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Cheng CT, Ooyang CH, Kang SC, Liao CH. Applications of Deep Learning in Trauma Radiology: A Narrative Review. Biomed J 2024:100743. [PMID: 38679199 DOI: 10.1016/j.bj.2024.100743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 03/26/2024] [Accepted: 04/24/2024] [Indexed: 05/01/2024] Open
Abstract
Diagnostic imaging is essential in modern trauma care for initial evaluation and identifying injuries requiring intervention. Deep learning (DL) has become mainstream in medical image analysis and has shown promising efficacy for classification, segmentation, and lesion detection. This narrative review provides the fundamental concepts for developing DL algorithms in trauma imaging and presents an overview of current progress 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, enhancing model explainability and transparency to build clinician trust, and integrating multimodal data to provide more meaningful insights into traumatic injuries. Though some commercial artificial intelligence products are Food and Drug Administration-approved for clinical use in the trauma field, adoption remains limited, highlighting the need for multi-disciplinary teams to engineer practical, real-world solutions. Overall, DL shows immense potential to improve the efficiency and accuracy of trauma imaging, but thoughtful development and validation are critical to ensure these technologies positively impact patient care.
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Affiliation(s)
- Chi-Tung Cheng
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou, Chang Gung University, Taoyuan Taiwan
| | - Chun-Hsiang Ooyang
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou, Chang Gung University, Taoyuan Taiwan
| | - Shih-Ching Kang
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou, Chang Gung University, Taoyuan Taiwan.
| | - Chien-Hung Liao
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou, Chang Gung University, Taoyuan Taiwan
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2
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Holland L, Hernandez Torres SI, Snider EJ. Using AI Segmentation Models to Improve Foreign Body Detection and Triage from Ultrasound Images. Bioengineering (Basel) 2024; 11:128. [PMID: 38391614 PMCID: PMC10886314 DOI: 10.3390/bioengineering11020128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 01/20/2024] [Accepted: 01/26/2024] [Indexed: 02/24/2024] Open
Abstract
Medical imaging can be a critical tool for triaging casualties in trauma situations. In remote or military medicine scenarios, triage is essential for identifying how to use limited resources or prioritize evacuation for the most serious cases. Ultrasound imaging, while portable and often available near the point of injury, can only be used for triage if images are properly acquired, interpreted, and objectively triage scored. Here, we detail how AI segmentation models can be used for improving image interpretation and objective triage evaluation for a medical application focused on foreign bodies embedded in tissues at variable distances from critical neurovascular features. Ultrasound images previously collected in a tissue phantom with or without neurovascular features were labeled with ground truth masks. These image sets were used to train two different segmentation AI frameworks: YOLOv7 and U-Net segmentation models. Overall, both approaches were successful in identifying shrapnel in the image set, with U-Net outperforming YOLOv7 for single-class segmentation. Both segmentation models were also evaluated with a more complex image set containing shrapnel, artery, vein, and nerve features. YOLOv7 obtained higher precision scores across multiple classes whereas U-Net achieved higher recall scores. Using each AI model, a triage distance metric was adapted to measure the proximity of shrapnel to the nearest neurovascular feature, with U-Net more closely mirroring the triage distances measured from ground truth labels. Overall, the segmentation AI models were successful in detecting shrapnel in ultrasound images and could allow for improved injury triage in emergency medicine scenarios.
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Affiliation(s)
- Lawrence Holland
- Organ Support and Automation Technologies Group, U.S. Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA
| | - Sofia I Hernandez Torres
- Organ Support and Automation Technologies Group, U.S. Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA
| | - Eric J Snider
- Organ Support and Automation Technologies Group, U.S. Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA
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3
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Huang L, Lin Y, Cao P, Zou X, Qin Q, Lin Z, Liang F, Li Z. Automated detection and segmentation of pleural effusion on ultrasound images using an Attention U-net. J Appl Clin Med Phys 2024; 25:e14231. [PMID: 38088928 PMCID: PMC10795456 DOI: 10.1002/acm2.14231] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 11/19/2023] [Accepted: 11/21/2023] [Indexed: 01/19/2024] Open
Abstract
BACKGROUND Ultrasonic for detecting and evaluating pleural effusion is an essential part of the Extended Focused Assessment with Sonography in Trauma (E-FAST) in emergencies. Our study aimed to develop an Artificial Intelligence (AI) diagnostic model that automatically identifies and segments pleural effusion areas on ultrasonography. METHODS An Attention U-net and a U-net model were used to detect and segment pleural effusion on ultrasound images of 848 subjects through fully supervised learning. Sensitivity, specificity, precision, accuracy, F1 score, the receiver operating characteristic (ROC) curve, and the area under the curve (AUC) were used to assess the model's effectiveness in classifying the data. The dice coefficient was used to evaluate the segmentation performance of the model. RESULTS In 10 random tests, the Attention U-net and U-net 's average sensitivity of 97% demonstrated that the pleural effusion was well detectable. The Attention U-net performed better at identifying negative images than the U-net, which had an average specificity of 91% compared to 86% for the U-net. Additionally, the Attention U-net was more accurate in predicting the pleural effusion region because its average dice coefficient was 0.86 as opposed to the U-net's average dice coefficient of 0.82. CONCLUSIONS The Attention U-net showed excellent performance in detecting and segmenting pleural effusion on ultrasonic images, which is expected to enhance the operation and application of E-FAST in clinical work.
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Affiliation(s)
- Libing Huang
- Department of UltrasoundShenzhen Second People's HospitalThe First Affiliated Hospital of Shenzhen UniversityShenzhenChina
- Shenzhen University School of MedicineShenzhenChina
| | - Yingying Lin
- Department of Diagnostic RadiologyThe University of Hong KongHong KongChina
| | - Peng Cao
- Department of Diagnostic RadiologyThe University of Hong KongHong KongChina
| | - Xia Zou
- Department of UltrasoundShenzhen Second People's HospitalThe First Affiliated Hospital of Shenzhen UniversityShenzhenChina
| | - Qian Qin
- Shenzhen University School of MedicineShenzhenChina
| | - Zhanye Lin
- Department of UltrasoundLonggang District People's Hospital of ShenzhenShenzhenChina
| | - Fengting Liang
- Department of UltrasoundShenzhen Second People's HospitalThe First Affiliated Hospital of Shenzhen UniversityShenzhenChina
| | - Zhengyi Li
- Department of UltrasoundShenzhen Second People's HospitalThe First Affiliated Hospital of Shenzhen UniversityShenzhenChina
- Shenzhen University School of MedicineShenzhenChina
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4
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Leo MM, Potter IY, Zahiri M, Vaziri A, Jung CF, Feldman JA. Using Deep Learning to Detect the Presence and Location of Hemoperitoneum on the Focused Assessment with Sonography in Trauma (FAST) Examination in Adults. J Digit Imaging 2023; 36:2035-2050. [PMID: 37286904 PMCID: PMC10501965 DOI: 10.1007/s10278-023-00845-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 04/13/2023] [Accepted: 05/04/2023] [Indexed: 06/09/2023] Open
Abstract
Abdominal ultrasonography has become an integral component of the evaluation of trauma patients. Internal hemorrhage can be rapidly diagnosed by finding free fluid with point-of-care ultrasound (POCUS) and expedite decisions to perform lifesaving interventions. However, the widespread clinical application of ultrasound is limited by the expertise required for image interpretation. This study aimed to develop a deep learning algorithm to identify the presence and location of hemoperitoneum on POCUS to assist novice clinicians in accurate interpretation of the Focused Assessment with Sonography in Trauma (FAST) exam. We analyzed right upper quadrant (RUQ) FAST exams obtained from 94 adult patients (44 confirmed hemoperitoneum) using the YoloV3 object detection algorithm. Exams were partitioned via fivefold stratified sampling for training, validation, and hold-out testing. We assessed each exam image-by-image using YoloV3 and determined hemoperitoneum presence for the exam using the detection with highest confidence score. We determined the detection threshold as the score that maximizes the geometric mean of sensitivity and specificity over the validation set. The algorithm had 95% sensitivity, 94% specificity, 95% accuracy, and 97% AUC over the test set, significantly outperforming three recent methods. The algorithm also exhibited strength in localization, while the detected box sizes varied with a 56% IOU averaged over positive cases. Image processing demonstrated only 57-ms latency, which is adequate for real-time use at the bedside. These results suggest that a deep learning algorithm can rapidly and accurately identify the presence and location of free fluid in the RUQ of the FAST exam in adult patients with hemoperitoneum.
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Affiliation(s)
- Megan M Leo
- Boston University School of Medicine, Boston, MA, USA.
- Department of Emergency Medicine, Boston Medical Center, BCD Building, 800 Harrison Ave1St Floor, Boston, MA, 02118, USA.
| | | | | | | | - Christine F Jung
- Division of Emergency Ultrasound, Department of Emergency Medicine, John H. Stroger Jr. Hospital of Cook County, Chicago, IL, USA
- Department of Emergency Medicine, Chicago Medical School of Rosalind, Franklin University of Medical Sciences, Chicago, IL, USA
- Department of Emergency Medicine, Rush Medical College, Chicago, IL, USA
| | - James A Feldman
- Boston University School of Medicine, Boston, MA, USA
- Department of Emergency Medicine, Boston Medical Center, BCD Building, 800 Harrison Ave1St Floor, Boston, MA, 02118, USA
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5
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Fallahdizcheh A, Laroia S, Wang C. Sequential Active Contour Based on Morphological-Driven Thresholding for Ultrasound Image Segmentation of Ascites. IEEE J Biomed Health Inform 2023; 27:4305-4316. [PMID: 37335794 DOI: 10.1109/jbhi.2023.3286869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/21/2023]
Abstract
Paracentesis is a high-demanding and routine operation, which has great potentials and benefits if semi-autonomous procedures can be developed. One of the most important techniques that facilitate semi-autonomous paracentesis is to segment the ascites from ultrasound images accurately and efficiently. The ascites, however, is usually with significantly different shapes and noise among different patients, and its shape/size changes dynamically during the paracentesis. This makes most of existing image segmentation methods either time consuming or inaccurate for segmenting ascites from its background. In this article, we propose a two-stage active contour method to facilitate accurate and efficient segmentation of ascites. First, a morphological-driven thresholding method is developed to locate the initial contour of the ascites automatically. Then, the identified initial contour is fed into a novel sequential active contour algorithm to segment the ascites from background accurately. The proposed method is tested and compared with state-of-the-art active contour methods on over 100 real ultrasound images of ascites, and the results show the superiority of our method in both accuracy and time efficiency.
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6
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Yıldız Potter İ, Leo MM, Vaziri A, Feldman JA. Automated detection and localization of pericardial effusion from point-of-care cardiac ultrasound examination. Med Biol Eng Comput 2023; 61:1947-1959. [PMID: 37243852 PMCID: PMC11194944 DOI: 10.1007/s11517-023-02855-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 05/17/2023] [Indexed: 05/29/2023]
Abstract
Focused Assessment with Sonography in Trauma (FAST) exam is the standard of care for pericardial and abdominal free fluid detection in emergency medicine. Despite its life saving potential, FAST is underutilized due to requiring clinicians with appropriate training and practice. To aid ultrasound interpretation, the role of artificial intelligence has been studied, while leaving room for improvement in localization information and computation time. The purpose of this study was to develop and test a deep learning approach to rapidly and accurately identify both the presence and location of pericardial effusion on point-of-care ultrasound (POCUS) exams. Each cardiac POCUS exam is analyzed image-by-image via the state-of-the-art YoloV3 algorithm and pericardial effusion presence is determined from the most confident detection. We evaluate our approach over a dataset of POCUS exams (cardiac component of FAST and ultrasound), comprising 37 cases with pericardial effusion and 39 negative controls. Our algorithm attains 92% specificity and 89% sensitivity in pericardial effusion identification, outperforming existing deep learning approaches, and localizes pericardial effusion by 51% Intersection Over Union with ground-truth annotations. Moreover, image processing demonstrates only 57 ms latency. Experimental results demonstrate the feasibility of rapid and accurate pericardial effusion detection from POCUS exams for physician overread.
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Affiliation(s)
| | - Megan M Leo
- School of Medicine, Boston University (BU), Chobanian & Avedisian, Boston, MA, USA
- Department of Emergency Medicine, Boston Medical Center (BMC), Boston, MA, USA
| | | | - James A Feldman
- School of Medicine, Boston University (BU), Chobanian & Avedisian, Boston, MA, USA
- Department of Emergency Medicine, Boston Medical Center (BMC), Boston, MA, USA
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7
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Hernandez-Torres SI, Hennessey RP, Snider EJ. Performance Comparison of Object Detection Networks for Shrapnel Identification in Ultrasound Images. Bioengineering (Basel) 2023; 10:807. [PMID: 37508834 PMCID: PMC10376403 DOI: 10.3390/bioengineering10070807] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Revised: 06/20/2023] [Accepted: 06/30/2023] [Indexed: 07/30/2023] Open
Abstract
Ultrasound imaging is a critical tool for triaging and diagnosing subjects but only if images can be properly interpreted. Unfortunately, in remote or military medicine situations, the expertise to interpret images can be lacking. Machine-learning image interpretation models that are explainable to the end user and deployable in real time with ultrasound equipment have the potential to solve this problem. We have previously shown how a YOLOv3 (You Only Look Once) object detection algorithm can be used for tracking shrapnel, artery, vein, and nerve fiber bundle features in a tissue phantom. However, real-time implementation of an object detection model requires optimizing model inference time. Here, we compare the performance of five different object detection deep-learning models with varying architectures and trainable parameters to determine which model is most suitable for this shrapnel-tracking ultrasound image application. We used a dataset of more than 16,000 ultrasound images from gelatin tissue phantoms containing artery, vein, nerve fiber, and shrapnel features for training and evaluating each model. Every object detection model surpassed 0.85 mean average precision except for the detection transformer model. Overall, the YOLOv7tiny model had the higher mean average precision and quickest inference time, making it the obvious model choice for this ultrasound imaging application. Other object detection models were overfitting the data as was determined by lower testing performance compared with higher training performance. In summary, the YOLOv7tiny object detection model had the best mean average precision and inference time and was selected as optimal for this application. Next steps will implement this object detection algorithm for real-time applications, an important next step in translating AI models for emergency and military medicine.
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Affiliation(s)
| | - Ryan P Hennessey
- U.S. Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA
| | - Eric J Snider
- U.S. Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA
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8
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Obaid AM, Turki A, Bellaaj H, Ksantini M, AlTaee A, Alaerjan A. Detection of Gallbladder Disease Types Using Deep Learning: An Informative Medical Method. Diagnostics (Basel) 2023; 13:diagnostics13101744. [PMID: 37238227 DOI: 10.3390/diagnostics13101744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 05/09/2023] [Accepted: 05/10/2023] [Indexed: 05/28/2023] Open
Abstract
Nowadays, despite all the conducted research and the provided efforts in advancing the healthcare sector, there is a strong need to rapidly and efficiently diagnose various diseases. The complexity of some disease mechanisms on one side and the dramatic life-saving potential on the other side raise big challenges for the development of tools for the early detection and diagnosis of diseases. Deep learning (DL), an area of artificial intelligence (AI), can be an informative medical tomography method that can aid in the early diagnosis of gallbladder (GB) disease based on ultrasound images (UI). Many researchers considered the classification of only one disease of the GB. In this work, we successfully managed to apply a deep neural network (DNN)-based classification model to a rich built database in order to detect nine diseases at once and to determine the type of disease using UI. In the first step, we built a balanced database composed of 10,692 UI of the GB organ from 1782 patients. These images were carefully collected from three hospitals over roughly three years and then classified by professionals. In the second step, we preprocessed and enhanced the dataset images in order to achieve the segmentation step. Finally, we applied and then compared four DNN models to analyze and classify these images in order to detect nine GB disease types. All the models produced good results in detecting GB diseases; the best was the MobileNet model, with an accuracy of 98.35%.
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Affiliation(s)
- Ahmed Mahdi Obaid
- CEMLab, National School of Electronics and Telecommunications of Sfax, University of Sfax, Sfax 3029, Tunisia
| | - Amina Turki
- CEMLab, National Engineering School of Sfax, University of Sfax, Sfax 3029, Tunisia
| | - Hatem Bellaaj
- ReDCAD, National Engineering School of Sfax, University of Sfax, Sfax 3029, Tunisia
| | - Mohamed Ksantini
- CEMLab, National Engineering School of Sfax, University of Sfax, Sfax 3029, Tunisia
| | | | - Alaa Alaerjan
- College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia
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Fanni SC, Greco G, Rossi S, Aghakhanyan G, Masala S, Scaglione M, Tonerini M, Neri E. Role of artificial intelligence in oncologic emergencies: a narrative review. EXPLORATION OF TARGETED ANTI-TUMOR THERAPY 2023; 4:344-354. [PMID: 37205309 PMCID: PMC10185441 DOI: 10.37349/etat.2023.00138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Accepted: 02/13/2023] [Indexed: 05/21/2023] Open
Abstract
Oncologic emergencies are a wide spectrum of oncologic conditions caused directly by malignancies or their treatment. Oncologic emergencies may be classified according to the underlying physiopathology in metabolic, hematologic, and structural conditions. In the latter, radiologists have a pivotal role, through an accurate diagnosis useful to provide optimal patient care. Structural conditions may involve the central nervous system, thorax, or abdomen, and emergency radiologists have to know the characteristics imaging findings of each one of them. The number of oncologic emergencies is growing due to the increased incidence of malignancies in the general population and also to the improved survival of these patients thanks to the advances in cancer treatment. Artificial intelligence (AI) could be a solution to assist emergency radiologists with this rapidly increasing workload. To our knowledge, AI applications in the setting of the oncologic emergency are mostly underexplored, probably due to the relatively low number of oncologic emergencies and the difficulty in training algorithms. However, cancer emergencies are defined by the cause and not by a specific pattern of radiological symptoms and signs. Therefore, it can be expected that AI algorithms developed for the detection of these emergencies in the non-oncological field can be transferred to the clinical setting of oncologic emergency. In this review, a craniocaudal approach was followed and central nervous system, thoracic, and abdominal oncologic emergencies have been addressed regarding the AI applications reported in literature. Among the central nervous system emergencies, AI applications have been reported for brain herniation and spinal cord compression. In the thoracic district the addressed emergencies were pulmonary embolism, cardiac tamponade and pneumothorax. Pneumothorax was the most frequently described application for AI, to improve sensibility and to reduce the time-to-diagnosis. Finally, regarding abdominal emergencies, AI applications for abdominal hemorrhage, intestinal obstruction, intestinal perforation, and intestinal intussusception have been described.
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Affiliation(s)
- Salvatore Claudio Fanni
- Department of Translational Research, Academic Radiology, University of Pisa, 56126 Pisa, Italy
| | - Giuseppe Greco
- Department of Translational Research, Academic Radiology, University of Pisa, 56126 Pisa, Italy
| | - Sara Rossi
- Department of Translational Research, Academic Radiology, University of Pisa, 56126 Pisa, Italy
| | - Gayane Aghakhanyan
- Department of Translational Research, Academic Radiology, University of Pisa, 56126 Pisa, Italy
| | - Salvatore Masala
- Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100 Sassari, Italy
| | - Mariano Scaglione
- Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100 Sassari, Italy
| | - Michele Tonerini
- Department of Surgical, Medical, Molecular and Critical Area Pathology, University of Pisa, 56126 Pisa, Italy
| | - Emanuele Neri
- Department of Translational Research, Academic Radiology, University of Pisa, 56126 Pisa, Italy
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Boice EN, Hernandez Torres SI, Knowlton ZJ, Berard D, Gonzalez JM, Avital G, Snider EJ. Training Ultrasound Image Classification Deep-Learning Algorithms for Pneumothorax Detection Using a Synthetic Tissue Phantom Apparatus. J Imaging 2022; 8:jimaging8090249. [PMID: 36135414 PMCID: PMC9502699 DOI: 10.3390/jimaging8090249] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Revised: 08/20/2022] [Accepted: 09/07/2022] [Indexed: 11/17/2022] Open
Abstract
Ultrasound (US) imaging is a critical tool in emergency and military medicine because of its portability and immediate nature. However, proper image interpretation requires skill, limiting its utility in remote applications for conditions such as pneumothorax (PTX) which requires rapid intervention. Artificial intelligence has the potential to automate ultrasound image analysis for various pathophysiological conditions. Training models require large data sets and a means of troubleshooting in real-time for ultrasound integration deployment, and they also require large animal models or clinical testing. Here, we detail the development of a dynamic synthetic tissue phantom model for PTX and its use in training image classification algorithms. The model comprises a synthetic gelatin phantom cast in a custom 3D-printed rib mold and a lung mimicking phantom. When compared to PTX images acquired in swine, images from the phantom were similar in both PTX negative and positive mimicking scenarios. We then used a deep learning image classification algorithm, which we previously developed for shrapnel detection, to accurately predict the presence of PTX in swine images by only training on phantom image sets, highlighting the utility for a tissue phantom for AI applications.
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Affiliation(s)
- Emily N. Boice
- U.S. Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA
| | | | - Zechariah J. Knowlton
- U.S. Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA
| | - David Berard
- U.S. Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA
| | - Jose M. Gonzalez
- U.S. Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA
| | - Guy Avital
- U.S. Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA
- Trauma & Combat Medicine Branch, Surgeon General’s Headquarters, Israel Defense Forces, Ramat-Gan 52620, Israel
- Division of Anesthesia, Intensive Care & Pain Management, Tel-Aviv Sourasky Medical Center, Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv 64239, Israel
| | - Eric J. Snider
- U.S. Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA
- Correspondence: ; Tel.: +210-539-8721
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