The natural language processing of radiology requests and reports of chest imaging: Comparing five transformer models' multilabel classification and a proof-of-concept study.

Background: Radiology requests and reports contain valuable information about diagnostic findings and indications, and transformer-based language models are promising for more accurate text classification.

Methods: In a retrospective study, 2256 radiologist-annotated radiology requests (8 classes) and reports (10 classes) were divided into training and testing datasets (90% and 10%, respectively) and used to train 32 models. Performance metrics were compared by model type (LSTM, Bertje, RobBERT, BERT-clinical, BERT-multilingual, BERT-base), text length, data prevalence, and training strategy.

Diagnostic reliability of low dose multidetector CT and cone beam CT in maxillofacial trauma-an experimental blinded and randomized study.

Objectives: To assess the diagnostic reliability of low dose multidetector CT (MDCT) and cone beam CT (CBCT) for zygomaticomaxillary fracture diagnosis.

Methods: Unilateral zygomaticomaxillary fractures were inflicted on four out of six fresh frozen human cadaver head specimens. All specimens were scanned using four MDCT and two CBCT imaging protocols of which the radiation exposure was systematically reduced.

Relationship between nodule count and lung cancer probability in baseline CT lung cancer screening: The NELSON study.

Objectives: To explore the relationship between nodule count and lung cancer probability in baseline low-dose CT lung cancer screening.

Materials And Methods: Included were participants from the NELSON trial with at least one baseline nodule (3392 participants [45% of screen-group], 7258 nodules). We determined nodule count per participant.