Researchers at the University of California, San Francisco (UCSF) have therefore developed a deep learning framework that not only identifies meningiomas and calculates their volume, but also shows clinicians how certain the AI is about its assessment.
The approach uses Evidential Deep Learning (EDL) to generate interpretable uncertainty maps alongside tumor segmentations.
According to the researchers, making uncertainty visible could increase confidence in AI assisted imaging and support safer use in clinical practice.
The framework achieved high segmentation accuracy, while areas identified by the AI as uncertain corresponded well with regions that specialists also considered ambiguous.
The system additionally generated calibrated estimates of tumor volume, meaning its reported level of uncertainty closely reflected the reliability of its measurements.
According to Andreas Rauschecker, UCSF assistant professor of radiology and co chief of Intelligent Imaging Research, some degree of uncertainty will always remain because AI can make segmentation errors.
Quantifying that uncertainty provides clinicians with additional information when interpreting automated measurements.
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