Centro de Informática, Universidade Federal de Pernambuco, Recife, PE, Brazil.
Comissão Nacional de Energia Nuclear, Centro Regional de Ciências Nucleares do Nordeste, Recife, PE, Brazil.
Sci Rep. 2024 Jan 10;14(1):956. doi: 10.1038/s41598-023-50332-9.
The timely and accurate diagnosis of candidemia, a severe bloodstream infection caused by Candida spp., remains challenging in clinical practice. Blood culture, the current gold standard technique, suffers from lengthy turnaround times and limited sensitivity. To address these limitations, we propose a novel approach utilizing an Electronic Nose (E-nose) combined with Time Series-based classification techniques to analyze and identify Candida spp. rapidly, using culture species of C. albicans, C.kodamaea ohmeri, C. glabrara, C. haemulonii, C. parapsilosis and C. krusei as control samples. This innovative method not only enhances diagnostic accuracy and reduces decision time for healthcare professionals in selecting appropriate treatments but also offers the potential for expanded usage and cost reduction due to the E-nose's low production costs. Our proof-of-concept experimental results, carried out with culture samples, demonstrate promising outcomes, with the Inception Time classifier achieving an impressive average accuracy of 97.46% during the test phase. This paper presents a groundbreaking advancement in the field, empowering medical practitioners with an efficient and reliable tool for early and precise identification of candidemia, ultimately leading to improved patient outcomes.
及时准确地诊断念珠菌血症（由念珠菌属引起的严重血流感染）仍然是临床实践中的挑战。目前的金标准技术——血培养，存在检测周期长和灵敏度有限的问题。为了解决这些局限性，我们提出了一种新的方法，利用电子鼻（E-nose）结合基于时间序列的分类技术，使用白色念珠菌、冲绳念珠菌、光滑念珠菌、近平滑念珠菌、近平滑念珠菌和克柔念珠菌等培养物种作为对照样本，快速分析和识别念珠菌属。这种创新方法不仅提高了医疗保健专业人员选择适当治疗方法的诊断准确性和决策时间，而且由于电子鼻的低成本，还有望扩大使用范围并降低成本。我们使用培养样本进行的概念验证实验结果表明，该方法具有很大的应用潜力，在测试阶段，Inception Time 分类器的平均准确率达到了 97.46%。本文提出了该领域的一项突破性进展，为医疗从业者提供了一种高效可靠的工具，用于早期、准确地识别念珠菌血症，最终改善患者的治疗效果。
