New AI-based Method Detects Brain Response to MS Treatment
|
By HospiMedica International staff writers Posted on 06 Jul 2019 |
|
Researchers at University College London {(UCL), London, UK} and King's College London {(KCL) London, UK} have developed a new artificial intelligence (AI)-based method for detecting the brain's response to treatment in multiple sclerosis (MS). The new method has substantially higher sensitivity than conventional, radiologist-derived measures permit.
The researchers studied patients with relapsing–remitting MS who were treated with the disease-modifying drug natalizumab, where serial magnetic resonance imaging (MRI) scans were available before and after initiation of treatment. The team used machine vision to extract an "imaging fingerprint" of the state of the brain from each scan, capturing detailed changes in white and grey matter and yielding a rich set of regional trajectories over time.
In comparison to the conventional analysis of the traditional measures of total lesion and grey matter volume that a radiologist is able to extract, the AI-assisted modeling of the complex imaging fingerprints was able to discriminate between pre- and post-treatment trajectories of change with much higher accuracy. The study demonstrated that AI can be used to detect brain imaging changes in treated MS with greater sensitivity than measures simple enough to be quantified by radiologists, enabling "superhuman" performance in the task. The approach could be used to guide therapy in individual patients, detect treatment success or failure faster, and to conduct trials of new drugs more effectively and with smaller patient cohorts.
Dr. Parashkev Nachev from UCL Queen Square Institute of Neurology who led the study, said, "Rather than attempting to copy what radiologists do perfectly well already, complex computational modeling in neurology is best deployed on tasks human experts cannot do at all: to synthesize a rich multiplicity of clinical and imaging features into a coherent, quantified description of the individual patient as a whole. This allows us to combine the flexibility and finesse of a clinician with the rigor and objectivity of a machine."
Related Links:
University College London
King's College London
The researchers studied patients with relapsing–remitting MS who were treated with the disease-modifying drug natalizumab, where serial magnetic resonance imaging (MRI) scans were available before and after initiation of treatment. The team used machine vision to extract an "imaging fingerprint" of the state of the brain from each scan, capturing detailed changes in white and grey matter and yielding a rich set of regional trajectories over time.
In comparison to the conventional analysis of the traditional measures of total lesion and grey matter volume that a radiologist is able to extract, the AI-assisted modeling of the complex imaging fingerprints was able to discriminate between pre- and post-treatment trajectories of change with much higher accuracy. The study demonstrated that AI can be used to detect brain imaging changes in treated MS with greater sensitivity than measures simple enough to be quantified by radiologists, enabling "superhuman" performance in the task. The approach could be used to guide therapy in individual patients, detect treatment success or failure faster, and to conduct trials of new drugs more effectively and with smaller patient cohorts.
Dr. Parashkev Nachev from UCL Queen Square Institute of Neurology who led the study, said, "Rather than attempting to copy what radiologists do perfectly well already, complex computational modeling in neurology is best deployed on tasks human experts cannot do at all: to synthesize a rich multiplicity of clinical and imaging features into a coherent, quantified description of the individual patient as a whole. This allows us to combine the flexibility and finesse of a clinician with the rigor and objectivity of a machine."
Related Links:
University College London
King's College London
Gold Member
STI Test
Vivalytic Sexually Transmitted Infection (STI) Array
New
Surgical System
Stealth AXiS
New
Blood Gas Analyzer
i-Check200
Latest AI News
- Machine Learning Approach Enhances Liver Cancer Risk Stratification
- New AI Approach Monitors Brain Health Using Passive Wearable Data
- AI Tool Maps Early Risk Patterns in Bloodstream Infections
- AI Model Identifies Rare Endocrine Disorder from Hand Images
- AI Tool Promises to Reduce Length of Hospital Stays and Free Up Beds
- Machine Learning Model Cuts Canceled Liver Transplants By 60%
Channels
Artificial Intelligence
view channel
Machine Learning Approach Enhances Liver Cancer Risk Stratification
Hepatocellular carcinoma, the most common form of primary liver cancer, is often detected late despite targeted surveillance programs. Current screening guidelines emphasize patients with known cirrhosis,... Read more
New AI Approach Monitors Brain Health Using Passive Wearable Data
Brain health spans cognitive and emotional functions and can fluctuate even in adults without diagnosed disease. Detecting early changes remains difficult in routine care and burdens specialty services... Read more
Critical Care
view channel
Automated IV Labeling Solution Improves Infusion Safety and Efficiency
Medication administration in high-acuity settings is often complicated by multiple concurrent infusions, making accurate line identification essential. In a 10-hospital intensive care unit study, 60% of... Read more
First-Of-Its-Kind AI Tool Detects Pulmonary Hypertension from Standard ECGs
Pulmonary hypertension is a progressive, life‑threatening disease that is frequently missed early because symptoms such as dyspnea are nonspecific and diagnostic delays can exceed two years.... Read more
Surgical Techniques
view channel
Continuous Monitoring with Wearables Enhances Postoperative Patient Safety
Postoperative hypoxemia on general surgical wards is common and often missed by intermittent vital sign checks. Undetected low oxygen levels can delay recovery and raise the risk of complications that... Read more
New Approach Enables Customized Muscle Tissue Without Biomaterial Scaffolds
Volumetric muscle loss is a traumatic loss of skeletal muscle that often leads to permanent functional impairment and limited reconstructive options. Current experimental strategies struggle to deliver... Read more
Patient Care
view channel
Wearable Sleep Data Predict Adherence to Pulmonary Rehabilitation
Chronic obstructive pulmonary disease (COPD) is a long-term lung disorder that makes breathing difficult and often disturbs sleep, reducing energy for daily activities. Limited engagement in pulmonary... Read more
Revolutionary Automatic IV-Line Flushing Device to Enhance Infusion Care
More than 80% of in-hospital patients receive intravenous (IV) therapy. Every dose of IV medicine delivered in a small volume (<250 mL) infusion bag should be followed by subsequent flushing to ensure... Read more
Health IT
view channel
EMR-Based Tool Predicts Graft Failure After Kidney Transplant
Kidney transplantation offers patients with end-stage kidney disease longer survival and better quality of life than dialysis, yet graft failure remains a major challenge. Although a successful transplant... Read more
Printable Molecule-Selective Nanoparticles Enable Mass Production of Wearable Biosensors
The future of medicine is likely to focus on the personalization of healthcare—understanding exactly what an individual requires and delivering the appropriate combination of nutrients, metabolites, and... Read more
Business
view channel
GE HealthCare Leads Major European Initiative to Advance Cardio-Oncology Care
Cardiovascular complications are increasingly recognized among people undergoing or recovering from cancer treatment, driven by pre‑existing disease and the cardiotoxic effects of chemotherapy, radiotherapy,... Read more
