Deep learning-enhanced dual-mode multiplexed optical sensor for point-of-care diagnostics of cardiovascular diseases
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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866917390866972672 |
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| author | Han, Gyeo-Re Eryilmaz, Merve Goncharov, Artem Li, Yuzhu Ye, Shun Tomoeda, Aoi Ngo, Emily Scussat, Margherita Wang, Xiao Ji, Zixiang Zhang, Max Hsu, Jeffrey J. Garner, Omai B. Di Carlo, Dino Ozcan, Aydogan |
| author_facet | Han, Gyeo-Re Eryilmaz, Merve Goncharov, Artem Li, Yuzhu Ye, Shun Tomoeda, Aoi Ngo, Emily Scussat, Margherita Wang, Xiao Ji, Zixiang Zhang, Max Hsu, Jeffrey J. Garner, Omai B. Di Carlo, Dino Ozcan, Aydogan |
| contents | Rapid and accessible cardiac biomarker testing is essential for the timely diagnosis and risk assessment of myocardial infarction (MI) and heart failure (HF), two interrelated conditions that frequently coexist and drive recurrent hospitalizations with high mortality. However, current laboratory and point-of-care testing systems are limited by long turnaround times, narrow dynamic ranges for the tested biomarkers, and single-analyte formats that fail to capture the complexity of cardiovascular disease. Here, we present a deep learning-enhanced dual-mode multiplexed vertical flow assay (xVFA) with a portable optical reader and a neural network-based quantification pipeline. This optical sensor integrates colorimetric and chemiluminescent detection within a single paper-based cartridge to complementarily cover a large dynamic range (spanning ~6 orders of magnitude) for both low- and high-abundance biomarkers, while maintaining quantitative accuracy. Using 50 uL of serum, the optical sensor simultaneously quantifies cardiac troponin I (cTnI), creatine kinase-MB (CK-MB), and N-terminal pro-B-type natriuretic peptide (NT-proBNP) within 23 min. The xVFA achieves sub-pg/mL sensitivity for cTnI and sub-ng/mL sensitivity for CK-MB and NT-proBNP, spanning the clinically relevant ranges for these biomarkers. Neural network models trained and blindly tested on 92 patient serum samples yielded a robust quantification performance (Pearson's r > 0.96 vs. reference assays). By combining high sensitivity, multiplexing, and automation in a compact and cost-effective optical sensor format, the dual-mode xVFA enables rapid and quantitative cardiovascular diagnostics at the point of care. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_21389 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep learning-enhanced dual-mode multiplexed optical sensor for point-of-care diagnostics of cardiovascular diseases Han, Gyeo-Re Eryilmaz, Merve Goncharov, Artem Li, Yuzhu Ye, Shun Tomoeda, Aoi Ngo, Emily Scussat, Margherita Wang, Xiao Ji, Zixiang Zhang, Max Hsu, Jeffrey J. Garner, Omai B. Di Carlo, Dino Ozcan, Aydogan Medical Physics Machine Learning Applied Physics Biological Physics Rapid and accessible cardiac biomarker testing is essential for the timely diagnosis and risk assessment of myocardial infarction (MI) and heart failure (HF), two interrelated conditions that frequently coexist and drive recurrent hospitalizations with high mortality. However, current laboratory and point-of-care testing systems are limited by long turnaround times, narrow dynamic ranges for the tested biomarkers, and single-analyte formats that fail to capture the complexity of cardiovascular disease. Here, we present a deep learning-enhanced dual-mode multiplexed vertical flow assay (xVFA) with a portable optical reader and a neural network-based quantification pipeline. This optical sensor integrates colorimetric and chemiluminescent detection within a single paper-based cartridge to complementarily cover a large dynamic range (spanning ~6 orders of magnitude) for both low- and high-abundance biomarkers, while maintaining quantitative accuracy. Using 50 uL of serum, the optical sensor simultaneously quantifies cardiac troponin I (cTnI), creatine kinase-MB (CK-MB), and N-terminal pro-B-type natriuretic peptide (NT-proBNP) within 23 min. The xVFA achieves sub-pg/mL sensitivity for cTnI and sub-ng/mL sensitivity for CK-MB and NT-proBNP, spanning the clinically relevant ranges for these biomarkers. Neural network models trained and blindly tested on 92 patient serum samples yielded a robust quantification performance (Pearson's r > 0.96 vs. reference assays). By combining high sensitivity, multiplexing, and automation in a compact and cost-effective optical sensor format, the dual-mode xVFA enables rapid and quantitative cardiovascular diagnostics at the point of care. |
| title | Deep learning-enhanced dual-mode multiplexed optical sensor for point-of-care diagnostics of cardiovascular diseases |
| topic | Medical Physics Machine Learning Applied Physics Biological Physics |
| url | https://arxiv.org/abs/2512.21389 |