National Centre for Epidemiology and Population Health, ANU College of Health and Medicine, The Australian National University, Acton, Australian Capital Territory, Australia.
Department of Microbiology, Faculty of Pure and Applied Sciences, Kwara State University, Malete, Nigeria.
Sci Rep. 2023 Feb 24;13(1):3244. doi: 10.1038/s41598-023-30440-2.
Access to Hepatitis B Virus (HBV) testing for people in low-resource settings has long been challenging due to the gold standard, enzyme immunoassay, being prohibitively expensive, and requiring specialised skills and facilities that are not readily available, particularly in remote and isolated laboratories. Routine pathology data in tandem with cutting-edge machine learning shows promising diagnostic potential. In this study, recursive partitioning ("trees") and Support Vector Machines (SVMs) were applied to interrogate patient dataset (n = 916) that comprised results for Hepatitis B Surface Antigen (HBsAg) and routine clinical chemistry and haematology blood tests. These algorithms were used to develop a predictive diagnostic model of HBV infection. Our SVM-based diagnostic model of infection (accuracy = 85.4%, sensitivity = 91%, specificity = 72.6%, precision = 88.2%, F1-score = 0.89, Area Under the Receiver Operating Curve, AUC = 0.90) proved to be highly accurate for discriminating HBsAg positive from negative patients, and thus rivals with immunoassay. Therefore, we propose a predictive model based on routine blood tests as a novel diagnostic for early detection of HBV infection. Early prediction of HBV infection via routine pathology markers and pattern recognition algorithms will offer decision-support to clinicians and enhance early diagnosis, which is critical for optimal clinical management and improved patient outcomes.
在资源匮乏的环境中，由于酶联免疫吸附测定法（enzyme immunoassay）作为金标准价格昂贵，且需要专业技能和设施，而这些在偏远和孤立的实验室中并不容易获得，因此乙型肝炎病毒（HBV）检测一直难以普及到人群。常规病理学数据与先进的机器学习相结合显示出有希望的诊断潜力。在这项研究中，递归分区（"树"）和支持向量机（Support Vector Machines，SVMs）被应用于分析包含乙型肝炎表面抗原（HBsAg）和常规临床化学及血液学检测结果的患者数据集（n=916）。这些算法被用于开发 HBV 感染的预测诊断模型。我们基于 SVM 的感染诊断模型（准确性=85.4%，灵敏度=91%，特异性=72.6%，精度=88.2%，F1 分数=0.89，接收者操作特征曲线下面积，AUC=0.90）在区分 HBsAg 阳性和阴性患者方面非常准确，可与免疫测定相媲美。因此，我们提出了一种基于常规血液测试的预测模型，作为一种新的 HBV 感染早期诊断方法。通过常规病理标志物和模式识别算法早期预测 HBV 感染，将为临床医生提供决策支持，并增强早期诊断，这对优化临床管理和改善患者预后至关重要。
