
Researchers at UC Irvine used deep learning to automate head CT reformatting, improving workflow standardization and efficiency.
Key Details
- 1Manual head CT reformatting can be variable and resource-intensive due to patient and technologist factors.
- 2Automated deep learning algorithms produced expert-level reformats with high accuracy and consistency.
- 3Automation could reduce diagnostic errors and turnaround times.
- 4Improved standardization and operational cost reduction are expected outcomes.
- 5The findings are from researchers in UC Irvine's Department of Radiological Sciences, published in JACR.
Why It Matters

Source
Radiology Business
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