Machine learning-based segmentation and risk modeling for real-time prediction of major arterial bleeding after pelvic fractures

项目摘要/摘要：骨盆骨折后动脉出血是导致骨盆骨折的主要可逆性原因

PROJECT SUMMARY/ABSTRACT: Arterial hemorrhage after pelvic fractures is a leading reversible cause of death after blunt trauma. Prediction of arterial bleeding risk is difficult, and currently determined using subjective criteria, often based on qualitative results of admission computed tomography (CT). Segmented hematoma and contrast extravasation (CE) volumes predict need for angioembolization, major transfusion, and mortality but cannot be applied in real-time. The ill-defined multi-focal nature of pelvic hematomas and CE prevents reliable estimation using diameter-based measurements. Dr. Dreizin is a trauma radiologist at the University of Maryland School of Medicine. His early work has focused on improving the speed and reliability of volumetric analysis of pelvic hematomas using semi-automated techniques, and derivation of a logistic regression-based prediction tool for major arterial injury after pelvic fractures. Dr. Dreizin’s goal for this four- year K08 mentored career development award proposal is to gain the skills needed to 1) implement deep learning architectures for automated hematoma volume segmentation and 2) develop computational models for outcome prediction after pelvic trauma. These tools could greatly improve the speed and accuracy of clinical decision making in the setting of life-threatening traumatic pelvic bleeding. Fully convolutional neural networks (FCNs) have emerged as the most robust and scalable method for automated medical image segmentation. Intuitive software platforms for training FCN implementations and generating multivariable machine learning models have been developed in the Python programming environment. The training objectives and research activities of this proposal are necessary to provide Dr. Dreizin with new skills and practical experience in Python programming, deep learning software, and computational modeling software. By understanding the principles and computational infrastructure behind modern machine learning, Dr. Dreizin will be able to train and validate state-of-the-art algorithms independently and effectively lead a team of researchers in this area. To achieve his goals, Dr. Dreizin has assembled a multidisciplinary team of mentors, advisors, and collaborators with world-leading expertise in computer vision in medical imaging, probability theory, data science, and comparative effectiveness research. Dr. Dreizin will focus on two specific aims. In Aim 1, he will train and validate deep learning architectures for segmentation of traumatic pelvic hematomas and CE by computing the Dice metric, time effort, and correlation with clinical outcomes. In Aim 2, he will generate and test quantitative models for predicting major arterial bleeding after pelvic trauma based on a rich multi-label dataset of segmented features. The training and pilot data will be necessary for Dr. Dreizin’s long- term goal of research independence and R01 support to develop automated segmentation algorithms for the spectrum of clinically important imaging features after pelvic trauma, as well as fully automated multivariable clinical prediction tools with potential for translation to industry and as an FDA-cleared product.