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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...

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Semi-quantitative Assessment Using [18F]FDG Tracer in Patients with Severe Brain Injury

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AI-Driven Radiology Report Generation for Traumatic Brain Injuries.

Riadh Bouslimi¹, Houda Trabelsi², Wahiba Ben Abdessalem Karaa²

¹Higher School of Digital Economics, Manouba University, Manouba, Tunisia. riadh.bouslimi@esen.tn.

Journal of Imaging Informatics in Medicine

|January 30, 2025

Summary

This study introduces an AI model for generating radiology reports for traumatic brain injuries. The novel approach improves diagnostic accuracy and aids radiologists and trainees in emergency medicine.

Keywords:

AC-BiFPN Intracranial hemorrhage detection Radiology report generation Transformer architecture Traumatic brain injury

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Published on: November 9, 2018

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Advanced Diffusion Imaging in The Hippocampus of Rats with Mild Traumatic Brain Injury

Advanced Diffusion Imaging in The Hippocampus of Rats with Mild Traumatic Brain Injury

Published on: August 14, 2019

Area of Science:

Medical Imaging and Artificial Intelligence
Radiology and Emergency Medicine

Background:

Traumatic brain injuries (TBIs) pose diagnostic challenges in emergency medicine.
Timely interpretation of medical images like CT and MRI is critical for patient outcomes.

Purpose of the Study:

To develop a novel AI-based approach for automatic radiology report generation in cranial trauma cases.
To improve diagnostic accuracy and support clinical decision-making for TBIs.

Main Methods:

Integration of an AC-BiFPN for multi-scale feature extraction and anomaly detection (e.g., intracranial hemorrhages).
Utilization of a Transformer architecture for coherent, contextually relevant report generation by modeling long-range dependencies.
Evaluation on the RSNA Intracranial Hemorrhage Detection dataset.

Main Results:

The proposed AI model outperforms traditional CNN-based models in diagnostic accuracy.
The model demonstrates superior performance in automatic radiology report generation.
The AI solution enhances diagnostic support for radiologists and serves as an educational tool for trainees.

Conclusions:

Combining advanced feature extraction (AC-BiFPN) with transformer-based text generation offers significant potential for TBI diagnosis.
The AI approach can improve clinical decision-making in high-pressure emergency medicine settings.
This technology can enhance the learning experience for physicians in training.