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ƒ(Cell): Software for Reproducible Analysis of Optoretinograms.

Robert F Cooper1,2, Mina Gaffney1, Brea D Brennan1

  • 1Joint Department of Biomedical Engineering, Marquette University and the Medical College of Wisconsin, Milwaukee, WI, USA.

Translational Vision Science & Technology
|March 24, 2026
PubMed
Summary
This summary is machine-generated.

A new software library, ƒ(Cell), enhances the repeatability of optoretinography (ORG) data analysis. It identifies key parameters like segmentation radius and normalization methods for consistent results in clinical trials.

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Area of Science:

  • Ophthalmology and Vision Science
  • Biomedical Imaging
  • Computational Biology

Background:

  • Optoretinography (ORG) is a noninvasive functional imaging technique with increasing use.
  • Standardized methodologies are crucial for repeatable ORG analyses, especially for clinical applications.
  • Existing ORG data analysis workflows can vary, impacting consistency.

Purpose of the Study:

  • To introduce ƒ(Cell), an open-source Python software library for reproducible and repeatable optoretinography data analysis.
  • To provide tools for co-registration, analysis, and process auditing of ORG datasets.
  • To identify key analysis parameters influencing the repeatability of ORG data.

Main Methods:

  • Developed ƒ(Cell) as a Python library for ORG data analysis.
  • Validated the software using normative ORG datasets and data from six external research groups.
  • Performed variance decomposition analysis on ƒ(Cell) parameters across multiple datasets and individuals.

Main Results:

  • ƒ(Cell) successfully processed diverse ORG datasets regardless of format or protocol.
  • A full factorial analysis identified segmentation radius, normalization, and relativization method as significant parameters (P < 0.001).
  • Mean subtraction relativization, cell-specific segmentation radius, and score-based normalization yielded the lowest coefficient of variance.

Conclusions:

  • ƒ(Cell) enables consistent, reproducible, and auditable analyses of intensity-based ORG (iORG) data.
  • The software facilitates the standardization of ORG analysis for improved reliability.
  • This work supports the use of ORG in clinical trials through enhanced data analysis consistency.