Rapid and automated interpretation of CRISPR-Cas13-based lateral flow assay test results using machine learning.

ABSTRACT: CRISPR-Cas-based lateral flow assays (LFAs) have emerged as a promising diagnostic tool for ultrasensitive detection of nucleic acids, offering improved speed, simplicity and cost-effectiveness compared to polymerase chain reaction (PCR)-based assays. However, visual interpretation of CRISPR-Cas-based LFA test results is prone to human error, potentially leading to false-positive or false-negative outcomes when analyzing test/control lines. To address this limitation, we have developed two neural network models: one based on a fully convolutional neural network and the other on a lightweight mobile-optimized neural network for automated interpretation of CRISPR-Cas-based LFA test results. To demonstrate proof of concept, these models were applied to interpret results from a CRISPR-Cas13-ba

SUBMITTER: Xue M 

PROVIDER: S-EPMC11726308 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature