背景:有效检测疟疾,包括在非常低的密度下检测感染,对于成功消除这种疾病至关重要。不幸的是,现有的方法要么便宜但敏感性差,要么敏感但昂贵。最近的研究表明,中红外光谱与机器学习(MIR-ML)相结合具有快速检测疟疾感染的潜力,但需要对代表流行地区自然感染的各种样本进行进一步评估。这项研究的目的是,因此,为了展示一个简单的人工智能,无试剂,和用户友好的方法,利用干血点的中红外光谱,在不同的寄生虫密度和贫血条件下准确检测疟疾感染。
方法:培养恶性疟原虫菌株NF54和FCR3,并与来自70个无疟疾个体的血液混合,以产生各种疟疾寄生虫血症和贫血状况。血液稀释产生了三个血细胞比容比(50%,25%,12.5%)和五种寄生虫血症水平(6%,0.1%,0.002%,0.00003%,0%)。在Whatman™滤纸上制备干燥的血点,并使用衰减全反射-傅里叶变换红外(ATR-FTIR)扫描以进行机器学习分析。在4655个光谱的80%/20%分裂上训练了三个分类器:(I)高对比度(6%寄生虫血症与负),(二)低对比度(0.00003%vs.阴性)和(III)所有浓度(所有阳性水平与负)。使用未知数据集对分类器进行了验证,以检测各种寄生虫血症水平和贫血状况下的疟疾。此外,这些分类器在坦桑尼亚东南部疟疾流行村庄的人口调查样本上进行了测试。
结果:AI分类器在检测疟疾感染方面的准确率超过90%,低至每微升血液一个寄生虫,常规RDT和显微镜无法达到的灵敏度。这些实验室开发的分类器无缝过渡到现场适用性,在实地调查期间收集的血液样本中预测自然恶性疟原虫感染的准确率超过80%。至关重要的是,表现不受各种贫血程度的影响,疟疾患者的常见并发症。
结论:这些发现表明,人工智能驱动的中红外光谱方法有望简化,敏感且具有成本效益的疟疾筛查方法,尽管寄生虫密度和贫血条件存在差异,但仍表现良好。该技术仅涉及使用台式中红外扫描仪扫描干燥的血点,并使用预先训练的AI分类器分析光谱,使其易于适应低资源环境中的现场条件。在这项研究中,该方法成功地适应了现场使用,从坦桑尼亚的人口水平调查中有效预测血液样本中的自然疟疾感染。通过额外的现场试验和验证,这项技术可以大大加强疟疾监测,并有助于加快消除疟疾的努力。
BACKGROUND: Effective testing for malaria, including the detection of infections at very low densities, is vital for the successful elimination of the disease. Unfortunately, existing methods are either inexpensive but poorly sensitive or sensitive but costly. Recent studies have shown that mid-infrared spectroscopy coupled with machine learning (MIRs-ML) has potential for rapidly detecting malaria infections but requires further evaluation on diverse samples representative of natural infections in endemic areas. The aim of this study was, therefore, to demonstrate a simple AI-powered, reagent-free, and user-friendly approach that uses mid-infrared spectra from dried blood spots to accurately detect malaria infections across varying parasite densities and anaemic conditions.
METHODS: Plasmodium falciparum strains NF54 and FCR3 were cultured and mixed with blood from 70 malaria-free individuals to create various malaria parasitaemia and anaemic conditions. Blood dilutions produced three haematocrit ratios (50%, 25%, 12.5%) and five parasitaemia levels (6%, 0.1%, 0.002%, 0.00003%, 0%). Dried blood spots were prepared on Whatman™ filter papers and scanned using attenuated total reflection-Fourier Transform Infrared (ATR-FTIR) for machine-learning analysis. Three classifiers were trained on an 80%/20% split of 4655 spectra: (I) high contrast (6% parasitaemia vs. negative), (II) low contrast (0.00003% vs. negative) and (III) all concentrations (all positive levels vs. negative). The classifiers were validated with unseen datasets to detect malaria at various parasitaemia levels and anaemic conditions. Additionally, these classifiers were tested on samples from a population survey in malaria-endemic villages of southeastern Tanzania.
RESULTS: The AI classifiers attained over 90% accuracy in detecting malaria infections as low as one parasite per microlitre of blood, a sensitivity unattainable by conventional RDTs and microscopy. These laboratory-developed classifiers seamlessly transitioned to field applicability, achieving over 80% accuracy in predicting natural P. falciparum infections in blood samples collected during the field survey. Crucially, the performance remained unaffected by various levels of anaemia, a common complication in malaria patients.
CONCLUSIONS: These findings suggest that the AI-driven mid-infrared spectroscopy approach holds promise as a simplified, sensitive and cost-effective method for malaria screening, consistently performing well despite variations in parasite densities and anaemic conditions. The technique simply involves scanning dried blood spots with a desktop mid-infrared scanner and analysing the spectra using pre-trained AI classifiers, making it readily adaptable to field conditions in low-resource settings. In this study, the approach was successfully adapted to field use, effectively predicting natural malaria infections in blood samples from a population-level survey in Tanzania. With additional field trials and validation, this technique could significantly enhance malaria surveillance and contribute to accelerating malaria elimination efforts.