PyVisualFields

PyVisualFields implements visual field (VF) analysis methods in Python to support detection and prediction of vision loss progression in ophthalmology research.

Key Features:

• Integration of AI Methods: Leverages artificial intelligence techniques for detection and prediction of visual field loss progression.
• R-to-Python Translation: Translates functionality from the R packages vfprogression and visualFields into Python using the rpy2 wrapper.
• Data Presentation: Provides functions for structured presentation of visual field measurement data.
• Normalization and Deviation Analysis: Implements normalization and deviation analyses for visual field datasets.
• Plotting: Includes plotting capabilities for visual field maps and related visualizations.
• Scoring: Implements scoring methods for visual field metrics.
• Progression Analysis: Offers progression analysis algorithms for longitudinal assessment of visual fields.
• R Independence (planned): Initial versions rely on R libraries, with future iterations aiming to remove R dependencies.

Scientific Applications:

• Ophthalmic Research: Supports statistical analysis, visualization, and progression prediction of visual fields in ophthalmic studies.
• Clinical Algorithm Development: Facilitates development and evaluation of algorithms for diagnosing and monitoring vision loss.

Methodology:

Translation of vfprogression and visualFields from R to Python via the rpy2 wrapper and integration of AI techniques for detection and progression prediction of visual field loss.