Advancing Neurosurgery through Intelligence Augmentation (IA)

As a neurosurgeon, you get one chance to get it right.
Each incision is a trade-off between functional impact and therapeutic benefit. Yet, finding that balance remains an elusive task in an organ of such complexity.

Consider, for example, the pathway of a GBM patient today:

Dive Deeper

The patient

  • With a brain deformed by glioblastoma, how can one confidently predict what will still be functioning, and what will not post-surgery?
  • How does one personalize a surgery so that the functions the patient values are accurately mapped to anatomy and are preserved?
  • How does one set the right expectation to the patient and their families in detail?

The planning

  • How can one efficiently obtain functional information when acquiring mapping prior to surgery remains an expensive and laborious task?
  • With advanced imaging techniques such as DTI, one can see the position of thousands of white matter tracts, but what makes one approach better than any other?

The surgery

  • How does one distinguish tumor from brain, and one functional area from another?

The rehab

  • How does one explain what happened during surgery in relation to outcome and expected deficit?
  • How does one design the best rehabilitation algorithm specific to the patient?

The need for more actionable insight, not information

Our ability to collect more information of the human brain continues to accelerate. For example, DTI tractography may now allow neurosurgeons to visualize the millions of subcortical tracts, and greater attention can be placed on not severing connections between important networks.

More recently, the Human Connectome Project has made available volumes of new information about the human brain, and reinforced the brain’s position as the most complex object in the known universe

Yet how does one efficiently make sense of this complexity? 


The bridge to personalized brain medicine

To effectively translate data and complexity into practical clinical approaches, data science techniques including dimensionality reduction are required. 

For example, by discerning 360 distinct functional areas of the brain and how they’re connected from millions of data points, the Human Connectome Project has made breakthroughs concerning the true nature of cognitive function.

However, achieving true patient-specific, precision-neurosurgery is a daily big-data problem, and most neurosurgeons are not data scientists.

Therefore, a necessary tool is one that streamlines and automates delivery of meaningful insights from data. Such solutions are driven by machine learning and are ‘intelligence augmenting (IA)’; a departure from an ‘artificial intelligence (AI)’ mindset of replicating and automating current clinical practice. 





Omniscient’s vision for a data-driven surgical planning solution

Enter a machine learning platform, built with ease-of-use and integration with clinical workflow in mind. Millions of data points derived from current DWI and fMRI imaging can be automatically turned into a patient specific, parcellated brain map to drive more informed decision making, without the need for expertise in data analytics.

We aim for a neurosurgical pathway empowered by insight:

The patient

  • Expectations and consequences of surgical decisions can be made clearer to patients

The planning

  • Networks are identified by their function, helping you to design approaches that may preserve what is most important to the patient

The surgery

  • Visualization of functional networks of the patient are easily co-registered to intraoperative image guidance

The rehab

  • Areas of deficit in the brain are measured through data, allowing you to design optimal rehabilitation strategies

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