Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Computed Tomography01:10

Computed Tomography

4.5K
Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
4.5K
Ultrasonography01:17

Ultrasonography

4.5K
Ultrasonography is an imaging technique that uses high-frequency sound waves to visualize the body's internal structures. It is a non-invasive and safe procedure that does not involve the use of ionizing radiation, making it widely used in various medical fields. Ultrasonography is used to study heart function, blood flow in the neck or extremities, certain conditions such as gallbladder disease, and fetal growth and development.
During an ultrasonography procedure, a handheld device called...
4.5K
Imaging Studies I: CT and MRI01:14

Imaging Studies I: CT and MRI

242
Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
Description of the Procedures
Computed Tomography (CT) scan:
Computed Tomography (CT) scans use X-ray technology to generate detailed images of bones, organs, and tissues. During the scan, the patient lies on a moving table...
242

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Revised cardiac risk index and adverse outcomes in patients undergoing surgical stabilization of rib fractures.

BMC surgery·2026
Same author

Comparative long-term risks of chronic kidney disease and dialysis following conservative treatment, renal artery embolization, or nephrectomy in patients with blunt kidney injuries: retrospective cohort study.

BJS open·2026
Same author

Continuous monitoring of intra-abdominal pressure: cumulative pressure exposure predicts early acute kidney injury in animal model.

World journal of emergency surgery : WJES·2026
Same author

Advancing trauma scoring through large language models: automated estimation of injury severity.

BMC emergency medicine·2026
Same author

Does When We Operate Matter? Revisiting Surgical Timing in Pancreatic Trauma.

World journal of surgery·2026
Same author

Withdrawal of life support following interfacility transfer in older adults with traumatic brain injury.

Surgery·2026

Related Experiment Video

Updated: Jul 2, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

401

Deep Learning for Automated Detection and Localization of Traumatic Abdominal Solid Organ Injuries on CT Scans.

Chi-Tung Cheng1, Hou-Hsien Lin1, Chih-Po Hsu1

  • 1Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou, Chang Gung University, Taoyuan, Taiwan.

Journal of Imaging Informatics in Medicine
|February 17, 2024
PubMed
Summary

Deep learning models (DLMs) were developed to detect solid organ injuries in patients with blunt abdominal trauma (BAT) using CT scans. These AI tools show promise in aiding clinicians to quickly identify critical injuries, improving trauma care decisions.

Keywords:
Artificial intelligenceBlunt abdominal traumaComputed tomographyDeep learningLiver injuryRenal injurySpleen injury

More Related Videos

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.8K
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.6K

Related Experiment Videos

Last Updated: Jul 2, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

401
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.8K
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.6K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Trauma Surgery

Background:

  • Computed tomography (CT) is standard for blunt abdominal trauma (BAT) diagnosis.
  • Deep learning models (DLMs) offer potential for enhancing clinical practice, but their use in trauma imaging is under-explored.
  • Rapid identification of life-threatening injuries is crucial in trauma care.

Purpose of the Study:

  • To develop and evaluate a DLM for automated detection of solid organ injuries (spleen, liver, kidney) in abdominal CT scans.
  • To assess the diagnostic performance of the DLM in identifying traumatic injuries.
  • To explore the potential of DLMs as assistive tools in trauma management.

Main Methods:

  • Development of DLMs using a dataset of abdominal CT scans from a single trauma center (2008-2017).
  • Training and validation on 1302 scans (87%), with testing on 194 scans (13%) from the final year.
  • Performance evaluation using metrics including accuracy, sensitivity, specificity, and AUC.

Main Results:

  • The spleen injury DLM achieved 0.938 accuracy and 0.952 specificity.
  • The liver injury DLM reported 0.820 accuracy and 0.847 specificity.
  • The kidney injury DLM demonstrated 0.959 accuracy and 0.989 specificity.

Conclusions:

  • A DLM was successfully developed to automate the detection of solid organ injuries from abdominal CT scans.
  • The DLM exhibits acceptable diagnostic accuracy for spleen, liver, and kidney injuries.
  • While not replacing clinicians, the DLM can serve as a valuable tool to expedite therapeutic decision-making in trauma care.