Classification of anatomic patterns of peripheral artery disease with automated machine learning (AutoML)

CONCLUSION: After a brief training period with a limited dataset, AutoML has demonstrated its potential in identifying and classifying the anatomical patterns of PAD, operating unhindered by the factors that can affect human analysts, such as fatigue or lack of experience. This technology bears the potential to revolutionize outcome prediction and standardize evidence-based revascularization strategies for patients with PAD, leveraging its adaptability and ability to continuously improve with additional data. The pursuit of further research in AutoML within the field of vascular medicine is both promising and warranted. However, it necessitates additional financial support to realize its full potential.PMID:38404043 | DOI:10.1177/17085381241236571

Source: Vascular - February 26, 2024 Category: Surgery Authors: Yury Rusinovich Volha Rusinovich Aliaksei Buhayenka Vitalii Liashko Arsen Sabanov David J F Holstein Samer Aldmour Markus Doss Daniela Branzan Source Type: research