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

Download Mobile App




Events

ATTENTION: Due to the COVID-19 PANDEMIC, many events are being rescheduled for a later date, converted into virtual venues, or altogether cancelled. Please check with the event organizer or website prior to planning for any forthcoming event.
30 Jan 2023 - 02 Feb 2023

AI Algorithm Automatically Detects Brain Abnormalities from MRI Scans

By MedImaging International staff writers
Posted on 15 Aug 2022
Print article
Image: An AI algorithm detects subtle brain abnormalities which cause epileptic seizures (Photo courtesy of University College London)
Image: An AI algorithm detects subtle brain abnormalities which cause epileptic seizures (Photo courtesy of University College London)

Around 1% of the world’s population suffers from the serious neurological condition epilepsy that is characterized by frequent seizures. Drugs treatments are available for the majority of people with epilepsy, although 20-30% do not respond to medications. In children who have had surgery to control their epilepsy, focal cortical dysplasia (FCD) is the most common cause, and in adults it is the third most common cause. FCD are areas of the brain that have developed abnormally and often cause drug-resistant epilepsy. It is typically treated with surgery, although identifying the lesions from an MRI is an ongoing challenge for clinicians, as MRI scans in FCDs can look normal. Now, an artificial intelligence (AI) algorithm can detect subtle brain abnormalities which cause epileptic seizures.

In the Multicentre Epilepsy Lesion Detection project (MELD), a team of international researchers led by the University College London (London, UK) used over 1,000 patient MRI scans from 22 global epilepsy centers to develop the algorithm, which provides reports of where abnormalities are in cases of drug-resistant FCD – a leading cause of epilepsy. To develop the algorithm, the team quantified cortical features from the MRI scans, such as how thick or folded the cortex/brain surface was, and used around 300,000 locations across the brain. Researchers then trained the algorithm on examples labeled by expert radiologists as either being a healthy brain or having FCD – dependant on their patterns and features.

The researchers found that overall the algorithm was able to detect the FCD in 67% of cases in the cohort (538 participants). Previously, 178 of the participants had been considered MRI negative, which means that radiologists had been unable to find the abnormality – yet the MELD algorithm was able to identify the FCD in 63% of these cases. This is particularly important, as if doctors can find the abnormality in the brain scan, then surgery to remove it can provide a cure.

“This algorithm could help to find more of these hidden lesions in children and adults with epilepsy, and enable more patients with epilepsy to be considered for brain surgery that could cure the epilepsy and improve their cognitive development,” said study co-senior author, Dr. Konrad Wagstyl from the UCL Queen Square Institute of Neurology.

“Our algorithm automatically learns to detect lesions from thousands of MRI scans of patients. It can reliably detect lesions of different types, shapes and sizes, and even many of those lesions that were previously missed by radiologists,” added study co-first author, Dr. Hannah Spitzer (Helmholtz Munich).

“We hope that this technology will help to identify epilepsy-causing abnormalities that are currently being missed. Ultimately it could enable more people with epilepsy to have potentially curative brain surgery,” said study co-senior author, Dr. Sophie Adler from the UCL Great Ormond Street Institute of Child Health.

Related Links:
University College London 

Gold Supplier
SBRT Phantom with Removable Spine
E2E SBRT Phantom with Removable Spine Model 036S-CVXX-xx
New
Motorized DR System I-Arm
Alizé BRS
New
Mobile Radiographic Table
CT160
New
Vertical Bucky
IMX-18A

Print article
CIRS -  MIRION

Channels

Radiography

view channel
Researchers used AI to triage patients with chest pain (Photo courtesy of Pexels)

First Deep Learning AI Model Triages Patients with Chest Pain Using X-Rays

Acute chest pain syndrome can involve tightness, burning or other discomfort in the chest or a severe pain that spreads to the back, neck, shoulders, arms, or jaw, accompanied by shortness of breath.... Read more

Ultrasound

view channel
Image: Dr. Derek Cool demonstrating the new robotic 3D ultrasound system (Photo courtesy of Lawson Health)

Robotic 3D Ultrasound System Improves Accuracy of Liver Cancer Treatment

Liver cancer is the fourth-leading cause of cancer death in the world. Surgery is one treatment option for liver cancer, although thermal ablation which uses heat to destroy the cancerous tumor has less... Read more

Nuclear Medicine

view channel
Image: Tracking radiation treatment in real time promises safer, more effective cancer therapy (Photo courtesy of Pexels)

Real-Time 3D Imaging Provides First-of-Its-Kind View of X-Rays Hitting Inside Body During Radiation Therapy

Radiation is used in treatment for hundreds of thousands of cancer patients each year, bombarding an area of the body with high energy waves and particles, usually X-rays. The radiation can kill cancer... Read more

General/Advanced Imaging

view channel
Image: The HIAS-29000 brain PET scanner with motion correction (Photo courtesy of Hamamatsu Photonics)

New Brain PET Scanner Corrects Blurring in Images Caused by Body Motion

Ordinary brain PET (positron emission tomography) scanners are unable to accurately measure the distribution of radiopharmaceuticals in the brain if the patients move their head during the examination process.... 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 © 2000-2023 Globetech Media. All rights reserved.