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Medical Imaging


“Our vision is that all the information that's needed will be available when and where it is needed. In addition to pre-op and real-time image fusion from multiple sources, we will have live patient monitoring, such as heart rate and blood pressure.”


Adding Expert Systems to the Picture


“Our vision is that all the information that's needed will be available when and where it is needed,” says Yu. “In addition to pre-op and real-time image fusion from multiple sources, we will have live patient monitoring, such as heart rate and blood pressure.” There are also plans to consolidate similar cases together, opening the door to virtual consultation and analysis opportunities.


On-the-spot simulation functions might, for instance, provide guidance as to the best spot to clip an aneurysm based on real-time computational fluid dynamics. Virtual angiography, individualized anesthesia and drug dose interactions could all be simulated during a procedure and then tracked, as administered, to refine underlying algorithms.


Last, but not least, data fusion can save money. “It will provide a method for automatically recording procedures,” says Yu; “This will support effective systems for reimbursement, and can be exploited by learning systems to further refine treatments.”


For all its potential, multimodality data fusion will need to overcome many challenges. Software from different systems will need to become more compatible, standards for everything from image quality to transmission speed will need to be developed, and a virtually unlimited appetite for bandwidth will demand ever-increasing processing power and energy efficiency. “It is still early days for real-time data fusion,” says Yu; “but when you add up everything that is happening in this field, you see that we are in the process of creating a system that will transform the way we plan, perform, document and learn from a vast range of treatments.”


“Data fusion can save money. It will provide a method for automatically recording procedures. This will support effective systems for reimbursement, and can be exploited by learning systems to further refine treatments.”


Daphne Yu, Head of Visualization Lab at Siemens Corporate Technology in Princeton, New Jersey.


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INSIGHT ON


HOSPITAL & HEALTHCARE MANAGEMENT VOL. 3 ISSUE 3 August 2014


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