The use of artificial intelligence in the diagnosis of peripheral arterial disease: a systematic review

• Main Authors: Sri P Negoro, -, Yan Efrata Sembiring, Yan, Latifah A Zati, -, I G Putra, -, Jeffreey J Dillon, -
• Format: Article PeerReviewed
• Language: English; Indonesian; English; English
• Published: Edizioni Minerva Medica
• Subjects: R5-920 Medicine (General)
• Online Access: https://repository.unair.ac.id/133150/1/8%20artikel.pdf; https://repository.unair.ac.id/133150/2/karil%2008.pdf; https://repository.unair.ac.id/133150/3/8%20turnitin.pdf; https://repository.unair.ac.id/133150/4/08%20Korespondensi.pdf; https://repository.unair.ac.id/133150/; https://www.minervamedica.it/it/riviste/vascular-endovascular-surgery/articolo.php?cod=R46Y2023N04A0142; https://doi.org/10.23736/S1824-4777.23.01620-0
• Institution: Universitas Airlangga

INTRODUCTION: Peripheral artery disease (PAD) affects more than 200 million people worldwide. Despite this, doctors often fail to detect it due to inconsistencies in screening criteria, inadequate patients, and a high prevalence of quiet or unusual symptoms. It is believed that the use of artificial intelligence (AI) will overcome these problems. This systematic review aims to summarize various previous studies that have investigated the use of artificial intelligence in managing PAD. EVIDENCE ACQUISITION: This is a systematic review using high-quality articles from the PubMed, Science Direct, and ProQuest databases published between 2011-2023. The method of selection and analysis of articles followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA). EVIDENCE SYNTHESIS: A total of six research articles were included in the analysis. Four studies documented its use to diagnose PAD based on clinical characteristics, with two of these studies revealing AI’s capacity to predict prognosis and give automated risk stratification for cardiovascular diseases. One research also indicated that it was used to classify PAD more precisely and more effectively. There were three studies that described the use of AI in radiological modalities such as Doppler ultrasonography, Angiography, and Multispectral Imaging. CONCLUSIONS: The use of AI based on clinical features and radiological examination AI based on clinical characteristics and radiological test findings can be utilized to manage PAD, particularly in the diagnostic and prognosis stratification processes.