However, the time sensitive nature of these injuries is compounded by the constant and drastic shortage of experienced radiologists. Their efficacy could be highly improved by an automated prioritization of incoming cases, that relies on improving patient mortality and outcomes.
TBI injuries are often too critical to be left to trainee radiologists. Even if they conduct the CT scan in time, an inaccurate measurement could lead to wrong treatment choices which would directly affect the patient’s survival and recovery. It also opens up the possibility of legal action against the radiologist and the healthcare provider.
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AI has played a crucial role in accurately and speedily identifying TBI patients with critical abnormalities, as well as quantifying and localizing the bleeding in their brains. It then combines these findings to prioritize the cases that need immediate physician attention.
Some AI solutions also display the detected abnormality overlaid on the original CT scan image, so that the radiologist can quickly verify the accuracy of the AI-powered decision. The radiologist can then swiftly browse across multiple CT scans.
Pointers that will aid in selecting a smart AI solution:
Extent of global deployments the solution provider has is a statement to their success.
The number of datasets on which the algorithm has been trained. Higher the dataset, better the solution.
Number of peer reviews the solution provider has with reputed research firms add to their credibility
Always validate the solution on your own datasets before implementing.
Using AI in such time sensitive scenarios demonstrates the value intelligent neuroimaging offers to precision medicine, targeted care and the potential for improved outcomes.
About the author: Pooja Rao is the co-founder and R&D head of Qure.ai.Back to HCB News