🤖 AI Summary
To address diagnostic delays caused by delayed interpretation of cranial trauma imaging in emergency settings, this study proposes an end-to-end AI system integrating AC-BiFPN and Transformer architectures for multi-scale feature extraction from CT/MRI scans and automatic generation of natural-language radiology reports. The framework uniquely co-optimizes lesion detection accuracy and report semantic coherence in the cranial trauma domain: AC-BiFPN enhances multi-scale lesion localization, while the Transformer captures long-range semantic dependencies to produce structured, clinically interpretable reports. Evaluated on the RSNA Intracranial Hemorrhage dataset, the model achieves significantly higher diagnostic accuracy and report quality compared to conventional CNN-based approaches. This work advances emergency department efficiency and provides an interpretable, deployable solution for clinical decision support and medical education.
📝 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.