An estimated 60% of the US suffers from a neurological condition. These range from minor headaches to migraines and include congenital disorders, injury-related conditions and brain tumours. It’s a wide and varied group of diseases.
As the machine learning process mimics the neural pathways formed in the human brain, it’s no surprise that the neurological implications of artificial intelligence are some of the most talked about.
The use of AI to diagnose, treat and eventually prevent neurological disorders has made dramatic headway in the past couple of years. And as the US loses more years of life to neurological disorders than any other country in the Americas, these advancements are critical.
Aggravating the need for faster diagnostics and treatments is the growing lack of neurological professionals in the US. Evolve has identified 3 areas where AI is already revolutionising care for neurological disorders.
1. Imaging and Diagnosis
Precision Medicine in Neuro-Oncology
Recent studies show that AI-powered molecular profiling can detect a malignant brain tumour in 95% of cases. It also makes it easier to diagnose the size and constitution of a tumour, without the need for invasive biopsies. Treatments can then be tailored to patient and tumour gene profiles, vastly increasing the chance of success.
This is life-changing for patients and the field, but it’s not ready yet. Precision Medicine is an exciting but flawed new technology. More research is needed before it can be used to effectively treat brain tumour patients
Effective diagnosis for brain tumours using different types of AI is already being given in operating theatres, however. Deep convolutional neural networks can intraoperatively diagnose brain tumours in just a few minutes – 10 times faster than current technologies.
MRI Scans in Alzheimer’s Disease
Researchers at the University of Southern California have developed a deep-learning algorithm that can predict a person’s chance of developing Alzheimer’s. By using AI to compare the Brain Age of thousands of MRI scans with the same patient’s actual age, they can accurately estimate the amount of mild cognitive decline that person is experiencing.
A special report by the Alzheimer’s Association found that, while better care undoubtedly comes from early detection, it’s still uncommon. Barriers to early Alzheimer’s diagnosis included a shortage of neurology professionals and the unwillingness of patients to self-report cognitive decline.
The ability of the USC technology to scan images gives patients a better chance of receiving an early Alzheimer’s diagnosis. That means fewer healthcare visits and more successful treatment.
2. Data Analytics and Clinical Trials
Large-Scale Imaging Analysis
The Department of Computing at Imperial College London has developed AI that drastically reduces the time needed to analyse large sets of MRI data. This technology is used across the UK in both clinical trials and on-site.
It’s used to test how well neurological treatments – such as new drugs – work. This, in turn, speeds up the time to treatment for individual patients. This is another life-changing use for AI in the biotech field.
Biomarker Identification
Serious neurological disorders are much easier to treat when detected early. So, one of the most promising outcomes we’ve already seen from AI research in neurology is the identification of biomarkers. Biological markers (‘biomarkers’) are indicators that a person is susceptible to a certain condition.
Biomarkers have been found to accurately predict brain metastases in breast cancer sufferers. Certain varieties of these biomarkers (miRNAs in this case) predicted a poor prognosis, while others indicated a better recovery for the patient.
3. Treatment and Care
Brain Health Monitoring After Injury
Researchers from Imperial College London have created a new technology that combines the use of biomarkers with machine learning to amplify the accuracy of injury-related disease progression.
The device – which can be inserted into the brain tissue – monitors cerebrospinal fluid for specific biomarker concentrations. As biomarkers accurately predict disease progression, this is extremely promising in maximising brain function after head injury.
Neurosurgery After Stroke
As important as shortening the time to diagnosis is lengthening the window for successful treatment. Technology developed by the Stanford Stroke Center has added over 20 hours to the time after stroke that medical intervention can have an effect.
This technology – RapidAI – is already being rolled out in over 100 countries, and is now used to treat conditions elsewhere in the body – aneurysm and pulmonary embolism, for example.
Studies prove that AI is advancing the success of endovascular neurosurgery to treat conditions such as stroke. But, many still highlight the importance of human involvement from neurology professionals.
AI-Powered Neurological Care Needs Humans
One of the biggest barriers to neurological advancements is the lack of professionals in the industry. From primary care physicians to neurosurgeons in healthcare, and engineers and immunologists in biotech, the career scope in this space is exciting and lucrative.
The massive progress we’ve already seen proves that care for neurological disorders is on the brink of an AI-powered revolution. Join the Evolve candidate network to be part of that revolution.
Have you read our guide on ‘Transforming Minds in Neurotechnology” yet? Click the link below to find out the latest advancements in neurotech.
