2026-04-29T22:57:42Zhttps://repisalud.isciii.es/rest/oai/request

oai:repisalud.isciii.es:20.500.12105/254942025-12-18T12:59:33Zcom_20.500.12105_2052com_20.500.12105_2051col_20.500.12105_19609

00925njm 22002777a 4500 dc 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 author 2024-08-30 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. 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 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