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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?
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.
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