DEEP LERNING FOR AUTOMATED DIAGNOSIS IN POLYTRAUMA: SIGNAL ANALYSIS AND CLINICAL PERSPECTIVES

Abstract

Polytrauma, particularly when associated with traumatic brain injury (TBI), remains a critical challenge in emergency care due to the urgency of diagnosis and the limitations of conventional methods. Invasive monitoring, neuroimaging, and neurological scoring systems such as the Glasgow Coma Scale are often time-consuming, resource-intensive, or unavailable during pre-hospital triage. Recent advances in deep learning (DL), combined with physiological signal analysis, offer promising opportunities for automated, non-invasive diagnostics capable of supporting real-time clinical decision-making.

This review explores recent approaches to using DL for interpreting biomedical signals to detect TBI. Based on seven key studies published between 2022 and 2024, we analyze models built on convolutional neural networks (CNNs), long short-term memory (LSTM), EEGNet, U-Net, and transfer learning techniques. Several models achieve AUROC values exceeding 0.90. Systems such as BioscoreNet and aICP demonstrate practical applications for detecting intracranial hypertension and assessing injury severity using passive or bedside-acquired signals, including EEG, speech, extracranial waveforms, and mobile sensor data.

Special attention is given to model interpretability (e.g., Grad-CAM visualizations), multisensory data fusion, and the integration of AI tools into mobile triage and monitoring platforms. The review also outlines current challenges, including signal variability, small datasets, and the need for clinical standardization. Ultimately, DL-based signal analysis represents a significant step toward scalable, accessible, and efficient diagnostic solutions in polytrauma management. Further clinical validation will be essential to enable seamless integration of these technologies into routine emergency medical practice.