Moving Beyond Flatland
- 3D medical imaging provides context and diagnostic value by giving clinicians more than a flat stack of images.
- When combined with additional detail, which itself can be pulled from the models; it is possible to create powerful visualizations, improve diagnostic accuracy or more effectively educate patients.
Assessing Atrial Fibrillation Treatment Efficacy
Atrial fibrillation (AF) is the most common arrhythmia in the world and impacts tens of millions of people. Patients suffering from AF live a diminished quality of life and are at risk for other serious conditions such as stroke.
Machine learning allows the building of patient specific models.
It can be difficult to apply new therapies because of how labor intensive it can be to create models outside of the research lab. Machine learning can be leveraged to segment images rapidly and efficiently without losing accuracy.
Quantifying the Severity of Atrial Fibrillation
Structural changes in the heart correlate with electrical changes.
- These structural changes aren't always reflected in the way the disease manifests; but strongly predict response to therapy.
- They are really hard to see without an invasive study, though. Medical imaging alongside computer vision, segmentation, and machine learning assessment can give insight to how far along the disease really is. This information then informs treatment options leading to better outcomes.
Creating positive feedback loops.
- The resulting models provide key insights into how a patient will respond to treatment. Patients with more observed structural change and higher degree of scarring require much more aggressive therapy then those with less change.
- Over time, as more data is acquired, it is possible to even more effectively target therapy and predict outcome.