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AI-Driven Radiology Report Generation for Traumatic Brain Injuries

Riadh Bouslimi, Houda Trabelsi, Wahiba Ben Abdssalem Karaa, Hana Hedhli

TL;DR

The paper addresses the challenge of automatically generating radiology reports for traumatic brain injuries in emergency settings. It introduces a hybrid AC-BiFPN + Transformer architecture that performs multi-scale image feature extraction from CT/MRI and generates coherent, clinically relevant reports. On the RSNA Intracranial Hemorrhage Detection dataset, the approach outperforms CNN-based baselines in both image analysis and report generation, as evidenced by improvements in standard NLP metrics. The work highlights practical benefits for rapid decision-making and education, while acknowledging limitations such as the lack of longitudinal data and the need for broader validation and dataset diversity.

Abstract

Traumatic brain injuries present significant diagnostic challenges in emergency medicine, where the timely interpretation of medical images is crucial for patient outcomes. In this paper, we propose a novel AI-based approach for automatic radiology report generation tailored to cranial trauma cases. Our model integrates an AC-BiFPN with a Transformer architecture to capture and process complex medical imaging data such as CT and MRI scans. The AC-BiFPN extracts multi-scale features, enabling the detection of intricate anomalies like intracranial hemorrhages, while the Transformer generates coherent, contextually relevant diagnostic reports by modeling long-range dependencies. We evaluate the performance of our model on the RSNA Intracranial Hemorrhage Detection dataset, where it outperforms traditional CNN-based models in both diagnostic accuracy and report generation. This solution not only supports radiologists in high-pressure environments but also provides a powerful educational tool for trainee physicians, offering real-time feedback and enhancing their learning experience. Our findings demonstrate the potential of combining advanced feature extraction with transformer-based text generation to improve clinical decision-making in the diagnosis of traumatic brain injuries.

AI-Driven Radiology Report Generation for Traumatic Brain Injuries

TL;DR

The paper addresses the challenge of automatically generating radiology reports for traumatic brain injuries in emergency settings. It introduces a hybrid AC-BiFPN + Transformer architecture that performs multi-scale image feature extraction from CT/MRI and generates coherent, clinically relevant reports. On the RSNA Intracranial Hemorrhage Detection dataset, the approach outperforms CNN-based baselines in both image analysis and report generation, as evidenced by improvements in standard NLP metrics. The work highlights practical benefits for rapid decision-making and education, while acknowledging limitations such as the lack of longitudinal data and the need for broader validation and dataset diversity.

Abstract

Traumatic brain injuries present significant diagnostic challenges in emergency medicine, where the timely interpretation of medical images is crucial for patient outcomes. In this paper, we propose a novel AI-based approach for automatic radiology report generation tailored to cranial trauma cases. Our model integrates an AC-BiFPN with a Transformer architecture to capture and process complex medical imaging data such as CT and MRI scans. The AC-BiFPN extracts multi-scale features, enabling the detection of intricate anomalies like intracranial hemorrhages, while the Transformer generates coherent, contextually relevant diagnostic reports by modeling long-range dependencies. We evaluate the performance of our model on the RSNA Intracranial Hemorrhage Detection dataset, where it outperforms traditional CNN-based models in both diagnostic accuracy and report generation. This solution not only supports radiologists in high-pressure environments but also provides a powerful educational tool for trainee physicians, offering real-time feedback and enhancing their learning experience. Our findings demonstrate the potential of combining advanced feature extraction with transformer-based text generation to improve clinical decision-making in the diagnosis of traumatic brain injuries.

Paper Structure

This paper contains 18 sections, 7 equations, 3 figures, 5 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Works
  3. Methodology
  4. Problem Definition
  5. AC-BiFPN for Feature Extraction
  6. Transformer Model
  7. Radiology report creation using the Transformer model
  8. Experimental settings
  9. Model training
  10. Evaluation of generated radiology reports
  11. Clinical Context-Aware Evaluation
  12. Metrics for Evaluation
  13. Experiments
  14. Datasets
  15. Model training
  16. ...and 3 more sections

Figures (3)

  • Figure 1: Comparative analysis of CNN+Transformer (A) and AC-BiFPN+Transformer (B) for automated radiology report eneration
  • Figure 2: The architecture of the proposed AC-BiFPN + Transformer model for intracranial hemorrhage radiology report generation.
  • Figure 3: Examples of brain CT images showing different types of intracranial hemorrhages: epidural, subdural, subarachnoid, intraparenchymal, and intraventricular hemorrhages from the RSNA Intracranial Hemorrhage Detection Dataset.