By harnessing computer models that simulate human intelligence, AI/ML can help us unlock long-standing mysteries about how our brains work. Already, AI is making new discoveries about how the brain processes information6. And the better we understand the intricacies of this mysterious organ, the better we can grasp how to treat neurological diseases like Multiple Sclerosis (MS), Alzheimer’s disease (AD), depression or Parkinson’s disease - conditions that currently affect hundreds of millions of people.
In the context of neurology, AI/ML could prove truly revolutionary. Radiology is an area of healthcare with some of the greatest progress in applying AI/ML in clinical care and drug discovery for Neurology patients. Today, in clinical practice, Radiologists typically take a rather abstract qualitative approach through visual assessment of medical images to detect, monitor, and characterize diseases to support clinical evaluation. Rather than relying on the human eye, AI/ML can automatically provide quantitative assessments of medical image characteristics through pattern recognition.
Radiomics – where features are abstracted for medical images like MRIs, X-ray, and CT scans – use AI algorithms to uncover patterns characteristics such as intensity of signal, texture, shape, and interrelationships between pixels that are otherwise invisible to the human eye. The output of radiomics holds potential to provide quantitative local disease characterization and sub-phenotyping across clinical conditions. In collaboration with Radiologists, Neurologists can then identify anomalies linked to clinical signs at the onset of or progression of disease to make more informed decision-making for an individual patients’ care plan. Potential use cases where AI/ML is applied to medical imaging are broad: for Multiple Sclerosis patients, there could be automatic identification and quantification of demyelination of lesions to aid clinical diagnosis. For Alzheimer’s Disease patients, detection of brain cell degeneration BDH is actively working to hone use cases for AI/ML that may be applied in clinical trials and clinical care. A recent partnership7 with TheraPanacea8, for instance, aims to leverage AI/ML analysis to create algorithmic solutions that improve patient care, advance personalized medicine, improve benefit-risk of treatments, and further our understanding of the underlying pathologies and heterogeneity of neurological diseases.
Paired with Biogen’s data, TheraPanacea’s AI/ML-based biomarker discovery capability has already led to some major discoveries in our understanding of MS – which like cancer - is not a single disease. MS encompasses various sub-disease types that affect patients differently. Together, we’ve created an ensemble-based learning algorithm using single brain MRI scans. The AI can classify MS lesions as acute, chronic ‘inactive’ or slowly expanding based on a single MRI scan. The technology can even predict where new lesions may form and has potential to eventually lead to better personalized treatment selection.
Obviously, the impact on patients is significant, particularly because early detection, diagnosis, and enhanced disease phenotyping are key9 for a condition like MS. For optimal patient outcomes, we must arm clinicians with personalized rather than disease-level information to inform clinical decision making.
The development with TheraPanacea is just one example of a future in which data-driven augmented clinical decisions can pave the way for patients to receive truly personalized care as early on in their condition’s progression as possible.