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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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Cheng CT, Kuo LW, Ouyang CH, Hsu CP, Lin WC, Fu CY, Kang SC, Liao CH. Development and evaluation of a deep learning-based model for simultaneous detection and localization of rib and clavicle fractures in trauma patients' chest radiographs. Trauma Surg Acute Care Open 2024; 9:e001300. [PMID: 38646620 PMCID: PMC11029226 DOI: 10.1136/tsaco-2023-001300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/23/2024] Open
Abstract
Purpose To develop a rib and clavicle fracture detection model for chest radiographs in trauma patients using a deep learning (DL) algorithm. Materials and methods We retrospectively collected 56 145 chest X-rays (CXRs) from trauma patients in a trauma center between August 2008 and December 2016. A rib/clavicle fracture detection DL algorithm was trained using this data set with 991 (1.8%) images labeled by experts with fracture site locations. The algorithm was tested on independently collected 300 CXRs in 2017. An external test set was also collected from hospitalized trauma patients in a regional hospital for evaluation. The receiver operating characteristic curve with area under the curve (AUC), accuracy, sensitivity, specificity, precision, and negative predictive value of the model on each test set was evaluated. The prediction probability on the images was visualized as heatmaps. Results The trained DL model achieved an AUC of 0.912 (95% CI 87.8 to 94.7) on the independent test set. The accuracy, sensitivity, and specificity on the given cut-off value are 83.7, 86.8, and 80.4, respectively. On the external test set, the model had a sensitivity of 88.0 and an accuracy of 72.5. While the model exhibited a slight decrease in accuracy on the external test set, it maintained its sensitivity in detecting fractures. Conclusion The algorithm detects rib and clavicle fractures concomitantly in the CXR of trauma patients with high accuracy in locating lesions through heatmap visualization.
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Affiliation(s)
- Chi-Tung Cheng
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital Linkou, Taoyuan, Taiwan
- Department of medicine, Chang Gung university, Taoyuan, Taiwan
| | - Ling-Wei Kuo
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital Linkou, Taoyuan, Taiwan
- Department of medicine, Chang Gung university, Taoyuan, Taiwan
| | - Chun-Hsiang Ouyang
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital Linkou, Taoyuan, Taiwan
- Department of medicine, Chang Gung university, Taoyuan, Taiwan
| | - Chi-Po Hsu
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital Linkou, Taoyuan, Taiwan
- Department of medicine, Chang Gung university, Taoyuan, Taiwan
| | - Wei-Cheng Lin
- Department of Electrical Engineering, Chang Gung University, Taoyuan, Taiwan
| | - Chih-Yuan Fu
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital Linkou, Taoyuan, Taiwan
- Department of medicine, Chang Gung university, Taoyuan, Taiwan
| | - Shih-Ching Kang
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital Linkou, Taoyuan, Taiwan
- Department of medicine, Chang Gung university, Taoyuan, Taiwan
| | - Chien-Hung Liao
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital Linkou, Taoyuan, Taiwan
- Department of medicine, Chang Gung university, Taoyuan, Taiwan
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Cote MP, Lubowitz JH. Recommended Requirements and Essential Elements for Proper Reporting of the Use of Artificial Intelligence Machine Learning Tools in Biomedical Research and Scientific Publications. Arthroscopy 2024; 40:1033-1038. [PMID: 38300189 DOI: 10.1016/j.arthro.2023.12.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Accepted: 12/30/2023] [Indexed: 02/02/2024]
Abstract
Essential elements required for proper use of artificial intelligence machine learning tools in biomedical research and scientific publications include (1) explanation justifying why a machine learning approach contributes to the purpose of the study; (2) description of the adequacy of the data (input) to produce the desired results (output); (3) details of the algorithmic (i.e., computational) approach including methods for organizing the data (preprocessing); the machine learning computational algorithm(s) assessed; on what data the models were trained; the presence of bias and efforts to mitigate these effects; and the methods for quantifying the variables (features) most influential in determining the results (e.g., Shapley values); (4) description of methods, and reporting of results, quantitating performance in terms of both model accuracy and model calibration (level of confidence in the model's predictions); (5) availability of the programming code (including a link to the code when available-ideally, the code should be available); (6) discussion of model internal validation (results applicable and sensitive to the population investigated and data on which the model was trained) and external validation (were the results investigated as to whether they are generalizable to different populations? If not, consideration of this limitation and discussion of plans for external validation, i.e., next steps). As biomedical research submissions using artificial intelligence technology increase, these requirements could facilitate purposeful use and comprehensive methodological reporting.
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Khoriati AA, Shahid Z, Fok M, Frank RM, Voss A, D'Hooghe P, Imam MA. Artificial intelligence and the orthopaedic surgeon: A review of the literature and potential applications for future practice: Current concepts. J ISAKOS 2024; 9:227-233. [PMID: 37949113 DOI: 10.1016/j.jisako.2023.10.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 10/28/2023] [Accepted: 10/30/2023] [Indexed: 11/12/2023]
Affiliation(s)
- Al-Achraf Khoriati
- Rowley Bristow Orthopaedic Centre, Ashford and St Peter's NHS Foundation Trust, Chertsey, KT106PZ, UK.
| | - Zuhaib Shahid
- Rowley Bristow Orthopaedic Centre, Ashford and St Peter's NHS Foundation Trust, Chertsey, KT106PZ, UK.
| | - Margaret Fok
- Department of Orthopaedics and Traumatology, Queen Mary Hospital, The University of Hong Kong, Pok Fu Lam Rd, High West, Hong Kong, China; Asia Pacific Orthopaedic Association, 57000, Malaysia.
| | - Rachel M Frank
- Department of Orthopaedic Surgery, Joint Preservation Program, University of Colorado School of Medicine, 12631 E 17th Ave, Mail Stop B202, Aurora, CO 80045, USA.
| | - Andreas Voss
- Sporthopaedicum Regensburg, Street, Hildegard-von-Bingen-Straße 1, 93053, Regensburg, Germany.
| | - Pieter D'Hooghe
- Aspetar Orthopedic and Sports Medicine Hospital, Aspire Zone, Sportscity Street 1, P.O. Box 29222, Doha, Qatar
| | - Mohamed A Imam
- Rowley Bristow Orthopaedic Centre, Ashford and St Peter's NHS Foundation Trust, Chertsey, KT106PZ, UK; Smart Health Centre, University of East London, University Way, London, E16 2RD, United Kingdom.
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Xing Z, Zhu Z, Jiang Z, Zhao J, Chen Q, Xing W, Pan L, Zeng Y, Liu A, Ding J. Automatic Urinary Stone Detection System for Abdominal Non-Enhanced CT Images Reduces the Burden on Radiologists. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:444-454. [PMID: 38343222 PMCID: PMC11031534 DOI: 10.1007/s10278-023-00946-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 10/18/2023] [Accepted: 10/18/2023] [Indexed: 04/20/2024]
Abstract
To develop a fully automatic urinary stone detection system (kidney, ureter, and bladder) and to test it in a real clinical environment. The local institutional review board approved this retrospective single-center study that used non-enhanced abdominopelvic CT scans from patients admitted urology (uPatients) and emergency (ePatients). The uPatients were randomly divided into training and validation sets in a ratio of 3:1. We designed a cascade urinary stone map location-feature pyramid networks (USm-FPNs) and innovatively proposed a ureter distance heatmap method to estimate the ureter position on non-enhanced CT to further reduce the false positives. The performances of the system were compared using the free-response receiver operating characteristic curve and the precision-recall curve. This study included 811 uPatients and 356 ePatients. At stone level, the cascade detector USm-FPNs has the mean of false positives per scan (mFP) 1.88 with the sensitivity 0.977 in validation set, and mFP was further reduced to 1.18 with the sensitivity 0.977 after combining the ureter distance heatmap. At patient level, the sensitivity and precision were as high as 0.995 and 0.990 in validation set, respectively. In a real clinical set of ePatients (27.5% of patients contain stones), the mFP was 1.31 with as high as sensitivity 0.977, and the diagnostic time reduced by > 20% with the system help. A fully automatic detection system for entire urinary stones on non-enhanced CT scans was proposed and reduces obviously the burden on junior radiologists without compromising sensitivity in real emergency data.
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Affiliation(s)
- Zhaoyu Xing
- Department of Urology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, China
| | - Zuhui Zhu
- Department of Radiology, Nantong Hospital of Traditional Chinese Medicine, Nantong, Jiangsu, China
| | - Zhenxing Jiang
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, China
| | - Jingshi Zhao
- Department of Radiology, Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Qin Chen
- Department of Radiology, People's Hospital of Pengzhou, Chengdu, Sichuan, China
| | - Wei Xing
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, China
| | - Liang Pan
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, China
| | - Yan Zeng
- Department of Research Center, Shanghai United Imaging Intelligence Co. Ltd, Shanghai, China.
| | - Aie Liu
- Department of Research Center, Shanghai United Imaging Intelligence Co. Ltd, Shanghai, China.
| | - Jiule Ding
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, China.
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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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Beyraghi S, Ghorbani F, Shabanpour J, Lajevardi ME, Nayyeri V, Chen PY, Ramahi OM. Microwave bone fracture diagnosis using deep neural network. Sci Rep 2023; 13:16957. [PMID: 37805642 PMCID: PMC10560237 DOI: 10.1038/s41598-023-44131-5] [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: 06/01/2023] [Accepted: 10/04/2023] [Indexed: 10/09/2023] Open
Abstract
This paper studies the feasibility of a deep neural network (DNN) approach for bone fracture diagnosis based on the non-invasive propagation of radio frequency waves. In contrast to previous "semi-automated" techniques, where X-ray images were used as the network input, in this work, we use S-parameters profiles for DNN training to avoid labeling and data collection problems. Our designed network can simultaneously classify different complex fracture types (normal, transverse, oblique, and comminuted) and estimate the length of the cracks. The proposed system can be used as a portable device in ambulances, retirement houses, and low-income settings for fast preliminary diagnosis in emergency locations when expert radiologists are not available. Using accurate modeling of the human body as well as changing tissue diameters to emulate various anatomical regions, we have created our datasets. Our numerical results show that our design DNN is successfully trained without overfitting. Finally, for the validation of the numerical results, different sets of experiments have been done on the sheep femur bones covered by the liquid phantom. Experimental results demonstrate that fracture types can be correctly classified without using potentially harmful and ionizing X-rays.
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Affiliation(s)
- Sina Beyraghi
- Department of Information and Communications Technologies, Pompeu Fabra University, Barcelona, Spain
| | - Fardin Ghorbani
- School of Electrical Engineering, Iran University of Science and Technology, Tehran, 1684613114, Iran
| | - Javad Shabanpour
- Department of Electronics and Nanoengineering, School of Electrical Engineering, Aalto University, 02150, Espoo, Finland
| | - Mir Emad Lajevardi
- Department of Electrical Engineering, Faculty of Electrical and Electronics, South Tehran Branch, Islamic Azad University, Tehran, 113654435, Iran
| | - Vahid Nayyeri
- School of Advanced Technologies, Iran University of Science and Technology, Tehran, 1684613114, Iran.
| | - Pai-Yen Chen
- Department of Electrical and Computer Engineering, University of Illinois, Chicago, IL, 60607, USA
| | - Omar M Ramahi
- Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, N2L3G1, Canada
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Zhou Q, Qin P, Luo J, Hu Q, Sun W, Chen B, Wang G. Evaluating AI rib fracture detections using follow-up CT scans. Am J Emerg Med 2023; 72:34-38. [PMID: 37478635 DOI: 10.1016/j.ajem.2023.07.018] [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: 12/13/2022] [Revised: 06/25/2023] [Accepted: 07/11/2023] [Indexed: 07/23/2023] Open
Abstract
PURPOSE This study compares the results of Artificial Intelligence (AI) diagnosis of rib fractures using initial CT and follow-up CT as the final diagnostic criteria, and studies AI-assisted diagnosis in improving the detection rate of rib fractures. METHODS A retrospective study was conducted on 113 patients who underwent initial and follow-up CT scans due to trauma. The initial and follow-up CT were used as diagnostic criteria, respectively. All images were transmitted to the AI software (V2.1.0, Huiying Medical Technology Co., Beijing, China) for rib fracture detection. The radiologist group (Group 1), AI group (Group 2), and Radiologist with AI group (Group 3) reviewed CT images at an interval of one month, recorded and compared the differences in the sensitivity and specificity for diagnosing rib fractures. RESULTS 589 and 712 rib fractures were diagnosed by the initial and follow-up CT, respectively. The initial CT diagnosis failed to detect 127 rib fractures, resulting in a missed rate of 17.84%. In addition, four normal ribs were mistakenly identified as being fractured. The follow-up CT was regarded as the diagnostic standard for rib fractures. The sensitivity and specificity were 82.16% and 99.80% for Group 1, 79.35% and 84.90% for Group 2, and 91.57% and 99.70% for Group 3. The sensitivity of Group 3 was higher than that of Group 1 and Group 2 (p < 0.05). The specificity was lower for Group 2 compared with Group 1 and Group 3 (p < 0.05). CONCLUSION AI-assisted diagnosis improved the detection rate of rib fractures, the follow-up CT should be used for the diagnosis standard of rib fractures, and AI misdiagnoses can be greatly reduced when a radiologist reviews the diagnosis.
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Affiliation(s)
- Quanshuai Zhou
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China; Guangzhou Xinhua University, Guangzhou, China
| | - Peixin Qin
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
| | - Junqi Luo
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
| | - Qiyi Hu
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
| | - Weiqian Sun
- Huiying Medical Technology Co., Ltd, Beijing, China
| | - Binghui Chen
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China.
| | - Guojie Wang
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China.
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Sun H, Wang X, Li Z, Liu A, Xu S, Jiang Q, Li Q, Xue Z, Gong J, Chen L, Xiao Y, Liu S. Automated Rib Fracture Detection on Chest X-Ray Using Contrastive Learning. J Digit Imaging 2023; 36:2138-2147. [PMID: 37407842 PMCID: PMC10501970 DOI: 10.1007/s10278-023-00868-z] [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: 03/19/2023] [Revised: 06/05/2023] [Accepted: 06/06/2023] [Indexed: 07/07/2023] Open
Abstract
To develop a deep learning-based model for detecting rib fractures on chest X-Ray and to evaluate its performance based on a multicenter study. Chest digital radiography (DR) images from 18,631 subjects were used for the training, testing, and validation of the deep learning fracture detection model. We first built a pretrained model, a simple framework for contrastive learning of visual representations (simCLR), using contrastive learning with the training set. Then, simCLR was used as the backbone for a fully convolutional one-stage (FCOS) objective detection network to identify rib fractures from chest X-ray images. The detection performance of the network for four different types of rib fractures was evaluated using the testing set. A total of 127 images from Data-CZ and 109 images from Data-CH with the annotations for four types of rib fractures were used for evaluation. The results showed that for Data-CZ, the sensitivities of the detection model with no pretraining, pretrained ImageNet, and pretrained DR were 0.465, 0.735, and 0.822, respectively, and the average number of false positives per scan was five in all cases. For the Data-CH test set, the sensitivities of three different pretraining methods were 0.403, 0.655, and 0.748. In the identification of four fracture types, the detection model achieved the highest performance for displaced fractures, with sensitivities of 0.873 and 0.774 for the Data-CZ and Data-CH test sets, respectively, with 5 false positives per scan, followed by nondisplaced fractures, buckle fractures, and old fractures. A pretrained model can significantly improve the performance of the deep learning-based rib fracture detection based on X-ray images, which can reduce missed diagnoses and improve the diagnostic efficacy.
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Affiliation(s)
- Hongbiao Sun
- Department of Radiology, Shanghai Changzheng Hospital, Navy Medical University, No.415, Fengyang Road, Huangpu District, Shanghai, 200003, China
| | - Xiang Wang
- Department of Radiology, Shanghai Changzheng Hospital, Navy Medical University, No.415, Fengyang Road, Huangpu District, Shanghai, 200003, China
| | - Zheren Li
- Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, China
- Shanghai United Imaging Intelligence Co., Ltd., No.701, Yunjin Road, Xuhui District, Shanghai, 200232, China
| | - Aie Liu
- Shanghai United Imaging Intelligence Co., Ltd., No.701, Yunjin Road, Xuhui District, Shanghai, 200232, China
| | - Shaochun Xu
- Department of Radiology, Shanghai Changzheng Hospital, Navy Medical University, No.415, Fengyang Road, Huangpu District, Shanghai, 200003, China
| | - Qinling Jiang
- Department of Radiology, Shanghai Changzheng Hospital, Navy Medical University, No.415, Fengyang Road, Huangpu District, Shanghai, 200003, China
| | - Qingchu Li
- Department of Radiology, Shanghai Changzheng Hospital, Navy Medical University, No.415, Fengyang Road, Huangpu District, Shanghai, 200003, China
| | - Zhong Xue
- Shanghai United Imaging Intelligence Co., Ltd., No.701, Yunjin Road, Xuhui District, Shanghai, 200232, China
| | - Jing Gong
- Departments of Radiology, Changhai Hospital, Navy Medical University, Shanghai, 200433, China
| | - Lei Chen
- Shanghai United Imaging Intelligence Co., Ltd., No.701, Yunjin Road, Xuhui District, Shanghai, 200232, China.
| | - Yi Xiao
- Department of Radiology, Shanghai Changzheng Hospital, Navy Medical University, No.415, Fengyang Road, Huangpu District, Shanghai, 200003, China.
| | - Shiyuan Liu
- Department of Radiology, Shanghai Changzheng Hospital, Navy Medical University, No.415, Fengyang Road, Huangpu District, Shanghai, 200003, China.
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Wang HC, Wang SC, Yan JL, Ko LW. Artificial Intelligence Model Trained with Sparse Data to Detect Facial and Cranial Bone Fractures from Head CT. J Digit Imaging 2023; 36:1408-1418. [PMID: 37095310 PMCID: PMC10407005 DOI: 10.1007/s10278-023-00829-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 02/15/2023] [Accepted: 03/31/2023] [Indexed: 04/26/2023] Open
Abstract
The presence of cranial and facial bone fractures is an important finding on non-enhanced head computed tomography (CT) scans from patients who have sustained head trauma. Some prior studies have proposed automatic cranial fracture detections, but studies on facial fractures are lacking. We propose a deep learning system to automatically detect both cranial and facial bone fractures. Our system incorporated models consisting of YOLOv4 for one-stage fracture detection and improved ResUNet (ResUNet++) for the segmentation of cranial and facial bones. The results from the two models mapped together provided the location of the fracture and the name of the fractured bone as the final output. The training data for the detection model were the soft tissue algorithm images from a total of 1,447 head CT studies (a total of 16,985 images), and the training data for the segmentation model included 1,538 selected head CT images. The trained models were tested on a test dataset consisting of 192 head CT studies (a total of 5,890 images). The overall performance achieved a sensitivity of 88.66%, a precision of 94.51%, and an F1 score of 0.9149. Specifically, the cranial and facial regions were evaluated and resulted in a sensitivity of 84.78% and 80.77%, a precision of 92.86% and 87.50%, and F1 scores of 0.8864 and 0.8400, respectively. The average accuracy for the segmentation labels concerning all predicted fracture bounding boxes was 80.90%. Our deep learning system could accurately detect cranial and facial bone fractures and identify the fractured bone region simultaneously.
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Affiliation(s)
- Huan-Chih Wang
- Division of Neurosurgery, Department of Surgery, National Taiwan University Hospital, Chungshan Rd, No. 7, Taipei City 100, Taiwan
- Division of Neurosurgery, Department of Surgery, National Taiwan University Hospital Hsinchu Branch, Hsinchu, Taiwan
- Department of Biological Science & Technology, College of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Shao-Chung Wang
- Department of Medical Imaging and Intervention, Gung Medical Foundation, New Taipei Municipal Tucheng Hospital, Chang
, New Taipei City, Taiwan
| | - Jiun-Lin Yan
- Department of Neurosurgery, Keelung Chang Gung Memorial Hospital, Keelung, Taiwan
- College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Li-Wei Ko
- Department of Biological Science & Technology, College of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
- Present Address: Institute of Electrical and Control Engineering, College of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
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Edamadaka S, Brown DW, Swaroop R, Kolodner M, Spain DA, Forrester JD, Choi J. FasterRib: A deep learning algorithm to automate identification and characterization of rib fractures on chest computed tomography scans. J Trauma Acute Care Surg 2023; 95:181-185. [PMID: 36872505 DOI: 10.1097/ta.0000000000003913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2023]
Abstract
OBJECTIVE Characterizing and enumerating rib fractures are critical to informing clinical decisions, yet in-depth characterization is rarely performed because of the manual burden of annotating these injuries on computed tomography (CT) scans. We hypothesized that our deep learning model, FasterRib , could predict the location and percentage displacement of rib fractures using chest CT scans. METHODS The development and internal validation cohort comprised more than 4,700 annotated rib fractures from 500 chest CT scans within the public RibFrac. We trained a convolutional neural network to predict bounding boxes around each fracture per CT slice. Adapting an existing rib segmentation model, FasterRib outputs the three-dimensional locations of each fracture (rib number and laterality). A deterministic formula analyzed cortical contact between bone segments to compute percentage displacements. We externally validated our model on our institution's data set. RESULTS FasterRib predicted precise rib fracture locations with 0.95 sensitivity, 0.90 precision, 0.92 f1 score, with an average of 1.3 false-positive fractures per scan. On external validation, FasterRib achieved 0.97 sensitivity, 0.96 precision, and 0.97 f1 score, and 2.24 false-positive fractures per scan. Our publicly available algorithm automatically outputs the location and percent displacement of each predicted rib fracture for multiple input CT scans. CONCLUSION We built a deep learning algorithm that automates rib fracture detection and characterization using chest CT scans. FasterRib achieved the highest recall and the second highest precision among known algorithms in literature. Our open source code could facilitate FasterRib's adaptation for similar computer vision tasks and further improvements via large-scale external validation. LEVEL OF EVIDENCE Diagnostic Tests/Criteria; Level III.
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Affiliation(s)
- Sathya Edamadaka
- From the Department of Electrical Engineering (S.E.), Stanford Center for Professional Development (D.J.B.), Department of Computer Science (R.S., M.K.), and Department of Surgery (D.A.S, J.D.F.,J.C.), Stanford University, Stanford, California
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12
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Jeong D, Jeong W, Lee JH, Park SY. Use of Automated Machine Learning for Classifying Hemoperitoneum on Ultrasonographic Images of Morrison's Pouch: A Multicenter Retrospective Study. J Clin Med 2023; 12:4043. [PMID: 37373736 DOI: 10.3390/jcm12124043] [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: 03/08/2023] [Revised: 06/09/2023] [Accepted: 06/11/2023] [Indexed: 06/29/2023] Open
Abstract
This study evaluated automated machine learning (AutoML) in classifying the presence or absence of hemoperitoneum in ultrasonography (USG) images of Morrison's pouch. In this multicenter, retrospective study, 864 trauma patients from trauma and emergency medical centers in South Korea were included. In all, 2200 USG images (1100 hemoperitoneum and 1100 normal) were collected. Of these, 1800 images were used for training and 200 were used for the internal validation of AutoML. External validation was performed using 100 hemoperitoneum images and 100 normal images collected separately from a trauma center that were not included in the training and internal validation sets. Google's open-source AutoML was used to train the algorithm in classifying hemoperitoneum in USG images, followed by internal and external validation. In the internal validation, the sensitivity, specificity, and area under the receiver operating characteristic (AUROC) curve were 95%, 99%, and 0.97, respectively. In the external validation, the sensitivity, specificity, and AUROC were 94%, 99%, and 0.97, respectively. The performances of AutoML in the internal and external validation were not statistically different (p = 0.78). A publicly available, general-purpose AutoML can accurately classify the presence or absence of hemoperitoneum in USG images of the Morrison's pouch of real-world trauma patients.
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Affiliation(s)
- Dongkil Jeong
- Department of Emergency Medicine, College of Medicine, Soonchunhyang University, Cheonan 31151, Republic of Korea
| | - Wonjoon Jeong
- Department of Emergency Medicine, School of Medicine, Chungnam National University, Daejeon 35015, Republic of Korea
| | - Ji Han Lee
- Division of Emergency Medicine, Department of Medicine, The Catholic University of Korea, Seoul 11765, Republic of Korea
| | - Sin-Youl Park
- Department of Emergency Medicine, College of Medicine, Yeungnam University, Daegu 42415, Republic of Korea
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13
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Tan H, Xu H, Yu N, Yu Y, Duan H, Fan Q, Zhanyu T. The value of deep learning-based computer aided diagnostic system in improving diagnostic performance of rib fractures in acute blunt trauma. BMC Med Imaging 2023; 23:55. [PMID: 37055752 PMCID: PMC10099632 DOI: 10.1186/s12880-023-01012-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 04/04/2023] [Indexed: 04/15/2023] Open
Abstract
BACKGROUND To evaluate the value of a deep learning-based computer-aided diagnostic system (DL-CAD) in improving the diagnostic performance of acute rib fractures in patients with chest trauma. MATERIALS AND METHODS CT images of 214 patients with acute blunt chest trauma were retrospectively analyzed by two interns and two attending radiologists independently firstly and then with the assistance of a DL-CAD one month later, in a blinded and randomized manner. The consensusdiagnosis of fib fracture by another two senior thoracic radiologists was regarded as reference standard. The rib fracture diagnostic sensitivity, specificity, positive predictive value, diagnostic confidence and mean reading time with and without DL-CAD were calculated and compared. RESULTS There were 680 rib fracture lesions confirmed as reference standard among all patients. The diagnostic sensitivity and positive predictive value of interns weresignificantly improved from (68.82%, 84.50%) to (91.76%, 93.17%) with the assistance of DL-CAD, respectively. Diagnostic sensitivity and positive predictive value of attendings aided by DL-CAD (94.56%, 95.67%) or not aided (86.47%, 93.83%), respectively. In addition, when radiologists were assisted by DL-CAD, the mean reading time was significantly reduced, and diagnostic confidence was significantly enhanced. CONCLUSIONS DL-CAD improves the diagnostic performance of acute rib fracture in chest trauma patients, which increases the diagnostic confidence, sensitivity, and positive predictive value for radiologists. DL-CAD can advance the diagnostic consistency of radiologists with different experiences.
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Affiliation(s)
- Hui Tan
- Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, China
| | - Hui Xu
- Peter Boris Centre for Addiction Research, McMaster University & St. Joseph's Health Care Hamilton, 100 West 5th Street, Hamilton, ON, L8P 3R2, Canada.
| | - Nan Yu
- Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, China
| | - Yong Yu
- Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, China
| | - Haifeng Duan
- Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, China
| | - Qiuju Fan
- Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, China.
| | - Tian Zhanyu
- Institute of Medical Technology, Shaanxi University of Chinese Medicine, Xianyang, China
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Ghosh A, Bose S, Patton D, Kumar I, Khalkhali V, Henry MK, Ouyang M, Huang H, Vossough A, Sze RW, Sotardi S, Francavilla M. Deep learning-based prediction of rib fracture presence in frontal radiographs of children under two years of age: a proof-of-concept study. Br J Radiol 2023; 96:20220778. [PMID: 36802807 PMCID: PMC10161923 DOI: 10.1259/bjr.20220778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 02/08/2023] [Accepted: 02/10/2023] [Indexed: 02/22/2023] Open
Abstract
OBJECTIVE In this proof-of-concept study, we aimed to develop deep-learning-based classifiers to identify rib fractures on frontal chest radiographs in children under 2 years of age. METHODS This retrospective study included 1311 frontal chest radiographs (radiographs with rib fractures, n = 653) from 1231 unique patients (median age: 4 m). Patients with more than one radiograph were included only in the training set. A binary classification was performed to identify the presence or absence of rib fractures using transfer learning and Resnet-50 and DenseNet-121 architectures. The area under the receiver operating characteristic curve (AUC-ROC) was reported. Gradient-weighted class activation mapping was used to highlight the region most relevant to the deep learning models' predictions. RESULTS On the validation set, the ResNet-50 and DenseNet-121 models obtained an AUC-ROC of 0.89 and 0.88, respectively. On the test set, the ResNet-50 model demonstrated an AUC-ROC of 0.84 with a sensitivity of 81% and specificity of 70%. The DenseNet-50 model obtained an AUC of 0.82 with 72% sensitivity and 79% specificity. CONCLUSION In this proof-of-concept study, a deep learning-based approach enabled the automatic detection of rib fractures in chest radiographs of young children with performances comparable to pediatric radiologists. Further evaluation of this approach on large multi-institutional data sets is needed to assess the generalizability of our results. ADVANCES IN KNOWLEDGE In this proof-of-concept study, a deep learning-based approach performed well in identifying chest radiographs with rib fractures. These findings provide further impetus to develop deep learning algorithms for identifying rib fractures in children, especially those with suspected physical abuse or non-accidental trauma.
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Affiliation(s)
| | - Saurav Bose
- Department of Radiology, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Daniella Patton
- Department of Radiology, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Ishaan Kumar
- Department of Radiology, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Vahid Khalkhali
- Department of Radiology, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
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Kim T, Goh TS, Lee JS, Lee JH, Kim H, Jung ID. Transfer learning-based ensemble convolutional neural network for accelerated diagnosis of foot fractures. Phys Eng Sci Med 2023; 46:265-277. [PMID: 36625995 DOI: 10.1007/s13246-023-01215-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 01/02/2023] [Indexed: 01/11/2023]
Abstract
The complex shape of the foot, consisting of 26 bones, variable ligaments, tendons, and muscles leads to misdiagnosis of foot fractures. Despite the introduction of artificial intelligence (AI) to diagnose fractures, the accuracy of foot fracture diagnosis is lower than that of conventional methods. We developed an AI assistant system that assists with consistent diagnosis and helps interns or non-experts improve their diagnosis of foot fractures, and compared the effectiveness of the AI assistance on various groups with different proficiency. Contrast-limited adaptive histogram equalization was used to improve the visibility of original radiographs and data augmentation was applied to prevent overfitting. Preprocessed radiographs were fed to an ensemble model of a transfer learning-based convolutional neural network (CNN) that was developed for foot fracture detection with three models: InceptionResNetV2, MobilenetV1, and ResNet152V2. After training the model, score class activation mapping was applied to visualize the fracture based on the model prediction. The prediction result was evaluated by the receiver operating characteristic (ROC) curve and its area under the curve (AUC), and the F1-Score. Regarding the test set, the ensemble model exhibited better classification ability (F1-Score: 0.837, AUC: 0.95, Accuracy: 86.1%) than other single models that showed an accuracy of 82.4%. With AI assistance for the orthopedic fellow, resident, intern, and student group, the accuracy of each group improved by 3.75%, 7.25%, 6.25%, and 7% respectively and diagnosis time was reduced by 21.9%, 14.7%, 24.4%, and 34.6% respectively.
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Affiliation(s)
- Taekyeong Kim
- Department of Mechanical Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919, Republic of Korea
| | - Tae Sik Goh
- Department of Orthopaedic Surgery, Biomedical Research Institute, Pusan National University Hospital, Pusan National University School of Medicine, Busan, 49241, Republic of Korea
| | - Jung Sub Lee
- Department of Orthopaedic Surgery, Biomedical Research Institute, Pusan National University Hospital, Pusan National University School of Medicine, Busan, 49241, Republic of Korea
| | - Ji Hyun Lee
- Health Insurance Review & Assessment Service, Wonju, 26465, Republic of Korea
| | - Hayeol Kim
- Department of Mechanical Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919, Republic of Korea
| | - Im Doo Jung
- Department of Mechanical Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919, Republic of Korea.
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Hongbiao S, Shaochun X, Xiang W, YuRun T, Yang L, Mingzi Z, Hua Y, Keyang Z, Chi-Cheng F, Qu F, Pengchen G, Yi X, Shiyuan L. Comparison and verification of two deep learning models for the detection of chest CT rib fractures. Acta Radiol 2023; 64:542-551. [PMID: 35300519 DOI: 10.1177/02841851221083519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
BACKGROUND A high false-positive rate remains a technical glitch hindering the broad spectrum of application of deep-learning-based diagnostic tools in routine radiological practice from assisting in diagnosing rib fractures. PURPOSE To examine the performance of two versions of deep-learning-based software tools in aiding radiologists in diagnosing rib fractures on chest computed tomography (CT) images. MATERIAL AND METHODS In total, 123 patients (708 rib fractures) were included in this retrospective study. Two groups of radiologists with different experience levels retrospectively reviewed images for rib fractures in the concurrent mode aided with RibFrac-High Sensitivity (HS) and RibFrac-High Precision (HP). We compared their diagnostic performance against the reference standard in terms of sensitivity and positive predictive value (PPV). RESULTS On a per-patient basis, RibFrac-HS exhibited a higher sensitivity compared with RibFrac-HP (mean difference=0.051, 95% CI=0.012-0.090; P = 0.011), whereas the latter significantly outperformed the former in terms of the PPV (mean difference=0.273, 95% CI=0.238-0.308; P < 0.0001). The use of RibFrac-HP significantly improved the junior and the senior groups' sensitivities respectively by 0.058 (95% CI=0.033-0.083; P < 0.0001) and 0.058 (95% CI=0.034-0.081; P < 0.0001), and decreased the diagnosis time by 206 s (95% CI=191-220; P < 0.0001) and 79 s (95% CI=67-92; P < 0.0001), respectively, when compared to no software assistance. CONCLUSION The sensitivity and efficiency of radiologists in identifying rib fractures can be improved by using RibFrac-HS and/or RibFrac-HP. With an added module for false-positive suppression, RibFrac-HP maintains the sensitivity and increases the PPV in fracture detection compared to Rib-Frac-HS.
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Affiliation(s)
- Sun Hongbiao
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, PR China
| | - Xu Shaochun
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, PR China
| | - Wang Xiang
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, PR China
| | - Tang YuRun
- Company 13, College of Basic Medical Sciences, Naval Medical University, Shanghai, PR China
| | - Lu Yang
- Shanghai Aitrox Technology Corporation Limited, Shanghai, PR China
| | - Zhang Mingzi
- Shanghai Aitrox Technology Corporation Limited, Shanghai, PR China
| | - Yang Hua
- Shanghai Aitrox Technology Corporation Limited, Shanghai, PR China
| | - Zhao Keyang
- Shanghai Aitrox Technology Corporation Limited, Shanghai, PR China
| | - Fu Chi-Cheng
- Shanghai Aitrox Technology Corporation Limited, Shanghai, PR China
| | - Fang Qu
- Shanghai Aitrox Technology Corporation Limited, Shanghai, PR China
| | - Gu Pengchen
- Shanghai Aitrox Technology Corporation Limited, Shanghai, PR China
| | - Xiao Yi
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, PR China
| | - Liu Shiyuan
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, PR China
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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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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] [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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Yang C, Yang L, Gao GD, Zong HQ, Gao D. Assessment of artificial intelligence-aided reading in the detection of nasal bone fractures. Technol Health Care 2022; 31:1017-1025. [PMID: 36442167 DOI: 10.3233/thc-220501] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
BACKGROUND: Artificial intelligence (AI) technology is a promising diagnostic adjunct in fracture detection. However, few studies describe the improvement of clinicians’ diagnostic accuracy for nasal bone fractures with the aid of AI technology. OBJECTIVE: This study aims to determine the value of the AI model in improving the diagnostic accuracy for nasal bone fractures compared with manual reading. METHODS: A total of 252 consecutive patients who had undergone facial computed tomography (CT) between January 2020 and January 2021 were enrolled in this study. The presence or absence of a nasal bone fracture was determined by two experienced radiologists. An AI algorithm based on the deep-learning algorithm was engineered, trained and validated to detect fractures on CT images. Twenty readers with various experience were invited to read CT images with or without AI. The accuracy, sensitivity and specificity with the aid of the AI model were calculated by the readers. RESULTS: The deep-learning AI model had 84.78% sensitivity, 86.67% specificity, 0.857 area under the curve (AUC) and a 0.714 Youden index in identifying nasal bone fractures. For all readers, regardless of experience, AI-aided reading had higher sensitivity ([94.00 ± 3.17]% vs [83.52 ± 10.16]%, P< 0.001), specificity ([89.75 ± 6.15]% vs [77.55 ± 11.38]%, P< 0.001) and AUC (0.92 ± 0.04 vs 0.81 ± 0.10, P< 0.001) compared with reading without AI. With the aid of AI, the sensitivity, specificity and AUC were significantly improved in readers with 1–5 years or 6–10 years of experience (all P< 0.05, Table 4). For readers with 11–15 years of experience, no evidence suggested that AI could improve sensitivity and AUC (P= 0.124 and 0.152, respectively). CONCLUSION: The AI model might aid less experienced physicians and radiologists in improving their diagnostic performance for the localisation of nasal bone fractures on CT images.
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Affiliation(s)
- Cun Yang
- Department of Medical Equipment, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Lei Yang
- Department of Medical Imaging, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Guo-Dong Gao
- Department of Medical Imaging, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Hui-Qian Zong
- Department of Medical Equipment, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Duo Gao
- Department of Medical Imaging, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
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Dankelman LHM, Schilstra S, IJpma FFA, Doornberg JN, Colaris JW, Verhofstad MHJ, Wijffels MME, Prijs J. Artificial intelligence fracture recognition on computed tomography: review of literature and recommendations. Eur J Trauma Emerg Surg 2022; 49:681-691. [PMID: 36284017 PMCID: PMC10175338 DOI: 10.1007/s00068-022-02128-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 10/02/2022] [Indexed: 11/26/2022]
Abstract
Abstract
Purpose
The use of computed tomography (CT) in fractures is time consuming, challenging and suffers from poor inter-surgeon reliability. Convolutional neural networks (CNNs), a subset of artificial intelligence (AI), may overcome shortcomings and reduce clinical burdens to detect and classify fractures. The aim of this review was to summarize literature on CNNs for the detection and classification of fractures on CT scans, focusing on its accuracy and to evaluate the beneficial role in daily practice.
Methods
Literature search was performed according to the PRISMA statement, and Embase, Medline ALL, Web of Science Core Collection, Cochrane Central Register of Controlled Trials and Google Scholar databases were searched. Studies were eligible when the use of AI for the detection of fractures on CT scans was described. Quality assessment was done with a modified version of the methodologic index for nonrandomized studies (MINORS), with a seven-item checklist. Performance of AI was defined as accuracy, F1-score and area under the curve (AUC).
Results
Of the 1140 identified studies, 17 were included. Accuracy ranged from 69 to 99%, the F1-score ranged from 0.35 to 0.94 and the AUC, ranging from 0.77 to 0.95. Based on ten studies, CNN showed a similar or improved diagnostic accuracy in addition to clinical evaluation only.
Conclusions
CNNs are applicable for the detection and classification fractures on CT scans. This can improve automated and clinician-aided diagnostics. Further research should focus on the additional value of CNN used for CT scans in daily clinics.
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Affiliation(s)
- Lente H. M. Dankelman
- Trauma Research Unit, Department of Surgery, Erasmus MC, University Medical Center Rotterdam, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands
| | - Sanne Schilstra
- Department of Orthopedic Surgery, Groningen University Medical Centre, Groningen, The Netherlands
- Department of Surgery, Groningen University Medical Centre, Groningen, The Netherlands
| | - Frank F. A. IJpma
- Department of Surgery, Groningen University Medical Centre, Groningen, The Netherlands
| | - Job N. Doornberg
- Department of Orthopedic Surgery, Groningen University Medical Centre, Groningen, The Netherlands
- Department of Surgery, Groningen University Medical Centre, Groningen, The Netherlands
- Department of Orthopedic & Trauma Surgery, Flinders Medical Centre, Flinders University, Adelaide, Australia
| | - Joost W. Colaris
- Department of Orthopedics, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | - Michael H. J. Verhofstad
- Trauma Research Unit, Department of Surgery, Erasmus MC, University Medical Center Rotterdam, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands
| | - Mathieu M. E. Wijffels
- Trauma Research Unit, Department of Surgery, Erasmus MC, University Medical Center Rotterdam, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands
| | - Jasper Prijs
- Department of Orthopedic Surgery, Groningen University Medical Centre, Groningen, The Netherlands
- Department of Surgery, Groningen University Medical Centre, Groningen, The Netherlands
- Department of Orthopedic & Trauma Surgery, Flinders Medical Centre, Flinders University, Adelaide, Australia
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Artificial Intelligence in Orthopedic Radiography Analysis: A Narrative Review. Diagnostics (Basel) 2022; 12:diagnostics12092235. [PMID: 36140636 PMCID: PMC9498096 DOI: 10.3390/diagnostics12092235] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 09/12/2022] [Accepted: 09/13/2022] [Indexed: 11/17/2022] Open
Abstract
Artificial intelligence (AI) in medicine is a rapidly growing field. In orthopedics, the clinical implementations of AI have not yet reached their full potential. Deep learning algorithms have shown promising results in computed radiographs for fracture detection, classification of OA, bone age, as well as automated measurements of the lower extremities. Studies investigating the performance of AI compared to trained human readers often show equal or better results, although human validation is indispensable at the current standards. The objective of this narrative review is to give an overview of AI in medicine and summarize the current applications of AI in orthopedic radiography imaging. Due to the different AI software and study design, it is difficult to find a clear structure in this field. To produce more homogeneous studies, open-source access to AI software codes and a consensus on study design should be aimed for.
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22
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Development and assessment of deep learning system for the location and classification of rib fractures via computed tomography. Eur J Radiol 2022; 154:110434. [DOI: 10.1016/j.ejrad.2022.110434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Revised: 05/26/2022] [Accepted: 06/30/2022] [Indexed: 11/18/2022]
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Rib Fracture Detection with Dual-Attention Enhanced U-Net. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:8945423. [PMID: 36035283 PMCID: PMC9410867 DOI: 10.1155/2022/8945423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 07/24/2022] [Accepted: 08/02/2022] [Indexed: 11/18/2022]
Abstract
Rib fractures are common injuries caused by chest trauma, which may cause serious consequences. It is essential to diagnose rib fractures accurately. Low-dose thoracic computed tomography (CT) is commonly used for rib fracture diagnosis, and convolutional neural network- (CNN-) based methods have assisted doctors in rib fracture diagnosis in recent years. However, due to the lack of rib fracture data and the irregular, various shape of rib fractures, it is difficult for CNN-based methods to extract rib fracture features. As a result, they cannot achieve satisfying results in terms of accuracy and sensitivity in detecting rib fractures. Inspired by the attention mechanism, we proposed the CFSG U-Net for rib fracture detection. The CSFG U-Net uses the U-Net architecture and is enhanced by a dual-attention module, including a channel-wise fusion attention module (CFAM) and a spatial-wise group attention module (SGAM). CFAM uses the channel attention mechanism to reweight the feature map along the channel dimension and refine the U-Net's skip connections. SGAM uses the group technique to generate spatial attention to adjust feature maps in the spatial dimension, which allows the spatial attention module to capture more fine-grained semantic information. To evaluate the effectiveness of our proposed methods, we established a rib fracture dataset in our research. The experimental results on our dataset show that the maximum sensitivity of our proposed method is 89.58%, and the average FROC score is 81.28%, which outperforms the existing rib fracture detection methods and attention modules.
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Nadkarni P, Merchant SA. Enhancing medical-imaging artificial intelligence through holistic use of time-tested key imaging and clinical parameters: Future insights. Artif Intell Med Imaging 2022; 3:55-69. [DOI: 10.35711/aimi.v3.i3.55] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 04/12/2022] [Accepted: 06/17/2022] [Indexed: 02/06/2023] Open
Abstract
Much of the published literature in Radiology-related Artificial Intelligence (AI) focuses on single tasks, such as identifying the presence or absence or severity of specific lesions. Progress comparable to that achieved for general-purpose computer vision has been hampered by the unavailability of large and diverse radiology datasets containing different types of lesions with possibly multiple kinds of abnormalities in the same image. Also, since a diagnosis is rarely achieved through an image alone, radiology AI must be able to employ diverse strategies that consider all available evidence, not just imaging information. Using key imaging and clinical signs will help improve their accuracy and utility tremendously. Employing strategies that consider all available evidence will be a formidable task; we believe that the combination of human and computer intelligence will be superior to either one alone. Further, unless an AI application is explainable, radiologists will not trust it to be either reliable or bias-free; we discuss some approaches aimed at providing better explanations, as well as regulatory concerns regarding explainability (“transparency”). Finally, we look at federated learning, which allows pooling data from multiple locales while maintaining data privacy to create more generalizable and reliable models, and quantum computing, still prototypical but potentially revolutionary in its computing impact.
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Affiliation(s)
- Prakash Nadkarni
- College of Nursing, University of Iowa, Iowa City, IA 52242, United States
| | - Suleman Adam Merchant
- Department of Radiology, LTM Medical College & LTM General Hospital, Mumbai 400022, Maharashtra, India
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Niiya A, Murakami K, Kobayashi R, Sekimoto A, Saeki M, Toyofuku K, Kato M, Shinjo H, Ito Y, Takei M, Murata C, Ohgiya Y. Development of an artificial intelligence-assisted computed tomography diagnosis technology for rib fracture and evaluation of its clinical usefulness. Sci Rep 2022; 12:8363. [PMID: 35589847 PMCID: PMC9119970 DOI: 10.1038/s41598-022-12453-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 05/03/2022] [Indexed: 11/20/2022] Open
Abstract
Artificial intelligence algorithms utilizing deep learning are helpful tools for diagnostic imaging. A deep learning-based automatic detection algorithm was developed for rib fractures on computed tomography (CT) images of high-energy trauma patients. In this study, the clinical effectiveness of this algorithm was evaluated. A total of 56 cases were retrospectively examined, including 46 rib fractures and 10 control cases from our hospital, between January and June 2019. Two radiologists annotated the fracture lesions (complete or incomplete) for each CT image, which is considered the “ground truth.” Thereafter, the algorithm’s diagnostic results for all cases were compared with the ground truth, and the sensitivity and number of false positive (FP) results per case were assessed. The radiologists identified 199 images with a fracture. The sensitivity of the algorithm was 89.8%, and the number of FPs per case was 2.5. After additional learning, the sensitivity increased to 93.5%, and the number of FPs was 1.9 per case. FP results were found in the trabecular bone with the appearance of fracture, vascular grooves, and artifacts. The sensitivity of the algorithm used in this study was sufficient to aid the rapid detection of rib fractures within the evaluated validation set of CT images.
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Affiliation(s)
- Akifumi Niiya
- Department of Radiology, Showa University, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo, 142-8666, Japan.
| | - Kouzou Murakami
- Department of Radiology, Showa University, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo, 142-8666, Japan
| | - Rei Kobayashi
- Department of Radiology, Showa University, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo, 142-8666, Japan
| | - Atsuhito Sekimoto
- Department of Radiology, Showa University, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo, 142-8666, Japan
| | - Miho Saeki
- Department of Radiology, Showa University, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo, 142-8666, Japan
| | - Kosuke Toyofuku
- Department of Radiology, Showa University, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo, 142-8666, Japan
| | - Masako Kato
- Department of Radiology, Showa University, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo, 142-8666, Japan
| | - Hidenori Shinjo
- Department of Radiology, Showa University, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo, 142-8666, Japan
| | - Yoshinori Ito
- Department of Radiology, Showa University, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo, 142-8666, Japan
| | - Mizuki Takei
- Fujifilm Corporation, Nishiazabu 2-Chome, Minato-ku, Tokyo, 26-30, Japan
| | - Chiori Murata
- Fujifilm Corporation, Nishiazabu 2-Chome, Minato-ku, Tokyo, 26-30, Japan
| | - Yoshimitsu Ohgiya
- Department of Radiology, Showa University, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo, 142-8666, Japan
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CCE-Net: A rib fracture diagnosis network based on contralateral, contextual, and edge enhanced modules. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Assessment of deep convolutional neural network models for mandibular fracture detection in panoramic radiographs. Int J Oral Maxillofac Surg 2022; 51:1488-1494. [DOI: 10.1016/j.ijom.2022.03.056] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Revised: 01/11/2022] [Accepted: 03/21/2022] [Indexed: 01/17/2023]
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An Algorithm for Automatic Rib Fracture Recognition Combined with nnU-Net and DenseNet. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE 2022; 2022:5841451. [PMID: 35251210 PMCID: PMC8896936 DOI: 10.1155/2022/5841451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 01/31/2022] [Indexed: 11/29/2022]
Abstract
Rib fracture is the most common thoracic clinical trauma. Most patients have multiple different types of rib fracture regions, so accurate and rapid identification of all trauma regions is crucial for the treatment of rib fracture patients. In this study, a two-stage rib fracture recognition model based on nnU-Net is proposed. First, a deep learning segmentation model is trained to generate candidate rib fracture regions, and then, a deep learning classification model is trained in the second stage to classify the segmented local fracture regions according to the candidate fracture regions generated in the first stage to determine whether they are fractures or not. The results show that the two-stage deep learning model proposed in this study improves the accuracy of rib fracture recognition and reduces the false-positive and false-negative rates of rib fracture detection, which can better assist doctors in fracture region recognition.
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Offiah AC. Current and emerging artificial intelligence applications for pediatric musculoskeletal radiology. Pediatr Radiol 2022; 52:2149-2158. [PMID: 34272573 PMCID: PMC9537230 DOI: 10.1007/s00247-021-05130-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 04/28/2021] [Accepted: 06/10/2021] [Indexed: 12/03/2022]
Abstract
Artificial intelligence (AI) is playing an ever-increasing role in radiology (more so in the adult world than in pediatrics), to the extent that there are unfounded fears it will completely take over the role of the radiologist. In relation to musculoskeletal applications of AI in pediatric radiology, we are far from the time when AI will replace radiologists; even for the commonest application (bone age assessment), AI is more often employed in an AI-assist mode rather than an AI-replace or AI-extend mode. AI for bone age assessment has been in clinical use for more than a decade and is the area in which most research has been conducted. Most other potential indications in children (such as appendicular and vertebral fracture detection) remain largely in the research domain. This article reviews the areas in which AI is most prominent in relation to the pediatric musculoskeletal system, briefly summarizing the current literature and highlighting areas for future research. Pediatric radiologists are encouraged to participate as members of the research teams conducting pediatric radiology artificial intelligence research.
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Affiliation(s)
- Amaka C. Offiah
- grid.11835.3e0000 0004 1936 9262Department of Oncology and Metabolism, University of Sheffield, Damer Street Building, Sheffield, S10 2TH UK ,grid.419127.80000 0004 0463 9178Department of Radiology, Sheffield Children’s NHS Foundation Trust, Sheffield, UK
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Azuma M, Nakada H, Takei M, Nakamura K, Katsuragawa S, Shinkawa N, Terada T, Masuda R, Hattori Y, Ide T, Kimura A, Shimomura M, Kawano M, Matsumura K, Meiri T, Ochiai H, Hirai T. Detection of acute rib fractures on CT images with convolutional neural networks: effect of location and type of fracture and reader's experience. Emerg Radiol 2021; 29:317-328. [PMID: 34855002 DOI: 10.1007/s10140-021-02000-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 11/10/2021] [Indexed: 11/25/2022]
Abstract
PURPOSE The evaluation of all ribs on thin-slice CT images is time consuming and it can be difficult to accurately assess the location and type of rib fracture in an emergency. The aim of our study was to develop and validate a convolutional neural network (CNN) algorithm for the detection of acute rib fractures on thoracic CT images and to investigate the effect of the CNN algorithm on radiologists' performance. METHODS The dataset for development of a CNN consisted of 539 thoracic CT scans with 4906 acute rib fractures. A three-dimensional faster region-based CNN was trained and evaluated by using tenfold cross-validation. For an observer performance study to investigate the effect of CNN outputs on radiologists' performance, 30 thoracic CT scans (28 scans with 90 acute rib fractures and 2 without rib fractures) which were not included in the development dataset were used. Observer performance study involved eight radiologists who evaluated CT images first without and second with CNN outputs. The diagnostic performance was assessed by using figure of merit (FOM) values obtained from the jackknife free-response receiver operating characteristic (JAFROC) analysis. RESULTS When radiologists used the CNN output for detection of rib fractures, the mean FOM value significantly increased for all readers (0.759 to 0.819, P = 0.0004) and for displaced (0.925 to 0.995, P = 0.0028) and non-displaced fractures (0.678 to 0.732, P = 0.0116). At all rib levels except for the 1st and 12th ribs, the radiologists' true-positive fraction of the detection became significantly increased by using the CNN outputs. CONCLUSION The CNN specialized for the detection of acute rib fractures on CT images can improve the radiologists' diagnostic performance regardless of the type of fractures and reader's experience. Further studies are needed to clarify the usefulness of the CNN for the detection of acute rib fractures on CT images in actual clinical practice.
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Affiliation(s)
- Minako Azuma
- Department of Radiology, Faculty of Medicine, University of Miyazaki, 5200 Kihara, Kiyotake, Miyazaki, 889-1692, Japan.
| | - Hiroshi Nakada
- Department of Radiology, Faculty of Medicine, University of Miyazaki, 5200 Kihara, Kiyotake, Miyazaki, 889-1692, Japan
| | | | | | | | - Norihiro Shinkawa
- Department of Radiology, Faculty of Medicine, University of Miyazaki, 5200 Kihara, Kiyotake, Miyazaki, 889-1692, Japan
| | - Tamasa Terada
- Department of Radiology, Faculty of Medicine, University of Miyazaki, 5200 Kihara, Kiyotake, Miyazaki, 889-1692, Japan
| | - Rie Masuda
- Department of Radiology, Faculty of Medicine, University of Miyazaki, 5200 Kihara, Kiyotake, Miyazaki, 889-1692, Japan
| | - Youhei Hattori
- Department of Radiology, Faculty of Medicine, University of Miyazaki, 5200 Kihara, Kiyotake, Miyazaki, 889-1692, Japan
| | - Takakazu Ide
- Department of Radiology, Faculty of Medicine, University of Miyazaki, 5200 Kihara, Kiyotake, Miyazaki, 889-1692, Japan
| | - Aya Kimura
- Department of Radiology, Faculty of Medicine, University of Miyazaki, 5200 Kihara, Kiyotake, Miyazaki, 889-1692, Japan
| | - Mei Shimomura
- Department of Radiology, Faculty of Medicine, University of Miyazaki, 5200 Kihara, Kiyotake, Miyazaki, 889-1692, Japan
| | - Masatsugu Kawano
- Department of Radiology, Faculty of Medicine, University of Miyazaki, 5200 Kihara, Kiyotake, Miyazaki, 889-1692, Japan
| | - Kengo Matsumura
- Department of Radiology, Faculty of Medicine, University of Miyazaki, 5200 Kihara, Kiyotake, Miyazaki, 889-1692, Japan
| | - Takayuki Meiri
- Department of Radiology, Faculty of Medicine, University of Miyazaki, 5200 Kihara, Kiyotake, Miyazaki, 889-1692, Japan
| | - Hidenobu Ochiai
- Center for Emergency and Critical Care Medicine, Faculty of Medicine, University of Miyazaki, Miyazaki, Japan
| | - Toshinori Hirai
- Department of Diagnostic Radiology, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan
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Ajmera P, Kharat A, Botchu R, Gupta H, Kulkarni V. Real-world analysis of artificial intelligence in musculoskeletal trauma. J Clin Orthop Trauma 2021; 22:101573. [PMID: 34527511 PMCID: PMC8427222 DOI: 10.1016/j.jcot.2021.101573] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 08/20/2021] [Accepted: 08/20/2021] [Indexed: 11/30/2022] Open
Abstract
Musculoskeletal trauma accounts for a large percentage of emergency room visits and is amongst the top causes of unscheduled patient visits to the emergency room. Musculoskeletal trauma results in expenditure of billions of dollars and protracted losses of quality-adjusted life years. New and innovative methods are needed to minimise the impact by ensuring quick and accurate assessment. However, each of the currently utilised radiological procedures, such as radiography, ultrasonography, computed tomography, and magnetic resonance imaging, has resulted in implosion of medical imaging data. Deep learning, a recent advancement in artificial intelligence, has demonstrated the potential to analyse medical images with sensitivity and specificity at par with experts. In this review article, we intend to summarise and showcase the various developments which have occurred in the dynamic field of artificial intelligence and machine learning and how their applicability to different aspects of imaging in trauma can be explored to improvise our existing reporting systems and improvise on patient outcomes.
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Affiliation(s)
- Pranav Ajmera
- Department of Radiology, Dr D.Y. Patil Medical College, Hospital and Research Center, DPU, Pune, India
| | - Amit Kharat
- Department of Radiology, Dr D.Y. Patil Medical College, Hospital and Research Center, DPU, Pune, India
| | - Rajesh Botchu
- Department of Musculoskeletal Radiology, Royal Orthopedic Hospital, Birmingham, UK
| | - Harun Gupta
- Department of Musculoskeletal Radiology, Leeds Teaching Hospitals, Leeds, UK
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Ibanez V, Gunz S, Erne S, Rawdon EJ, Ampanozi G, Franckenberg S, Sieberth T, Affolter R, Ebert LC, Dobay A. RiFNet: Automated rib fracture detection in postmortem computed tomography. Forensic Sci Med Pathol 2021; 18:20-29. [PMID: 34709561 PMCID: PMC8921053 DOI: 10.1007/s12024-021-00431-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/20/2021] [Indexed: 12/31/2022]
Abstract
Imaging techniques are widely used for medical diagnostics. In some cases, a lack of medical practitioners who can manually analyze the images can lead to a bottleneck. Consequently, we developed a custom-made convolutional neural network (RiFNet = Rib Fracture Network) that can detect rib fractures in postmortem computed tomography. In a retrospective cohort study, we retrieved PMCT data from 195 postmortem cases with rib fractures from July 2017 to April 2018 from our database. The computed tomography data were prepared using a plugin in the commercial imaging software Syngo.via whereby the rib cage was unfolded on a single-in-plane image reformation. Out of the 195 cases, a total of 585 images were extracted and divided into two groups labeled "with" and "without" fractures. These two groups were subsequently divided into training, validation, and test datasets to assess the performance of RiFNet. In addition, we explored the possibility of applying transfer learning techniques on our dataset by choosing two independent noncommercial off-the-shelf convolutional neural network architectures (ResNet50 V2 and Inception V3) and compared the performances of those two with RiFNet. When using pre-trained convolutional neural networks, we achieved an F1 score of 0.64 with Inception V3 and an F1 score of 0.61 with ResNet50 V2. We obtained an average F1 score of 0.91 ± 0.04 with RiFNet. RiFNet is efficient in detecting rib fractures on postmortem computed tomography. Transfer learning techniques are not necessarily well adapted to make classifications in postmortem computed tomography.
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Affiliation(s)
- Victor Ibanez
- Zurich Institute of Forensic Medicine, University of Zurich, Winterthurerstrasse 190/52, CH-8057, Zurich, Switzerland
| | - Samuel Gunz
- Zurich Institute of Forensic Medicine, University of Zurich, Winterthurerstrasse 190/52, CH-8057, Zurich, Switzerland
| | - Svenja Erne
- Zurich Institute of Forensic Medicine, University of Zurich, Winterthurerstrasse 190/52, CH-8057, Zurich, Switzerland
| | - Eric J Rawdon
- Department of Mathematics, University of St. Thomas, St. Paul, Minnesota, 55105-1079, USA
| | - Garyfalia Ampanozi
- Zurich Institute of Forensic Medicine, University of Zurich, Winterthurerstrasse 190/52, CH-8057, Zurich, Switzerland
| | - Sabine Franckenberg
- Zurich Institute of Forensic Medicine, University of Zurich, Winterthurerstrasse 190/52, CH-8057, Zurich, Switzerland.,Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Rämistrasse 100, 8091, Zurich, Switzerland
| | - Till Sieberth
- Zurich Institute of Forensic Medicine, University of Zurich, Winterthurerstrasse 190/52, CH-8057, Zurich, Switzerland
| | - Raffael Affolter
- Zurich Institute of Forensic Medicine, University of Zurich, Winterthurerstrasse 190/52, CH-8057, Zurich, Switzerland
| | - Lars C Ebert
- Zurich Institute of Forensic Medicine, University of Zurich, Winterthurerstrasse 190/52, CH-8057, Zurich, Switzerland
| | - Akos Dobay
- Zurich Institute of Forensic Medicine, University of Zurich, Winterthurerstrasse 190/52, CH-8057, Zurich, Switzerland.
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