the role of machine learning in the diagnosis of helminth zoonotic diseases: a comprehensive review

Traditional methods of diagnosing helminth zoonotic diseases are usually based on serological and imaging tests, which, although useful, are often time-consuming, expensive, and associated with low diagnostic accuracy in the early stages. the purpose of this review is to investigate the use of machine learning in the diagnosis of helminthic zoonotic diseases. with the emergence of new technologies such as machine learning, it is possible to diagnose these diseases more quickly and accurately. machine learning can use large and complex data to recognize hidden patterns in clinical, molecular, and image data and achieve highly accurate prediction models. using machine learning algorithms, medical imaging data, such as computed tomography and magnetic resonance imaging, can be used for echinococcosis diagnosis to distinguish hydatid cysts from other suspicious cysts accurately. advanced algorithms, such as deep neural networks, can be used to analyze molecular data, and fascioliasis can be diagnosed earlier and more accurately with advanced algorithms. additionally, machine learning can help to optimize diagnostic processes in areas where healthcare facilities are limited or there is a lack of access to them. based on imaging, serological tests, and genetic information, supervised and unsupervised learning algorithms can be applied to detect helminth zoonotic diseases with high accuracy, reducing costs, and reducing the time needed for diagnosis.