Potential applications of artificial intelligence and machine learning on diagnosis, treatment, and outcome prediction to address health care disparities of chronic limb-threatening ischemia

• Published in: Seminars in vascular surgery Vol. 36; no. 3; pp. 454 - 459
• Main Authors: Bagheri, Amir Behzad, Rouzi, Mohammad Dehghan, Koohbanani, Navid Alemi, Mahoor, Mohammad H., Finco, M.G., Lee, Myeounggon, Najafi, Bijan, Chung, Jayer
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.09.2023
• Subjects: Artificial intelligence; Chronic limb-threatening ischemia; Convoluted neural networks; Health care disparities; Machine learning; Surgery; Health care disparities; Convoluted neural networks; Artificial intelligence; Machine learning; Chronic limb-threatening ischemia
• ISSN: 0895-7967; 1558-4518; 1558-4518
• DOI: 10.1053/j.semvascsurg.2023.06.003

Chronic limb-threatening ischemia (CLTI) is the most advanced form of peripheral artery disease. CLTI has an extremely poor prognosis and is associated with considerable risk of major amputation, cardiac morbidity, mortality, and poor quality of life. Early diagnosis and targeted treatment of CLTI is critical for improving patient's prognosis. However, this objective has proven elusive, time-consuming, and challenging due to existing health care disparities among patients. In this article, we reviewed how artificial intelligence (AI) and machine learning (ML) can be helpful to accurately diagnose, improve outcome prediction, and identify disparities in the treatment of CLTI. We demonstrate the importance of AI/ML approaches for management of these patients and how available data could be used for computer-guided interventions. Although AI/ML applications to mitigate health care disparities in CLTI are in their infancy, we also highlighted specific AI/ML methods that show potential for addressing health care disparities in CLTI.