pyPOCQuant

pyPOCQuant quantifies lateral flow Point-Of-Care Test (POCT) images to produce objective numerical measurements of test-line intensities for diagnostic and research applications.

Key Features:

• Automated Quantification: Converts visual POCT bands into precise numerical intensity values from digital images.
• Standardized Algorithms: Employs standardized algorithms to measure test-line intensities for reproducibility and robustness.
• Unbiased Analysis: Eliminates human interpretation bias to provide objective measurements of presence or absence signals.
• Image Processing Techniques: Uses image processing methods to extract and quantify signal intensities from test lines.
• Python 3 Implementation: Implemented in Python 3, providing a computational environment for image analysis routines.

Scientific Applications:

• SARS-CoV-2 on-site testing: Facilitates quantitative analysis of lateral flow assays used in SARS-CoV-2 testing to support on-site diagnostics.
• Lateral flow assay quantification: Applicable to any lateral flow POCTs for detecting pathogens or immune responses by measuring test-line intensities.

Methodology:

Analysis of digital POCT images and extraction of quantitative data from test lines using image processing techniques and standardized algorithms to measure line intensities; implemented in Python 3.