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Zhao T, Meng X, Wang Z, Hu Y, Fan H, Han J, Zhu N, Niu F. Diagnostic evaluation of blunt chest trauma by imaging-based application of artificial intelligence: A review. Am J Emerg Med 2024; 85:35-43. [PMID: 39213808 DOI: 10.1016/j.ajem.2024.08.019] [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: 06/19/2024] [Accepted: 08/12/2024] [Indexed: 09/04/2024] Open
Abstract
Artificial intelligence (AI) is becoming increasingly integral in clinical practice, such as during imaging tasks associated with the diagnosis and evaluation of blunt chest trauma (BCT). Due to significant advances in imaging-based deep learning, recent studies have demonstrated the efficacy of AI in the diagnosis of BCT, with a focus on rib fractures, pulmonary contusion, hemopneumothorax and others, demonstrating significant clinical progress. However, the complicated nature of BCT presents challenges in providing a comprehensive diagnosis and prognostic evaluation, and current deep learning research concentrates on specific clinical contexts, limiting its utility in addressing BCT intricacies. Here, we provide a review of the available evidence surrounding the potential utility of AI in BCT, and additionally identify the challenges impeding its development. This review offers insights on how to optimize the role of AI in the diagnostic evaluation of BCT, which can ultimately enhance patient care and outcomes in this critical clinical domain.
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Affiliation(s)
- Tingting Zhao
- The Department of Radiology, Tianjin University Tianjin Hospital, 406 Jiefang Southern Road, Tianjin, China; Graduate School, Tianjin University, Tianjin, China
| | - Xianghong Meng
- The Department of Radiology, Tianjin University Tianjin Hospital, 406 Jiefang Southern Road, Tianjin, China; Graduate School, Tianjin University, Tianjin, China.
| | - Zhi Wang
- The Department of Radiology, Tianjin University Tianjin Hospital, 406 Jiefang Southern Road, Tianjin, China; Graduate School, Tianjin University, Tianjin, China.
| | - Yongcheng Hu
- The Department of Radiology, Tianjin University Tianjin Hospital, 406 Jiefang Southern Road, Tianjin, China
| | - Hongxing Fan
- The Department of Radiology, Tianjin University Tianjin Hospital, 406 Jiefang Southern Road, Tianjin, China; Graduate School, Tianjin Medical University, Tianjin, China
| | - Jun Han
- The Department of Radiology, Tianjin University Tianjin Hospital, 406 Jiefang Southern Road, Tianjin, China; Graduate School, Tianjin University, Tianjin, China
| | - Nana Zhu
- The Department of Radiology, Tianjin University Tianjin Hospital, 406 Jiefang Southern Road, Tianjin, China; Graduate School, Tianjin Medical University, Tianjin, China
| | - Feige Niu
- The Department of Radiology, Tianjin University Tianjin Hospital, 406 Jiefang Southern Road, Tianjin, China; Graduate School, Tianjin Medical University, Tianjin, China
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Lee CW, Huang CC, Jang YC, Chen KC, Ho SY, Chou CT, Wu WP. Diagnostic Accuracy for Acute Rib Fractures: A Cross-sectional Study Utilizing Automatic Rib Unfolding and 3D Volume-Rendered Reformation. Acad Radiol 2024; 31:1538-1547. [PMID: 37845164 DOI: 10.1016/j.acra.2023.08.037] [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] [Received: 05/20/2023] [Revised: 08/25/2023] [Accepted: 08/26/2023] [Indexed: 10/18/2023]
Abstract
RATIONALE AND OBJECTIVES The aim of this study was to compare the use of computed tomography (CT) with automatic rib unfolding and three-dimensional (3D) volume-rendered imaging in the detection and characterization of rib fractures and flail chest. MATERIALS AND METHODS A total of 130 patients with blunt chest trauma underwent whole-body CT, and five independent readers assessed the presence and characterization of rib fractures using traditional CT images, automatic rib unfolding, and 3D volume-rendered images in separate readout sessions at least 2 weeks apart. A gold standard was established by consensus among the readers based on the combined analysis of conventional and reformatted images. RESULTS Automatic rib unfolding significantly reduced mean reading time by 47.5%-74.9% (P < 0.0001) while maintaining a comparable diagnostic performance for rib fractures (positive predictive value [PPV] of 82.1%-93.5%, negative predictive value [NPV] of 96.8%-98.2%, and 69.4%-94.2% and 96.9%-99.1% for conventional axial images and 70.4%-85.1% and 95.2%-96.6% for 3D images) and better interobserver agreement (kappa of 0.74-0.87). For flail chest, automatic rib unfolding showed a PPV of 85.7%-100%, NPV of 90.4%-99.0%, and 80.0%-100% and 89.7%-100% for conventional axial images and 76.9%-100% and 89.0%-92.1% for 3D images. CONCLUSION Automatic rib unfolding demonstrated equivalent diagnostic performance to conventional images in detecting acute rib fractures and flail chest, with good interobserver agreement and time-saving benefits.
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Affiliation(s)
- Chih-Wei Lee
- Department of Radiology, Changhua Christian Hospital, Changhua, Taiwan (C.-W.L., Y.-C.J., S.-Y.H., C.T.C., W.-P.W.)
| | - Cheng-Chieh Huang
- Department of Emergency and Critical Care Medicine, Changhua Christian Hospital, Changhua, Taiwan (C.-C.H., K.-C.C.); Department of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan (C.-C.H.)
| | - Yong-Ching Jang
- Department of Radiology, Changhua Christian Hospital, Changhua, Taiwan (C.-W.L., Y.-C.J., S.-Y.H., C.T.C., W.-P.W.)
| | - Kuan-Chih Chen
- Department of Emergency and Critical Care Medicine, Changhua Christian Hospital, Changhua, Taiwan (C.-C.H., K.-C.C.)
| | - Shang-Yun Ho
- Department of Radiology, Changhua Christian Hospital, Changhua, Taiwan (C.-W.L., Y.-C.J., S.-Y.H., C.T.C., W.-P.W.); Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung, Taiwan (S.-Y.H.)
| | - Chen-Te Chou
- Department of Radiology, Changhua Christian Hospital, Changhua, Taiwan (C.-W.L., Y.-C.J., S.-Y.H., C.T.C., W.-P.W.); Kaohsiung Medical University, Kaohsiung, Taiwan (C.-T.C., W.-P.W)
| | - Wen-Pei Wu
- Department of Radiology, Changhua Christian Hospital, Changhua, Taiwan (C.-W.L., Y.-C.J., S.-Y.H., C.T.C., W.-P.W.); Kaohsiung Medical University, Kaohsiung, Taiwan (C.-T.C., W.-P.W); Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan (W.-P.W.).
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Lopez-Melia M, Magnin V, Marchand-Maillet S, Grabherr S. Deep learning for acute rib fracture detection in CT data: a systematic review and meta-analysis. Br J Radiol 2024; 97:535-543. [PMID: 38323515 PMCID: PMC11027249 DOI: 10.1093/bjr/tqae014] [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: 09/12/2023] [Revised: 12/16/2023] [Accepted: 01/12/2024] [Indexed: 02/08/2024] Open
Abstract
OBJECTIVES To review studies on deep learning (DL) models for classification, detection, and segmentation of rib fractures in CT data, to determine their risk of bias (ROB), and to analyse the performance of acute rib fracture detection models. METHODS Research articles written in English were retrieved from PubMed, Embase, and Web of Science in April 2023. A study was only included if a DL model was used to classify, detect, or segment rib fractures, and only if the model was trained with CT data from humans. For the ROB assessment, the Quality Assessment of Diagnostic Accuracy Studies tool was used. The performance of acute rib fracture detection models was meta-analysed with forest plots. RESULTS A total of 27 studies were selected. About 75% of the studies have ROB by not reporting the patient selection criteria, including control patients or using 5-mm slice thickness CT scans. The sensitivity, precision, and F1-score of the subgroup of low ROB studies were 89.60% (95%CI, 86.31%-92.90%), 84.89% (95%CI, 81.59%-88.18%), and 86.66% (95%CI, 84.62%-88.71%), respectively. The ROB subgroup differences test for the F1-score led to a p-value below 0.1. CONCLUSION ROB in studies mostly stems from an inappropriate patient and data selection. The studies with low ROB have better F1-score in acute rib fracture detection using DL models. ADVANCES IN KNOWLEDGE This systematic review will be a reference to the taxonomy of the current status of rib fracture detection with DL models, and upcoming studies will benefit from our data extraction, our ROB assessment, and our meta-analysis.
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Affiliation(s)
- Manel Lopez-Melia
- University Centre of Legal Medicine Lausanne-Geneva, Geneva 1206, Switzerland
- University Hospital and University of Geneva, Geneva 1205, Switzerland
| | - Virginie Magnin
- University Centre of Legal Medicine Lausanne-Geneva, Geneva 1206, Switzerland
- University Hospital and University of Geneva, Geneva 1205, Switzerland
- University Hospital and University of Lausanne, Lausanne 1005, Switzerland
| | | | - Silke Grabherr
- University Centre of Legal Medicine Lausanne-Geneva, Geneva 1206, Switzerland
- University Hospital and University of Geneva, Geneva 1205, Switzerland
- University Hospital and University of Lausanne, Lausanne 1005, Switzerland
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Wong YC, Wang LJ, Kaewlai R, Wu CH. Watch Out for the Early Killers: Imaging Diagnosis of Thoracic Trauma. Korean J Radiol 2023; 24:752-760. [PMID: 37500576 PMCID: PMC10400372 DOI: 10.3348/kjr.2022.1021] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Revised: 06/06/2023] [Accepted: 06/11/2023] [Indexed: 07/29/2023] Open
Abstract
Radiologists and trauma surgeons should monitor for early killers among patients with thoracic trauma, such as tension pneumothorax, tracheobronchial injuries, flail chest, aortic injury, mediastinal hematomas, and severe pulmonary parenchymal injury. With the advent of cutting-edge technology, rapid volumetric computed tomography of the chest has become the most definitive diagnostic tool for establishing or excluding thoracic trauma. With the notion of "time is life" at emergency settings, radiologists must find ways to shorten the turnaround time of reports. One way to interpret chest findings is to use a systemic approach, as advocated in this study. Our interpretation of chest findings for thoracic trauma follows the acronym "ABC-Please" in which "A" stands for abnormal air, "B" stands for abnormal bones, "C" stands for abnormal cardiovascular system, and "P" in "Please" stands for abnormal pulmonary parenchyma and vessels. In the future, utilizing an artificial intelligence software can be an alternative, which can highlight significant findings as "warm zones" on the heatmap and can re-prioritize important examinations at the top of the reading list for radiologists to expedite the final reports.
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Affiliation(s)
- Yon-Cheong Wong
- Division of Emergency and Critical Care Radiology, Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Chang Gung University, Taiwan.
| | - Li-Jen Wang
- Department of Medical Imaging and Intervention, New Taipei Municipal TuCheng Hospital, Chang Gung University, Taiwan
| | - Rathachai Kaewlai
- Division of Diagnostic Radiology, Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Cheng-Hsien Wu
- Division of Emergency and Critical Care Radiology, Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Chang Gung University, Taiwan
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Li N, Wu Z, Jiang C, Sun L, Li B, Guo J, Liu F, Zhou Z, Qin H, Tan W, Tian L. An automatic fresh rib fracture detection and positioning system using deep learning. Br J Radiol 2023; 96:20221006. [PMID: 36972072 PMCID: PMC10230380 DOI: 10.1259/bjr.20221006] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 03/14/2023] [Accepted: 03/15/2023] [Indexed: 03/29/2023] Open
Abstract
OBJECTIVE To evaluate the performance and robustness of a deep learning-based automatic fresh rib fracture detection and positioning system (FRF-DPS). METHODS CT scans of 18,172 participants admitted to eight hospitals from June 2009 to March 2019 were retrospectively collected. Patients were divided into development set (14,241), multicenter internal test set (1612), and external test set (2319). In internal test set, sensitivity, false positives (FPs) and specificity were used to assess fresh rib fracture detection performance at the lesion- and examination-levels. In external test set, the performance of detecting fresh rib fractures by radiologist and FRF-DPS were evaluated at lesion, rib, and examination levels. Additionally, the accuracy of FRF-DPS in rib positioning was investigated by the ground-truth labeling. RESULTS In multicenter internal test set, FRF-DPS showed excellent performance at the lesion- (sensitivity: 0.933 [95%CI, 0.916-0.949], FPs: 0.50 [95%CI, 0.397-0.583]) and examination-level. In external test set, the sensitivity and FPs at the lesion-level of FRF-DPS (0.909 [95%CI, 0.883-0.926], p < 0.001; 0.379 [95%CI, 0.303-0.422], p = 0.001) were better than the radiologist (0.789 [95%CI, 0.766-0.807]; 0.496 [95%CI, 0.383-0.571]), so were the rib- and patient-levels. In subgroup analysis of CT parameters, FRF-DPS were robust (0.894-0.927). Finally, FRF-DPS(0.997 [95%CI, 0.992-1.000], p < 0.001) is more accurate than radiologist (0.981 [95%CI, 0.969-0.996]) in rib positioning and takes 20 times less time. CONCLUSION FRF-DPS achieved high detection rate of fresh rib fractures with low FP values, and precise positioning of ribs, thus can be used in clinical practice to improve the detection rate and work efficiency. ADVANCES IN KNOWLEDGE We developed the FRF-DPS system which can detect fresh rib fractures and rib position, and evaluated by a large amount of multicenter data.
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Affiliation(s)
- Ning Li
- Department of Radiology, Fushun Central Hospital of Liaoning Province, Fushun, Liaoning Province, China
| | - Zhe Wu
- Department of Radiology, Fushun Central Hospital of Liaoning Province, Fushun, Liaoning Province, China
| | - Chao Jiang
- Department of Radiology, Fushun Central Hospital of Liaoning Province, Fushun, Liaoning Province, China
| | - Lulu Sun
- Department of Radiology, Fushun Central Hospital of Liaoning Province, Fushun, Liaoning Province, China
| | - Bingyao Li
- Department of Radiology, Fushun Central Hospital of Liaoning Province, Fushun, Liaoning Province, China
| | - Jun Guo
- Department of Radiology, Fushun Central Hospital of Liaoning Province, Fushun, Liaoning Province, China
| | - Feng Liu
- Deepwise Artificial Intelligence (AI) Lab, Deepwise Inc., Beijing, China
| | - Zhen Zhou
- Deepwise Artificial Intelligence (AI) Lab, Deepwise Inc., Beijing, China
| | - Haibo Qin
- Department of Radiology, Fushun Central Hospital of Liaoning Province, Fushun, Liaoning Province, China
| | - Weixiong Tan
- Deepwise Artificial Intelligence (AI) Lab, Deepwise Inc., Beijing, China
| | - Lufeng Tian
- Department of Radiology, Fushun Central Hospital of Liaoning Province, Fushun, Liaoning Province, China
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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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Xiong S, Hu H, Liu S, Huang Y, Cheng J, Wan B. Improving diagnostic performance of rib fractures for the night shift in radiology department using a computer-aided diagnosis system based on deep learning: A clinical retrospective study. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2023; 31:265-276. [PMID: 36806541 DOI: 10.3233/xst-221343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
OBJECTIVE To investigate the application value of a computer-aided diagnosis (CAD) system based on deep learning (DL) of rib fractures for night shifts in radiology department. METHODS Chest computed tomography (CT) images and structured reports were retrospectively selected from the picture archiving and communication system (PACS) for 2,332 blunt chest trauma patients. In all CT imaging examinations, two on-duty radiologists (radiologists I and II) completed reports using three different reading patterns namely, P1 = independent reading during the day shift; P2 = independent reading during the night shift; and P3 = reading with the aid of a CAD system as the concurrent reader during the night shift. The locations and types of rib fractures were documented for each reading. In this study, the reference standard for rib fractures was established by an expert group. Sensitivity and false positives per scan (FPS) were counted and compared among P1, P2, and P3. RESULTS The reference standard verified 6,443 rib fractures in the 2,332 patients. The sensitivity of both radiologists decreased significantly in P2 compared to that in P1 (both p < 0.017). The sensitivities of both radiologists showed no statistical difference between P3 and P1 (both p > 0.017). Radiologist I's FPS increased significantly in P2 compared to P1 (p < 0.017). The FPS of radiologist I showed no statistically significant difference between P3 and P1 (p > 0.017). The FPS of Radiologist II showed no statistical difference among all three reading patterns (p > 0.05). CONCLUSIONS DL-based CAD systems can be integrated into the workflow of radiology departments during the night shift to improve the diagnostic performance of CT rib fractures.
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Affiliation(s)
- Shan Xiong
- Department of Radiology, Jingzhou Hospital Affiliated to Yangtze University, Jingzhou, China
| | - Hai Hu
- Department of Radiology, Jingzhou Hospital Affiliated to Yangtze University, Jingzhou, China
| | - Sibin Liu
- Department of Radiology, Jingzhou Hospital Affiliated to Yangtze University, Jingzhou, China
| | - Yuanyi Huang
- Department of Radiology, Jingzhou Hospital Affiliated to Yangtze University, Jingzhou, China
| | - Jianmin Cheng
- Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Bing Wan
- Department of Radiology, Renhe Hospital Affiliated to Three Gorges University, Yichang, China
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Radiomics-Based Machine Learning for Predicting the Injury Time of Rib Fractures in Gemstone Spectral Imaging Scans. BIOENGINEERING (BASEL, SWITZERLAND) 2022; 10:bioengineering10010008. [PMID: 36671582 PMCID: PMC9855073 DOI: 10.3390/bioengineering10010008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Revised: 12/07/2022] [Accepted: 12/16/2022] [Indexed: 12/24/2022]
Abstract
This retrospective study aimed to predict the injury time of rib fractures in distinguishing fresh (30 days) or old (90 days) rib fractures. We enrolled 111 patients with chest trauma who had been scanned for rib fractures at our hospital between January 2018 and December 2018 using gemstone spectral imaging (GSI). The volume of interest of each broken end of the rib fractures was segmented using calcium-based material decomposition images derived from the GSI scans. The training and testing sets were randomly assigned in a 7:3 ratio. All cases were divided into groups distinguishing the injury time at 30 and 90 days. We constructed radiomics-based models to predict the injury time of rib fractures. The model performance was assessed by the area under the curve (AUC) obtained by the receiver operating characteristic analysis. We included 54 patients with 259 rib fracture segmentations (34 men; mean age, 52 years ± 12.02; and range, 19-72 years). Nine features were excluded by the least absolute shrinkage and selection operator logistic regression to build the radiomics signature. For distinguishing the injury time at 30 days, the Support Vector Machine (SVM) model and human-model collaboration resulted in an accuracy and AUC of 0.85 and 0.871 and 0.91 and 0.912, respectively, and 0.81 and 0.804 and 0.83 and 0.85, respectively, at 90 days in the testing set. The radiomics-based model displayed good accuracy in differentiating between the injury time of rib fractures at 30 and 90 days, and the human-model collaboration generated more accurate outcomes, which may help to add value to clinical practice and distinguish artificial injury in forensic medicine.
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Zech JR, Santomartino SM, Yi PH. Artificial Intelligence (AI) for Fracture Diagnosis: An Overview of Current Products and Considerations for Clinical Adoption, From the AJR Special Series on AI Applications. AJR Am J Roentgenol 2022; 219:869-878. [PMID: 35731103 DOI: 10.2214/ajr.22.27873] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Fractures are common injuries that can be difficult to diagnose, with missed fractures accounting for most misdiagnoses in the emergency department. Artificial intelligence (AI) and, specifically, deep learning have shown a strong ability to accurately detect fractures and augment the performance of radiologists in proof-of-concept research settings. Although the number of real-world AI products available for clinical use continues to increase, guidance for practicing radiologists in the adoption of this new technology is limited. This review describes how AI and deep learning algorithms can help radiologists to better diagnose fractures. The article also provides an overview of commercially available U.S. FDA-cleared AI tools for fracture detection as well as considerations for the clinical adoption of these tools by radiology practices.
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Affiliation(s)
- John R Zech
- Department of Radiology, Columbia University Irving Medical Center/New York-Presbyterian Hospital, New York, NY
| | - Samantha M Santomartino
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland Medical Intelligent Imaging (UM2ii) Center, University of Maryland School of Medicine, 670 W Baltimore St, First Fl, Rm 1172, Baltimore, MD 21201
| | - Paul H Yi
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland Medical Intelligent Imaging (UM2ii) Center, University of Maryland School of Medicine, 670 W Baltimore St, First Fl, Rm 1172, Baltimore, MD 21201
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Automated fracture screening using an object detection algorithm on whole-body trauma computed tomography. Sci Rep 2022; 12:16549. [PMID: 36192521 PMCID: PMC9529907 DOI: 10.1038/s41598-022-20996-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 09/21/2022] [Indexed: 11/28/2022] Open
Abstract
The emergency department is an environment with a potential risk for diagnostic errors during trauma care, particularly for fractures. Convolutional neural network (CNN) deep learning methods are now widely used in medicine because they improve diagnostic accuracy, decrease misinterpretation, and improve efficiency. In this study, we investigated whether automatic localization and classification using CNN could be applied to pelvic, rib, and spine fractures. We also examined whether this fracture detection algorithm could help physicians in fracture diagnosis. A total of 7664 whole-body CT axial slices (chest, abdomen, pelvis) from 200 patients were used. Sensitivity, precision, and F1-score were calculated to evaluate the performance of the CNN model. For the grouped mean values for pelvic, spine, or rib fractures, the sensitivity was 0.786, precision was 0.648, and F1-score was 0.711. Moreover, with CNN model assistance, surgeons showed improved sensitivity for detecting fractures and the time of reading and interpreting CT scans was reduced, especially for less experienced orthopedic surgeons. Application of the CNN model may lead to reductions in missed fractures from whole-body CT images and to faster workflows and improved patient care through efficient diagnosis in polytrauma patients.
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