Available data on radiologists' missed cervical spine fractures are based primarily on studies using human reviewers to identify errors on re-evaluation; such studies do not capture the full extent of missed fractures. To use machine-learning (ML) models to identify cervical spine fractures on CT missed by interpreting radiologists, characterize the nature of these fractures, and assess their clinical significance. This retrospective study included all cervical spine CT examinations performed in adult patients in the emergency department between January 1, 2018 and December 31, 2022.
View Article and Find Full Text PDFAnimals (Basel)
November 2024
Radiol Artif Intell
November 2024
Purpose To evaluate the performance of the winning machine learning (ML) models from the 2023 RSNA Abdominal Trauma Detection Artificial Intelligence Challenge. Materials and Methods The competition was hosted on Kaggle and took place between July 26, 2023, to October 15, 2023. The multicenter competition dataset consisted of 4,274 abdominal trauma CT scans in which solid organs (liver, spleen and kidneys) were annotated as healthy, low-grade or high-grade injury.
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