Artificial Intelligence

Autonomous & Ultrafast Breast MRI

breast MRI AI

A new study out of the University of Groningen highlighted the scanning and diagnostic efficiency advantages that might come from combining ultrafast breast MRI with autonomous AI. That might make some readers uncomfortable, but the fact that autonomous AI is one of 2022’s most controversial topics makes this study worth some extra attention.

The researchers used 837 “TWIST” ultrafast breast MRI exams from 488 patients (118 abnormal breasts, 34 w/ malignant lesions) to train and validate a deep learning model to detect and automatically exclude normal exams from radiologist workloads. They then tested it against 178 exams from 149 patients from the same institution (55 abnormal, 30 w/ malignant lesions), achieving a 0.81 AUC.

When evaluated at a conservative 0.25 detection error threshold, the DL model:

  • Achieved 98% sensitivity and negative predictive values
  • Misclassified one abnormal exam as normal (out of 55)
  • Correctly classified all exams with malignant lesions
  • Would have reduced radiologists’ exam workload by 6.2% (-15.7% at breast level)

When evaluated at a 0.37 detection error threshold, the model:

  • Achieved 95% sensitivity and a 97% negative predictive value (still high)
  • Misclassified three abnormal exams (3 of 55), including one malignant lesion
  • Would have reduced radiologists’ exam workload by 15.7% (-30.6% at breast level)

These radiologist workflow improvements would complement the TWIST ultrafast MRI sequence’s far shorter magnet time than current protocols (2 vs. 20 minutes), while the DL model could further reduce scan times by automatically ending exams once they are flagged as normal. 

The Takeaway

Even if the world might not be ready for this type of autonomous AI workflow, this study is a good example of how abbreviated MRI protocols and AI could be able to improve both imaging team and radiologist efficiency. It’s also the latest in a series of studies exploring how AI could exclude normal scans from radiologist workflows, suggesting that the development and design of this type of autonomous AI will continue to mature.

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