This repository contains the code and resources for the Abdominal Trauma Detection Model competition organized by the Radiological Society of North America (RSNA).
Traumatic injury is the most common cause of death in the first four decades of life and a significant public health problem around the world. There are estimated to be more than 5 million annual deaths worldwide from traumatic injury. Prompt and accurate diagnosis of traumatic injuries is crucial for initiating appropriate and timely interventions, which can significantly improve patient outcomes and survival rates. Computed tomography (CT) has become an indispensable tool in evaluating patients with suspected abdominal injuries due to its ability to provide detailed cross-sectional images of the abdomen.
HOWEVER, interpreting CT scans for abdominal trauma can be a complex and time-consuming task, especially when multiple injuries or areas of subtle active bleeding are present. This challenge seeks to harness the power of artificial intelligence and machine learning to assist medical professionals in rapidly and precisely detecting injuries and grading their severity. The development of advanced algorithms for this purpose has the potential to improve trauma care and patient outcomes worldwide.
Blunt-force abdominal trauma is among the most common types of traumatic injury, with the most frequent cause being motor vehicle accidents. Abdominal trauma may result in damage and internal bleeding of the internal organs, including the liver, spleen, kidneys, and bowel. Detection and classification of injuries are essential to effective treatment and favorable outcomes. A large proportion of patients with abdominal trauma require urgent surgery. Abdominal trauma often cannot be diagnosed clinically by physical exam, patient symptoms, or laboratory tests.
Prompt diagnosis of abdominal trauma using medical imaging is thus critical to patient care. AI tools that assist and expedite the diagnosis of abdominal trauma have the potential to substantially improve patient care and health outcomes in the emergency setting.
The RSNA Abdominal Trauma Detection AI Challenge, organized by the RSNA in collaboration with the American Society of Emergency Radiology (ASER) and the Society for Abdominal Radiology (SAR), gives researchers the task of building models that detect severe injury to the internal abdominal organs, including the liver, kidneys, spleen, and bowel, as well as any active internal bleeding.
- bowel_injury - Damage or harm that occurs to the intestines.
- bowel_healthy - No damage or harm to the intestines.
- extravasation_injury - Refers to internal bleeding.
- extravasation_healthy - No internal bleeding detected.
- kidney_healthy - No kidney injury detected
- kidney_low - Low grade Kidney injury detected
- kidney_high - High grade Kidney injury detected
- liver_healthy - No liver injury detected
- liver_low - Low grade liver injury detected
- liver_high - High grade liver injury detected
- spleen_healthy - No spleen injury detected
- spleen_low - Low grade spleen injury detected
- spleen_high - High grade spleen injury detected
Clone this repository into your current working directory using the bash command below
git clone https://github.com/MartinKalema/RSNA-Abdominal-Trauma-Detection