Sawesi Suhila, Jadhav Arya, Rashrash Bushra
Health Informatics and Bioinformatics Program, College Of Computing, Grand Valley State University, 333 Michigan St. NE, Grand Rapids, MI, 49503, United States, 1 616-331-7827 ext 17827.
Data Science, College Of Computing, Grand Valley State University, Allendale, MI, United States.
JMIR Med Inform. 2025 May 29;13:e67859. doi: 10.2196/67859.
Leptospirosis, a zoonotic disease caused by Leptospira bacteria, continues to pose significant public health risks, particularly in tropical and subtropical regions.
This systematic review aimed to evaluate the application of machine learning (ML) and deep learning (DL) techniques in predicting and diagnosing leptospirosis, focusing on the most used algorithms, validation methods, data types, and performance metrics.
Using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS), and Prediction model Risk of Bias Assessment Tool (PROBAST) tools, we conducted a comprehensive review of studies applying ML and DL models for leptospirosis detection and prediction, examining algorithm performance, data sources, and validation approaches.
Out of a total of 374 articles screened, 17 studies were included in the qualitative synthesis, representing approximately 4.5% of the initial pool. The review identified frequent use of algorithms such as support vector machines, artificial neural networks, decision trees, and convolutional neural networks (CNNs). Among the included studies, 88% (15/17) used traditional ML methods, and 24% (4/17) used DL techniques. Several models demonstrated high predictive performance, with reported accuracy rates ranging from 80% to 98%, notably with the U-Net CNN achieving 98.02% accuracy. However, public datasets were underused, with only 35% (6/17) of studies incorporating publicly available data sources; the majority (65%, 11/17) relied primarily on private datasets from hospitals, clinical records, or regional surveillance systems.
ML and DL techniques demonstrate potential for improving leptospirosis prediction and diagnosis, but future research should focus on using larger, more diverse datasets, adopting transfer learning strategies, and integrating advanced ensemble and validation techniques to strengthen model accuracy and generalization.
钩端螺旋体病是一种由钩端螺旋体细菌引起的人畜共患病，仍然对公共卫生构成重大风险，特别是在热带和亚热带地区。
本系统评价旨在评估机器学习（ML）和深度学习（DL）技术在钩端螺旋体病预测和诊断中的应用，重点关注最常用的算法、验证方法、数据类型和性能指标。
我们使用系统评价和Meta分析的首选报告项目（PRISMA）指南、预测模型研究系统评价的关键评估和数据提取清单（CHARMS）以及预测模型偏倚风险评估工具（PROBAST）工具，对应用ML和DL模型进行钩端螺旋体病检测和预测的研究进行了全面综述，考察算法性能、数据来源和验证方法。
在总共筛选的374篇文章中，有17项研究纳入了定性综合分析，约占初始文献库的4.5%。该综述发现，支持向量机、人工神经网络、决策树和卷积神经网络（CNN）等算法被频繁使用。在纳入的研究中，88%（15/17）使用传统ML方法，24%（4/17）使用DL技术。几个模型表现出较高的预测性能，报告的准确率在80%至98%之间，特别是U-Net CNN的准确率达到98.02%。然而，公共数据集使用不足，只有35%（6/17）的研究纳入了公开可用的数据源；大多数（65%，11/17）主要依赖来自医院、临床记录或区域监测系统的私有数据集。
ML和DL技术在改善钩端螺旋体病预测和诊断方面显示出潜力，但未来的研究应侧重于使用更大、更多样化的数据集，采用迁移学习策略，并整合先进的集成和验证技术，以提高模型的准确性和泛化能力。
