Predictive modelling of vascular surgery trends using machine learning: a comparative study of Irish public and private tertiary referral centres

Clicks: 29
ID: 310321
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #20 of 490 articles by views in Frontiers in surgery

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 490 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
BackgroundVascular diseases are increasing in Ireland as well as worldwide alongside an ageing society, posing a growing demand for trained and qualified healthcare professionals. In this study, we have analysed current practices of vascular interventions by using the data from the vascular tertiary centre to predict the future size and capacity of the vascular surgery workforce through artificial intelligence (AI)-powered predictive models.MethodsWe employed supervised machine learning (ML) regression model to predict trends in the landscape of complex vascular and endovascular surgery over the next 22 years, utilising data from a high-volume public and private tertiary referral vascular centre spanning two decades (2002 to 2023) in the West of Ireland.ResultsWe conducted 1,653 aortic interventions, 1,185 carotid interventions, and 3,069 peripheral vascular interventions, with conversion rates from referral to surgery of 5%, 7.4%, and 9.8%, respectively. The private sector experienced a dramatic 73-fold increase in abdominal aortic aneurysm interventions, contrasted with a modest 1.25-fold increase in the public sector. Our model predicts a shortage of vascular surgeons, with the workforce potentially meeting demand by 2050. By 2030, each surgeon would need to increase yearly wRVU production by 22%–31% and by 2040 by 8%–11% to accommodate the workload.ConclusionsOur model predicts a shortage of the vascular surgery workforce over the next two decades. We can speculate that addressing future needs in vascular surgery requires either training more specialists or increasing the efficiency and wRUV through strategic planning and integration of AI/ML systems to ensure adequate compensation and the sustainability of the workforce. By focusing on these areas, we can navigate the evolving landscape of vascular surgery and continue providing high-quality patient care.
Reference Key
imported_1768925911_696faad702021 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Soliman, Osama
Journal Frontiers in surgery
Year 2026
DOI
10.3389/fsurg.2025.1733205
URL
https://www.frontiersin.org/journals/surgery/articles/10.3389/fsurg.2025.1733205/full
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.