AI-powered imaging: deep learning in trauma radiology.

Abstract

Diagnostic imaging plays a critical role in contemporary trauma care for preliminary assessment and detection of injuries that need intervention. Deep learning (DL) has gained mainstream application in medical image analysis and has demonstrated excellent efficacy for classification, segmentation, and lesion detection. This narrative review offers the underlying principles on creating DL algorithms in trauma imaging and offers an overview of recent developments in each modality. DL has been applied to detect free fluid on Focused Assessment with Sonography for Trauma (FAST), traumatic findings on chest and pelvic X-rays, and computed tomography (CT) scans, identify intracranial hemorrhage on head CT, detect vertebral fractures, and identify injuries to organs like the spleen, liver, and lungs on abdominal and chest CT. Future directions involve expanding dataset size and diversity through federated learning, improving the model explainability and transparency, which also would increase the clinicians' trust in the model, and in multimodal data to offer more meaningful insights into traumatic injuries. Although some commercial AI products are approved by the Food and Drug Administration for clinical use in the trauma field, yet the adoption is quite low, which calls for multi-disciplinary teams to engineer practical, real-world solutions. In general, DL demonstrates vast potential to enhance the effectiveness and accuracy of trauma imaging, but careful development and verification are essential to guarantee these technologies benefit patient care.