Jing Zhang

Objective: Trauma is a leading cause of death worldwide, with many incidents resulting in hemorrhage before the patient reaches the hospital. Despite advances in trauma care, the majority of deaths occur within the first three hours of hospital admission, offering a very limited window for effective intervention. Unfortunately, a significant increase in mortality from hemorrhagic trauma is primarily due to delays in hemorrhage control. Therefore, we propose a machine learning model to predict the need for urgent hemorrhage intervention. Methods: This study developed and validated an XGBoost-based machine learning model using data from the National Trauma Data Bank (NTDB) from 2017 to 2019. It focuses on demographic and clinical data from the initial hours following trauma for model training and validation, aiming to predict whether trauma patients require urgent hemorrhage intervention. Results: The XGBoost model demonstrated superior performance across multiple datasets, achieving an AUROC of 0.872 on the training set, 0.869 on the internal validation set, and 0.875 on the external validation set. The model also showed high sensitivity (77.8% on the external validation set) and specificity (82.1% on the external validation set), with an accuracy exceeding 81% across all datasets, highlighting its high reliability for clinical applications. Conclusions: Our study shows that the XGBoost model effectively predicts urgent hemorrhage interventions using data from the National Trauma Data Bank (NTDB). It outperforms other machine learning algorithms in accuracy and robustness across various datasets. These results highlight machine learning’s potential to improve emergency responses and decision-making in trauma care.

The rapid evolution of computer vision has elevated object detection to a central task within the field. In medicine, automated lesion detection has the potential to greatly improve diagnostic efficiency for clinicians. The extraordinary success of deep learning in computer vision has motivated researchers globally to apply these advancements to medical image analysis. Deep learning techniques have demonstrated superior performance in medical image classification, detection, segmentation, registration, and retrieval compared to traditional methods. Among these, the YOLO (You Only Look Once) series of algorithms stands out for their exceptional speed and accuracy, making them a popular choice for medical image detection. This paper presents the underlying principles and structure of the classic YOLO algorithms, reviews their current applications in medical image detection, addresses existing challenges, and explores future directions for the application of YOLO in this domain.