Deep Learning Algorithm for Traumatic Splenic Injury Detection and Sequential Localization

NCT05643612 | Completed

• 1 other identifier: SpleenTrNet
• Study Type: observational
• Target: 600
• Locations: 1 country
• Sites: 1

Brief Summary

Spleen laceration is a lethal abdominal trauma and usually be diagnosed by medical images such as computed tomography. Deep learning had been proved its usage in detect abnormalities in medical images. In this trial, we used de-identified registry databank to develop a novel deep-learning based algorithm to detect the spleen trauma and to identify the injury locations.

Conditions

Spleen Injury; Machine Learning

Keywords

deep learning; artificial intelligence; spleen trauma; spleen injury

Outcome Measures

Primary Outcomes (1)

• Diagnostic accuracy

  Diagnostic accuracy of the deep learning algorithm to detect splenic injury

  3 days

Secondary Outcomes (1)

• Comparison of difference backbone of algorithm

  3 days

Study Arms (2)

splenic injury group

We retrospectively collected data from patients who underwent contrast-enhanced abdominal CT in the emergency department of Chang Gung Memorial Hospital, Linko, due to trauma and acute abdomen from Jul 2008 to Dec 2017. We identified 300 venous phase scans with splenic injury.

Diagnostic Test: Deep learning algorithm

control group

We retrospectively collected data from patients who underwent contrast-enhanced abdominal CT in the emergency department of Chang Gung Memorial Hospital, Linko, due to trauma and acute abdomen from Jul 2008 to Dec 2017. We randomly selected 300 additional venous phase scans without splenic injury

Diagnostic Test: Deep learning algorithm

Interventions

Deep learning algorithm DIAGNOSTIC_TEST

A sequential two-stage 3D spleen injury detection framework to identify splenic injury in the CT scans

control group; splenic injury group

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

We enrolled images of the patients who underwent abdominal computed tomography in emergency department for trauma and acute abdominal survey from Jul 2008 to Dec 2017. Then we selected 300 images with splenic injury and 300 images without.

You may qualify if:

• patients who underwent abdominal computed tomography in emergency department for trauma and acute abdominal survey from Jul 2008 to Dec 2017.

You may not qualify if:

• poor quality images
• no contrast series of computed tomography images.
• images from other hospitals without proper evaluation

Sponsors & Collaborators

• Chang Gung Memorial Hospital: lead

Study Sites (1)

Chang Gung memorial hospital

Taoyuan District, 333, Taiwan

Location

MeSH Terms

Interventions

Detection Algorithms

Intervention Hierarchy (Ancestors)

Algorithms; Mathematical Concepts

Study Officials

• Chien-Hung Liao, MD.

  Chang Gung Memorial Hospital

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor

Study Record Dates

• First Submitted: November 30, 2022
• First Posted: December 9, 2022
• Study Start: February 1, 2022
• Primary Completion: November 1, 2022
• Study Completion: November 1, 2022
• Last Updated: December 9, 2022

Record last verified: 2022-11

Data Sharing

• IPD Sharing: Will not share

Locations

TW (1)