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

Area of Science:

• Ophthalmic informatics
• Medical image analysis
• Data interoperability in vision research

Background:

• Standardization of ophthalmic visual field (OPV) data is crucial for research.
• Existing DICOM data formats present interoperability and accessibility challenges for vision researchers.
• A need exists for robust tools to manage and analyze OPV DICOM files.

Purpose of the Study:

• To introduce PyOPV, a novel Python-based software package for managing and analyzing OPV DICOM data.
• To address limitations in data interoperability and accessibility for vision research.
• To provide tools for DICOM compliance checking, parsing, and conversion of OPV data.

Main Methods:

• PyOPV was developed using Python 3.8.2, leveraging DICOM Supplement 146 for compliance checking.
• The software was designed and tested using OPV DICOM files from three different vendors.
• Functionalities were validated at two independent institutions using sample data.

Main Results:

• PyOPV successfully extracted and converted OPV DICOM data into Pandas DataFrames and JSON formats.
• Validation showed excellent agreement with existing institutional workflows, demonstrating data accuracy.
• Significant interoperability issues were identified, with 17%-51% of required DICOM tags missing across vendors.

Conclusions:

• PyOPV offers an efficient solution for handling ophthalmic visual field data, enhancing research scalability.
• The software bridges a critical gap in data interoperability, enabling bulk processing from diverse sources.
• PyOPV supports integration into large-scale health data warehouses, advancing ophthalmic informatics and clinical research.