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Deep learning-based identification of aberrant anterior tibial artery on knee MRI: a brazilian multicenter study.

To develop and validate a deep learning model for the detection of aberrant anterior tibial artery (AATA) on axial T2-weighted knee MRI, given the surgical relevance of unrecognized AATA and the lack of automated detection tools.
www.ncbi.nlm.nih.gov

Two-step deep-learning candidemia prediction model using two large time-sequence electronic health datasets.

Candidemia is a rare but life-threatening bloodstream infection that remains difficult to predict using conventional risk stratification approaches, highlighting the need for improved predictive strategies. As a result, empiric antifungal therapy is often delayed even in high-risk patients.
www.ncbi.nlm.nih.gov

Deep learning-based upsampling of 2D detector array measurements for patient plan verification in radiotherapy.

Detector arrays are commonly used for treatment plan verifications in intensity modulated radiation therapy. However, the intrinsic resolution of detector arrays is limited by the physical dimensions of each single detector and the detector-to-detector distance. This may lead to inaccurate representations of steep gradients and narrow dose peaks.
www.ncbi.nlm.nih.gov