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Surgical site infections in facial trauma patients pose significant clinical challenges. This chapter presents AI-driven approaches for predicting and managing post-surgical infection risk. It outlines models that integrate clinical, imaging, and intraoperative data to enhance early detection and intervention. An AI project is described as a framework for identifying high-risk patients within 7 and 30 days post-surgery. Additionally, a multimodal AI system is introduced, combining imaging, clinical notes, and physiological data for remote detection of surgical site infections, with particular attention to its impact on healthcare workload.

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AI for Postoperative Surgical Site Infection Prediction

  • 章节

  • 发表日期：

    2026年

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  • Tuan D. Pham,
  • Simon Holmes,
  • Domniki Chatzopoulou,
  • Paul Coulthard

摘要

Surgical site infections in facial trauma patients pose significant clinical challenges. This chapter presents AI-driven approaches for predicting and managing post-surgical infection risk. It outlines models that integrate clinical, imaging, and intraoperative data to enhance early detection and intervention. An AI project is described as a framework for identifying high-risk patients within 7 and 30 days post-surgery. Additionally, a multimodal AI system is introduced, combining imaging, clinical notes, and physiological data for remote detection of surgical site infections, with particular attention to its impact on healthcare workload.