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Related Concept Videos

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Related Experiment Video

Updated: Oct 15, 2025

Cardiac Pressure-Volume Loop Analysis Using Conductance Catheters in Mice
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CLiP, catheter and line position dataset.

Jennifer S N Tang1, Jarrel C Y Seah2, Adil Zia3

  • 1Department of Radiology, The Royal Melbourne Hospital, Melbourne, Victoria, Australia. Jennifer.Tang@mh.org.au.

Scientific Data
|October 29, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a new dataset of chest X-rays with detailed annotations for endotracheal tubes, nasoenteric tubes, and central venous catheters. This resource aids in verifying correct catheter placement and preventing patient complications.

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Area of Science:

  • Medical Imaging
  • Radiology
  • Data Science

Background:

  • Accurate placement of medical devices like catheters is essential for patient care and preventing adverse events.
  • Chest radiographs are frequently used to confirm the position of various catheters in clinical practice.
  • Existing datasets may lack comprehensive annotations for critical airway and vascular access devices.

Purpose of the Study:

  • To introduce and describe a novel, large-scale dataset of annotated chest radiographs.
  • To provide manually labeled segmentations for endotracheal tubes (ETT), nasoenteric tubes (NET), and central venous catheters (CVCs).
  • To facilitate research in automated detection and position verification of these critical medical devices.

Main Methods:

  • Utilized the publicly available NIH ChestXRay14 dataset, comprising 30,083 chest radiographs.
  • Performed manual annotation and segmentation of 50,612 image-level labels and 17,999 specific device annotations.
  • Focused on three key devices: endotracheal tubes (ETT), nasoenteric tubes (NET), and central venous catheters (CVCs).

Main Results:

  • A comprehensive dataset with 50,612 image-level and 17,999 manually labeled annotations was created.
  • The dataset includes segmentations for endotracheal tubes, nasoenteric tubes, and central venous catheters.
  • The dataset is derived from 30,083 chest radiographs, offering significant scale for machine learning applications.

Conclusions:

  • The described dataset represents a valuable resource for developing and validating algorithms for medical device placement verification.
  • Availability of this annotated data can advance research in AI-driven diagnostic tools in radiology.
  • Accurate catheter placement, supported by such datasets, is key to improving patient outcomes and reducing healthcare-associated complications.