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van Nistelrooij N, Schitter S, van Lierop P, Ghoul KE, König D, Hanisch M, Tel A, Xi T, Thiem DGE, Smeets R, Dubois L, Flügge T, van Ginneken B, Bergé S, Vinayahalingam S. Detecting Mandible Fractures in CBCT Scans Using a 3-Stage Neural Network. J Dent Res 2024:220345241256618. [PMID: 38910411 DOI: 10.1177/00220345241256618] [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: 06/25/2024] Open
Abstract
After nasal bone fractures, fractures of the mandible are the most frequently encountered injuries of the facial skeleton. Accurate identification of fracture locations is critical for effectively managing these injuries. To address this need, JawFracNet, an innovative artificial intelligence method, has been developed to enable automated detection of mandibular fractures in cone-beam computed tomography (CBCT) scans. JawFracNet employs a 3-stage neural network model that processes 3-dimensional patches from a CBCT scan. Stage 1 predicts a segmentation mask of the mandible in a patch, which is subsequently used in stage 2 to predict a segmentation of the fractures and in stage 3 to classify whether the patch contains any fracture. The final output of JawFracNet is the fracture segmentation of the entire scan, obtained by aggregating and unifying voxel-level and patch-level predictions. A total of 164 CBCT scans without mandibular fractures and 171 CBCT scans with mandibular fractures were included in this study. Evaluation of JawFracNet demonstrated a precision of 0.978 and a sensitivity of 0.956 in detecting mandibular fractures. The current study proposes the first benchmark for mandibular fracture detection in CBCT scans. Straightforward replication is promoted by publicly sharing the code and providing access to JawFracNet on grand-challenge.org.
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Affiliation(s)
- N van Nistelrooij
- Department of Oral and Maxillofacial Surgery, Radboud University Medical Center, Nijmegen, The Netherlands
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Oral and Maxillofacial Surgery, Berlin, Germany
| | - S Schitter
- Department of Oral and Maxillofacial Surgery, Division of Regenerative, Orofacial Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - P van Lierop
- Department of Oral and Maxillofacial Surgery, Radboud University Medical Center, Nijmegen, The Netherlands
| | - K El Ghoul
- Department of Oral and Maxillofacial Surgery, Erasmus Medical Center, Rotterdam, The Netherlands
| | - D König
- Department of Oral and Maxillofacial Surgery, Division of Regenerative, Orofacial Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - M Hanisch
- Department of Oral and Maxillofacial Surgery, Universitätsklinikum, Münster, Münster, Germany
| | - A Tel
- Clinic of Maxillofacial Surgery, Head-Neck and NeuroScience Department University Hospital of Udine, Udine, Italy
| | - T Xi
- Department of Oral and Maxillofacial Surgery, Radboud University Medical Center, Nijmegen, The Netherlands
| | - D G E Thiem
- Department of Oral and Maxillofacial Surgery, Facial Plastic Surgery, University Medical Centre Mainz, Mainz, Germany
| | - R Smeets
- Department of Oral and Maxillofacial Surgery, Division of Regenerative, Orofacial Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - L Dubois
- Department of Oral and Maxillofacial Surgery, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - T Flügge
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Oral and Maxillofacial Surgery, Berlin, Germany
| | - B van Ginneken
- Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands
| | - S Bergé
- Department of Oral and Maxillofacial Surgery, Radboud University Medical Center, Nijmegen, The Netherlands
| | - S Vinayahalingam
- Department of Oral and Maxillofacial Surgery, Radboud University Medical Center, Nijmegen, The Netherlands
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Zhou T, Wang H, Du Y, Liu F, Guo Y, Lu H. M 3YOLOv5: Feature enhanced YOLOv5 model for mandibular fracture detection. Comput Biol Med 2024; 173:108291. [PMID: 38522254 DOI: 10.1016/j.compbiomed.2024.108291] [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: 12/29/2023] [Revised: 02/28/2024] [Accepted: 03/12/2024] [Indexed: 03/26/2024]
Abstract
BACKGROUND It is very important to detect mandibular fracture region. However, the size of mandibular fracture region is different due to different anatomical positions, different sites and different degrees of force. It is difficult to locate and recognize fracture region accurately. METHODS To solve these problems, M3YOLOv5 model is proposed in this paper. Three feature enhancement strategies are designed, which improve the ability of model to locate and recognize mandibular fracture region. Firstly, Global-Local Feature Extraction Module (GLFEM) is designed. By effectively combining Convolutional Neural Network (CNN) and Transformer, the problem of insufficient global information extraction ability of CNN is complemented, and the positioning ability of the model to the fracture region is improved. Secondly, in order to improve the interaction ability of context information, Deep-Shallow Feature Interaction Module (DSFIM) is designed. In this module, the spatial information in the shallow feature layer is embedded to the deep feature layer by the spatial attention mechanism, and the semantic information in the deep feature layer is embedded to the shallow feature layer by the channel attention mechanism. The fracture region recognition ability of the model is improved. Finally, Multi-scale Multi receptive-field Feature Mixing Module (MMFMM) is designed. Deep separate convolution chains are used in this modal, which is composed by multiple layers of different scales and different dilation coefficients. This method provides richer receptive field for the model, and the ability to detect fracture region of different scales is improved. RESULTS The precision rate, mAP value, recall rate and F1 value of M3YOLOv5 model on mandibular fracture CT data set are 97.18%, 96.86%, 94.42% and 95.58% respectively. The experimental results show that there is better performance about M3YOLOv5 model than the mainstream detection models. CONCLUSION The M3YOLOv5 model can effectively recognize and locate the mandibular fracture region, which is of great significance for doctors' clinical diagnosis.
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Affiliation(s)
- Tao Zhou
- School of Computer Science and Engineering, North Minzu University, Yinchuan, 750021, China; Key Laboratory of Image and Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan, 750021, China
| | - Hongwei Wang
- School of Computer Science and Engineering, North Minzu University, Yinchuan, 750021, China; Key Laboratory of Image and Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan, 750021, China.
| | - Yuhu Du
- School of Computer Science and Engineering, North Minzu University, Yinchuan, 750021, China; Key Laboratory of Image and Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan, 750021, China
| | - Fengzhen Liu
- School of Computer Science and Engineering, North Minzu University, Yinchuan, 750021, China; Key Laboratory of Image and Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan, 750021, China
| | - Yujie Guo
- School of Computer Science and Engineering, North Minzu University, Yinchuan, 750021, China; Key Laboratory of Image and Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan, 750021, China
| | - Huiling Lu
- School of Medical Information and Engineering, Ningxia Medical University, Yinchuan, 750004, China.
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Gontarz M, Bargiel J, Gąsiorowski K, Marecik T, Szczurowski P, Zapała J, Wyszyńska-Pawelec G. "Air Sign" in Misdiagnosed Mandibular Fractures Based on CT and CBCT Evaluation. Diagnostics (Basel) 2024; 14:362. [PMID: 38396403 PMCID: PMC10888197 DOI: 10.3390/diagnostics14040362] [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: 01/08/2024] [Revised: 01/29/2024] [Accepted: 02/05/2024] [Indexed: 02/25/2024] Open
Abstract
BACKGROUND Diagnostic errors constitute one of the reasons for the improper and often delayed treatment of mandibular fractures. The aim of this study was to present a series of cases involving undiagnosed concomitant secondary fractures in the mandibular body during preoperative diagnostics. Additionally, this study aimed to describe the "air sign" as an indirect indicator of a mandibular body fracture. METHODS A retrospective analysis of CT/CBCT scans conducted before surgery was performed on patients misdiagnosed with a mandibular body fracture within a one-year period. RESULTS Among the 75 patients who underwent surgical treatment for mandibular fractures, mandibular body fractures were missed in 3 cases (4%) before surgery. The analysis of CT/CBCT before surgery revealed the presence of an air collection, termed the "air sign", in the soft tissue adjacent to each misdiagnosed fracture of the mandibular body. CONCLUSIONS The "air sign" in a CT/CBCT scan may serve as an additional indirect indication of a fracture in the mandibular body. Its presence should prompt the surgeon to conduct a more thorough clinical examination of the patient under general anesthesia after completing the ORIF procedure in order to rule-out additional fractures.
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Affiliation(s)
- Michał Gontarz
- Department of Cranio-Maxillofacial Surgery, Jagiellonian University Medical College, 30-688 Cracow, Poland; (J.B.); (K.G.); (T.M.); (P.S.); (J.Z.); (G.W.-P.)
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Pham TD, Holmes SB, Coulthard P. A review on artificial intelligence for the diagnosis of fractures in facial trauma imaging. Front Artif Intell 2024; 6:1278529. [PMID: 38249794 PMCID: PMC10797131 DOI: 10.3389/frai.2023.1278529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 12/11/2023] [Indexed: 01/23/2024] Open
Abstract
Patients with facial trauma may suffer from injuries such as broken bones, bleeding, swelling, bruising, lacerations, burns, and deformity in the face. Common causes of facial-bone fractures are the results of road accidents, violence, and sports injuries. Surgery is needed if the trauma patient would be deprived of normal functioning or subject to facial deformity based on findings from radiology. Although the image reading by radiologists is useful for evaluating suspected facial fractures, there are certain challenges in human-based diagnostics. Artificial intelligence (AI) is making a quantum leap in radiology, producing significant improvements of reports and workflows. Here, an updated literature review is presented on the impact of AI in facial trauma with a special reference to fracture detection in radiology. The purpose is to gain insights into the current development and demand for future research in facial trauma. This review also discusses limitations to be overcome and current important issues for investigation in order to make AI applications to the trauma more effective and realistic in practical settings. The publications selected for review were based on their clinical significance, journal metrics, and journal indexing.
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Affiliation(s)
- Tuan D. Pham
- Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
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Ha EG, Jeon KJ, Lee C, Kim HS, Han SS. Development of deep learning model and evaluation in real clinical practice of lingual mandibular bone depression (Stafne cyst) on panoramic radiographs. Dentomaxillofac Radiol 2023; 52:20220413. [PMID: 37192044 PMCID: PMC10304844 DOI: 10.1259/dmfr.20220413] [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: 12/06/2022] [Revised: 03/31/2023] [Accepted: 04/03/2023] [Indexed: 05/18/2023] Open
Abstract
OBJECTIVES Lingual mandibular bone depression (LMBD) is a developmental bony defect in the lingual aspect of the mandible that does not require any surgical treatment. It is sometimes confused with a cyst or another radiolucent pathologic lesion on panoramic radiography. Thus, it is important to differentiate LMBD from true pathological radiolucent lesions requiring treatment. This study aimed to develop a deep learning model for the fully automatic differential diagnosis of LMBD from true pathological radiolucent cysts or tumors on panoramic radiographs without a manual process and evaluate the model's performance using a test dataset that reflected real clinical practice. METHODS A deep learning model using the EfficientDet algorithm was developed with training and validation data sets (443 images) consisting of 83 LMBD patients and 360 patients with true pathological radiolucent lesions. The test data set (1500 images) consisted of 8 LMBD patients, 53 patients with pathological radiolucent lesions, and 1439 healthy patients based on the clinical prevalence of these conditions in order to simulate real-world conditions, and the model was evaluated in terms of accuracy, sensitivity, and specificity using this test data set. RESULTS The model's accuracy, sensitivity, and specificity were more than 99.8%, and only 10 out of 1500 test images were erroneously predicted. CONCLUSION Excellent performance was found for the proposed model, in which the number of patients in each group was composed to reflect the prevalence in real-world clinical practice. The model can help dental clinicians make accurate diagnoses and avoid unnecessary examinations in real clinical settings.
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Affiliation(s)
- Eun-Gyu Ha
- Department of Electrical and Electronic Engineering, Yonsei University College of Engineering, Seoul, Republic of Korea
| | - Kug Jin Jeon
- Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, Republic of Korea
| | - Chena Lee
- Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, Republic of Korea
| | - Hak-Sun Kim
- Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, Republic of Korea
| | - Sang-Sun Han
- Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, Republic of Korea
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Tong Y, Jie B, Wang X, Xu Z, Ding P, He Y. Is Convolutional Neural Network Accurate for Automatic Detection of Zygomatic Fractures on Computed Tomography? J Oral Maxillofac Surg 2023:S0278-2391(23)00393-2. [PMID: 37217163 DOI: 10.1016/j.joms.2023.04.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 03/29/2023] [Accepted: 04/23/2023] [Indexed: 05/24/2023]
Abstract
PURPOSE Zygomatic fractures involve complex anatomical structures of the mid-face and the diagnosis can be challenging and labor-consuming. This research is aimed to evaluate the performance of an automatic algorithm for the detection of zygomatic fractures based on convolutional neural network (CNN) on spiral computed tomography (CT). MATERIALS AND METHODS We designed a cross-sectional retrospective diagnostic trial study. Clinical records and CT scans of patients with zygomatic fractures were reviewed. The sample consisted of two types of patients with different zygomatic fractures statuses (positive or negative) in Peking University School of Stomatology from 2013 to 2019. All CT samples were randomly divided into three groups at a ratio of 6:2:2 as training set, validation set, and test set, respectively. All CT scans were viewed and annotated by three experienced maxillofacial surgeons, serving as the gold standard. The algorithm consisted of two modules as follows: (1) segmentation of the zygomatic region of CT based on U-Net, a type of CNN model; (2) detection of fractures based on Deep Residual Network 34(ResNet34). The region segmentation model was used first to detect and extract the zygomatic region, then the detection model was used to detect the fracture status. The Dice coefficient was used to evaluate the performance of the segmentation algorithm. The sensitivity and specificity were used to assess the performance of the detection model. The covariates included age, gender, duration of injury, and the etiology of fractures. RESULTS A total of 379 patients with an average age of 35.43 ± 12.74 years were included in the study. There were 203 nonfracture patients and 176 fracture patients with 220 sites of zygomatic fractures (44 patients underwent bilateral fractures). The Dice coefficientof zygomatic region detection model and gold standard verified by manual labeling were 0.9337 (coronal plane) and 0.9269 (sagittal plane), respectively. The sensitivity and specificity of the fracture detection model were 100% (p>.05). CONCLUSION The performance of the algorithm based on CNNs was not statistically different from the gold standard (manual diagnosis) for zygomatic fracture detection in order for the algorithm to be applied clinically.
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Affiliation(s)
- Yanhang Tong
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology; National Engineering Laboratory for Digital and Material Technology of Stomatology; Beijing Key Laboratory of Digital Stomatology; National Clinical Research Center for Oral Diseases, Beijing, China
| | - Bimeng Jie
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology; National Engineering Laboratory for Digital and Material Technology of Stomatology; Beijing Key Laboratory of Digital Stomatology; National Clinical Research Center for Oral Diseases, Beijing, China
| | - Xuebing Wang
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology; National Engineering Laboratory for Digital and Material Technology of Stomatology; Beijing Key Laboratory of Digital Stomatology; National Clinical Research Center for Oral Diseases, Beijing, China
| | | | | | - Yang He
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology; National Engineering Laboratory for Digital and Material Technology of Stomatology; Beijing Key Laboratory of Digital Stomatology; National Clinical Research Center for Oral Diseases, Beijing, China.
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