The Contribution of Real-Time Artificial Intelligence Segmentation in Maxillofacial Trauma Emergencies.

Clicks: 91
ID: 281981
2025
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 #14 of 16 articles by views in Diagnostics (Basel, Switzerland)

Most read Least read

Bar heights use a square-root scale.

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
Abstract
Maxillofacial trauma poses significant challenges in emergency medicine, requiring rapid interventions to minimize morbidity and mortality. Traditional segmentation methods are time-consuming and error-prone, particularly in high-pressure settings. Real-time artificial intelligence (AI) segmentation offers a transformative solution to streamline workflows and enhance clinical decision-making. This study evaluated the potential of real-time AI segmentation to improve diagnostic efficiency and support decision-making in maxillofacial trauma emergencies. This study evaluated 53 trauma patients with moderate to severe maxillofacial injuries treated over 16 months at Galilee Medical Center. AI-assisted segmentation using Materialise Mimics Viewer and Romexis Smart Tool was compared to semi-automated methods in terms of time and accuracy. The clinical impact of AI on diagnosis and treatment planning was also assessed. AI segmentation was significantly faster than semi-automated methods (9.87 vs. 63.38 min) with comparable accuracy (DSC: 0.92-0.93 for AI; 0.95 for semi-automated). AI tools provided rapid 3D visualization of key structures, enabling faster decisions for airway management, fracture assessment, and foreign body localization. Specific trauma cases illustrate the potential of real-time AI segmentation to enhance the efficiency of diagnosis, treatment planning, and overall management of maxillofacial emergencies. The highest clinical benefit was observed in complex cases, such as orbital injuries or combined mandible and midface fractures. Real-time AI segmentation has the potential to enhance efficiency and clinical utility in managing maxillofacial trauma by providing precise, actionable data in time-sensitive scenarios. However, the expertise of oral and maxillofacial surgeons remains critical, with AI serving as a complementary tool to aid, rather than replace, clinical decision-making.
Reference Key
shhadeh2025the Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Shhadeh, Amjad; Daoud, Shadi; Redenski, Idan; Oren, Daniel; Zoabi, Adeeb; Kablan, Fares; Srouji, Samer
Journal Diagnostics (Basel, Switzerland)
Year 2025
DOI
10.3390/diagnostics15080984
URL
Keywords

Citations

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

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