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

Radiological Investigation II: MRI and Ventilation Perfusion Scan01:30

Radiological Investigation II: MRI and Ventilation Perfusion Scan

111
Description
Magnetic Resonance Imaging (MRI) and Ventilation Perfusion Scans are two radiological investigations that offer detailed diagnostic images of the body, particularly lung structures.
MRI
MRI uses magnetic fields and radiofrequency signals to distinguish between normal and abnormal tissues. This technology provides a more detailed diagnostic image than CT scans, enabling it to characterize pulmonary nodules, stage bronchogenic carcinoma, and evaluate inflammatory activity in...
111
Ultrasonography01:17

Ultrasonography

4.4K
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...
4.4K

You might also read

Related Articles

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

Sort by
Same author

Improving cost-efficiency in port-site fascial closure: a novel Veress-needle technique and a comprehensive literature review.

Updates in surgery·2026
Same author

An integrated surveillance and management model for <i>Baylisascaris procyonis</i> in invasive raccoons (<i>Procyon lotor</i>) in Italy.

Frontiers in veterinary science·2026
Same author

Artificial Intelligence-Assisted Quantification of Longitudinal HRCT Changes During Treatment of Pulmonary Tuberculosis: An Exploratory Proof-of-Concept Study.

Diagnostics (Basel, Switzerland)·2026
Same author

Vertebrogenic Low Back Pain and Basivertebral Nerve Ablation: A Review of Mechanisms, Imaging-Driven Selection, and Clinical Outcomes.

Diagnostics (Basel, Switzerland)·2026
Same author

A Structured Preoperative Imaging Checklist for Pancreatoduodenectomy: Translating Precision Anatomy Into Surgical Practice.

Journal of hepato-biliary-pancreatic sciences·2026
Same author

Leishmania infantum infection in Phlebotomus perniciosus and Phlebotomus perfiliewi (Diptera, Psychodidae) and their abundance in central Italy.

Parasites & vectors·2026

Related Experiment Video

Updated: Jun 22, 2025

Author Spotlight: Enhancing Transplantation Research Through MicroCT Angiography in Murine Models
09:23

Author Spotlight: Enhancing Transplantation Research Through MicroCT Angiography in Murine Models

Published on: September 22, 2023

2.7K

Future Perspectives on Radiomics in Acute Liver Injury and Liver Trauma.

Maria Chiara Brunese1, Pasquale Avella2,3, Micaela Cappuccio2

  • 1Department of Medicine and Health Science "V. Tiberio", University of Molise, 86100 Campobasso, Italy.

Journal of Personalized Medicine
|June 27, 2024
PubMed
Summary

Artificial intelligence (AI) shows promise in detecting and quantifying acute liver injury in both children and adults. Radiomics models demonstrate reliability for clinical use, though larger validation cohorts are needed.

Keywords:
abdominal traumaacute liver injuryartificial intelligencedrug-induced liver injuryliver traumaradiomics

More Related Videos

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K
Novel In Vivo Micro-Computed Tomography Imaging Techniques for Assessing the Progression of Non-Alcoholic Fatty Liver Disease
08:41

Novel In Vivo Micro-Computed Tomography Imaging Techniques for Assessing the Progression of Non-Alcoholic Fatty Liver Disease

Published on: March 24, 2023

1.1K

Related Experiment Videos

Last Updated: Jun 22, 2025

Author Spotlight: Enhancing Transplantation Research Through MicroCT Angiography in Murine Models
09:23

Author Spotlight: Enhancing Transplantation Research Through MicroCT Angiography in Murine Models

Published on: September 22, 2023

2.7K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K
Novel In Vivo Micro-Computed Tomography Imaging Techniques for Assessing the Progression of Non-Alcoholic Fatty Liver Disease
08:41

Novel In Vivo Micro-Computed Tomography Imaging Techniques for Assessing the Progression of Non-Alcoholic Fatty Liver Disease

Published on: March 24, 2023

1.1K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Radiology

Background:

  • Acute liver injury is commonly caused by trauma, but also by sepsis or drug toxicity.
  • Accurate detection and quantification of injured liver areas are crucial for patient management.
  • Existing diagnostic methods may have limitations in speed or precision.

Purpose of the Study:

  • To review and analyze the capabilities of artificial intelligence (AI) in detecting and quantifying acute liver injury.
  • To assess AI's performance in both adult and pediatric populations.
  • To evaluate the clinical applicability of AI-driven radiomics for liver injury.

Main Methods:

  • A systematic literature analysis was conducted using the PubMed Dataset.
  • Original research articles published between 2018 and 2023 were included.
  • Studies with cohorts of at least 10 adult or pediatric patients were selected.

Main Results:

  • Six studies involving 564 patients (170 children, 394 adults) were analyzed.
  • AI was applied to liver trauma (66%), sepsis (17%), and chemotherapy-induced injury (17%).
  • Computed Tomography (CT) was the primary imaging modality (83%), with high diagnostic performance reported, including specificity >80% in three studies.

Conclusions:

  • AI-driven radiomics models appear reliable for clinical application in acute liver injury.
  • These AI tools show potential for accurate detection and quantification of liver damage.
  • Further research with larger patient cohorts is necessary for broader validation.