Mr. Dipayan Banik Profile

Dipayan Banik

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Proceedings Article | 2 March 2022 Presentation + Paper

Customization and testing of a mobile reader app for an open-access SARS-CoV-2 antigen lateral flow assay

Wenbo Wang, James Stafford, Dipayan Banik, Matthew Keller

Proceedings Volume 11950, 1195009 (2022) https://doi.org/10.1117/12.2609795

KEYWORDS: Visualization, Image resolution, Detection and tracking algorithms, Signal detection, RGB color model, Diagnostic tests, Machine learning, Image quality, Video, Receivers

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Lateral flow assays (LFA’s) are a common diagnostic test form, particularly in low-to-middle income countries (LMIC’s). Visual interpretation of LFA’s can be subjective and inconsistent, especially with faint positive results, and commercial readers are expensive and challenging to implement in LMIC’s. We report a phone-agnostic Android app to acquire images and interpret results of a variety of LFA’s with no additional hardware. Starting from the open-source “rdt-scan” codebase, we integrated new features and revamped the peak detection method. This included improved perspective corrections, phone level check to eliminate shadows, high resolution still-image capture besides existing video frame capture, and new peak detection method. This peak detection incorporated smoothing and baseline removal from the one-dimensional profiles of a given color channel’s intensity averaged across the read window’s width, with location and relative size constraints to correctly report locations and peak heights of control and test lines. The app was tested in a real-world setting in conjunction with an open-access LFA for SARS-CoV-2 antigen developed by GH Labs. The app acquired 155 images of LFA cassettes, and results were compared against both visual interpretation by trained clinical staff and PCR results from the same patients. With an appropriate setting for test line intensity threshold, the app matched visual read for all cases but one missed visual positive. From ROC analyses against PCR, the app outperformed visual read by 1-3% across sensitivity, specificity, and AUC. The app thus demonstrated promise for accurate, consistent interpretation of LFA’s while generating digital records that could also be useful for health surveillance.

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