Digital analysis of medical images is getting more sophisticated, and it has already been shown to be more objective and accurate than human readers. By taking a systematic approach to the identification of markers within artificial intelligence driven learning systems, we can significantly improve the care we offer patients.
Automated digital analysis will be the first step in all routine imaging and screening, for the speed and accuracy with which it can identify suspected conditions before further work is planned. By studying progression over time and inter related markers, a machine learning system will be able to detect symptoms early enough to be able to promote a preventative approach to care.
We enable digital analysis to create capacity for medical imaging to meet unprecedented healthcare demand and improve care:
- Deliver digital best practice at the point-of-care
- Support high volumes of patients
- Relieve reporting bottlenecks and speed up diagnosis
- Improve public health and patient outcomes
Example: Express 3D Visualisation of knee flexion before surgery
Surgeons treating hip and knee injuries arthroscopically are using 3D visualisation techniques applied to MR and CT scan slices to virtually reconstruct a 3D view of the joint as it flexes thereby informing the surgical plan before an incision is made.