Artificial intelligence-driven mobile interpretation of a semi-quantitative cryptococcal antigen lateral flow assay
Abstract Objectives Cryptococcosis remains a severe global health concern, underscoring the urgent need for rapid and reliable diagnostic solutions. Point-of-care tests (POCTs), such as the cryptococcal antigen semi-quantitative (CrAgSQ) lateral flow assay (LFA), offer promise in addressing this cha...
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BMC
2024-08-01
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| Series: | IMA Fungus |
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| Online Access: | https://doi.org/10.1186/s43008-024-00158-5 |
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| author | David Bermejo-Peláez Ana Alastruey-Izquierdo Narda Medina Daniel Capellán-Martín Oscar Bonilla Miguel Luengo-Oroz Juan Luis Rodríguez-Tudela |
| author_facet | David Bermejo-Peláez Ana Alastruey-Izquierdo Narda Medina Daniel Capellán-Martín Oscar Bonilla Miguel Luengo-Oroz Juan Luis Rodríguez-Tudela |
| author_sort | David Bermejo-Peláez |
| collection | DOAJ |
| description | Abstract Objectives Cryptococcosis remains a severe global health concern, underscoring the urgent need for rapid and reliable diagnostic solutions. Point-of-care tests (POCTs), such as the cryptococcal antigen semi-quantitative (CrAgSQ) lateral flow assay (LFA), offer promise in addressing this challenge. However, their subjective interpretation poses a limitation. Our objectives encompass the development and validation of a digital platform based on Artificial Intelligence (AI), assessing its semi-quantitative LFA interpretation performance, and exploring its potential to quantify CrAg concentrations directly from LFA images. Methods We tested 53 cryptococcal antigen (CrAg) concentrations spanning from 0 to 5000 ng/ml. A total of 318 CrAgSQ LFAs were inoculated and systematically photographed twice, employing two distinct smartphones, resulting in a dataset of 1272 images. We developed an AI algorithm designed for the automated interpretation of CrAgSQ LFAs. Concurrently, we explored the relationship between quantified test line intensities and CrAg concentrations. Results Our algorithm surpasses visual reading in sensitivity, and shows fewer discrepancies (p < 0.0001). The system exhibited capability of predicting CrAg concentrations exclusively based on a photograph of the LFA (Pearson correlation coefficient of 0.85). Conclusions This technology's adaptability for various LFAs suggests broader applications. AI-driven interpretations have transformative potential, revolutionizing cryptococcosis diagnosis, offering standardized, reliable, and efficient POCT results. |
| format | Article |
| id | doaj-art-20bf56408e694af09df733209ae7d9c4 |
| institution | Kabale University |
| issn | 2210-6359 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | BMC |
| record_format | Article |
| series | IMA Fungus |
| spelling | doaj-art-20bf56408e694af09df733209ae7d9c42025-02-03T12:00:27ZengBMCIMA Fungus2210-63592024-08-011511910.1186/s43008-024-00158-5Artificial intelligence-driven mobile interpretation of a semi-quantitative cryptococcal antigen lateral flow assayDavid Bermejo-Peláez0Ana Alastruey-Izquierdo1Narda Medina2Daniel Capellán-Martín3Oscar Bonilla4Miguel Luengo-Oroz5Juan Luis Rodríguez-Tudela6SpotlabMycology Reference Laboratory, National Center for Microbiology, Instituto de Salud Carlos IIIAsociación de Salud IntegralSpotlabMycology Reference Laboratory, National Center for Microbiology, Instituto de Salud Carlos IIISpotlabGlobal Action for Fungal InfectionsAbstract Objectives Cryptococcosis remains a severe global health concern, underscoring the urgent need for rapid and reliable diagnostic solutions. Point-of-care tests (POCTs), such as the cryptococcal antigen semi-quantitative (CrAgSQ) lateral flow assay (LFA), offer promise in addressing this challenge. However, their subjective interpretation poses a limitation. Our objectives encompass the development and validation of a digital platform based on Artificial Intelligence (AI), assessing its semi-quantitative LFA interpretation performance, and exploring its potential to quantify CrAg concentrations directly from LFA images. Methods We tested 53 cryptococcal antigen (CrAg) concentrations spanning from 0 to 5000 ng/ml. A total of 318 CrAgSQ LFAs were inoculated and systematically photographed twice, employing two distinct smartphones, resulting in a dataset of 1272 images. We developed an AI algorithm designed for the automated interpretation of CrAgSQ LFAs. Concurrently, we explored the relationship between quantified test line intensities and CrAg concentrations. Results Our algorithm surpasses visual reading in sensitivity, and shows fewer discrepancies (p < 0.0001). The system exhibited capability of predicting CrAg concentrations exclusively based on a photograph of the LFA (Pearson correlation coefficient of 0.85). Conclusions This technology's adaptability for various LFAs suggests broader applications. AI-driven interpretations have transformative potential, revolutionizing cryptococcosis diagnosis, offering standardized, reliable, and efficient POCT results.https://doi.org/10.1186/s43008-024-00158-5CryptococcosisLateral flow assay (LFA)Artificial intelligence (AI)SmartphoneSemiquantitative assayAntigen quantification |
| spellingShingle | David Bermejo-Peláez Ana Alastruey-Izquierdo Narda Medina Daniel Capellán-Martín Oscar Bonilla Miguel Luengo-Oroz Juan Luis Rodríguez-Tudela Artificial intelligence-driven mobile interpretation of a semi-quantitative cryptococcal antigen lateral flow assay IMA Fungus Cryptococcosis Lateral flow assay (LFA) Artificial intelligence (AI) Smartphone Semiquantitative assay Antigen quantification |
| title | Artificial intelligence-driven mobile interpretation of a semi-quantitative cryptococcal antigen lateral flow assay |
| title_full | Artificial intelligence-driven mobile interpretation of a semi-quantitative cryptococcal antigen lateral flow assay |
| title_fullStr | Artificial intelligence-driven mobile interpretation of a semi-quantitative cryptococcal antigen lateral flow assay |
| title_full_unstemmed | Artificial intelligence-driven mobile interpretation of a semi-quantitative cryptococcal antigen lateral flow assay |
| title_short | Artificial intelligence-driven mobile interpretation of a semi-quantitative cryptococcal antigen lateral flow assay |
| title_sort | artificial intelligence driven mobile interpretation of a semi quantitative cryptococcal antigen lateral flow assay |
| topic | Cryptococcosis Lateral flow assay (LFA) Artificial intelligence (AI) Smartphone Semiquantitative assay Antigen quantification |
| url | https://doi.org/10.1186/s43008-024-00158-5 |
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