imaging Suite Makes Imaging Healthcare Data More Accessible

Medical imaging is a critical tool used to diagnose patients, and there are billions of medical images scanned globally each year. Imaging data accounts for about 90% of all healthcare data1 and, until... Read more
Copyright © 2000-2024 Globetech Media. All rights reserved.