RSNA Abdominal Trauma Detection AI Challenge (2023)

The 2023 RSNA Abdominal Trauma Detection AI Challenge invited participants to develop machine learning models that match radiologists’ performance in detecting, locating and classifying the severity of traumatic abdominal injuries. 

AI’s impact on traumatic abdominal injuries

Traumatic injury occurs in people of all ages and is a leading cause of death worldwide. Nearly 5 million people die each year as a result of traumatic injury, according to the World Health Organization.

Abdominal trauma often causes damage to the internal organs, which may result in internal bleeding and injuries to the kidneys, spleen, liver and bowel. Motor vehicle accidents are the most common cause of abdominal trauma in the U.S. Rapid and accurate detection and classification of injuries is key to effective treatment and favorable patient outcomes.

Researchers hope that AI can assist in expeditiously identifying and classifying traumatic abdominal injuries. “The artificial intelligence models developed as part of this challenge have significant potential to advance patient care by assisting radiologists and other physicians to detect and grade different traumatic abdominal injuries, which is a particularly difficult task, requiring a lot of careful image review,” said Jeff Rudie, MD, PhD, emergency radiologist and Scripps Clinic and adjunct assistant professor in the Department of Radiology at the University of California, San Diego. “These models may have the potential both to help prioritize positive studies for faster reading and to identify higher grade injuries that might require prompt intervention.”

About the imaging data

To create the ground truth dataset, the challenge planning task force collected imaging data sourced from 23 sites in 14 countries on six continents, including more than 4,000 CT exams with various abdominal injuries and a roughly equal number of cases without injury.

“This year’s RSNA AI Challenge is the most ambitious challenge yet, given that it encompasses detection and classification of traumatic injuries across multiple organs,” Dr. Rudie said. “Our team has compiled and annotated a broad set of trauma CTs from institutions across six continents. The dataset is annotated at multiple levels, including the presence of injuries in four solid organs with injury grading, image level annotations for active extravasations and bowel injury, and voxelwise segmentations of each of the potentially injured organs.”


The dataset description is coming soon.


Access the challenge results on the Kaggle website.

Access results

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