• PyOPV: An Open-Source Python P...

*Result*: PyOPV: An Open-Source Python Package for Ophthalmic Visual Field Data Management.

Title:
PyOPV: An Open-Source Python Package for Ophthalmic Visual Field Data Management.
Authors:
Hallaj S; Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, Hamilton Glaucoma Center, University of California San Diego, La Jolla, CA.; Department of Medicine, Division of Biomedical Informatics, University of California San Diego, La Jolla, CA., Boland MV; Department of Ophthalmology, Mass Eye and Ear, Harvard Medical School, Boston, MA., Halfpenny W; Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, Hamilton Glaucoma Center, University of California San Diego, La Jolla, CA.; Department of Medicine, Division of Biomedical Informatics, University of California San Diego, La Jolla, CA., Myers JS; Glaucoma Service, Wills Eye Hospital, Thomas Jefferson University, Philadelphia, PA., Weinreb RN; Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, Hamilton Glaucoma Center, University of California San Diego, La Jolla, CA., Zangwill LM; Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, Hamilton Glaucoma Center, University of California San Diego, La Jolla, CA., Baxter SL; Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, Hamilton Glaucoma Center, University of California San Diego, La Jolla, CA.; Department of Medicine, Division of Biomedical Informatics, University of California San Diego, La Jolla, CA.
Source:
Journal of glaucoma [J Glaucoma] 2026 Mar 01; Vol. 35 (3), pp. 150-156. Date of Electronic Publication: 2026 Feb 17.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Wolters Kluwer Health, Inc Country of Publication: United States NLM ID: 9300903 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1536-481X (Electronic) Linking ISSN: 10570829 NLM ISO Abbreviation: J Glaucoma Subsets: MEDLINE
Imprint Name(s):
Publication: <2015- > : Philadelphia, PA : Wolters Kluwer Health, Inc.
Original Publication: New York, N.Y. : Raven Press, c1992-
MeSH Terms:
Visual Fields*/physiology , Visual Field Tests*/methods , Data Management*/methods , Glaucoma*/physiopathology , Glaucoma*/diagnosis , Software*, Humans
References:
Jayaram H, Kolko M, Friedman DS, et al. Glaucoma: now and beyond. Lancet. 2023;402:1788–1801.
Cavanaugh MR, Blanchard LM, McDermott M, et al. Efficacy of visual retraining in the hemianopic field after stroke: results of a randomized clinical trial. Ophthalmology. 2021;128:1091–1101.
Mollan SP, Bodoza S, Ní Mhéalóid Á, et al. Visual field pointwise analysis of the idiopathic intracranial hypertension weight trial (IIH:WT). Transl Vis Sci Technol. 2023;12:1.
Arnold AC. Visual field defects in the optic neuritis treatment trial: central vs peripheral, focal vs global. Am J Ophthalmol. 1999;128:632–634.
De Moraes CG, Liebmann JM, Levin LA. Detection and measurement of clinically meaningful visual field progression in clinical trials for glaucoma. Prog Retin Eye Res. 2017;56:107–147.
Wall M, Johnson CA, Cello KE, et al. Visual field outcomes for the idiopathic intracranial hypertension treatment trial (IIHTT). Invest Ophthalmol Vis Sci. 2016;57:805–812.
Radgoudarzi N, Hallaj S, Boland MV, et al. Barriers to extracting and harmonizing glaucoma testing data: gaps, shortcomings, and the pursuit of FAIRness. Ophthalmol Sci. 2024;4:100621.
Hallaj S, Halfpenny W, Radgoudarzi N, et al. Gap analysis of standard automated perimetry concept representation in medical terminologies. J Glaucoma. 2025;34(8):644–649; 10.1097/IJG.0000000000002575. (PMID: 10.1097/IJG.0000000000002575)
PeriData. https://www.peridata.org/index_e.htm .
Saifee M, Wu J, Liu Y, et al. Development and validation of automated visual field report extraction platform using computer vision tools. Front Med (Lausanne). 2021;8:625487.
DICOM-Working-Group-9. Ophthalmic Visual Field (OPV) Static Perimetry Measurements Storage SOP Class. https://www.dicomstandard.org/News-dir/ftsup/docs/sups/sup146.pdf .
Eslami M, Kazeminasab S, Sharma V, et al. PyVisualFields: a Python package for visual field analysis. Transl Vis Sci Technol. 2023;12:6.
Marin-Franch I, Swanson WH. The visualFields package: a tool for analysis and visualization of visual fields. J Vis. 2013;13:10.
Shi B PyVF 2022. https://github.com/constructor-s/PyVF/tree/master .
Glaucoma Data Standards. https://eyewiki.org/Glaucoma_Data_Standards .
Tang S-T, Tjia V, Noga T, et al. Creating a medical imaging workflow based on FHIR, DICOMweb, and SVG. J Digit Imag. 2023;36:794–803.
Contributed Indexing:
Keywords: OMOP CDM; OPV DICOM; PyOPV; interoperability; ophthalmic informatics; standard automated perimetry; visual field data
Entry Date(s):
Date Created: 20260226 Date Completed: 20260306 Latest Revision: 20260306
Update Code:
20260307
DOI:
10.1097/IJG.0000000000002654
PMID:
41746848
Database:
MEDLINE

*Further Information*

*Prcis: PyOPV is a software designed and validated for handling standard visual field DICOM files, enabling multiple functionalities for glaucoma researchers.
Purpose: To introduce PyOPV, a novel vendor-agnostic Python-based software package we designed for the management and analysis of OPhthalmic Visual field (OPV) DICOM data. PyOPV addresses limitations in interoperability and data accessibility encountered by vision researchers by providing tools that check DICOM compliance, parse, and convert OPV DICOM files into formats easily usable for research and integration with research data systems (eg, Pandas Dataframes, JSON).
Methods: PyOPV was developed using Python 3.8.2. It uses Supplement 146 of the DICOM standard to check compliance, which defines the "ophthalmic-visual-field-static-perimetry-measurements" Composite Information Object Definition. Sample OPV DICOM files from 3 vendors that provide perimetry devices were used to design the package and analyzed for DICOM. The functionalities were then validated at 2 different institutions.
Results: PyOPV successfully extracted and converted OPV DICOM data into Pandas DataFrames and JSON formats, facilitating data access, analysis, and visualization. The validation on longitudinal files from different protocols demonstrated excellent agreement between PyOPV outputs and ground truth data extracted using in-place workflows of each institution. Further, it highlighted significant interoperability challenges by demonstrating missing attributes across vendors, with a considerable proportion (range: 17%-51%) of the required tags missing from the files.
Conclusions: PyOPV provides an efficient solution for handling ophthalmic visual field data, bridging a critical gap in data interoperability and research scalability. It can incorporate OPV files from different vendors and distinct protocols in bulk, thereby enhancing the ability to analyze and integrate visual field data into large-scale health data warehouses, supporting ophthalmic informatics and advancing clinical research. PyOPV is limited by the vendors' failure to provide all data elements.
(Copyright © 2026 Wolters Kluwer Health, Inc. All rights reserved.)*

*Disclosure: The authors declare no conflict of interest.*