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. 2021 Dec 30;11:24507. doi: 10.1038/s41598-021-04298-1

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© The Author(s) 2021

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Quantitative assessment of hemagglutination titers using NanoSpot.ai. (a) Master palette of hemagglutination samples selected based on visual appearance and reflecting a range of agglutination from 0 (no agglutination) to 9 (strongest agglutination). (b) Confusion matrix based on visual and AI-based interpretation of agglutination samples (holdout set), which had not been shown to AI before. (c–g) Correlation of AB-titer with visual appearance of hemagglutination (c), AI-based interpretation (d,f) and ELISA (e,g). Serum samples of two COVID-19-positive patients were titrated (in COVID-19-negative serum) and analyzed by indicated methods. Agglutination was terminated when close to fully developed (corresponding to AI-bin 6–8). twofold (c–e) and 1.5-fold (f,g) dilution steps were used as indicated. Error bars represent mean ± Std dev based on six (d,f (AI)) or three (e,g (ELISA)) replicates. (h) Setting used for data acquisition, including test card, cell phone and mobile app capturing image.