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IA Model for Acute Appendicitis in CT

NCT06175169 · Status: RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 568

Last updated 2023-12-18

No results posted yet for this study

Summary

the investigators's study group has developed a fully automated 3D convolutional neural network (CNN)-based diagnostic framework using information of appendix (IA) model to identify non-appendicitis and simple and complicated appendicitis on CT scan images based on the two-stage binary classification algorithm, as a clinician does for deciding treatment. The dataset was built from a large population of patients visiting emergency departments who underwent intravenous contrast-enhanced abdominopelvic CT examinations to evaluate abdominal pain in the right or lower quadrant area as the chief complaint. Recently, the IA model was externally validated using a dataset of multicenter institutions through data exfiltration. In this study, the investigators hypothesized that the IA model would show a comparable negative appendicitis rate of \<10% non-inferior margins compared to non-radiologists with a shorter interpretation time in a prospectively randomized dataset.

Conditions

  • Acute Appendicitis

Interventions

DEVICE

Information of Appendix (IA) model

Deep learning model

Sponsors & Collaborators

  • Doheon Institute for Digital Innovation In Medicine

    collaborator UNKNOWN

  • Medical A.I. Center of Hallym University

    collaborator UNKNOWN

  • Hallym University Medical Center

    lead OTHER

Principal Investigators

  • Iltae Son · Hallym University Medical Center

Study Design

Allocation
RANDOMIZED
Purpose
DIAGNOSTIC
Masking
SINGLE
Model
PARALLEL

Eligibility

Min Age
12 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2023-07-04
Primary Completion
2024-12-31
Completion
2025-12-31

Countries

  • South Korea

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06175169 on ClinicalTrials.gov