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Computed Tomography01:10

Computed Tomography

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.
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Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is formed in...

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CNN-Based Device-Agnostic Feature Extraction From ONH OCT Scans.

Sjoerd J Driessen1,2, Karin A van Garderen1,2,3, Danilo Andrade De Jesus4,5,6

  • 1Department of Ophthalmology, Erasmus Medical Center, Rotterdam, The Netherlands.

Translational Vision Science & Technology
|December 3, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an AI method for consistent optic nerve head (ONH) measurements from optical coherence tomography (OCT) scans across devices. The AI approach improves reliability for retinal nerve fiber layer (RNFL) and minimal rim width (MRW) biomarkers, aiding research and patient care.

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Measurements of the optic nerve head (ONH) using optical coherence tomography (OCT) vary significantly between different devices.
  • This lack of interchangeability complicates patient monitoring and hinders collaborative research efforts in ophthalmology.
  • Existing manufacturer-specific algorithms limit the consistency and comparability of OCT-derived biomarkers.

Purpose of the Study:

  • To develop and validate a device-agnostic artificial intelligence (AI) method for extracting ONH biomarkers from OCT scans.
  • To assess the reliability of AI-extracted biomarkers compared to manufacturer-specific algorithms across multiple OCT devices.

Main Methods:

  • ONH-centered OCT volumes from Heidelberg SPECTRALIS, ZEISS CIRRUS HD-OCT 5000, and Topcon 3D OCT devices were annotated.
  • A convolutional neural network (CNN) was trained on segmented B-scans to extract biomarkers like retinal nerve fiber layer (RNFL) and minimal rim width (MRW).
  • CNN-derived biomarker values were compared between devices and against manufacturer-reported values using an independent test set.

Main Results:

  • The AI method demonstrated higher reliability for circumpapillary RNFL (cpRNFL) measurements compared to manufacturer values.
  • Intraclass correlation coefficients (ICCs) for AI-derived cpRNFL were 0.667 and 0.656 across different devices and scan parameters.
  • AI-derived minimal rim width (MRW) measurements showed excellent agreement among devices (ICC = 0.917) and with manufacturer values (ICC = 0.983).

Conclusions:

  • The developed device-agnostic AI method offers a more reliable approach for extracting ONH OCT biomarkers, particularly cpRNFL.
  • MRW measurements demonstrated strong consistency across different OCT devices using the AI method.
  • This open-source software provides a robust solution for consistent biomarker extraction, reducing reliance on manufacturer algorithms and benefiting clinical practice and research.