5403. Visual Communication of Traumatic Injuries by Automatic Rendering of a Graphic Summary Based on Natural Language Processing of CT Reports
Authors * Denotes Presenting Author
  1. Nitai Bar *; Rambam Healthcare Campus
  2. Anat Illivitzki; Rambam Healthcare Campus
  3. Eyal Bercovich; Rambam Healthcare Campus
Initial medical response in traumatic injury scenarios requires a multidisciplinary approach involving radiologists, as well as trauma surgeons, emergency medicine physicians, and lead administrators. In such settings, immediate and efficient data flow is essential, but nevertheless remains a major challenge. This is particularly pronounced in mass casualty incidents, where triage, patient navigation, and team coordination become critical. We aim to develop a tool leveraging an advanced language model to automatically extract and classify findings from CT reports, and channel data to achieve real-time rendering of a graphical summary of traumatic findings. Graphical illustrations are an efficient means of communicating complex data, which is required for prioritization of patients and supports life-saving decisions taken under time pressure in the trauma room.

Materials and Methods:
Our dataset comprised all chest CTA trauma protocol scans performed between 1.1.2012 – 1.4.2022, at a tertiary trauma center in the north of Israel. Traumatic injuries were manually annotated to produce “gold-standard” labels. GPT models were subsequently used to extract meaningful data from the reports and classify them into predefined categories, enabling a rule-based automatic rendering of a graphical summary illustration using open-source graphical tools. Performance of the language model was assessed with mean AUC, F1, and exact match scores as compared to the gold standard labels.

Preliminary findings suggest exact match scores > 80% and F1 scores > 90%, demonstrating the model's ability to accurately predict and classify traumatic injury labels, including rare ones. The NLP model thus allows real-time generation of structured data, which can be utilized to streamline communication between teams, promote notification of acute findings and ensure adequate patient prioritization.

We introduce a pipeline harnessing natural language models and graphic tools to allow real-time data analysis. Graphical illustrations of medical data based on automatically generated classifiers from medical reports are a promising novel tool contributing to optimize the trauma workflow.