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New research suggests artificial intelligence could be a game-changer in detecting subtle brain abnormalities that frequently enough elude human reviewers, potentially leading to more accurate epilepsy diagnoses and treatment.
Imagine a world where a computer programme could help doctors pinpoint the source of seizures with greater accuracy. That’s the promise of a groundbreaking AI algorithm developed by researchers at University College London. Presented at the American Epilepsy Society’s 78th Annual Meeting, the tool focuses on identifying two common types of lesions – focal cortical dysplasia (FCD) and hippocampal sclerosis – often responsible for epilepsy.
“An estimated 42%-55% of adult and pediatric patients with epilepsy who undergo surgery with negative MRI findings have FCDs,” explained Sophie Adler, a medical student at University College London, during her presentation. “These malformations of cortical progress can be difficult to spot on standard MRI scans.”
The AI program, trained on data from 22 epilepsy centers worldwide, analyzes surface-based features of the brain, looking for patterns that might indicate a lesion. Initial testing showed promising results, with the algorithm detecting lesions in 63% of MRI-negative patients. However, the initial version also produced a important number of false positives, requiring careful review by neurologists.to refine the algorithm, the researchers incorporated a graph convolutional neural network. This advanced technique allows the AI to consider the context of each point on the brain’s surface, improving its ability to distinguish between normal and abnormal tissue.
“This strategy considerably reduced false positives, raising the positive predictive value to 67%,” said Adler. “So, if the algorithm flags something, it’s nearly 70% likely that it’s a real FCD.”
The researchers have made their algorithm open-source, allowing clinicians and researchers worldwide to access and utilize the tool.They are also collaborating with radiologists and surgeons to integrate the algorithm into clinical practice.
“we’re working with surgeons to use the algorithm to identify suspected lesions before implanting electrodes,” Adler explained. The team is also developing an algorithm to detect hippocampal sclerosis, with the ultimate goal of creating a single algorithm capable of detecting multiple types of epileptic lesions.The Future of epilepsy Diagnosis
The potential of AI in epilepsy diagnosis is immense. As Dr.sara Inati, an assistant clinical investigator in the NINDS Intramural Research Program, pointed out, standardized imaging protocols are crucial for ensuring the accuracy and consistency of AI-based diagnoses.
“Following these standards and using similar protocols across centers will make it easier to utilize these powerful AI tools,” Dr. Inati stated. “While we’re still in the early stages, the work being done by researchers like Adler and her team is making these tools more accessible to clinicians, ultimately leading to better diagnosis and treatment for epilepsy patients.”
Could this be the dawn of a new era in epilepsy care? Only time will tell, but the possibilities are certainly exciting.
New research suggests an artificial intelligence (AI) algorithm could revolutionize epilepsy diagnosis by identifying subtle brain lesions often missed by customary methods.
The algorithm, developed by a team from University College London, focuses on detecting focal cortical dysplasia (FCD), a common cause of epilepsy that can be difficult to spot on standard MRI scans.
“A significant percentage of epilepsy patients who undergo surgery have FCD, even though their initial MRI scans don’t show it,” explained Dr. Olivia Ramirez, a neurologist specializing in epilepsy.
The AI,trained on data from 22 epilepsy centers worldwide,analyzes the surface of the brain,looking for patterns indicative of a lesion.
“It’s not magic, but it’s pretty remarkable,” Dr. Ramirez said. “The neural network learns to recognize subtle variations in brain structure that might escape the human eye.”
Early versions of the algorithm detected lesions in a surprising number of patients, around 63%, but also produced a high number of false positives.
To improve accuracy, researchers incorporated a more sophisticated technique to help the algorithm distinguish between normal and abnormal tissue.
“The refined algorithm now has a positive predictive value of 67%,” Dr. Ramirez noted. “This means if it flags something, there’s a 67% chance it’s actually an FCD.”
Perhaps even more exciting, the researchers have made the algorithm open source, allowing othre scientists and clinicians to access and utilize it freely.
“This open-source approach is crucial for accelerating progress in epilepsy research and treatment,” Dr. Ramirez emphasized.
The team is currently collaborating with surgeons to explore the algorithm’s potential in identifying lesions before electrode implantation, a procedure used to pinpoint the source of seizures.
This groundbreaking AI technology holds immense promise for improving epilepsy diagnosis and treatment, potentially leading to more effective therapies and better outcomes for patients.
AI Could Revolutionize Epilepsy Diagnosis and Treatment
New technology promises faster, more accurate detection of brain lesions
Millions of Americans live with epilepsy, a neurological disorder characterized by recurrent seizures. Diagnosing epilepsy can be a complex process, often involving multiple tests and specialist consultations. But a groundbreaking new development using artificial intelligence (AI) could change the game,offering faster,more accurate detection of brain lesions associated with the condition.
“This is just incredible,” said Sarah, a patient advocate for epilepsy awareness. “It gives me so much hope for people living with epilepsy.”
The AI algorithm, still in development, analyzes MRI scans to identify subtle abnormalities in brain tissue that may be missed by the human eye. These abnormalities, known as lesions, can be a key indicator of epilepsy.”It certainly has the potential to be a game-changer for epilepsy diagnosis and treatment,” explained Olivia,a neuroscientist working on the project. “They’re also working on expanding the algorithm to detect other types of lesions, like hippocampal sclerosis.”
Hippocampal sclerosis, a common cause of epilepsy, involves scarring in the hippocampus, a part of the brain crucial for memory.
While the technology is still being refined, its potential impact is immense. faster and more accurate diagnosis could lead to earlier intervention, potentially reducing the severity and frequency of seizures. This could substantially improve the quality of life for millions of Americans living with epilepsy.
New AI Algorithm Shows Promise in Detecting Subtle Brain Abnormalities
New research suggests artificial intelligence could be a game-changer in detecting subtle brain abnormalities that frequently elude human reviewers, potentially leading to more accurate epilepsy diagnoses and treatment.สั่ง
Imagine a world where a computer program could help doctors pinpoint the source of seizures with greater accuracy.That’s the promise of a groundbreaking AI algorithm developed by researchers at University College london. Presented at the American Epilepsy Society’s 78th Annual Meeting, the tool focuses on identifying two common types of lesions – focal cortical dysplasia (FCD) and hippocampal sclerosis – frequently enough responsible for epilepsy.
“An estimated 42%-55% of adult and pediatric patients with epilepsy who undergo surgery with negative MRI findings have FCDs,” explained Sophie adler, a medical student at University College London, during her presentation. “These malformations of cortical progress can be arduous to spot on standard MRI scans.”
How the AI Algorithm Works
The AI program, trained on data from 22 epilepsy centers worldwide, analyzes surface-based features of the brain, looking for patterns that might indicate a lesion. Initial testing showed promising results, with the algorithm detecting lesions in 63% of MRI-negative patients. However, the initial version also produced a important number of false positives, requiring careful review by neurologists.
To refine the algorithm, the researchers incorporated a graph convolutional neural network. This advanced technique allows the AI to consider the context of each point on the brain’s surface, improving its ability to distinguish between normal and abnormal tissue.
“this strategy considerably reduced false positives, raising the positive predictive value to 67%,” said Adler. “So, if the algorithm flags something, it’s nearly 70% likely that it’s a real FCD.”
Open-Source and Collaborative
The researchers have made their algorithm open-source,allowing clinicians and researchers worldwide to access and utilize the tool. They are also collaborating with radiologists and surgeons to integrate the algorithm into clinical practise.
“we’re working with surgeons to use the algorithm to identify suspected lesions before implanting electrodes,” Adler explained. The team is also developing an algorithm to detect hippocampal sclerosis, with the ultimate goal of creating a single algorithm capable of detecting multiple types of epileptic lesions.
The Future of Epilepsy Diagnosis
The potential of AI in epilepsy diagnosis is immense. As Dr. Sara Inati, an assistant clinical investigator in the NINDS Intramural Research Program, pointed out, standardized imaging protocols are crucial for ensuring the accuracy and consistency of AI-based diagnoses.
“Following these standards and using similar protocols across centers will make it easier to utilize these powerful AI tools,” Dr. Inati stated. “While we’re still in the early stages, the work being done by researchers like Adler and her team is making these tools more accessible to clinicians, ultimately leading to better diagnosis and treatment for epilepsy patients.”
Could this be the dawn of a new era in epilepsy care? Only time will tell, but the possibilities are certainly exciting.
