Performance of recurrent neural networks with Monte Carlo dropout for predicting pharmacokinetic parameters from dynamic contrast-enhanced magnetic resonance imaging data.

J Appl Clin Med Phys · 2025 · PMC11799911 · PMID 39716853

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0.0740

closest p · 1.5× alpha

0.0740

boldest claim

The sentences

did not reach statistical significancep = 0.074so close (0.05 < p ≤ 0.1)

The NRMSE for LSTM(0.25) was smaller than that for LSTM(0) in v p ; however, the difference between them did not reach statistical significance ( p = 0.074) (Figure 5b , upper right).

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