AI-Enhanced Lateral Flow Assay Enables 3-Minute Quantitative Detection with Laboratory-Grade Accuracy
Bibliographic record
Abstract
Lateral flow immunoassay (LFA) remains one of the most widely used point-of-care testing (POCT) platforms for disease diagnosis, food safety assessment, and environmental monitoring. However, traditional LFAs typically require up to 30 min and offer only qualitative results, limiting their application where precise and rapid quantification is needed. In this study, we propose a Rapid and Accurate Deep Learning-Based Quantitative Lateral Flow Assay (RAD-LFA), specifically designed to overcome the inherent limitations of conventional LFA techniques. RAD-LFA integrates a Residual Network (ResNet) module for spatial feature extraction and a DyFormer module for dynamic temporal modeling, enabling precise quantification of target analytes within the first 3 min of the assay. This integrated framework significantly reduces detection time and enhances quantification accuracy, as demonstrated through extensive validation on Coronavirus disease 2019 (COVID-19) and hepatitis B virus (HBV) data sets. Experimental results show that RAD-LFA improves qualitative detection accuracy by 15% over expert visual interpretation and achieves robust quantitative performance with a coefficient of determination ( R 2 ) of 0.97. In clinical blind tests, RAD-LFA demonstrated excellent diagnostic performance, achieving 94% overall accuracy, 95% sensitivity, 92% specificity, and an R 2 of 0.9985 while markedly reducing the analysis time. Overall, RAD-LFA represents a promising, portable, and highly reliable POCT solution that effectively bridges the gap between decentralized testing and laboratory-grade quantification.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".