2026-04-29T22:57:44Zhttps://repisalud.isciii.es/rest/oai/requestoai:repisalud.isciii.es:20.500.12105/254942025-12-18T12:59:33Zcom_20.500.12105_2052com_20.500.12105_2051col_20.500.12105_19609
Repisalud
author
Bermejo-Peláez, David
author
Alastruey-Izquierdo, Ana
author
Medina, Narda
author
Capellán-Martín, Daniel
author
Bonilla, Oscar
author
Luengo-Oroz, Miguel
author
Rodriguez-Tudela, Juan Luis
funder
Instituto de Salud Carlos III
funder
Agencia Estatal de Investigación (España)
funder
Global Action Fund for Fungal Infections
funder
Fondation JYLAG
2024-11-12T14:19:14Z
2024-11-12T14:19:14Z
2024-08-30
IMA Fungus. 2024 Aug 30;15(1):27.
10.1186/s43008-024-00158-5
2210-6359
2210-6340
IMA fungus
39215368
https://hdl.handle.net/20.500.12105/25494
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.
eng
Antigen quantification
Artificial intelligence (AI)
Cryptococcosis
Lateral flow assay (LFA)
Semiquantitative assay
Smartphone
Artificial intelligence-driven mobile interpretation of a semi-quantitative cryptococcal antigen lateral flow assay
research article
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URL
https://repisalud.isciii.es/bitstreams/98e7ae50-16b8-46ef-b343-fbb85d869f9e/download
File
MD5
055700fc3fa147fa414859551c1fa881
2114862
application/pdf
ArtificialIntelligence-drivenMobile_2024.pdf
URL
https://repisalud.isciii.es/bitstreams/a719e6a4-69f6-488b-9f53-47f585c0868d/download
File
MD5
c8f471831aca4f0a327c01ab067be0c1
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application/pdf
Supplementary_ArtificialIntelligence-drivenMobile_2024.pdf