Artificial intelligence-driven mobile interpretation of a semi-quantitative cryptococcal antigen lateral flow assay.

IF 5.2 1区 生物学 Q1 MYCOLOGY Ima Fungus Pub Date : 2024-08-30 DOI:10.1186/s43008-024-00158-5
David Bermejo-Peláez, Ana Alastruey-Izquierdo, Narda Medina, Daniel Capellán-Martín, Oscar Bonilla, Miguel Luengo-Oroz, Juan Luis Rodríguez-Tudela
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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.

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人工智能驱动的半定量隐球菌抗原侧流分析移动解读。
目的:隐球菌病仍然是全球严重的健康问题,因此迫切需要快速可靠的诊断解决方案。隐球菌抗原半定量(CrAgSQ)侧流检测法(LFA)等床旁检测(POCT)有望解决这一难题。但是,它们的主观解释存在局限性。我们的目标包括开发和验证基于人工智能(AI)的数字平台,评估其半定量 LFA 解释性能,并探索其直接从 LFA 图像量化 CrAg 浓度的潜力:我们测试了 53 种隐球菌抗原(CrAg)浓度,范围从 0 到 5000 ng/ml。我们共接种了 318 个 CrAgSQ LFA,并使用两部不同的智能手机系统地拍摄了两次,得到了 1272 张图像的数据集。我们开发了一种人工智能算法,用于自动解读 CrAgSQ LFAs。同时,我们还探索了量化检测线强度与 CrAg 浓度之间的关系:结果:我们的算法在灵敏度上超过了目测读数,并且显示出较少的差异(p):该技术对各种 LFA 的适应性表明其应用范围更广。人工智能驱动的解释具有变革潜力,能彻底改变隐球菌病诊断,提供标准化、可靠和高效的 POCT 结果。
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来源期刊
Ima Fungus
Ima Fungus Agricultural and Biological Sciences-Agricultural and Biological Sciences (miscellaneous)
CiteScore
11.00
自引率
3.70%
发文量
18
审稿时长
20 weeks
期刊介绍: The flagship journal of the International Mycological Association. IMA Fungus is an international, peer-reviewed, open-access, full colour, fast-track journal. Papers on any aspect of mycology are considered, and published on-line with final pagination after proofs have been corrected; they are then effectively published under the International Code of Nomenclature for algae, fungi, and plants. The journal strongly supports good practice policies, and requires voucher specimens or cultures to be deposited in a public collection with an online database, DNA sequences in GenBank, alignments in TreeBASE, and validating information on new scientific names, including typifications, to be lodged in MycoBank. News, meeting reports, personalia, research news, correspondence, book news, and information on forthcoming international meetings are included in each issue
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