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Cheng CT, Lin HH, Hsu CP, Chen HW, Huang JF, Hsieh CH, Fu CY, Chung IF, Liao CH. Deep Learning for Automated Detection and Localization of Traumatic Abdominal Solid Organ Injuries on CT Scans. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:1113-1123. [PMID: 38366294 PMCID: PMC11169164 DOI: 10.1007/s10278-024-01038-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Revised: 01/31/2024] [Accepted: 02/01/2024] [Indexed: 02/18/2024]
Abstract
Computed tomography (CT) is the most commonly used diagnostic modality for blunt abdominal trauma (BAT), significantly influencing management approaches. Deep learning models (DLMs) have shown great promise in enhancing various aspects of clinical practice. There is limited literature available on the use of DLMs specifically for trauma image evaluation. In this study, we developed a DLM aimed at detecting solid organ injuries to assist medical professionals in rapidly identifying life-threatening injuries. The study enrolled patients from a single trauma center who received abdominal CT scans between 2008 and 2017. Patients with spleen, liver, or kidney injury were categorized as the solid organ injury group, while others were considered negative cases. Only images acquired from the trauma center were enrolled. A subset of images acquired in the last year was designated as the test set, and the remaining images were utilized to train and validate the detection models. The performance of each model was assessed using metrics such as the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value based on the best Youden index operating point. The study developed the models using 1302 (87%) scans for training and tested them on 194 (13%) scans. The spleen injury model demonstrated an accuracy of 0.938 and a specificity of 0.952. The accuracy and specificity of the liver injury model were reported as 0.820 and 0.847, respectively. The kidney injury model showed an accuracy of 0.959 and a specificity of 0.989. We developed a DLM that can automate the detection of solid organ injuries by abdominal CT scans with acceptable diagnostic accuracy. It cannot replace the role of clinicians, but we can expect it to be a potential tool to accelerate the process of therapeutic decisions for trauma 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
| | - Hou-Hsien Lin
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou, Chang Gung University, Taoyuan, Taiwan
| | - Chih-Po Hsu
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou, Chang Gung University, Taoyuan, Taiwan
| | - Huan-Wu Chen
- Department of Medical Imaging & Intervention, Chang Gung Memorial Hospital, Linkou, Chang Gung University, Taoyuan, Taiwan
| | - Jen-Fu Huang
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou, Chang Gung University, Taoyuan, Taiwan
| | - Chi-Hsun Hsieh
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou, Chang Gung University, Taoyuan, Taiwan
| | - Chih-Yuan Fu
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou, Chang Gung University, Taoyuan, Taiwan
| | - I-Fang Chung
- Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Chien-Hung Liao
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou, Chang Gung University, Taoyuan, Taiwan.
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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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Sarkar N, Kumagai M, Meyr S, Pothapragada S, Unberath M, Li G, Ahmed SR, Smith EB, Davis MA, Khatri GD, Agrawal A, Delproposto ZS, Chen H, Caballero CG, Dreizin D. An ASER AI/ML expert panel formative user research study for an interpretable interactive splenic AAST grading graphical user interface prototype. Emerg Radiol 2024; 31:167-178. [PMID: 38302827 PMCID: PMC11257379 DOI: 10.1007/s10140-024-02202-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 01/08/2024] [Indexed: 02/03/2024]
Abstract
PURPOSE The AAST Organ Injury Scale is widely adopted for splenic injury severity but suffers from only moderate inter-rater agreement. This work assesses SpleenPro, a prototype interactive explainable artificial intelligence/machine learning (AI/ML) diagnostic aid to support AAST grading, for effects on radiologist dwell time, agreement, clinical utility, and user acceptance. METHODS Two trauma radiology ad hoc expert panelists independently performed timed AAST grading on 76 admission CT studies with blunt splenic injury, first without AI/ML assistance, and after a 2-month washout period and randomization, with AI/ML assistance. To evaluate user acceptance, three versions of the SpleenPro user interface with increasing explainability were presented to four independent expert panelists with four example cases each. A structured interview consisting of Likert scales and free responses was conducted, with specific questions regarding dimensions of diagnostic utility (DU); mental support (MS); effort, workload, and frustration (EWF); trust and reliability (TR); and likelihood of future use (LFU). RESULTS SpleenPro significantly decreased interpretation times for both raters. Weighted Cohen's kappa increased from 0.53 to 0.70 with AI/ML assistance. During user acceptance interviews, increasing explainability was associated with improvement in Likert scores for MS, EWF, TR, and LFU. Expert panelists indicated the need for a combined early notification and grading functionality, PACS integration, and report autopopulation to improve DU. CONCLUSIONS SpleenPro was useful for improving objectivity of AAST grading and increasing mental support. Formative user research identified generalizable concepts including the need for a combined detection and grading pipeline and integration with the clinical workflow.
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Affiliation(s)
- Nathan Sarkar
- University of Maryland School of Medicine, 655 W. Baltimore Street, Baltimore, MD, 21201, USA
| | - Mitsuo Kumagai
- University of Maryland College Park, 4603 Calvert Rd, College Park, MD, 20740, USA
| | - Samantha Meyr
- University of Maryland College Park, 4603 Calvert Rd, College Park, MD, 20740, USA
| | - Sriya Pothapragada
- University of Maryland College Park, 4603 Calvert Rd, College Park, MD, 20740, USA
| | - Mathias Unberath
- Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD, 21218, USA
| | - Guang Li
- University of Maryland School of Medicine, 655 W. Baltimore Street, Baltimore, MD, 21201, USA
| | - Sagheer Rauf Ahmed
- University of Maryland School of Medicine, 655 W. Baltimore Street, Baltimore, MD, 21201, USA
- R Adams Cowley Shock Trauma Center, 22 S Greene St, Baltimore, MD, 21201, USA
| | - Elana Beth Smith
- University of Maryland School of Medicine, 655 W. Baltimore Street, Baltimore, MD, 21201, USA
- R Adams Cowley Shock Trauma Center, 22 S Greene St, Baltimore, MD, 21201, USA
| | | | | | - Anjali Agrawal
- Teleradiology Solutions, 22 Lianfair Road Unit 6, Ardmore, PA, 19003, USA
| | | | - Haomin Chen
- Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD, 21218, USA
| | | | - David Dreizin
- University of Maryland School of Medicine, 655 W. Baltimore Street, Baltimore, MD, 21201, USA.
- R Adams Cowley Shock Trauma Center, 22 S Greene St, Baltimore, MD, 21201, USA.
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Angthong C, Rungrattanawilai N, Pundee C. Artificial intelligence assistance in deciding management strategies for polytrauma and trauma patients. POLISH JOURNAL OF SURGERY 2023; 96:114-117. [PMID: 38348980 DOI: 10.5604/01.3001.0053.9857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/15/2024]
Abstract
<b><br>Introduction:</b> Artificial intelligence (AI) is an emerging technology with vast potential for use in several fields of medicine. However, little is known about the application of AI in treatment decisions for patients with polytrauma. In this systematic review, we investigated the benefits and performance of AI in predicting the management of patients with polytrauma and trauma.</br> <b><br>Methods:</b> This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Studies were extracted from the PubMed and Google Scholar databases from their inception until November 2022, using the search terms "Artificial intelligence," "polytrauma," and "decision." Seventeen articles were identified and screened for eligibility. Animal studies, review articles, systematic reviews, meta-analyses, and studies that did not involve polytrauma or severe trauma management decisions were excluded. Eight studies were eligible for final review.</br> <b><br>Results:</b> Eight studies focusing on patients with trauma, including two on military trauma, were included. The AI applications were mainly implemented for predictions and/or decisions on shock, bleeding, and blood transfusion. Few studies predicted death/survival. The identification of trauma patients using AI was proposed in a previous study. The overall performance of AI was good (six studies), excellent (one study), and acceptable (one study).</br> <b><br>Discussion:</b> AI demonstrated satisfactory performance in decision-making and management prediction in patients with polytrauma/severe trauma, especially in situations of shock/bleeding.</br> <b><br>Importance:</b> The present study serves as a basis for further research to develop practical AI applications for the management of patients with trauma.</br>.
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Affiliation(s)
- Chayanin Angthong
- Division of Digital and Innovative Medicine, Faculty of Medicine, King Mongkut's Institute of Technology Ladkrabang (KMITL), Bangkok, Thailand
| | | | - Chaiyapruk Pundee
- Department of Orthopaedics, Samitivej Srinakarin Hospital, Bangkok Dusit Medical Services (BDMS), Bangkok, Thailand
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Zhang L, LaBelle W, Unberath M, Chen H, Hu J, Li G, Dreizin D. A vendor-agnostic, PACS integrated, and DICOM-compatible software-server pipeline for testing segmentation algorithms within the clinical radiology workflow. Front Med (Lausanne) 2023; 10:1241570. [PMID: 37954555 PMCID: PMC10637622 DOI: 10.3389/fmed.2023.1241570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 10/09/2023] [Indexed: 11/14/2023] Open
Abstract
Background Reproducible approaches are needed to bring AI/ML for medical image analysis closer to the bedside. Investigators wishing to shadow test cross-sectional medical imaging segmentation algorithms on new studies in real-time will benefit from simple tools that integrate PACS with on-premises image processing, allowing visualization of DICOM-compatible segmentation results and volumetric data at the radiology workstation. Purpose In this work, we develop and release a simple containerized and easily deployable pipeline for shadow testing of segmentation algorithms within the clinical workflow. Methods Our end-to-end automated pipeline has two major components- 1. A router/listener and anonymizer and an OHIF web viewer backstopped by a DCM4CHEE DICOM query/retrieve archive deployed in the virtual infrastructure of our secure hospital intranet, and 2. An on-premises single GPU workstation host for DICOM/NIfTI conversion steps, and image processing. DICOM images are visualized in OHIF along with their segmentation masks and associated volumetry measurements (in mL) using DICOM SEG and structured report (SR) elements. Since nnU-net has emerged as a widely-used out-of-the-box method for training segmentation models with state-of-the-art performance, feasibility of our pipleine is demonstrated by recording clock times for a traumatic pelvic hematoma nnU-net model. Results Mean total clock time from PACS send by user to completion of transfer to the DCM4CHEE query/retrieve archive was 5 min 32 s (± SD of 1 min 26 s). This compares favorably to the report turnaround times for whole-body CT exams, which often exceed 30 min, and illustrates feasibility in the clinical setting where quantitative results would be expected prior to report sign-off. Inference times accounted for most of the total clock time, ranging from 2 min 41 s to 8 min 27 s. All other virtual and on-premises host steps combined ranged from a minimum of 34 s to a maximum of 48 s. Conclusion The software worked seamlessly with an existing PACS and could be used for deployment of DL models within the radiology workflow for prospective testing on newly scanned patients. Once configured, the pipeline is executed through one command using a single shell script. The code is made publicly available through an open-source license at "https://github.com/vastc/," and includes a readme file providing pipeline config instructions for host names, series filter, other parameters, and citation instructions for this work.
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Affiliation(s)
- Lei Zhang
- School of Medicine, University of Maryland, Baltimore, MD, United States
| | - Wayne LaBelle
- School of Medicine, University of Maryland, Baltimore, MD, United States
| | - Mathias Unberath
- Department of Computer Science, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Haomin Chen
- Department of Computer Science, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Jiazhen Hu
- Department of Computer Science, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Guang Li
- School of Medicine, University of Maryland, Baltimore, MD, United States
| | - David Dreizin
- School of Medicine, University of Maryland, Baltimore, MD, United States
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Sarkar N, Zhang L, Campbell P, Liang Y, Li G, Khedr M, Khetan U, Dreizin D. Pulmonary contusion: automated deep learning-based quantitative visualization. Emerg Radiol 2023; 30:435-441. [PMID: 37318609 PMCID: PMC10527354 DOI: 10.1007/s10140-023-02149-2] [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: 04/11/2023] [Accepted: 06/07/2023] [Indexed: 06/16/2023]
Abstract
PURPOSE Rapid automated CT volumetry of pulmonary contusion may predict progression to Acute Respiratory Distress Syndrome (ARDS) and help guide early clinical management in at-risk trauma patients. This study aims to train and validate state-of-the-art deep learning models to quantify pulmonary contusion as a percentage of total lung volume (Lung Contusion Index, or auto-LCI) and assess the relationship between auto-LCI and relevant clinical outcomes. METHODS 302 adult patients (age ≥ 18) with pulmonary contusion were retrospectively identified from reports between 2016 and 2021. nnU-Net was trained on manual contusion and whole-lung segmentations. Point-of-care candidate variables for multivariate regression included oxygen saturation, heart rate, and systolic blood pressure on admission. Logistic regression was used to assess ARDS risk, and Cox proportional hazards models were used to determine differences in ICU length of stay and mechanical ventilation time. RESULTS Mean Volume Similarity Index and mean Dice scores were 0.82 and 0.67. Interclass correlation coefficient and Pearson r between ground-truth and predicted volumes were 0.90 and 0.91. 38 (14%) patients developed ARDS. In bivariate analysis, auto-LCI was associated with ARDS (p < 0.001), ICU admission (p < 0.001), and need for mechanical ventilation (p < 0.001). In multivariate analyses, auto-LCI was associated with ARDS (p = 0.04), longer length of stay in the ICU (p = 0.02) and longer time on mechanical ventilation (p = 0.04). AUC of multivariate regression to predict ARDS using auto-LCI and clinical variables was 0.70 while AUC using auto-LCI alone was 0.68. CONCLUSION Increasing auto-LCI values corresponded with increased risk of ARDS, longer ICU admissions, and longer periods of mechanical ventilation.
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Affiliation(s)
- Nathan Sarkar
- Department of Diagnostic Radiology and Nuclear Medicine, R Adams Cowley Shock Trauma Center, University of Maryland School of Medicine, 22 S Greene St, Baltimore, MD, 21201, USA
| | - Lei Zhang
- Department of Diagnostic Radiology and Nuclear Medicine, R Adams Cowley Shock Trauma Center, University of Maryland School of Medicine, 22 S Greene St, Baltimore, MD, 21201, USA
| | - Peter Campbell
- Department of Diagnostic Radiology and Nuclear Medicine, R Adams Cowley Shock Trauma Center, University of Maryland School of Medicine, 22 S Greene St, Baltimore, MD, 21201, USA
| | - Yuanyuan Liang
- Department of Epidemiology & Public Health, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Guang Li
- Department of Diagnostic Radiology and Nuclear Medicine, R Adams Cowley Shock Trauma Center, University of Maryland School of Medicine, 22 S Greene St, Baltimore, MD, 21201, USA
| | - Mustafa Khedr
- Department of Diagnostic Radiology and Nuclear Medicine, R Adams Cowley Shock Trauma Center, University of Maryland School of Medicine, 22 S Greene St, Baltimore, MD, 21201, USA
| | - Udit Khetan
- Department of Diagnostic Radiology and Nuclear Medicine, R Adams Cowley Shock Trauma Center, University of Maryland School of Medicine, 22 S Greene St, Baltimore, MD, 21201, USA
| | - David Dreizin
- Department of Diagnostic Radiology and Nuclear Medicine, R Adams Cowley Shock Trauma Center, University of Maryland School of Medicine, 22 S Greene St, Baltimore, MD, 21201, USA.
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Dreizin D, Zhang L, Sarkar N, Bodanapally UK, Li G, Hu J, Chen H, Khedr M, Khetan U, Campbell P, Unberath M. Accelerating voxelwise annotation of cross-sectional imaging through AI collaborative labeling with quality assurance and bias mitigation. FRONTIERS IN RADIOLOGY 2023; 3:1202412. [PMID: 37485306 PMCID: PMC10362988 DOI: 10.3389/fradi.2023.1202412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Accepted: 06/22/2023] [Indexed: 07/25/2023]
Abstract
Background precision-medicine quantitative tools for cross-sectional imaging require painstaking labeling of targets that vary considerably in volume, prohibiting scaling of data annotation efforts and supervised training to large datasets for robust and generalizable clinical performance. A straight-forward time-saving strategy involves manual editing of AI-generated labels, which we call AI-collaborative labeling (AICL). Factors affecting the efficacy and utility of such an approach are unknown. Reduction in time effort is not well documented. Further, edited AI labels may be prone to automation bias. Purpose In this pilot, using a cohort of CTs with intracavitary hemorrhage, we evaluate both time savings and AICL label quality and propose criteria that must be met for using AICL annotations as a high-throughput, high-quality ground truth. Methods 57 CT scans of patients with traumatic intracavitary hemorrhage were included. No participant recruited for this study had previously interpreted the scans. nnU-net models trained on small existing datasets for each feature (hemothorax/hemoperitoneum/pelvic hematoma; n = 77-253) were used in inference. Two common scenarios served as baseline comparison- de novo expert manual labeling, and expert edits of trained staff labels. Parameters included time effort and image quality graded by a blinded independent expert using a 9-point scale. The observer also attempted to discriminate AICL and expert labels in a random subset (n = 18). Data were compared with ANOVA and post-hoc paired signed rank tests with Bonferroni correction. Results AICL reduced time effort 2.8-fold compared to staff label editing, and 8.7-fold compared to expert labeling (corrected p < 0.0006). Mean Likert grades for AICL (8.4, SD:0.6) were significantly higher than for expert labels (7.8, SD:0.9) and edited staff labels (7.7, SD:0.8) (corrected p < 0.0006). The independent observer failed to correctly discriminate AI and human labels. Conclusion For our use case and annotators, AICL facilitates rapid large-scale curation of high-quality ground truth. The proposed quality control regime can be employed by other investigators prior to embarking on AICL for segmentation tasks in large datasets.
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Affiliation(s)
- David Dreizin
- Department of Diagnostic Radiology and Nuclear Medicine, School of Medicine, University of Maryland, Baltimore, MD, United States
| | - Lei Zhang
- Department of Diagnostic Radiology and Nuclear Medicine, School of Medicine, University of Maryland, Baltimore, MD, United States
| | - Nathan Sarkar
- Department of Diagnostic Radiology and Nuclear Medicine, School of Medicine, University of Maryland, Baltimore, MD, United States
| | - Uttam K. Bodanapally
- Department of Diagnostic Radiology and Nuclear Medicine, School of Medicine, University of Maryland, Baltimore, MD, United States
| | - Guang Li
- Department of Diagnostic Radiology and Nuclear Medicine, School of Medicine, University of Maryland, Baltimore, MD, United States
| | - Jiazhen Hu
- Johns Hopkins University, Baltimore, MD, United States
| | - Haomin Chen
- Johns Hopkins University, Baltimore, MD, United States
| | - Mustafa Khedr
- Department of Diagnostic Radiology and Nuclear Medicine, School of Medicine, University of Maryland, Baltimore, MD, United States
| | - Udit Khetan
- Department of Diagnostic Radiology and Nuclear Medicine, School of Medicine, University of Maryland, Baltimore, MD, United States
| | - Peter Campbell
- Department of Diagnostic Radiology and Nuclear Medicine, School of Medicine, University of Maryland, Baltimore, MD, United States
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Agrawal A, Khatri GD, Khurana B, Sodickson AD, Liang Y, Dreizin D. A survey of ASER members on artificial intelligence in emergency radiology: trends, perceptions, and expectations. Emerg Radiol 2023; 30:267-277. [PMID: 36913061 PMCID: PMC10362990 DOI: 10.1007/s10140-023-02121-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Accepted: 02/28/2023] [Indexed: 03/14/2023]
Abstract
PURPOSE There is a growing body of diagnostic performance studies for emergency radiology-related artificial intelligence/machine learning (AI/ML) tools; however, little is known about user preferences, concerns, experiences, expectations, and the degree of penetration of AI tools in emergency radiology. Our aim is to conduct a survey of the current trends, perceptions, and expectations regarding AI among American Society of Emergency Radiology (ASER) members. METHODS An anonymous and voluntary online survey questionnaire was e-mailed to all ASER members, followed by two reminder e-mails. A descriptive analysis of the data was conducted, and results summarized. RESULTS A total of 113 members responded (response rate 12%). The majority were attending radiologists (90%) with greater than 10 years' experience (80%) and from an academic practice (65%). Most (55%) reported use of commercial AI CAD tools in their practice. Workflow prioritization based on pathology detection, injury or disease severity grading and classification, quantitative visualization, and auto-population of structured reports were identified as high-value tasks. Respondents overwhelmingly indicated a need for explainable and verifiable tools (87%) and the need for transparency in the development process (80%). Most respondents did not feel that AI would reduce the need for emergency radiologists in the next two decades (72%) or diminish interest in fellowship programs (58%). Negative perceptions pertained to potential for automation bias (23%), over-diagnosis (16%), poor generalizability (15%), negative impact on training (11%), and impediments to workflow (10%). CONCLUSION ASER member respondents are in general optimistic about the impact of AI in the practice of emergency radiology and its impact on the popularity of emergency radiology as a subspecialty. The majority expect to see transparent and explainable AI models with the radiologist as the decision-maker.
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Affiliation(s)
- Anjali Agrawal
- New Delhi operations, Teleradiology Solutions, Delhi, India
| | - Garvit D Khatri
- Nuclear Medicine, Department of Radiology, University of Washington School of Medicine, Seattle, WA, USA
| | - Bharti Khurana
- Emergency Radiology, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Aaron D Sodickson
- Emergency Radiology, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Yuanyuan Liang
- Epidemiology & Public Health, University of Maryland School of Medicine, Baltimore, MD, USA
| | - David Dreizin
- Trauma and Emergency Radiology, Department of Diagnostic Radiology and Nuclear Medicine, R Adams Cowley Shock Trauma Center, University of Maryland School of Medicine, Baltimore, MD, USA.
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Dreizin D. The American Society of Emergency Radiology (ASER) AI/ML expert panel: inception, mandate, work products, and goals. Emerg Radiol 2023; 30:279-283. [PMID: 37071272 DOI: 10.1007/s10140-023-02135-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 04/11/2023] [Indexed: 04/19/2023]
Affiliation(s)
- David Dreizin
- Emergency and Trauma Imaging, Department of Diagnostic Radiology and Nuclear Medicine, R Adams Cowley Shock Trauma , Center, University of Maryland School of Medicine, Baltimore, MD, USA.
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Dreizin D, Staziaki PV, Khatri GD, Beckmann NM, Feng Z, Liang Y, Delproposto ZS, Klug M, Spann JS, Sarkar N, Fu Y. Artificial intelligence CAD tools in trauma imaging: a scoping review from the American Society of Emergency Radiology (ASER) AI/ML Expert Panel. Emerg Radiol 2023; 30:251-265. [PMID: 36917287 PMCID: PMC10640925 DOI: 10.1007/s10140-023-02120-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 02/27/2023] [Indexed: 03/16/2023]
Abstract
BACKGROUND AI/ML CAD tools can potentially improve outcomes in the high-stakes, high-volume model of trauma radiology. No prior scoping review has been undertaken to comprehensively assess tools in this subspecialty. PURPOSE To map the evolution and current state of trauma radiology CAD tools along key dimensions of technology readiness. METHODS Following a search of databases, abstract screening, and full-text document review, CAD tool maturity was charted using elements of data curation, performance validation, outcomes research, explainability, user acceptance, and funding patterns. Descriptive statistics were used to illustrate key trends. RESULTS A total of 4052 records were screened, and 233 full-text articles were selected for content analysis. Twenty-one papers described FDA-approved commercial tools, and 212 reported algorithm prototypes. Works ranged from foundational research to multi-reader multi-case trials with heterogeneous external data. Scalable convolutional neural network-based implementations increased steeply after 2016 and were used in all commercial products; however, options for explainability were narrow. Of FDA-approved tools, 9/10 performed detection tasks. Dataset sizes ranged from < 100 to > 500,000 patients, and commercialization coincided with public dataset availability. Cross-sectional torso datasets were uniformly small. Data curation methods with ground truth labeling by independent readers were uncommon. No papers assessed user acceptance, and no method included human-computer interaction. The USA and China had the highest research output and frequency of research funding. CONCLUSIONS Trauma imaging CAD tools are likely to improve patient care but are currently in an early stage of maturity, with few FDA-approved products for a limited number of uses. The scarcity of high-quality annotated data remains a major barrier.
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Affiliation(s)
- David Dreizin
- Department of Diagnostic Radiology and Nuclear Medicine, R Adams Cowley Shock Trauma Center, University of Maryland School of Medicine, Baltimore, MD, USA.
| | - Pedro V Staziaki
- Cardiothoracic Imaging, Department of Radiology, Larner College of Medicine, University of Vermont, Burlington, VT, USA
| | - Garvit D Khatri
- Department of Radiology, University of Washington School of Medicine, Seattle, WA, USA
| | - Nicholas M Beckmann
- Memorial Hermann Orthopedic & Spine Hospital, McGovern Medical School at UTHealth, Houston, TX, USA
| | - Zhaoyong Feng
- Epidemiology & Public Health, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Yuanyuan Liang
- Epidemiology & Public Health, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Zachary S Delproposto
- Division of Emergency Radiology, Department of Radiology, University of Michigan, Ann Arbor, MI, USA
| | | | - J Stephen Spann
- Department of Radiology, University of Alabama at Birmingham Heersink School of Medicine, Birmingham, AL, USA
| | - Nathan Sarkar
- University of Maryland School of Medicine, Baltimore, MD, USA
| | - Yunting Fu
- Health Sciences and Human Services Library, University of Maryland, Baltimore, Baltimore, MD, USA
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11
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Zhang L, LaBelle W, Unberath M, Chen H, Hu J, Li G, Dreizin D. A vendor-agnostic, PACS integrated, and DICOMcompatible software-server pipeline for testing segmentation algorithms within the clinical radiology workflow. RESEARCH SQUARE 2023:rs.3.rs-2837634. [PMID: 37163064 PMCID: PMC10168465 DOI: 10.21203/rs.3.rs-2837634/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Background Reproducible approaches are needed to bring AI/ML for medical image analysis closer to the bedside. Investigators wishing to shadow test cross-sectional medical imaging segmentation algorithms on new studies in real-time will benefit from simple tools that integrate PACS with on-premises image processing, allowing visualization of DICOM-compatible segmentation results and volumetric data at the radiology workstation. Purpose In this work, we develop and release a simple containerized and easily deployable pipeline for shadow testing of segmentation algorithms within the clinical workflow. Methods Our end-to-end automated pipeline has two major components-1. a router/listener and anonymizer and an OHIF web viewer backstopped by a DCM4CHEE DICOM query/retrieve archive deployed in the virtual infrastructure of our secure hospital intranet, and 2. An on-premises single GPU workstation host for DICOM/NIfTI conversion steps, and image processing. DICOM images are visualized in OHIF along with their segmentation masks and associated volumetry measurements (in mL) using DICOM SEG and structured report (SR) elements. Feasibility is demonstrated by recording clock times for a traumatic pelvic hematoma cascaded nnU-net model. Results Mean total clock time from PACS send by user to completion of transfer to the DCM4CHEE query/retrieve archive was 5 minutes 32 seconds (+/- SD of 1 min 26 sec). This compares favorably to the report turnaround times for whole-body CT exams, which often exceed 30 minutes. Inference times accounted for most of the total clock time, ranging from 2 minutes 41 seconds to 8 minutes 27 seconds. All other virtual and on-premises host steps combined ranged from a minimum of 34 seconds to a maximum of 48 seconds. Conclusion The software worked seamlessly with an existing PACS and could be used for deployment of DL models within the radiology workflow for prospective testing on newly scanned patients. Once configured, the pipeline is executed through one command using a single shell script. The code is made publicly available through an open-source license at "https://github.com/vastc/", and includes a readme file providing pipeline config instructions for host names, series filter, other parameters, and citation instructions for this work.
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Affiliation(s)
| | | | | | | | | | - Guang Li
- University of Maryland, Baltimore
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12
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Manuel Román-Belmonte J, De la Corte-Rodríguez H, Adriana Rodríguez-Damiani B, Carlos Rodríguez-Merchán E. Artificial Intelligence in Musculoskeletal Conditions. ARTIF INTELL 2023. [DOI: 10.5772/intechopen.110696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2023]
Abstract
Artificial intelligence (AI) refers to computer capabilities that resemble human intelligence. AI implies the ability to learn and perform tasks that have not been specifically programmed. Moreover, it is an iterative process involving the ability of computerized systems to capture information, transform it into knowledge, and process it to produce adaptive changes in the environment. A large labeled database is needed to train the AI system and generate a robust algorithm. Otherwise, the algorithm cannot be applied in a generalized way. AI can facilitate the interpretation and acquisition of radiological images. In addition, it can facilitate the detection of trauma injuries and assist in orthopedic and rehabilitative processes. The applications of AI in musculoskeletal conditions are promising and are likely to have a significant impact on the future management of these patients.
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13
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Predicting rare outcomes in abdominal wall reconstruction using image-based deep learning models. Surgery 2023; 173:748-755. [PMID: 36229252 DOI: 10.1016/j.surg.2022.06.048] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 06/04/2022] [Accepted: 06/27/2022] [Indexed: 11/05/2022]
Abstract
BACKGROUND Deep learning models with imbalanced data sets are a challenge in the fields of artificial intelligence and surgery. The aim of this study was to develop and compare deep learning models that predict rare but devastating postoperative complications after abdominal wall reconstruction. METHODS A prospectively maintained institutional database was used to identify abdominal wall reconstruction patients with preoperative computed tomography scans. Conventional deep learning models were developed using an 8-layer convolutional neural network and a 2-class training system (ie, learns negative and positive outcomes). Conventional deep learning models were compared to deep learning models that were developed using a generative adversarial network anomaly framework, which uses image augmentation and anomaly detection. The primary outcomes were receiver operating characteristic values for predicting mesh infection and pulmonary failure. RESULTS Computed tomography scans from 510 patients were used with a total of 10,004 images. Mesh infection and pulmonary failure occurred in 3.7% and 5.6% of patients, respectively. The conventional deep learning models were less effective than generative adversarial network anomaly for predicting mesh infection (receiver operating characteristic 0.61 vs 0.73, P < .01) and pulmonary failure (receiver operating characteristic 0.59 vs 0.70, P < .01). Although the conventional deep learning models had higher accuracies/specificities for predicting mesh infection (0.93 vs 0.78, P < .01/.96 vs .78, P < .01) and pulmonary failure (0.88 vs 0.68, P < .01/.92 vs .67, P < .01), they were substantially compromised by decreased model sensitivity (0.25 vs 0.68, P < .01/.27 vs .73, P < .01). CONCLUSION Compared to conventional deep learning models, generative adversarial network anomaly deep learning models showed improved performance on imbalanced data sets, predominantly by increasing model sensitivity. Understanding patients who are at risk for rare but devastating postoperative complications can improve risk stratification, resource utilization, and the consent process.
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Ota S, Takeuchi I, Hamada M, Fujita W, Muramatsu KI, Nagasawa H, Jitsuiki K, Ohsaka H, Ishikawa K, Mogami A, Yanagawa Y. Bladder deformity accompanied by pelvic fracture indirectly indicates clinical severity. Am J Emerg Med 2023; 67:108-111. [PMID: 36863261 DOI: 10.1016/j.ajem.2023.02.029] [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: 10/05/2022] [Revised: 02/13/2023] [Accepted: 02/22/2023] [Indexed: 02/27/2023] Open
Abstract
BACKGROUND That the bladder can be compressed by extraperitoneal hematoma induced by obstetrics and gynecologic diseases, is well known. However, there have been no reports on the clinical significance of compressed bladder induced by pelvic fracture (PF). We therefore retrospectively investigated the clinical features of compressed bladder induced by the PF. METHODS From January 2018 to December 2021, we performed a retrospective review of the hospital medical charts of all emergency outpatients who were treated by emergency physicians at the department of acute critical care medicine in our hospital, and who were diagnosed with PF based on computed tomography (CT) on arrival. The subjects were divided into two groups: the Deformity group, in which the bladder was compressed by extraperitoneal hematoma, and the Normal group. Variables were compared between the two groups. RESULTS During the investigation period, 147 patients with PF were enrolled as subjects. There were 44 patients in the Deformity group and 103 in the Normal group. There were no significant differences between the two groups with regard to sex, age, GCS, heart rate or final outcome. However, the average systolic blood pressure in the Deformity group was significantly lower, and the average respiratory rate, injury severity score, rate of unstable circulation, rate of transfusion and duration of hospitalization in the Deformity group were significantly greater in comparison to the Normal group. CONCLUSIONS The present study showed that bladder deformity induced by PF tended to be a poor physiological sign that was associated with severe anatomical abnormality, unstable circulation requiring transfusion, and long hospitalization. Accordingly, physicians should evaluate shape of bladder when treating PF.
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Affiliation(s)
- Soichiro Ota
- Department of Acute Critical Care Medicine, Shizuoka Hospital, Juntendo University, 1129 Nagaoka, Izunokuni City, Shizuoka 410-2295, Japan.
| | - Ikuto Takeuchi
- Department of Acute Critical Care Medicine, Shizuoka Hospital, Juntendo University, 1129 Nagaoka, Izunokuni City, Shizuoka 410-2295, Japan.
| | - Michika Hamada
- Department of Acute Critical Care Medicine, Shizuoka Hospital, Juntendo University, 1129 Nagaoka, Izunokuni City, Shizuoka 410-2295, Japan.
| | - Wataru Fujita
- Department of Acute Critical Care Medicine, Shizuoka Hospital, Juntendo University, 1129 Nagaoka, Izunokuni City, Shizuoka 410-2295, Japan.
| | - Ken-Ichi Muramatsu
- Department of Acute Critical Care Medicine, Shizuoka Hospital, Juntendo University, 1129 Nagaoka, Izunokuni City, Shizuoka 410-2295, Japan.
| | - Hiroki Nagasawa
- Department of Acute Critical Care Medicine, Shizuoka Hospital, Juntendo University, 1129 Nagaoka, Izunokuni City, Shizuoka 410-2295, Japan.
| | - Kei Jitsuiki
- Department of Acute Critical Care Medicine, Shizuoka Hospital, Juntendo University, 1129 Nagaoka, Izunokuni City, Shizuoka 410-2295, Japan.
| | - Hiromichi Ohsaka
- Department of Acute Critical Care Medicine, Shizuoka Hospital, Juntendo University, 1129 Nagaoka, Izunokuni City, Shizuoka 410-2295, Japan.
| | - Kouhei Ishikawa
- Department of Acute Critical Care Medicine, Shizuoka Hospital, Juntendo University, 1129 Nagaoka, Izunokuni City, Shizuoka 410-2295, Japan.
| | - Atsuhiko Mogami
- Department of Acute Critical Care Medicine, Shizuoka Hospital, Juntendo University, 1129 Nagaoka, Izunokuni City, Shizuoka 410-2295, Japan.
| | - Youichi Yanagawa
- Department of Acute Critical Care Medicine, Shizuoka Hospital, Juntendo University, 1129 Nagaoka, Izunokuni City, Shizuoka 410-2295, Japan.
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Chen H, Unberath M, Dreizin D. Toward automated interpretable AAST grading for blunt splenic injury. Emerg Radiol 2023; 30:41-50. [PMID: 36371579 DOI: 10.1007/s10140-022-02099-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 11/04/2022] [Indexed: 11/13/2022]
Abstract
BACKGROUND The American Association for the Surgery of Trauma (AAST) splenic organ injury scale (OIS) is the most frequently used CT-based grading system for blunt splenic trauma. However, reported inter-rater agreement is modest, and an algorithm that objectively automates grading based on transparent and verifiable criteria could serve as a high-trust diagnostic aid. PURPOSE To pilot the development of an automated interpretable multi-stage deep learning-based system to predict AAST grade from admission trauma CT. METHODS Our pipeline includes 4 parts: (1) automated splenic localization, (2) Faster R-CNN-based detection of pseudoaneurysms (PSA) and active bleeds (AB), (3) nnU-Net segmentation and quantification of splenic parenchymal disruption (SPD), and (4) a directed graph that infers AAST grades from detection and segmentation results. Training and validation is performed on a dataset of adult patients (age ≥ 18) with voxelwise labeling, consensus AAST grading, and hemorrhage-related outcome data (n = 174). RESULTS AAST classification agreement (weighted κ) between automated and consensus AAST grades was substantial (0.79). High-grade (IV and V) injuries were predicted with accuracy, positive predictive value, and negative predictive value of 92%, 95%, and 89%. The area under the curve for predicting hemorrhage control intervention was comparable between expert consensus and automated AAST grading (0.83 vs 0.88). The mean combined inference time for the pipeline was 96.9 s. CONCLUSIONS The results of our method were rapid and verifiable, with high agreement between automated and expert consensus grades. Diagnosis of high-grade lesions and prediction of hemorrhage control intervention produced accurate results in adult patients.
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Affiliation(s)
- Haomin Chen
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Mathias Unberath
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - David Dreizin
- Emergency and Trauma Imaging, Department of Diagnostic Radiology and Nuclear Medicine, R Adams Cowley Shock Trauma Center, University of Maryland School of Medicine, Baltimore, MD, USA.
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Dreizin D, Champ K, Dattwyler M, Bodanapally U, Smith EB, Li G, Singh R, Wang Z, Liang Y. Blunt splenic injury in adults: Association between volumetric quantitative CT parameters and intervention. J Trauma Acute Care Surg 2023; 94:125-132. [PMID: 35546417 PMCID: PMC9652480 DOI: 10.1097/ta.0000000000003684] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
BACKGROUND. Several ordinal grading systems are employed in deciding whether to perform angioembolization or splenectomy following blunt splenic injury. The 2018 AAST Organ Injury Scale (OIS) incorporates vascular lesions but not hemoperitoneum, which is considered in the Thompson classifier. Granular and verifiable quantitative measurements of these features may have a future role in facilitating objective decision-making. PURPOSE. To compare performance of CT volumetry-based quantitative modeling to the 1994 and 2018 AAST OIS and Thompson classifier for the following endpoints: decision to perform splenectomy (SPY), and the composite of SPY or angioembolization (AE) MATERIALS AND METHODS. Adult BSI patients (age ≥ 18 years) scanned with dual-phase CT prior to intervention at a single level I trauma center from 2017-2019 were included in this retrospective study (n=174). Scoring using 2018 AAST, 1994 AAST, and Thompson systems was performed retrospectively by two radiologists and arbitrated by a third. Endpoints included 1. SPY and 2. The composite of SPY or AE. Logistic regression models were developed from segmented active bleed, contained vascular lesion, splenic parenchymal disruption, and hemoperitoneum volumes. AUCs for ordinal systems and volumetric models were compared. RESULTS. Forty-seven BSI patients (27%) underwent SPY, and 87 patients (50%) underwent SPY or AE. Quantitative model AUCs (0.85- SPY, 0.82-composite) were not significantly different from 2018 AAST AUCs (0.81, 0.88, p=0.66, 0.14) for both endpoints, and were significantly improved over Thompson scoring (0.76, p=0.02; 0.77, p=0.04). CONCLUSION: Quantitative CT volumetry can be used to model intervention for BSI with accuracy comparable to 2018 AAST scoring and significantly higher than Thompson scoring. Study Type: Prognostic Level of Evidence: IV CT volumetry of blunt splenic injury-related features predicts splenectomy and angioembolization in adults and identifies clinically important target features for computer vision and automation research.
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Affiliation(s)
- David Dreizin
- From the Department of Diagnostic Radiology and Nuclear Medicine, R Adams Cowley Shock Trauma Center, University of Maryland School of Medicine (D.D., M.D., U.B., E.B.S., G.L., Z.W., K.C., R.S.); and Department of Epidemiology and Public Health (Y.L.), University of Maryland School of Medicine, Baltimore, Maryland
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17
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Dreizin D, Nixon B, Hu J, Albert B, Yan C, Yang G, Chen H, Liang Y, Kim N, Jeudy J, Li G, Smith EB, Unberath M. A pilot study of deep learning-based CT volumetry for traumatic hemothorax. Emerg Radiol 2022; 29:995-1002. [PMID: 35971025 DOI: 10.1007/s10140-022-02087-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 08/08/2022] [Indexed: 12/17/2022]
Abstract
PURPOSE We employ nnU-Net, a state-of-the-art self-configuring deep learning-based semantic segmentation method for quantitative visualization of hemothorax (HTX) in trauma patients, and assess performance using a combination of overlap and volume-based metrics. The accuracy of hemothorax volumes for predicting a composite of hemorrhage-related outcomes - massive transfusion (MT) and in-hospital mortality (IHM) not related to traumatic brain injury - is assessed and compared to subjective expert consensus grading by an experienced chest and emergency radiologist. MATERIALS AND METHODS The study included manually labeled admission chest CTs from 77 consecutive adult patients with non-negligible (≥ 50 mL) traumatic HTX between 2016 and 2018 from one trauma center. DL results of ensembled nnU-Net were determined from fivefold cross-validation and compared to individual 2D, 3D, and cascaded 3D nnU-Net results using the Dice similarity coefficient (DSC) and volume similarity index. Pearson's r, intraclass correlation coefficient (ICC), and mean bias were also determined for the best performing model. Manual and automated hemothorax volumes and subjective hemothorax volume grades were analyzed as predictors of MT and IHM using AUC comparison. Volume cut-offs yielding sensitivity or specificity ≥ 90% were determined from ROC analysis. RESULTS Ensembled nnU-Net achieved a mean DSC of 0.75 (SD: ± 0.12), and mean volume similarity of 0.91 (SD: ± 0.10), Pearson r of 0.93, and ICC of 0.92. Mean overmeasurement bias was only 1.7 mL despite a range of manual HTX volumes from 35 to 1503 mL (median: 178 mL). AUC of automated volumes for the composite outcome was 0.74 (95%CI: 0.58-0.91), compared to 0.76 (95%CI: 0.58-0.93) for manual volumes, and 0.76 (95%CI: 0.62-0.90) for consensus expert grading (p = 0.93). Automated volume cut-offs of 77 mL and 334 mL predicted the outcome with 93% sensitivity and 90% specificity respectively. CONCLUSION Automated HTX volumetry had high method validity, yielded interpretable visual results, and had similar performance for the hemorrhage-related outcomes assessed compared to manual volumes and expert consensus grading. The results suggest promising avenues for automated HTX volumetry in research and clinical care.
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Affiliation(s)
- David Dreizin
- Department of Diagnostic Radiology and Nuclear Medicine, R Adams Cowley Shock Trauma Center, University of Maryland School of Medicine, 22 S Greene St, Baltimore, MD, 21201, USA.
| | - Bryan Nixon
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Jiazhen Hu
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Benjamin Albert
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Chang Yan
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Gary Yang
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Haomin Chen
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Yuanyuan Liang
- Epidemiology & Public Health, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Nahye Kim
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Jean Jeudy
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Guang Li
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Elana B Smith
- Department of Diagnostic Radiology and Nuclear Medicine, R Adams Cowley Shock Trauma Center, University of Maryland School of Medicine, 22 S Greene St, Baltimore, MD, 21201, USA
| | - Mathias Unberath
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
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18
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Sciubba DM, Khanna N, Pennington Z, Singh RK. VIBe Scale: Validation of the Intraoperative Bleeding Severity Scale by Spine Surgeons. Int J Spine Surg 2022; 16:8304. [PMID: 35831060 PMCID: PMC9421269 DOI: 10.14444/8304] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
BACKGROUND The Validated Intraoperative Bleeding Scale (VIBe Scale) was initially validated with surgeons who operate on cardiothoracic, abdominal, and pelvic cavities and fulfilled criteria for a clinician-reported scale. However, there is a need for a tool to aid in intraoperative blood management during spine surgeries. The purpose of the present study was to establish the reliability and consistency of the VIBe Scale as a tool for spine surgeons to assess intraoperative bleeding. METHODS Orthopedic (n = 16) and neurological (n = 9) spine surgeons scored videos depicting surgical bleeding and assessed the VIBe Scale's relevance and clarity. Inter- and intraobserver agreement (Kendall's W) were calculated for all surgeons and pooled with responses from the original study to establish agreement across specialties. RESULTS All of the spine surgeons indicated that the scale was clinically relevant for evaluating hemostasis and could be implemented in a clinical study. Twenty-two spine surgeons (88%) reported that the scale represents the range of bleeding site sizes and severities expected in their practice. Twenty-four spine surgeons (96%) indicated that the scale would be useful in communicating bleeding severity with other members of the surgical team. Interobserver agreement was acceptable (0.79) for orthopedic specialists, appreciable (0.88) for neurological specialists, and appreciable (0.88) for the combined specialists. Intraobserver agreement was excellent for orthopedic (0.91) and neurological (0.91) spine surgeons and excellent (0.96) for the combined specialists. CONCLUSIONS The results highlight the reliability of the VIBe Scale and potential utility for quantifying intraoperative blood loss in spine surgery. LEVEL OF EVIDENCE: 3 CLINICAL RELEVANCE The VIBe Scale may be useful for evaluating the efficacy of untested intraoperative hemostatic agents and for comparing the relative efficacy of 2 or more analogous agents. It may also prove useful for intraoperative staff by quantifying ongoing intraoperative blood loss and correlating losses with the potential transfusion and intraoperative hemostatic agent requirements.
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Affiliation(s)
- Daniel M Sciubba
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Neurosurgery, Zucker School of Medicine at Hofstra, Long Island Jewish Medical Center and North Shore University Hospital, Northwell Health, Manhasset, NY, USA
| | - Nitin Khanna
- Department of Orthopedics, Indiana University School of Medicine, Munster, USA
| | - Zach Pennington
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Neurosurgery, Mayo Clinic, Rochester, MN, USA
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Yu TJ, Bangura A, Bodanapally U, Nascone J, O'Toole R, Liang Y, Dreizin D. Dual-Energy CT and Cinematic Rendering to Improve Assessment of Pelvic Fracture Instability. Radiology 2022; 304:353-362. [PMID: 35438566 PMCID: PMC9340240 DOI: 10.1148/radiol.211679] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Background Grading of pelvic fracture instability is challenging in patients with pelvic binders. Dual-energy CT (DECT) and cinematic rendering can provide ancillary information regarding osteoligamentous integrity, but the utility of these tools remains unknown. Purpose To assess the added diagnostic value of DECT and cinematic rendering, with respect to single-energy CT (SECT), for discriminating any instability and translational instability in patients with pelvic binders. Materials and Methods In this retrospective analysis, consecutive adult patients (age ≥18 years) were stabilized with pelvic binders and scanned in dual-energy mode using a 128-section CT scanner at one level I trauma center between August 2016 and January 2019. Young-Burgess grading by orthopedists served as the reference standard. Two radiologists performed blinded consensus grading with the Young-Burgess system in three reading sessions (session 1, SECT; session 2, SECT plus DECT; session 3, SECT plus DECT and cinematic rendering). Lateral compression (LC) type 1 (LC-1) and anteroposterior compression (APC) type 1 (APC-1) injuries were considered stable; LC type 2 and APC type 2, rotationally unstable; and LC type 3, APC type 3, and vertical shear, translationally unstable. Diagnostic performance for any instability and translational instability was compared between reading sessions using the McNemar and DeLong tests. Radiologist agreement with the orthopedic reference standard was calculated with the weighted κ statistic. Results Fifty-four patients (mean age, 41 years ± 16 [SD]; 41 men) were analyzed. Diagnostic performance was greater with SECT plus DECT and cinematic rendering compared with SECT alone for any instability, with an area under the receiver operating characteristic curve (AUC) of 0.67 for SECT alone and 0.82 for SECT plus DECT and cinematic rendering (P = .04); for translational instability, the AUCs were 0.80 for SECT alone and 0.95 for SECT plus DECT and cinematic rendering (P = .01). For any instability, corresponding sensitivities were 61% (22 of 36 patients) for SECT alone and 86% (31 of 36 patients) for SECT plus DECT and cinematic rendering (P < .001). The corresponding specificities were 72% (13 of 18 patients) and 78% (14 of 18 patients), respectively (P > .99). Agreement (κ value) between radiologists and orthopedist reference standard improved from 0.44 to 0.76 for SECT versus the combination of SECT, DECT, and cinematic rendering. Conclusion Combined use of single-energy CT, dual-energy CT, and cinematic rendering improved instability assessment over that with single-energy CT alone. © RSNA, 2022 Online supplemental material is available for this article.
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Affiliation(s)
- Theresa J Yu
- From the Department of Diagnostic Radiology and Nuclear Medicine (T.J.Y., U.B., D.D.) and Division of Orthopaedic Traumatology (A.B., J.N., R.O.), R. Adams Cowley Shock Trauma Center, University of Maryland School of Medicine, 22 S Greene St, Baltimore, MD 21201; and Department of Epidemiology and Public Health, University of Maryland School of Medicine (Y.L.)
| | - Abdulai Bangura
- From the Department of Diagnostic Radiology and Nuclear Medicine (T.J.Y., U.B., D.D.) and Division of Orthopaedic Traumatology (A.B., J.N., R.O.), R. Adams Cowley Shock Trauma Center, University of Maryland School of Medicine, 22 S Greene St, Baltimore, MD 21201; and Department of Epidemiology and Public Health, University of Maryland School of Medicine (Y.L.)
| | - Uttam Bodanapally
- From the Department of Diagnostic Radiology and Nuclear Medicine (T.J.Y., U.B., D.D.) and Division of Orthopaedic Traumatology (A.B., J.N., R.O.), R. Adams Cowley Shock Trauma Center, University of Maryland School of Medicine, 22 S Greene St, Baltimore, MD 21201; and Department of Epidemiology and Public Health, University of Maryland School of Medicine (Y.L.)
| | - Jason Nascone
- From the Department of Diagnostic Radiology and Nuclear Medicine (T.J.Y., U.B., D.D.) and Division of Orthopaedic Traumatology (A.B., J.N., R.O.), R. Adams Cowley Shock Trauma Center, University of Maryland School of Medicine, 22 S Greene St, Baltimore, MD 21201; and Department of Epidemiology and Public Health, University of Maryland School of Medicine (Y.L.)
| | - Robert O'Toole
- From the Department of Diagnostic Radiology and Nuclear Medicine (T.J.Y., U.B., D.D.) and Division of Orthopaedic Traumatology (A.B., J.N., R.O.), R. Adams Cowley Shock Trauma Center, University of Maryland School of Medicine, 22 S Greene St, Baltimore, MD 21201; and Department of Epidemiology and Public Health, University of Maryland School of Medicine (Y.L.)
| | - Yuanyuan Liang
- From the Department of Diagnostic Radiology and Nuclear Medicine (T.J.Y., U.B., D.D.) and Division of Orthopaedic Traumatology (A.B., J.N., R.O.), R. Adams Cowley Shock Trauma Center, University of Maryland School of Medicine, 22 S Greene St, Baltimore, MD 21201; and Department of Epidemiology and Public Health, University of Maryland School of Medicine (Y.L.)
| | - David Dreizin
- From the Department of Diagnostic Radiology and Nuclear Medicine (T.J.Y., U.B., D.D.) and Division of Orthopaedic Traumatology (A.B., J.N., R.O.), R. Adams Cowley Shock Trauma Center, University of Maryland School of Medicine, 22 S Greene St, Baltimore, MD 21201; and Department of Epidemiology and Public Health, University of Maryland School of Medicine (Y.L.)
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Laur O, Wang B. Musculoskeletal trauma and artificial intelligence: current trends and projections. Skeletal Radiol 2022; 51:257-269. [PMID: 34089338 DOI: 10.1007/s00256-021-03824-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 05/13/2021] [Accepted: 05/18/2021] [Indexed: 02/02/2023]
Abstract
Musculoskeletal trauma accounts for a significant fraction of emergency department visits and patients seeking urgent care, with a high financial cost to society. Diagnostic imaging is indispensable in the workup and management of trauma patients. However, diagnostic imaging represents a complex multifaceted system, with many aspects of its workflow prone to inefficiencies or human error. Recent technological innovations in artificial intelligence and machine learning have shown promise to revolutionize our systems for providing medical care to patients. This review will provide a general overview of the current state of artificial intelligence and machine learning applications in different aspects of trauma imaging and provide a vision for how such applications could be leveraged to enhance our diagnostic imaging systems and optimize patient outcomes.
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Affiliation(s)
- Olga Laur
- Division of Musculoskeletal Radiology, Department of Radiology, NYU Langone Health, 301 East 17th Street, 6th Floor, New York, NY, 10003, USA
| | - Benjamin Wang
- Division of Musculoskeletal Radiology, Department of Radiology, NYU Langone Health, 301 East 17th Street, 6th Floor, New York, NY, 10003, USA.
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Dreizin D, Rosales R, Li G, Syed H, Chen R. Volumetric Markers of Body Composition May Improve Personalized Prediction of Major Arterial Bleeding After Pelvic Fracture: A Secondary Analysis of the Baltimore CT Prediction Model Cohort. Can Assoc Radiol J 2021; 72:854-861. [PMID: 32910695 PMCID: PMC8011455 DOI: 10.1177/0846537120952508] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
METHODS This work is a retrospective secondary analysis of a single institution cohort used in the development of the Baltimore CT prediction model. The cohort includes 115 consecutive patients that underwent admission contrast-enhanced CT of the abdomen and pelvis for blunt trauma with pelvic ring disruption followed by conventional angiography. Major arterial injury requiring angioembolization served as the outcome variable. Angioembolization was required in 73/115 patients (63% of the cohort). Average age was 46.9 years (±SD 20.4). Body composition measurements were determined as 2-dimensional (2D) or 3-dimensional (3D) parameters and included mid-L3 trabecular bone attenuation, abdominal visceral fat area or volume, and percent muscle fat fraction (as a marker of sarcopenia) measured using segmentation and histogram analysis. RESULTS Models incorporating 2D (Model B) or 3D markers (model C) of body composition showed improvement over the original Baltimore model (model A) in all parameters of performance, quality, and fit (area under the receiver-operating curve [AUC], Akaike information criterion, Brier score, Hosmer-Lemeshow test, and adjusted-R2). Area under the receiver-operating curve increased from 0.83 (A), to 0.86 (B), and 0.88 (C). The greatest improvement was seen with 3D parameters. CONCLUSION Once automated, quantitative visualization tools providing "free" 3D body composition information can be expected to improve personalized precision diagnostics, outcome prediction, and decision support in patients with bleeding pelvic fractures.
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Affiliation(s)
- David Dreizin
- Department of Diagnostic Radiology and Nuclear Medicine, R Adams Cowley Shock Trauma Center, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Remberto Rosales
- Department of Diagnostic Radiology and Nuclear Medicine, R Adams Cowley Shock Trauma Center, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Guang Li
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Hassan Syed
- Department of Diagnostic Radiology and Nuclear Medicine, R Adams Cowley Shock Trauma Center, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Rong Chen
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
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22
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Elhage SA, Deerenberg EB, Ayuso SA, Murphy KJ, Shao JM, Kercher KW, Smart NJ, Fischer JP, Augenstein VA, Colavita PD, Heniford BT. Development and Validation of Image-Based Deep Learning Models to Predict Surgical Complexity and Complications in Abdominal Wall Reconstruction. JAMA Surg 2021; 156:933-940. [PMID: 34232255 DOI: 10.1001/jamasurg.2021.3012] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Importance Image-based deep learning models (DLMs) have been used in other disciplines, but this method has yet to be used to predict surgical outcomes. Objective To apply image-based deep learning to predict complexity, defined as need for component separation, and pulmonary and wound complications after abdominal wall reconstruction (AWR). Design, Setting, and Participants This quality improvement study was performed at an 874-bed hospital and tertiary hernia referral center from September 2019 to January 2020. A prospective database was queried for patients with ventral hernias who underwent open AWR by experienced surgeons and had preoperative computed tomography images containing the entire hernia defect. An 8-layer convolutional neural network was generated to analyze image characteristics. Images were batched into training (approximately 80%) or test sets (approximately 20%) to analyze model output. Test sets were blinded from the convolutional neural network until training was completed. For the surgical complexity model, a separate validation set of computed tomography images was evaluated by a blinded panel of 6 expert AWR surgeons and the surgical complexity DLM. Analysis started February 2020. Exposures Image-based DLM. Main Outcomes and Measures The primary outcome was model performance as measured by area under the curve in the receiver operating curve (ROC) calculated for each model; accuracy with accompanying sensitivity and specificity were also calculated. Measures were DLM prediction of surgical complexity using need for component separation techniques as a surrogate and prediction of postoperative surgical site infection and pulmonary failure. The DLM for predicting surgical complexity was compared against the prediction of 6 expert AWR surgeons. Results A total of 369 patients and 9303 computed tomography images were used. The mean (SD) age of patients was 57.9 (12.6) years, 232 (62.9%) were female, and 323 (87.5%) were White. The surgical complexity DLM performed well (ROC = 0.744; P < .001) and, when compared with surgeon prediction on the validation set, performed better with an accuracy of 81.3% compared with 65.0% (P < .001). Surgical site infection was predicted successfully with an ROC of 0.898 (P < .001). However, the DLM for predicting pulmonary failure was less effective with an ROC of 0.545 (P = .03). Conclusions and Relevance Image-based DLM using routine, preoperative computed tomography images was successful in predicting surgical complexity and more accurate than expert surgeon judgment. An additional DLM accurately predicted the development of surgical site infection.
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Affiliation(s)
- Sharbel Adib Elhage
- Department of Surgery, Franciscus Gasthuis en Vlietland, Rotterdam, the Netherlands
| | | | - Sullivan Armando Ayuso
- Division of Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Carolinas Medical Center, Charlotte, North Carolina
| | | | - Jenny Meng Shao
- Department of Surgery, University of Pennsylvania, Philadelphia
| | - Kent Williams Kercher
- Division of Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Carolinas Medical Center, Charlotte, North Carolina
| | - Neil James Smart
- Department of Colorectal Surgery, Royal Devon and Exeter NHS Foundation Trust, Royal Devon and Exeter Hospital, Exeter, United Kingdom
| | - John Patrick Fischer
- Division of Plastic Surgery, Department of Surgery, Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Vedra Abdomerovic Augenstein
- Division of Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Carolinas Medical Center, Charlotte, North Carolina
| | - Paul Dominick Colavita
- Division of Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Carolinas Medical Center, Charlotte, North Carolina
| | - B Todd Heniford
- Division of Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Carolinas Medical Center, Charlotte, North Carolina
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23
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Dreizin D, Goldmann F, LeBedis C, Boscak A, Dattwyler M, Bodanapally U, Li G, Anderson S, Maier A, Unberath M. An Automated Deep Learning Method for Tile AO/OTA Pelvic Fracture Severity Grading from Trauma whole-Body CT. J Digit Imaging 2021; 34:53-65. [PMID: 33479859 PMCID: PMC7886919 DOI: 10.1007/s10278-020-00399-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Revised: 10/14/2020] [Accepted: 11/10/2020] [Indexed: 01/13/2023] Open
Abstract
Admission trauma whole-body CT is routinely employed as a first-line diagnostic tool for characterizing pelvic fracture severity. Tile AO/OTA grade based on the presence or absence of rotational and translational instability corresponds with need for interventions including massive transfusion and angioembolization. An automated method could be highly beneficial for point of care triage in this critical time-sensitive setting. A dataset of 373 trauma whole-body CTs collected from two busy level 1 trauma centers with consensus Tile AO/OTA grading by three trauma radiologists was used to train and test a triplanar parallel concatenated network incorporating orthogonal full-thickness multiplanar reformat (MPR) views as input with a ResNeXt-50 backbone. Input pelvic images were first derived using an automated registration and cropping technique. Performance of the network for classification of rotational and translational instability was compared with that of (1) an analogous triplanar architecture incorporating an LSTM RNN network, (2) a previously described 3D autoencoder-based method, and (3) grading by a fourth independent blinded radiologist with trauma expertise. Confusion matrix results were derived, anchored to peak Matthews correlation coefficient (MCC). Associations with clinical outcomes were determined using Fisher's exact test. The triplanar parallel concatenated method had the highest accuracies for discriminating translational and rotational instability (85% and 74%, respectively), with specificity, recall, and F1 score of 93.4%, 56.5%, and 0.63 for translational instability and 71.7%, 75.7%, and 0.77 for rotational instability. Accuracy of this method was equivalent to the single radiologist read for rotational instability (74.0% versus 76.7%, p = 0.40), but significantly higher for translational instability (85.0% versus 75.1, p = 0.0007). Mean inference time was < 0.1 s per test image. Translational instability determined with this method was associated with need for angioembolization and massive transfusion (p = 0.002-0.008). Saliency maps demonstrated that the network focused on the sacroiliac complex and pubic symphysis, in keeping with the AO/OTA grading paradigm. A multiview concatenated deep network leveraging 3D information from orthogonal thick-MPR images predicted rotationally and translationally unstable pelvic fractures with accuracy comparable to an independent reader with trauma radiology expertise. Model output demonstrated significant association with key clinical outcomes.
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Affiliation(s)
- David Dreizin
- Emergency and Trauma Imaging, Department of Diagnostic Radiology and Nuclear Medicine, R Adams Cowley Shock Trauma Center, University of Maryland School of Medicine, Baltimore, MD USA
| | | | - Christina LeBedis
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, Baltimore, MD USA
| | - Alexis Boscak
- Emergency and Trauma Imaging, Department of Diagnostic Radiology and Nuclear Medicine, R Adams Cowley Shock Trauma Center, University of Maryland School of Medicine, Baltimore, MD USA
| | - Matthew Dattwyler
- Emergency and Trauma Imaging, Department of Diagnostic Radiology and Nuclear Medicine, R Adams Cowley Shock Trauma Center, University of Maryland School of Medicine, Baltimore, MD USA
| | - Uttam Bodanapally
- Emergency and Trauma Imaging, Department of Diagnostic Radiology and Nuclear Medicine, R Adams Cowley Shock Trauma Center, University of Maryland School of Medicine, Baltimore, MD USA
| | - Guang Li
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD USA
| | - Stephan Anderson
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, Baltimore, MD USA
| | - Andreas Maier
- Friedrich-Alexander University, Schloßplatz, Erlangen Germany
| | - Mathias Unberath
- Department of Computer Science, Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD USA
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24
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Dreizin D, Zhou Y, Fu S, Wang Y, Li G, Champ K, Siegel E, Wang Z, Chen T, Yuille AL. A Multiscale Deep Learning Method for Quantitative Visualization of Traumatic Hemoperitoneum at CT: Assessment of Feasibility and Comparison with Subjective Categorical Estimation. Radiol Artif Intell 2020; 2:e190220. [PMID: 33330848 PMCID: PMC7706875 DOI: 10.1148/ryai.2020190220] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Revised: 06/23/2020] [Accepted: 06/30/2020] [Indexed: 02/05/2023]
Abstract
PURPOSE To evaluate the feasibility of a multiscale deep learning algorithm for quantitative visualization and measurement of traumatic hemoperitoneum and to compare diagnostic performance for relevant outcomes with categorical estimation. MATERIALS AND METHODS This retrospective, single-institution study included 130 patients (mean age, 38 years; interquartile range, 25-50 years; 79 men) with traumatic hemoperitoneum who underwent CT of the abdomen and pelvis at trauma admission between January 2016 and April 2019. Labeled cases were separated into five combinations of training (80%) and test (20%) sets, and fivefold cross-validation was performed. Dice similarity coefficients (DSCs) were compared with those from a three-dimensional (3D) U-Net and a coarse-to-fine deep learning method. Areas under the receiver operating characteristic curve (AUCs) for a composite outcome, including hemostatic intervention, transfusion, and in-hospital mortality, were compared with consensus categorical assessment by two radiologists. An optimal cutoff was derived by using a radial basis function-based support vector machine. RESULTS Mean DSC for the multiscale algorithm was 0.61 ± 0.15 (standard deviation) compared with 0.32 ± 0.16 for the 3D U-Net method and 0.52 ± 0.17 for the coarse-to-fine method (P < .0001). Correlation and agreement between automated and manual volumes were excellent (Pearson r = 0.97, intraclass correlation coefficient = 0.93). The algorithm produced intuitive and explainable visual results. AUCs for automated volume measurement and categorical estimation were 0.86 and 0.77, respectively (P = .004). An optimal cutoff of 278.9 mL yielded accuracy of 84%, sensitivity of 82%, specificity of 93%, positive predictive value of 86%, and negative predictive value of 83%. CONCLUSION A multiscale deep learning method for traumatic hemoperitoneum quantitative visualization had improved diagnostic performance for predicting hemorrhage-control interventions and mortality compared with subjective volume estimation. Supplemental material is available for this article. © RSNA, 2020.
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Affiliation(s)
- David Dreizin
- From the Section of Trauma and Emergency Radiology, R. Adams Cowley Shock Trauma Center (D.D.) and Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland Medical Center, University of Maryland, 22 S Greene St, Baltimore, MD 21201 (G.L., K.C., E.S., Z.W., T.C.); and Department of Computer Science, Computational Cognition Vision and Learning, Johns Hopkins University, Baltimore, Md (Y.Z., S.F., Y.W., A.L.Y.)
| | - Yuyin Zhou
- From the Section of Trauma and Emergency Radiology, R. Adams Cowley Shock Trauma Center (D.D.) and Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland Medical Center, University of Maryland, 22 S Greene St, Baltimore, MD 21201 (G.L., K.C., E.S., Z.W., T.C.); and Department of Computer Science, Computational Cognition Vision and Learning, Johns Hopkins University, Baltimore, Md (Y.Z., S.F., Y.W., A.L.Y.)
| | - Shuhao Fu
- From the Section of Trauma and Emergency Radiology, R. Adams Cowley Shock Trauma Center (D.D.) and Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland Medical Center, University of Maryland, 22 S Greene St, Baltimore, MD 21201 (G.L., K.C., E.S., Z.W., T.C.); and Department of Computer Science, Computational Cognition Vision and Learning, Johns Hopkins University, Baltimore, Md (Y.Z., S.F., Y.W., A.L.Y.)
| | - Yan Wang
- From the Section of Trauma and Emergency Radiology, R. Adams Cowley Shock Trauma Center (D.D.) and Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland Medical Center, University of Maryland, 22 S Greene St, Baltimore, MD 21201 (G.L., K.C., E.S., Z.W., T.C.); and Department of Computer Science, Computational Cognition Vision and Learning, Johns Hopkins University, Baltimore, Md (Y.Z., S.F., Y.W., A.L.Y.)
| | - Guang Li
- From the Section of Trauma and Emergency Radiology, R. Adams Cowley Shock Trauma Center (D.D.) and Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland Medical Center, University of Maryland, 22 S Greene St, Baltimore, MD 21201 (G.L., K.C., E.S., Z.W., T.C.); and Department of Computer Science, Computational Cognition Vision and Learning, Johns Hopkins University, Baltimore, Md (Y.Z., S.F., Y.W., A.L.Y.)
| | - Kathryn Champ
- From the Section of Trauma and Emergency Radiology, R. Adams Cowley Shock Trauma Center (D.D.) and Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland Medical Center, University of Maryland, 22 S Greene St, Baltimore, MD 21201 (G.L., K.C., E.S., Z.W., T.C.); and Department of Computer Science, Computational Cognition Vision and Learning, Johns Hopkins University, Baltimore, Md (Y.Z., S.F., Y.W., A.L.Y.)
| | - Eliot Siegel
- From the Section of Trauma and Emergency Radiology, R. Adams Cowley Shock Trauma Center (D.D.) and Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland Medical Center, University of Maryland, 22 S Greene St, Baltimore, MD 21201 (G.L., K.C., E.S., Z.W., T.C.); and Department of Computer Science, Computational Cognition Vision and Learning, Johns Hopkins University, Baltimore, Md (Y.Z., S.F., Y.W., A.L.Y.)
| | - Ze Wang
- From the Section of Trauma and Emergency Radiology, R. Adams Cowley Shock Trauma Center (D.D.) and Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland Medical Center, University of Maryland, 22 S Greene St, Baltimore, MD 21201 (G.L., K.C., E.S., Z.W., T.C.); and Department of Computer Science, Computational Cognition Vision and Learning, Johns Hopkins University, Baltimore, Md (Y.Z., S.F., Y.W., A.L.Y.)
| | - Tina Chen
- From the Section of Trauma and Emergency Radiology, R. Adams Cowley Shock Trauma Center (D.D.) and Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland Medical Center, University of Maryland, 22 S Greene St, Baltimore, MD 21201 (G.L., K.C., E.S., Z.W., T.C.); and Department of Computer Science, Computational Cognition Vision and Learning, Johns Hopkins University, Baltimore, Md (Y.Z., S.F., Y.W., A.L.Y.)
| | - Alan L. Yuille
- From the Section of Trauma and Emergency Radiology, R. Adams Cowley Shock Trauma Center (D.D.) and Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland Medical Center, University of Maryland, 22 S Greene St, Baltimore, MD 21201 (G.L., K.C., E.S., Z.W., T.C.); and Department of Computer Science, Computational Cognition Vision and Learning, Johns Hopkins University, Baltimore, Md (Y.Z., S.F., Y.W., A.L.Y.)
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