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Ruitenbeek HC, Oei EHG, Visser JJ, Kijowski R. Artificial intelligence in musculoskeletal imaging: realistic clinical applications in the next decade. Skeletal Radiol 2024; 53:1849-1868. [PMID: 38902420 DOI: 10.1007/s00256-024-04684-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 04/06/2024] [Accepted: 04/15/2024] [Indexed: 06/22/2024]
Abstract
This article will provide a perspective review of the most extensively investigated deep learning (DL) applications for musculoskeletal disease detection that have the best potential to translate into routine clinical practice over the next decade. Deep learning methods for detecting fractures, estimating pediatric bone age, calculating bone measurements such as lower extremity alignment and Cobb angle, and grading osteoarthritis on radiographs have been shown to have high diagnostic performance with many of these applications now commercially available for use in clinical practice. Many studies have also documented the feasibility of using DL methods for detecting joint pathology and characterizing bone tumors on magnetic resonance imaging (MRI). However, musculoskeletal disease detection on MRI is difficult as it requires multi-task, multi-class detection of complex abnormalities on multiple image slices with different tissue contrasts. The generalizability of DL methods for musculoskeletal disease detection on MRI is also challenging due to fluctuations in image quality caused by the wide variety of scanners and pulse sequences used in routine MRI protocols. The diagnostic performance of current DL methods for musculoskeletal disease detection must be further evaluated in well-designed prospective studies using large image datasets acquired at different institutions with different imaging parameters and imaging hardware before they can be fully implemented in clinical practice. Future studies must also investigate the true clinical benefits of current DL methods and determine whether they could enhance quality, reduce error rates, improve workflow, and decrease radiologist fatigue and burnout with all of this weighed against the costs.
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Affiliation(s)
- Huibert C Ruitenbeek
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center, P.O. Box 2040, 3000 CA, Rotterdam, The Netherlands
| | - Edwin H G Oei
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center, P.O. Box 2040, 3000 CA, Rotterdam, The Netherlands
| | - Jacob J Visser
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center, P.O. Box 2040, 3000 CA, Rotterdam, The Netherlands
| | - Richard Kijowski
- Department of Radiology, New York University Grossman School of Medicine, 660 First Avenue, 3rd Floor, New York, NY, 10016, USA.
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Schousboe JT, Lewis JR, Monchka BA, Reid SB, Davidson MJ, Kimelman D, Jozani MJ, Smith C, Sim M, Gilani SZ, Suter D, Leslie WD. Simultaneous automated ascertainment of prevalent vertebral fracture and abdominal aortic calcification in clinical practice: role in fracture risk assessment. J Bone Miner Res 2024; 39:898-905. [PMID: 38699950 DOI: 10.1093/jbmr/zjae066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/20/2024] [Revised: 04/08/2024] [Accepted: 05/01/2024] [Indexed: 05/05/2024]
Abstract
Whether simultaneous automated ascertainments of prevalent vertebral fracture (auto-PVFx) and abdominal aortic calcification (auto-AAC) on vertebral fracture assessment (VFA) lateral spine bone density (BMD) images jointly predict incident fractures in routine clinical practice is unclear. We estimated the independent associations of auto-PVFx and auto-AAC primarily with incident major osteoporotic and secondarily with incident hip and any clinical fractures in 11 013 individuals (mean [SD] age 75.8 [6.8] years, 93.3% female) who had a BMD test combined with VFA between March 2010 and December 2017. Auto-PVFx and auto-AAC were ascertained using convolutional neural networks (CNNs). Proportional hazards models were used to estimate the associations of auto-PVFx and auto-AAC with incident fractures over a mean (SD) follow-up of 3.7 (2.2) years, adjusted for each other and other risk factors. At baseline, 17% (n = 1881) had auto-PVFx and 27% (n = 2974) had a high level of auto-AAC (≥ 6 on scale of 0 to 24). Multivariable-adjusted hazard ratios (HR) for incident major osteoporotic fracture (95% CI) were 1.85 (1.59, 2.15) for those with compared with those without auto-PVFx, and 1.36 (1.14, 1.62) for those with high compared with low auto-AAC. The multivariable-adjusted HRs for incident hip fracture were 1.62 (95% CI, 1.26 to 2.07) for those with compared to those without auto-PVFx, and 1.55 (95% CI, 1.15 to 2.09) for those high auto-AAC compared with low auto-AAC. The 5-year cumulative incidence of major osteoporotic fracture was 7.1% in those with no auto-PVFx and low auto-AAC, 10.1% in those with no auto-PVFx and high auto-AAC, 13.4% in those with auto-PVFx and low auto-AAC, and 18.0% in those with auto-PVFx and high auto-AAC. While physician manual review of images in clinical practice will still be needed to confirm image quality and provide clinical context for interpretation, simultaneous automated ascertainment of auto-PVFx and auto-AAC can aid fracture risk assessment.
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Affiliation(s)
- John T Schousboe
- Department of Rheumatology, Park Nicollet Clinic and HealthPartners Institute, Minneapolis MN 55416, United States
- Division of Health Policy and Management, University of Minnesota, Minneapolis, MN 55455, United States
| | - Joshua R Lewis
- Nutrition & Health Innovation Research Institute, Edith Cowan University, Joondalup 6027, Australia
- Medical School, University of Western Australia, Perth 6009, Australia
- Centre for Kidney Research, School of Public Health, The University of Sydney, Sydney 2006, Australia
| | - Barret A Monchka
- George & Fay Yee Centre for Healthcare Innovation, University of Manitoba, Winnipeg R3T 2N2, Canada
| | - Siobhan B Reid
- Department of Computer Science, Concordia University, Montreal H4B 1R6, Canada
| | - Michael J Davidson
- Department of Medicine, University of Manitoba, Winnipeg R3T 2N2, Canada
| | - Douglas Kimelman
- Department of Medicine, University of Manitoba, Winnipeg R3T 2N2, Canada
| | | | - Cassandra Smith
- Nutrition & Health Innovation Research Institute, Edith Cowan University, Joondalup 6027, Australia
- Medical School, University of Western Australia, Perth 6009, Australia
| | - Marc Sim
- Nutrition & Health Innovation Research Institute, Edith Cowan University, Joondalup 6027, Australia
- Medical School, University of Western Australia, Perth 6009, Australia
| | - Syed Zulqarnain Gilani
- Nutrition & Health Innovation Research Institute, Edith Cowan University, Joondalup 6027, Australia
- Centre for AI & ML, School of Science, Edith Cowan University, Joondalup 6027, Australia
- Department of Computer Science and Software Engineering, University of Western Australia, Perth 6009, Australia
| | - David Suter
- Nutrition & Health Innovation Research Institute, Edith Cowan University, Joondalup 6027, Australia
| | - William D Leslie
- Department of Medicine, University of Manitoba, Winnipeg R3T 2N2, Canada
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Kong SH, Cho W, Park SB, Choo J, Kim JH, Kim SW, Shin CS. A Computed Tomography-Based Fracture Prediction Model With Images of Vertebral Bones and Muscles by Employing Deep Learning: Development and Validation Study. J Med Internet Res 2024; 26:e48535. [PMID: 38995678 PMCID: PMC11282387 DOI: 10.2196/48535] [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: 04/27/2023] [Revised: 01/27/2024] [Accepted: 05/30/2024] [Indexed: 07/13/2024] Open
Abstract
BACKGROUND With the progressive increase in aging populations, the use of opportunistic computed tomography (CT) scanning is increasing, which could be a valuable method for acquiring information on both muscles and bones of aging populations. OBJECTIVE The aim of this study was to develop and externally validate opportunistic CT-based fracture prediction models by using images of vertebral bones and paravertebral muscles. METHODS The models were developed based on a retrospective longitudinal cohort study of 1214 patients with abdominal CT images between 2010 and 2019. The models were externally validated in 495 patients. The primary outcome of this study was defined as the predictive accuracy for identifying vertebral fracture events within a 5-year follow-up. The image models were developed using an attention convolutional neural network-recurrent neural network model from images of the vertebral bone and paravertebral muscles. RESULTS The mean ages of the patients in the development and validation sets were 73 years and 68 years, and 69.1% (839/1214) and 78.8% (390/495) of them were females, respectively. The areas under the receiver operator curve (AUROCs) for predicting vertebral fractures were superior in images of the vertebral bone and paravertebral muscles than those in the bone-only images in the external validation cohort (0.827, 95% CI 0.821-0.833 vs 0.815, 95% CI 0.806-0.824, respectively; P<.001). The AUROCs of these image models were higher than those of the fracture risk assessment models (0.810 for major osteoporotic risk, 0.780 for hip fracture risk). For the clinical model using age, sex, BMI, use of steroids, smoking, possible secondary osteoporosis, type 2 diabetes mellitus, HIV, hepatitis C, and renal failure, the AUROC value in the external validation cohort was 0.749 (95% CI 0.736-0.762), which was lower than that of the image model using vertebral bones and muscles (P<.001). CONCLUSIONS The model using the images of the vertebral bone and paravertebral muscle showed better performance than that using the images of the bone-only or clinical variables. Opportunistic CT screening may contribute to identifying patients with a high fracture risk in the future.
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Affiliation(s)
- Sung Hye Kong
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Wonwoo Cho
- Kim Jaechul Graduate School of AI, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Sung Bae Park
- Department of Neurosurgery, Seoul National University Boramae Hospital, Seoul, Republic of Korea
| | - Jaegul Choo
- Kim Jaechul Graduate School of AI, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Jung Hee Kim
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| | - Sang Wan Kim
- Department of Internal Medicine, Seoul National University Boramae Hospital, Seoul, Republic of Korea
| | - Chan Soo Shin
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea
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He Y, Lin J, Zhu S, Zhu J, Xu Z. Deep learning in the radiologic diagnosis of osteoporosis: a literature review. J Int Med Res 2024; 52:3000605241244754. [PMID: 38656208 PMCID: PMC11044779 DOI: 10.1177/03000605241244754] [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: 08/01/2023] [Accepted: 02/26/2024] [Indexed: 04/26/2024] Open
Abstract
OBJECTIVE Osteoporosis is a systemic bone disease characterized by low bone mass, damaged bone microstructure, increased bone fragility, and susceptibility to fractures. With the rapid development of artificial intelligence, a series of studies have reported deep learning applications in the screening and diagnosis of osteoporosis. The aim of this review was to summary the application of deep learning methods in the radiologic diagnosis of osteoporosis. METHODS We conducted a two-step literature search using the PubMed and Web of Science databases. In this review, we focused on routine radiologic methods, such as X-ray, computed tomography, and magnetic resonance imaging, used to opportunistically screen for osteoporosis. RESULTS A total of 40 studies were included in this review. These studies were divided into three categories: osteoporosis screening (n = 20), bone mineral density prediction (n = 13), and osteoporotic fracture risk prediction and detection (n = 7). CONCLUSIONS Deep learning has demonstrated a remarkable capacity for osteoporosis screening. However, clinical commercialization of a diagnostic model for osteoporosis remains a challenge.
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Affiliation(s)
- Yu He
- Suzhou Medical College, Soochow University, Suzhou, Jiangsu, China
| | - Jiaxi Lin
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Shiqi Zhu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Jinzhou Zhu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Zhonghua Xu
- Department of Orthopedics, Jintan Affiliated Hospital to Jiangsu University, Changzhou, China
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Wang H, Ying J, Liu J, Yu T, Huang D. Harnessing ResNet50 and SENet for enhanced ankle fracture identification. BMC Musculoskelet Disord 2024; 25:250. [PMID: 38561697 PMCID: PMC10983628 DOI: 10.1186/s12891-024-07355-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Accepted: 03/13/2024] [Indexed: 04/04/2024] Open
Abstract
BACKGROUND Ankle fractures are prevalent injuries that necessitate precise diagnostic tools. Traditional diagnostic methods have limitations that can be addressed using machine learning techniques, with the potential to improve accuracy and expedite diagnoses. METHODS We trained various deep learning architectures, notably the Adapted ResNet50 with SENet capabilities, to identify ankle fractures using a curated dataset of radiographic images. Model performance was evaluated using common metrics like accuracy, precision, and recall. Additionally, Grad-CAM visualizations were employed to interpret model decisions. RESULTS The Adapted ResNet50 with SENet capabilities consistently outperformed other models, achieving an accuracy of 93%, AUC of 95%, and recall of 92%. Grad-CAM visualizations provided insights into areas of the radiographs that the model deemed significant in its decisions. CONCLUSIONS The Adapted ResNet50 model enhanced with SENet capabilities demonstrated superior performance in detecting ankle fractures, offering a promising tool to complement traditional diagnostic methods. However, continuous refinement and expert validation are essential to ensure optimal application in clinical settings.
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Grants
- 2020AS0031 Science and Technology Projects in the Field of Agriculture and Social Development in Yinzhou District, Ningbo City, Zhejiang Province, China
- 2020AS0031 Science and Technology Projects in the Field of Agriculture and Social Development in Yinzhou District, Ningbo City, Zhejiang Province, China
- 2020AS0031 Science and Technology Projects in the Field of Agriculture and Social Development in Yinzhou District, Ningbo City, Zhejiang Province, China
- 2020AS0031 Science and Technology Projects in the Field of Agriculture and Social Development in Yinzhou District, Ningbo City, Zhejiang Province, China
- 2020AS0031 Science and Technology Projects in the Field of Agriculture and Social Development in Yinzhou District, Ningbo City, Zhejiang Province, China
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Affiliation(s)
- Hua Wang
- Department of Medical Imaging, Ningbo No. 6 Hospital, Ningbo, China
| | - Jichong Ying
- Department of Orthopedics, Ningbo No. 6 Hospital, Ningbo, China
| | - Jianlei Liu
- Department of Orthopedics, Ningbo No. 6 Hospital, Ningbo, China
| | - Tianming Yu
- Department of Orthopedics, Ningbo No. 6 Hospital, Ningbo, China
| | - Dichao Huang
- Department of Orthopedics, Ningbo No. 6 Hospital, Ningbo, China.
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Maki S, Furuya T, Inoue M, Shiga Y, Inage K, Eguchi Y, Orita S, Ohtori S. Machine Learning and Deep Learning in Spinal Injury: A Narrative Review of Algorithms in Diagnosis and Prognosis. J Clin Med 2024; 13:705. [PMID: 38337399 PMCID: PMC10856760 DOI: 10.3390/jcm13030705] [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: 01/14/2024] [Accepted: 01/18/2024] [Indexed: 02/12/2024] Open
Abstract
Spinal injuries, including cervical and thoracolumbar fractures, continue to be a major public health concern. Recent advancements in machine learning and deep learning technologies offer exciting prospects for improving both diagnostic and prognostic approaches in spinal injury care. This narrative review systematically explores the practical utility of these computational methods, with a focus on their application in imaging techniques such as computed tomography (CT) and magnetic resonance imaging (MRI), as well as in structured clinical data. Of the 39 studies included, 34 were focused on diagnostic applications, chiefly using deep learning to carry out tasks like vertebral fracture identification, differentiation between benign and malignant fractures, and AO fracture classification. The remaining five were prognostic, using machine learning to analyze parameters for predicting outcomes such as vertebral collapse and future fracture risk. This review highlights the potential benefit of machine learning and deep learning in spinal injury care, especially their roles in enhancing diagnostic capabilities, detailed fracture characterization, risk assessments, and individualized treatment planning.
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Affiliation(s)
- Satoshi Maki
- Department of Orthopaedic Surgery, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan
- Center for Frontier Medical Engineering, Chiba University, Chiba 263-8522, Japan
| | - Takeo Furuya
- Department of Orthopaedic Surgery, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan
| | - Masahiro Inoue
- Department of Orthopaedic Surgery, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan
| | - Yasuhiro Shiga
- Department of Orthopaedic Surgery, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan
| | - Kazuhide Inage
- Department of Orthopaedic Surgery, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan
| | - Yawara Eguchi
- Department of Orthopaedic Surgery, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan
| | - Sumihisa Orita
- Department of Orthopaedic Surgery, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan
- Center for Frontier Medical Engineering, Chiba University, Chiba 263-8522, Japan
| | - Seiji Ohtori
- Department of Orthopaedic Surgery, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan
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Jung J, Dai J, Liu B, Wu Q. Artificial intelligence in fracture detection with different image modalities and data types: A systematic review and meta-analysis. PLOS DIGITAL HEALTH 2024; 3:e0000438. [PMID: 38289965 PMCID: PMC10826962 DOI: 10.1371/journal.pdig.0000438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 12/25/2023] [Indexed: 02/01/2024]
Abstract
Artificial Intelligence (AI), encompassing Machine Learning and Deep Learning, has increasingly been applied to fracture detection using diverse imaging modalities and data types. This systematic review and meta-analysis aimed to assess the efficacy of AI in detecting fractures through various imaging modalities and data types (image, tabular, or both) and to synthesize the existing evidence related to AI-based fracture detection. Peer-reviewed studies developing and validating AI for fracture detection were identified through searches in multiple electronic databases without time limitations. A hierarchical meta-analysis model was used to calculate pooled sensitivity and specificity. A diagnostic accuracy quality assessment was performed to evaluate bias and applicability. Of the 66 eligible studies, 54 identified fractures using imaging-related data, nine using tabular data, and three using both. Vertebral fractures were the most common outcome (n = 20), followed by hip fractures (n = 18). Hip fractures exhibited the highest pooled sensitivity (92%; 95% CI: 87-96, p< 0.01) and specificity (90%; 95% CI: 85-93, p< 0.01). Pooled sensitivity and specificity using image data (92%; 95% CI: 90-94, p< 0.01; and 91%; 95% CI: 88-93, p < 0.01) were higher than those using tabular data (81%; 95% CI: 77-85, p< 0.01; and 83%; 95% CI: 76-88, p < 0.01), respectively. Radiographs demonstrated the highest pooled sensitivity (94%; 95% CI: 90-96, p < 0.01) and specificity (92%; 95% CI: 89-94, p< 0.01). Patient selection and reference standards were major concerns in assessing diagnostic accuracy for bias and applicability. AI displays high diagnostic accuracy for various fracture outcomes, indicating potential utility in healthcare systems for fracture diagnosis. However, enhanced transparency in reporting and adherence to standardized guidelines are necessary to improve the clinical applicability of AI. Review Registration: PROSPERO (CRD42021240359).
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Affiliation(s)
- Jongyun Jung
- Department of Biomedical Informatics (Dr. Qing Wu, Jongyun Jung, and Jingyuan Dai), College of Medicine, The Ohio State University, Columbus, Ohio, United States of America
| | - Jingyuan Dai
- Department of Biomedical Informatics (Dr. Qing Wu, Jongyun Jung, and Jingyuan Dai), College of Medicine, The Ohio State University, Columbus, Ohio, United States of America
| | - Bowen Liu
- Department of Mathematics and Statistics, Division of Computing, Analytics, and Mathematics, School of Science and Engineering (Bowen Liu), University of Missouri-Kansas City, Kansas City, Missouri, United States of America
| | - Qing Wu
- Department of Biomedical Informatics (Dr. Qing Wu, Jongyun Jung, and Jingyuan Dai), College of Medicine, The Ohio State University, Columbus, Ohio, United States of America
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Kim Y, Kim YG, Park JW, Kim BW, Shin Y, Kong SH, Kim JH, Lee YK, Kim SW, Shin CS. A CT-based Deep Learning Model for Predicting Subsequent Fracture Risk in Patients with Hip Fracture. Radiology 2024; 310:e230614. [PMID: 38289213 DOI: 10.1148/radiol.230614] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Background Patients have the highest risk of subsequent fractures in the first few years after an initial fracture, yet models to predict short-term subsequent risk have not been developed. Purpose To develop and validate a deep learning prediction model for subsequent fracture risk using digitally reconstructed radiographs from hip CT in patients with recent hip fractures. Materials and Methods This retrospective study included adult patients who underwent three-dimensional hip CT due to a fracture from January 2004 to December 2020. Two-dimensional frontal, lateral, and axial digitally reconstructed radiographs were generated and assembled to construct an ensemble model. DenseNet modules were used to calculate risk probability based on extracted image features and fracture-free probability plots were output. Model performance was assessed using the C index and area under the receiver operating characteristic curve (AUC) and compared with other models using the paired t test. Results The training and validation set included 1012 patients (mean age, 74.5 years ± 13.3 [SD]; 706 female, 113 subsequent fracture) and the test set included 468 patients (mean age, 75.9 years ± 14.0; 335 female, 22 subsequent fractures). In the test set, the ensemble model had a higher C index (0.73) for predicting subsequent fractures than that of other image-based models (C index range, 0.59-0.70 for five of six models; P value range, < .001 to < .05). The ensemble model achieved AUCs of 0.74, 0.74, and 0.73 at the 2-, 3-, and 5-year follow-ups, respectively; higher than that of most other image-based models at 2 years (AUC range, 0.57-0.71 for five of six models; P value range, < .001 to < .05) and 3 years (AUC range, 0.55-0.72 for four of six models; P value range, < .001 to < .05). Moreover, the AUCs achieved by the ensemble model were higher than that of a clinical model that included known risk factors (2-, 3-, and 5-year AUCs of 0.58, 0.64, and 0.70, respectively; P < .001 for all). Conclusion In patients with recent hip fractures, the ensemble deep learning model using digital reconstructed radiographs from hip CT showed good performance for predicting subsequent fractures in the short term. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Li and Jaremko in this issue.
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Affiliation(s)
- Yisak Kim
- From the Interdisciplinary Program in Bioengineering (Y.K., Y.S.) and Integrated Major in Innovative Medical Science (Y.K.), Seoul National University Graduate School, Seoul, Republic of Korea; Department of Radiology (Y.K.), Transdisciplinary Department of Medicine & Advanced Technology (Y.G.K., B.W.K., Y.S.), and Department of Internal Medicine (J.H.K., C.S.S.), Seoul National University Hospital, Seoul, Republic of Korea; Departments of Orthopaedic Surgery (J.W.P., Y.K.L.) and Internal Medicine (S.H.K.), Seoul National University Bundang Hospital, 82 Gumi-ro 173 Beon-gil, Bundang gu, Seongnam, Republic of Korea; Departments of Medicine (Y.G.K.) and Internal Medicine (S.H.K., J.H.K., S.W.K., C.S.S.), Seoul National University College of Medicine, Seoul, Republic of Korea; and Department of Internal Medicine, Seoul National University Boramae Hospital, Seoul, Republic of Korea (S.W.K.)
| | - Young-Gon Kim
- From the Interdisciplinary Program in Bioengineering (Y.K., Y.S.) and Integrated Major in Innovative Medical Science (Y.K.), Seoul National University Graduate School, Seoul, Republic of Korea; Department of Radiology (Y.K.), Transdisciplinary Department of Medicine & Advanced Technology (Y.G.K., B.W.K., Y.S.), and Department of Internal Medicine (J.H.K., C.S.S.), Seoul National University Hospital, Seoul, Republic of Korea; Departments of Orthopaedic Surgery (J.W.P., Y.K.L.) and Internal Medicine (S.H.K.), Seoul National University Bundang Hospital, 82 Gumi-ro 173 Beon-gil, Bundang gu, Seongnam, Republic of Korea; Departments of Medicine (Y.G.K.) and Internal Medicine (S.H.K., J.H.K., S.W.K., C.S.S.), Seoul National University College of Medicine, Seoul, Republic of Korea; and Department of Internal Medicine, Seoul National University Boramae Hospital, Seoul, Republic of Korea (S.W.K.)
| | - Jung-Wee Park
- From the Interdisciplinary Program in Bioengineering (Y.K., Y.S.) and Integrated Major in Innovative Medical Science (Y.K.), Seoul National University Graduate School, Seoul, Republic of Korea; Department of Radiology (Y.K.), Transdisciplinary Department of Medicine & Advanced Technology (Y.G.K., B.W.K., Y.S.), and Department of Internal Medicine (J.H.K., C.S.S.), Seoul National University Hospital, Seoul, Republic of Korea; Departments of Orthopaedic Surgery (J.W.P., Y.K.L.) and Internal Medicine (S.H.K.), Seoul National University Bundang Hospital, 82 Gumi-ro 173 Beon-gil, Bundang gu, Seongnam, Republic of Korea; Departments of Medicine (Y.G.K.) and Internal Medicine (S.H.K., J.H.K., S.W.K., C.S.S.), Seoul National University College of Medicine, Seoul, Republic of Korea; and Department of Internal Medicine, Seoul National University Boramae Hospital, Seoul, Republic of Korea (S.W.K.)
| | - Byung Woo Kim
- From the Interdisciplinary Program in Bioengineering (Y.K., Y.S.) and Integrated Major in Innovative Medical Science (Y.K.), Seoul National University Graduate School, Seoul, Republic of Korea; Department of Radiology (Y.K.), Transdisciplinary Department of Medicine & Advanced Technology (Y.G.K., B.W.K., Y.S.), and Department of Internal Medicine (J.H.K., C.S.S.), Seoul National University Hospital, Seoul, Republic of Korea; Departments of Orthopaedic Surgery (J.W.P., Y.K.L.) and Internal Medicine (S.H.K.), Seoul National University Bundang Hospital, 82 Gumi-ro 173 Beon-gil, Bundang gu, Seongnam, Republic of Korea; Departments of Medicine (Y.G.K.) and Internal Medicine (S.H.K., J.H.K., S.W.K., C.S.S.), Seoul National University College of Medicine, Seoul, Republic of Korea; and Department of Internal Medicine, Seoul National University Boramae Hospital, Seoul, Republic of Korea (S.W.K.)
| | - Youmin Shin
- From the Interdisciplinary Program in Bioengineering (Y.K., Y.S.) and Integrated Major in Innovative Medical Science (Y.K.), Seoul National University Graduate School, Seoul, Republic of Korea; Department of Radiology (Y.K.), Transdisciplinary Department of Medicine & Advanced Technology (Y.G.K., B.W.K., Y.S.), and Department of Internal Medicine (J.H.K., C.S.S.), Seoul National University Hospital, Seoul, Republic of Korea; Departments of Orthopaedic Surgery (J.W.P., Y.K.L.) and Internal Medicine (S.H.K.), Seoul National University Bundang Hospital, 82 Gumi-ro 173 Beon-gil, Bundang gu, Seongnam, Republic of Korea; Departments of Medicine (Y.G.K.) and Internal Medicine (S.H.K., J.H.K., S.W.K., C.S.S.), Seoul National University College of Medicine, Seoul, Republic of Korea; and Department of Internal Medicine, Seoul National University Boramae Hospital, Seoul, Republic of Korea (S.W.K.)
| | - Sung Hye Kong
- From the Interdisciplinary Program in Bioengineering (Y.K., Y.S.) and Integrated Major in Innovative Medical Science (Y.K.), Seoul National University Graduate School, Seoul, Republic of Korea; Department of Radiology (Y.K.), Transdisciplinary Department of Medicine & Advanced Technology (Y.G.K., B.W.K., Y.S.), and Department of Internal Medicine (J.H.K., C.S.S.), Seoul National University Hospital, Seoul, Republic of Korea; Departments of Orthopaedic Surgery (J.W.P., Y.K.L.) and Internal Medicine (S.H.K.), Seoul National University Bundang Hospital, 82 Gumi-ro 173 Beon-gil, Bundang gu, Seongnam, Republic of Korea; Departments of Medicine (Y.G.K.) and Internal Medicine (S.H.K., J.H.K., S.W.K., C.S.S.), Seoul National University College of Medicine, Seoul, Republic of Korea; and Department of Internal Medicine, Seoul National University Boramae Hospital, Seoul, Republic of Korea (S.W.K.)
| | - Jung Hee Kim
- From the Interdisciplinary Program in Bioengineering (Y.K., Y.S.) and Integrated Major in Innovative Medical Science (Y.K.), Seoul National University Graduate School, Seoul, Republic of Korea; Department of Radiology (Y.K.), Transdisciplinary Department of Medicine & Advanced Technology (Y.G.K., B.W.K., Y.S.), and Department of Internal Medicine (J.H.K., C.S.S.), Seoul National University Hospital, Seoul, Republic of Korea; Departments of Orthopaedic Surgery (J.W.P., Y.K.L.) and Internal Medicine (S.H.K.), Seoul National University Bundang Hospital, 82 Gumi-ro 173 Beon-gil, Bundang gu, Seongnam, Republic of Korea; Departments of Medicine (Y.G.K.) and Internal Medicine (S.H.K., J.H.K., S.W.K., C.S.S.), Seoul National University College of Medicine, Seoul, Republic of Korea; and Department of Internal Medicine, Seoul National University Boramae Hospital, Seoul, Republic of Korea (S.W.K.)
| | - Young-Kyun Lee
- From the Interdisciplinary Program in Bioengineering (Y.K., Y.S.) and Integrated Major in Innovative Medical Science (Y.K.), Seoul National University Graduate School, Seoul, Republic of Korea; Department of Radiology (Y.K.), Transdisciplinary Department of Medicine & Advanced Technology (Y.G.K., B.W.K., Y.S.), and Department of Internal Medicine (J.H.K., C.S.S.), Seoul National University Hospital, Seoul, Republic of Korea; Departments of Orthopaedic Surgery (J.W.P., Y.K.L.) and Internal Medicine (S.H.K.), Seoul National University Bundang Hospital, 82 Gumi-ro 173 Beon-gil, Bundang gu, Seongnam, Republic of Korea; Departments of Medicine (Y.G.K.) and Internal Medicine (S.H.K., J.H.K., S.W.K., C.S.S.), Seoul National University College of Medicine, Seoul, Republic of Korea; and Department of Internal Medicine, Seoul National University Boramae Hospital, Seoul, Republic of Korea (S.W.K.)
| | - Sang Wan Kim
- From the Interdisciplinary Program in Bioengineering (Y.K., Y.S.) and Integrated Major in Innovative Medical Science (Y.K.), Seoul National University Graduate School, Seoul, Republic of Korea; Department of Radiology (Y.K.), Transdisciplinary Department of Medicine & Advanced Technology (Y.G.K., B.W.K., Y.S.), and Department of Internal Medicine (J.H.K., C.S.S.), Seoul National University Hospital, Seoul, Republic of Korea; Departments of Orthopaedic Surgery (J.W.P., Y.K.L.) and Internal Medicine (S.H.K.), Seoul National University Bundang Hospital, 82 Gumi-ro 173 Beon-gil, Bundang gu, Seongnam, Republic of Korea; Departments of Medicine (Y.G.K.) and Internal Medicine (S.H.K., J.H.K., S.W.K., C.S.S.), Seoul National University College of Medicine, Seoul, Republic of Korea; and Department of Internal Medicine, Seoul National University Boramae Hospital, Seoul, Republic of Korea (S.W.K.)
| | - Chan Soo Shin
- From the Interdisciplinary Program in Bioengineering (Y.K., Y.S.) and Integrated Major in Innovative Medical Science (Y.K.), Seoul National University Graduate School, Seoul, Republic of Korea; Department of Radiology (Y.K.), Transdisciplinary Department of Medicine & Advanced Technology (Y.G.K., B.W.K., Y.S.), and Department of Internal Medicine (J.H.K., C.S.S.), Seoul National University Hospital, Seoul, Republic of Korea; Departments of Orthopaedic Surgery (J.W.P., Y.K.L.) and Internal Medicine (S.H.K.), Seoul National University Bundang Hospital, 82 Gumi-ro 173 Beon-gil, Bundang gu, Seongnam, Republic of Korea; Departments of Medicine (Y.G.K.) and Internal Medicine (S.H.K., J.H.K., S.W.K., C.S.S.), Seoul National University College of Medicine, Seoul, Republic of Korea; and Department of Internal Medicine, Seoul National University Boramae Hospital, Seoul, Republic of Korea (S.W.K.)
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9
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Su Z, Adam A, Nasrudin MF, Ayob M, Punganan G. Skeletal Fracture Detection with Deep Learning: A Comprehensive Review. Diagnostics (Basel) 2023; 13:3245. [PMID: 37892066 PMCID: PMC10606060 DOI: 10.3390/diagnostics13203245] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 10/12/2023] [Accepted: 10/13/2023] [Indexed: 10/29/2023] Open
Abstract
Deep learning models have shown great promise in diagnosing skeletal fractures from X-ray images. However, challenges remain that hinder progress in this field. Firstly, a lack of clear definitions for recognition, classification, detection, and localization tasks hampers the consistent development and comparison of methodologies. The existing reviews often lack technical depth or have limited scope. Additionally, the absence of explainable facilities undermines the clinical application and expert confidence in results. To address these issues, this comprehensive review analyzes and evaluates 40 out of 337 recent papers identified in prestigious databases, including WOS, Scopus, and EI. The objectives of this review are threefold. Firstly, precise definitions are established for the bone fracture recognition, classification, detection, and localization tasks within deep learning. Secondly, each study is summarized based on key aspects such as the bones involved, research objectives, dataset sizes, methods employed, results obtained, and concluding remarks. This process distills the diverse approaches into a generalized processing framework or workflow. Moreover, this review identifies the crucial areas for future research in deep learning models for bone fracture diagnosis. These include enhancing the network interpretability, integrating multimodal clinical information, providing therapeutic schedule recommendations, and developing advanced visualization methods for clinical application. By addressing these challenges, deep learning models can be made more intelligent and specialized in this domain. In conclusion, this review fills the gap in precise task definitions within deep learning for bone fracture diagnosis and provides a comprehensive analysis of the recent research. The findings serve as a foundation for future advancements, enabling improved interpretability, multimodal integration, clinical decision support, and advanced visualization techniques.
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Affiliation(s)
- Zhihao Su
- Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia; (Z.S.); (M.F.N.); (M.A.)
| | - Afzan Adam
- Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia; (Z.S.); (M.F.N.); (M.A.)
| | - Mohammad Faidzul Nasrudin
- Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia; (Z.S.); (M.F.N.); (M.A.)
| | - Masri Ayob
- Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia; (Z.S.); (M.F.N.); (M.A.)
| | - Gauthamen Punganan
- Department of Orthopedics and Traumatology, Hospital Raja Permaisuri Bainun, Ipoh 30450, Perak, Malaysia;
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10
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Constant C, Aubin CE, Kremers HM, Garcia DVV, Wyles CC, Rouzrokh P, Larson AN. The use of deep learning in medical imaging to improve spine care: A scoping review of current literature and clinical applications. NORTH AMERICAN SPINE SOCIETY JOURNAL 2023; 15:100236. [PMID: 37599816 PMCID: PMC10432249 DOI: 10.1016/j.xnsj.2023.100236] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Accepted: 06/14/2023] [Indexed: 08/22/2023]
Abstract
Background Artificial intelligence is a revolutionary technology that promises to assist clinicians in improving patient care. In radiology, deep learning (DL) is widely used in clinical decision aids due to its ability to analyze complex patterns and images. It allows for rapid, enhanced data, and imaging analysis, from diagnosis to outcome prediction. The purpose of this study was to evaluate the current literature and clinical utilization of DL in spine imaging. Methods This study is a scoping review and utilized the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to review the scientific literature from 2012 to 2021. A search in PubMed, Web of Science, Embased, and IEEE Xplore databases with syntax specific for DL and medical imaging in spine care applications was conducted to collect all original publications on the subject. Specific data was extracted from the available literature, including algorithm application, algorithms tested, database type and size, algorithm training method, and outcome of interest. Results A total of 365 studies (total sample of 232,394 patients) were included and grouped into 4 general applications: diagnostic tools, clinical decision support tools, automated clinical/instrumentation assessment, and clinical outcome prediction. Notable disparities exist in the selected algorithms and the training across multiple disparate databases. The most frequently used algorithms were U-Net and ResNet. A DL model was developed and validated in 92% of included studies, while a pre-existing DL model was investigated in 8%. Of all developed models, only 15% of them have been externally validated. Conclusions Based on this scoping review, DL in spine imaging is used in a broad range of clinical applications, particularly for diagnosing spinal conditions. There is a wide variety of DL algorithms, database characteristics, and training methods. Future studies should focus on external validation of existing models before bringing them into clinical use.
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Affiliation(s)
- Caroline Constant
- Orthopedic Surgery AI Laboratory, Mayo Clinic, 200 1st St Southwest, Rochester, MN, 55902, United States
- Polytechnique Montreal, 2500 Chem. de Polytechnique, Montréal, QC H3T 1J4, Canada
- AO Research Institute Davos, Clavadelerstrasse 8, CH 7270, Davos, Switzerland
| | - Carl-Eric Aubin
- Polytechnique Montreal, 2500 Chem. de Polytechnique, Montréal, QC H3T 1J4, Canada
| | - Hilal Maradit Kremers
- Orthopedic Surgery AI Laboratory, Mayo Clinic, 200 1st St Southwest, Rochester, MN, 55902, United States
| | - Diana V. Vera Garcia
- Orthopedic Surgery AI Laboratory, Mayo Clinic, 200 1st St Southwest, Rochester, MN, 55902, United States
| | - Cody C. Wyles
- Orthopedic Surgery AI Laboratory, Mayo Clinic, 200 1st St Southwest, Rochester, MN, 55902, United States
- Department of Orthopedic Surgery, Mayo Clinic, 200, 1st St Southwest, Rochester, MN, 55902, United States
| | - Pouria Rouzrokh
- Orthopedic Surgery AI Laboratory, Mayo Clinic, 200 1st St Southwest, Rochester, MN, 55902, United States
- Radiology Informatics Laboratory, Mayo Clinic, 200, 1st St Southwest, Rochester, MN, 55902, United States
| | - Annalise Noelle Larson
- Orthopedic Surgery AI Laboratory, Mayo Clinic, 200 1st St Southwest, Rochester, MN, 55902, United States
- Department of Orthopedic Surgery, Mayo Clinic, 200, 1st St Southwest, Rochester, MN, 55902, United States
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11
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Guo D, Liu X, Wang D, Tang X, Qin Y. Development and clinical validation of deep learning for auto-diagnosis of supraspinatus tears. J Orthop Surg Res 2023; 18:426. [PMID: 37308995 DOI: 10.1186/s13018-023-03909-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Accepted: 06/04/2023] [Indexed: 06/14/2023] Open
Abstract
BACKGROUND Accurately diagnosing supraspinatus tears based on magnetic resonance imaging (MRI) is challenging and time-combusting due to the experience level variability of the musculoskeletal radiologists and orthopedic surgeons. We developed a deep learning-based model for automatically diagnosing supraspinatus tears (STs) using shoulder MRI and validated its feasibility in clinical practice. MATERIALS AND METHODS A total of 701 shoulder MRI data (2804 images) were retrospectively collected for model training and internal test. An additional 69 shoulder MRIs (276 images) were collected from patients who underwent shoulder arthroplasty and constituted the surgery test set for clinical validation. Two advanced convolutional neural networks (CNN) based on Xception were trained and optimized to detect STs. The diagnostic performance of the CNN was evaluated according to its sensitivity, specificity, precision, accuracy, and F1 score. Subgroup analyses were performed to verify its robustness, and we also compared the CNN's performance with that of 4 radiologists and 4 orthopedic surgeons on the surgery and internal test sets. RESULTS Optimal diagnostic performance was achieved on the 2D model, from which F1-scores of 0.824 and 0.75, and areas under the ROC curves of 0.921 (95% confidence interval, 0.841-1.000) and 0.882 (0.817-0.947) were observed on the surgery and internal test sets. For the subgroup analysis, the 2D CNN model demonstrated a sensitivity of 0.33-1.000 and 0.625-1.000 for different degrees of tears on the surgery and internal test sets, and there was no significant performance difference between 1.5 and 3.0 T data. Compared with eight clinicians, the 2D CNN model exhibited better diagnostic performance than the junior clinicians and was equivalent to senior clinicians. CONCLUSIONS The proposed 2D CNN model realized the adequate and efficient automatic diagnoses of STs, which achieved a comparable performance of junior musculoskeletal radiologists and orthopedic surgeons. It might be conducive to assisting poor-experienced radiologists, especially in community scenarios lacking consulting experts.
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Affiliation(s)
- Deming Guo
- Orthopaedic Medical Center, The Second Hospital of Jilin University, Changchun, 130041, People's Republic of China
- Jilin Provincial Key Laboratory of Orhtopeadics, Changchun, People's Republic of China
| | - Xiaoning Liu
- Orthopaedic Medical Center, The Second Hospital of Jilin University, Changchun, 130041, People's Republic of China
| | - Dawei Wang
- Beijing Infervision Technology Co Ltd, Beijing, People's Republic of China
| | - Xiongfeng Tang
- Orthopaedic Medical Center, The Second Hospital of Jilin University, Changchun, 130041, People's Republic of China.
- Jilin Provincial Key Laboratory of Orhtopeadics, Changchun, People's Republic of China.
| | - Yanguo Qin
- Orthopaedic Medical Center, The Second Hospital of Jilin University, Changchun, 130041, People's Republic of China.
- Jilin Provincial Key Laboratory of Orhtopeadics, Changchun, People's Republic of China.
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12
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Dreizin D, Staziaki PV, Khatri GD, Beckmann NM, Feng Z, Liang Y, Delproposto ZS, Klug M, Spann JS, Sarkar N, Fu Y. Artificial intelligence CAD tools in trauma imaging: a scoping review from the American Society of Emergency Radiology (ASER) AI/ML Expert Panel. Emerg Radiol 2023; 30:251-265. [PMID: 36917287 PMCID: PMC10640925 DOI: 10.1007/s10140-023-02120-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 02/27/2023] [Indexed: 03/16/2023]
Abstract
BACKGROUND AI/ML CAD tools can potentially improve outcomes in the high-stakes, high-volume model of trauma radiology. No prior scoping review has been undertaken to comprehensively assess tools in this subspecialty. PURPOSE To map the evolution and current state of trauma radiology CAD tools along key dimensions of technology readiness. METHODS Following a search of databases, abstract screening, and full-text document review, CAD tool maturity was charted using elements of data curation, performance validation, outcomes research, explainability, user acceptance, and funding patterns. Descriptive statistics were used to illustrate key trends. RESULTS A total of 4052 records were screened, and 233 full-text articles were selected for content analysis. Twenty-one papers described FDA-approved commercial tools, and 212 reported algorithm prototypes. Works ranged from foundational research to multi-reader multi-case trials with heterogeneous external data. Scalable convolutional neural network-based implementations increased steeply after 2016 and were used in all commercial products; however, options for explainability were narrow. Of FDA-approved tools, 9/10 performed detection tasks. Dataset sizes ranged from < 100 to > 500,000 patients, and commercialization coincided with public dataset availability. Cross-sectional torso datasets were uniformly small. Data curation methods with ground truth labeling by independent readers were uncommon. No papers assessed user acceptance, and no method included human-computer interaction. The USA and China had the highest research output and frequency of research funding. CONCLUSIONS Trauma imaging CAD tools are likely to improve patient care but are currently in an early stage of maturity, with few FDA-approved products for a limited number of uses. The scarcity of high-quality annotated data remains a major barrier.
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Affiliation(s)
- David Dreizin
- Department of Diagnostic Radiology and Nuclear Medicine, R Adams Cowley Shock Trauma Center, University of Maryland School of Medicine, Baltimore, MD, USA.
| | - Pedro V Staziaki
- Cardiothoracic Imaging, Department of Radiology, Larner College of Medicine, University of Vermont, Burlington, VT, USA
| | - Garvit D Khatri
- Department of Radiology, University of Washington School of Medicine, Seattle, WA, USA
| | - Nicholas M Beckmann
- Memorial Hermann Orthopedic & Spine Hospital, McGovern Medical School at UTHealth, Houston, TX, USA
| | - Zhaoyong Feng
- Epidemiology & Public Health, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Yuanyuan Liang
- Epidemiology & Public Health, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Zachary S Delproposto
- Division of Emergency Radiology, Department of Radiology, University of Michigan, Ann Arbor, MI, USA
| | | | - J Stephen Spann
- Department of Radiology, University of Alabama at Birmingham Heersink School of Medicine, Birmingham, AL, USA
| | - Nathan Sarkar
- University of Maryland School of Medicine, Baltimore, MD, USA
| | - Yunting Fu
- Health Sciences and Human Services Library, University of Maryland, Baltimore, Baltimore, MD, USA
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13
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Zhang J, Liu J, Liang Z, Xia L, Zhang W, Xing Y, Zhang X, Tang G. Differentiation of acute and chronic vertebral compression fractures using conventional CT based on deep transfer learning features and hand-crafted radiomics features. BMC Musculoskelet Disord 2023; 24:165. [PMID: 36879285 PMCID: PMC9987077 DOI: 10.1186/s12891-023-06281-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Accepted: 02/28/2023] [Indexed: 03/08/2023] Open
Abstract
BACKGROUND We evaluated the diagnostic efficacy of deep learning radiomics (DLR) and hand-crafted radiomics (HCR) features in differentiating acute and chronic vertebral compression fractures (VCFs). METHODS A total of 365 patients with VCFs were retrospectively analysed based on their computed tomography (CT) scan data. All patients completed MRI examination within 2 weeks. There were 315 acute VCFs and 205 chronic VCFs. Deep transfer learning (DTL) features and HCR features were extracted from CT images of patients with VCFs using DLR and traditional radiomics, respectively, and feature fusion was performed to establish the least absolute shrinkage and selection operator. The MRI display of vertebral bone marrow oedema was used as the gold standard for acute VCF, and the model performance was evaluated using the receiver operating characteristic (ROC).To separately evaluate the effectiveness of DLR, traditional radiomics and feature fusion in the differential diagnosis of acute and chronic VCFs, we constructed a nomogram based on the clinical baseline data to visualize the classification evaluation. The predictive power of each model was compared using the Delong test, and the clinical value of the nomogram was evaluated using decision curve analysis (DCA). RESULTS Fifty DTL features were obtained from DLR, 41 HCR features were obtained from traditional radiomics, and 77 features fusion were obtained after feature screening and fusion of the two. The area under the curve (AUC) of the DLR model in the training cohort and test cohort were 0.992 (95% confidence interval (CI), 0.983-0.999) and 0.871 (95% CI, 0.805-0.938), respectively. While the AUCs of the conventional radiomics model in the training cohort and test cohort were 0.973 (95% CI, 0.955-0.990) and 0.854 (95% CI, 0.773-0.934), respectively. The AUCs of the features fusion model in the training cohort and test cohort were 0.997 (95% CI, 0.994-0.999) and 0.915 (95% CI, 0.855-0.974), respectively. The AUCs of nomogram constructed by the features fusion in combination with clinical baseline data were 0.998 (95% CI, 0.996-0.999) and 0.946 (95% CI, 0.906-0.987) in the training cohort and test cohort, respectively. The Delong test showed that the differences between the features fusion model and the nomogram in the training cohort and the test cohort were not statistically significant (P values were 0.794 and 0.668, respectively), and the differences in the other prediction models in the training cohort and the test cohort were statistically significant (P < 0.05). DCA showed that the nomogram had high clinical value. CONCLUSION The features fusion model can be used for the differential diagnosis of acute and chronic VCFs, and its differential diagnosis ability is improved when compared with that when either radiomics is used alone. At the same time, the nomogram has a high predictive value for acute and chronic VCFs and can be a potential decision-making tool to assist clinicians, especially when a patient is unable to undergo spinal MRI examination.
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Affiliation(s)
- Jun Zhang
- Department of Radiology, Clinical Medical College of Shanghai Tenth People's Hospital of Nanjing Medical University, 301 Middle Yanchang Road, Shanghai, 200072, P.R. China.,Department of Radiology, Sir RunRun Hospital affiliated to Nanjing Medical University, 109 Longmian Road, Nanjing, Jiangsu, 211002, P.R. China
| | - Jiayi Liu
- Department of Radiology, Sir RunRun Hospital affiliated to Nanjing Medical University, 109 Longmian Road, Nanjing, Jiangsu, 211002, P.R. China
| | - Zhipeng Liang
- Department of Radiology, Sir RunRun Hospital affiliated to Nanjing Medical University, 109 Longmian Road, Nanjing, Jiangsu, 211002, P.R. China
| | - Liang Xia
- Department of Radiology, Sir RunRun Hospital affiliated to Nanjing Medical University, 109 Longmian Road, Nanjing, Jiangsu, 211002, P.R. China
| | - Weixiao Zhang
- Department of Radiology, Sir RunRun Hospital affiliated to Nanjing Medical University, 109 Longmian Road, Nanjing, Jiangsu, 211002, P.R. China
| | - Yanfen Xing
- Department of Radiology, Sir RunRun Hospital affiliated to Nanjing Medical University, 109 Longmian Road, Nanjing, Jiangsu, 211002, P.R. China
| | - Xueli Zhang
- Department of Radiology, Shanghai TenthPeople's Hospital, Tongji University School of Medicine, 301 Middle Yanchang Road, Shanghai, 200072, P.R. China
| | - Guangyu Tang
- Department of Radiology, Clinical Medical College of Shanghai Tenth People's Hospital of Nanjing Medical University, 301 Middle Yanchang Road, Shanghai, 200072, P.R. China. .,Department of Radiology, Shanghai TenthPeople's Hospital, Tongji University School of Medicine, 301 Middle Yanchang Road, Shanghai, 200072, P.R. China.
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14
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Abstract
PURPOSE OF REVIEW Opportunistic screening is a combination of techniques to identify subjects of high risk for osteoporotic fracture using routine clinical CT scans prescribed for diagnoses unrelated to osteoporosis. The two main components are automated detection of vertebral fractures and measurement of bone mineral density (BMD) in CT scans, in which a phantom for calibration of CT to BMD values is not used. This review describes the particular challenges of opportunistic screening and provides an overview and comparison of current techniques used for opportunistic screening. The review further outlines the performance of opportunistic screening. RECENT FINDINGS A wide range of technologies for the automatic detection of vertebral fractures have been developed and successfully validated. Most of them are based on artificial intelligence algorithms. The automated differentiation of osteoporotic from traumatic fractures and vertebral deformities unrelated to osteoporosis, the grading of vertebral fracture severity, and the detection of mild vertebral fractures is still problematic. The accuracy of automated fracture detection compared to classical radiological semi-quantitative Genant scoring is about 80%. Accuracy errors of alternative BMD calibration methods compared to simultaneous phantom-based calibration used in standard quantitative CT (QCT) range from below 5% to about 10%. The impact of contrast agents, frequently administered in clinical CT on the determination of BMD and on fracture risk determination is still controversial. Opportunistic screening, the identification of vertebral fracture and the measurement of BMD using clinical routine CT scans, is feasible but corresponding techniques still need to be integrated into the clinical workflow and further validated with respect to the prediction of fracture risk.
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Affiliation(s)
- Klaus Engelke
- Department of Medicine III, Friedrich-Alexander University of Erlangen-Nürnberg, University Hospital Erlangen, Ulmenweg 18, 91054, Erlangen, Germany.
- Institute of Medical Physics (IMP), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Henkestr. 91, 91052, Erlangen, Germany.
| | - Oliver Chaudry
- Department of Medicine III, Friedrich-Alexander University of Erlangen-Nürnberg, University Hospital Erlangen, Ulmenweg 18, 91054, Erlangen, Germany
- Institute of Medical Physics (IMP), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Henkestr. 91, 91052, Erlangen, Germany
| | - Stefan Bartenschlager
- Department of Medicine III, Friedrich-Alexander University of Erlangen-Nürnberg, University Hospital Erlangen, Ulmenweg 18, 91054, Erlangen, Germany
- Institute of Medical Physics (IMP), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Henkestr. 91, 91052, Erlangen, Germany
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15
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Faraji N, Jafari Jozani M, Nematollahi N. Another look at regression analysis using ranked set samples with application to an osteoporosis study. Biometrics 2022; 78:1489-1502. [PMID: 34184755 DOI: 10.1111/biom.13513] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Revised: 05/05/2021] [Accepted: 06/17/2021] [Indexed: 12/30/2022]
Abstract
Statistical learning with ranked set samples has shown promising results in estimating various population parameters. Despite the vast literature on rank-based statistical learning methodologies, very little effort has been devoted to studying regression analysis with such samples. A pressing issue is how to incorporate the rank information of ranked set samples into the analysis. We propose two methodologies based on a weighted least squares approach and multilevel modeling to better incorporate the rank information of such samples into the estimation and prediction processes of regression-type models. Our approaches reveal significant improvements in both estimation and prediction problems over already existing methods in the literature and the corresponding ones with simple random samples. We study the robustness of our methods with respect to the misspecification of the distribution of the error terms. Also, we show that rank-based regression models can effectively predict simple random test data by assigning ranks to them a posteriori using judgment poststratification. Theoretical results are augmented with simulations and an osteoporosis study based on a real data set from the Bone Mineral Density (BMD) program of Manitoba to estimate the BMD level of patients using easy to obtain covariates.
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Affiliation(s)
- Nasrin Faraji
- Department of Statistics, Allameh Tabataba'i University, Tehran, Iran
| | | | - Nader Nematollahi
- Department of Statistics, Allameh Tabataba'i University, Tehran, Iran
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16
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Ren G, Yu K, Xie Z, Wang P, Zhang W, Huang Y, Wang Y, Wu X. Current Applications of Machine Learning in Spine: From Clinical View. Global Spine J 2022; 12:1827-1840. [PMID: 34628966 PMCID: PMC9609532 DOI: 10.1177/21925682211035363] [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] [Indexed: 12/17/2022] Open
Abstract
STUDY DESIGN Narrative review. OBJECTIVES This review aims to present current applications of machine learning (ML) in spine domain to clinicians. METHODS We conducted a comprehensive PubMed search of peer-reviewed articles that were published between 2006 and 2020 using terms (spine, spinal, lumbar, cervical, thoracic, machine learning) to examine ML in spine. Then exclude research of other domain, case report, review or meta-analysis, and which without available abstract or full text. RESULTS Total 1738 articles were retrieved from database, and 292 studies were finally included. Key findings of current applications were compiled and summarized in this review. Main clinical applications of those techniques including image processing, diagnosis, decision supporting, operative assistance, rehabilitation, surgery outcomes, complications, hospitalization and cost. CONCLUSIONS ML had achieved excellent performance and hold immense potential in spine. ML could help clinical staff to improve medical level, enhance work efficiency, and reduce adverse events. However more randomized controlled trials and improvement of interpretability are essential to clinicians accepting models' assistance in real work.
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Affiliation(s)
- GuanRui Ren
- Southeast University Medical College,
Nanjing, Jiangsu, China
| | - Kun Yu
- Nanjing Jiangbei Hospital, Nanjing,
Jiangsu, China
| | - ZhiYang Xie
- Department of Spine Surgery, Zhongda
Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China
| | - PeiYang Wang
- Southeast University Medical College,
Nanjing, Jiangsu, China
| | - Wei Zhang
- Southeast University Medical College,
Nanjing, Jiangsu, China
| | - Yong Huang
- Southeast University Medical College,
Nanjing, Jiangsu, China
| | - YunTao Wang
- Department of Spine Surgery, Zhongda
Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China,YunTao Wang, Department of Spine Surgery,
Zhongda Hospital, School of Medicine, Southeast University, No. 87, Dingjiaqiao
Road, Nanjing, Jiangsu 210009, China.
| | - XiaoTao Wu
- Department of Spine Surgery, Zhongda
Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China,XiaoTao Wu, Department of Spine Surgery,
Zhongda Hospital, School of Medicine, Southeast University, No. 87, Dingjiaqiao
Road, Nanjing, Jiangsu 210009, China.
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17
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Wani IM, Arora S. Osteoporosis diagnosis in knee X-rays by transfer learning based on convolution neural network. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 82:14193-14217. [PMID: 36185321 PMCID: PMC9510281 DOI: 10.1007/s11042-022-13911-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 08/17/2022] [Accepted: 09/12/2022] [Indexed: 06/16/2023]
Abstract
Osteoporosis degrades the quality of bones and is the primary cause of fractures in the elderly and women after menopause. The high diagnostic and treatment costs urge the researchers to find a cost-effective diagnostic system to diagnose osteoporosis in the early stages. X-ray imaging is the cheapest and most common imaging technique to detect bone pathologies butmanual interpretation of x-rays for osteoporosis is difficult and extraction of required features and selection of high-performance classifiers is a very challenging task. Deep learning systems have gained the popularity in image analysis field over the last few decades. This paper proposes a convolution neural network (CNN) based approach to detect osteoporosis from x-rays. In our study, we have used the transfer learning of deep learning-based CNNs namely AlexNet, VggNet-16, ResNet, and VggNet -19 to classify the x-ray images of knee joints into normal, osteopenia, and osteoporosis disease groups. The main objectives of the current study are: (i) to present a dataset of 381 knee x-rays medically validated by the T-scores obtained from the Quantitative Ultrasound System, and (ii) to propose a deep learning approach using transfer learning to classify different stages of the disease. The performance of these classifiers is compared and the best accuracy of 91.1% is achieved by pretrained Alexnet architecture on the presented dataset with an error rate of 0.09 and validation loss of 0.54 as compared to the accuracy of 79%, an error rate of 0.21, and validation loss of 0.544 when pretrained network was not used.. The results of the study suggest that a deep learning system with transfer learning can help clinicians to detect osteoporosis in its early stages hence reducing the risk of fractures.
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Affiliation(s)
- Insha Majeed Wani
- School of Computer Science Engineering, Shri Mata Vaishno Devi University, Katra, India
| | - Sakshi Arora
- School of Computer Science Engineering, Shri Mata Vaishno Devi University, Katra, India
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18
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Hipp JA, Grieco TF, Newman P, Reitman CA. Definition of normal vertebral morphometry using NHANES‐II radiographs. JBMR Plus 2022; 6:e10677. [PMID: 36248278 PMCID: PMC9549721 DOI: 10.1002/jbm4.10677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 08/08/2022] [Accepted: 08/26/2022] [Indexed: 11/26/2022] Open
Abstract
A robust definition of normal vertebral morphometry is required to confidently identify abnormalities such as fractures. The Second National Health and Nutrition Examination Survey (NHANES‐II) collected a nationwide probability sample to document the health status of the United States. Over 10,000 lateral cervical spine and 7,000 lateral lumbar spine X‐rays were collected. Demographic, anthropometric, health, and medical history data were also collected. The coordinates of the vertebral body corners were obtained for each lumbar and cervical vertebra using previously validated, automated technology consisting of a pipeline of neural networks and coded logic. These landmarks were used to calculate six vertebral body morphometry metrics. Descriptive statistics were generated and used to identify and trim outliers from the data. Descriptive statistics were tabulated using the trimmed data for use in quantifying deviation from average for each metric. The dependency of these metrics on sex, age, race, nation of origin, height, weight, and body mass index (BMI) was also assessed. There was low variation in vertebral morphometry after accounting for vertebrae (eg, L1, L2), and the R2 was high for ANOVAs. Excluding outliers, age, sex, race, nation of origin, height, weight, and BMI were statistically significant for most of the variables, though the F‐statistic was very small compared to that for vertebral level. Excluding all variables except vertebra changed the ANOVA R2 very little. Reference data were generated that could be used to produce standardized metrics in units of SD from mean. This allows for easy identification of abnormalities resulting from vertebral fractures, atypical vertebral body morphometries, and other congenital or degenerative conditions. Standardized metrics also remove the effect of vertebral level, facilitating easy interpretation and enabling data for all vertebrae to be pooled in research studies. © 2022 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.
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Affiliation(s)
- John A. Hipp
- Medical Metrics, Imaging Core Laboratory Houston TX
| | | | | | - Charles A. Reitman
- Orthopaedics and Physical Medicine Medical University of South Carolina Charleston SC
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Kong SH, Lee JW, Bae BU, Sung JK, Jung KH, Kim JH, Shin CS. Development of a Spine X-Ray-Based Fracture Prediction Model Using a Deep Learning Algorithm. Endocrinol Metab (Seoul) 2022; 37:674-683. [PMID: 35927066 PMCID: PMC9449110 DOI: 10.3803/enm.2022.1461] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 06/20/2022] [Indexed: 11/20/2022] Open
Abstract
BACKGRUOUND Since image-based fracture prediction models using deep learning are lacking, we aimed to develop an X-ray-based fracture prediction model using deep learning with longitudinal data. METHODS This study included 1,595 participants aged 50 to 75 years with at least two lumbosacral radiographs without baseline fractures from 2010 to 2015 at Seoul National University Hospital. Positive and negative cases were defined according to whether vertebral fractures developed during follow-up. The cases were divided into training (n=1,416) and test (n=179) sets. A convolutional neural network (CNN)-based prediction algorithm, DeepSurv, was trained with images and baseline clinical information (age, sex, body mass index, glucocorticoid use, and secondary osteoporosis). The concordance index (C-index) was used to compare performance between DeepSurv and the Fracture Risk Assessment Tool (FRAX) and Cox proportional hazard (CoxPH) models. RESULTS Of the total participants, 1,188 (74.4%) were women, and the mean age was 60.5 years. During a mean follow-up period of 40.7 months, vertebral fractures occurred in 7.5% (120/1,595) of participants. In the test set, when DeepSurv learned with images and clinical features, it showed higher performance than FRAX and CoxPH in terms of C-index values (DeepSurv, 0.612; 95% confidence interval [CI], 0.571 to 0.653; FRAX, 0.547; CoxPH, 0.594; 95% CI, 0.552 to 0.555). Notably, the DeepSurv method without clinical features had a higher C-index (0.614; 95% CI, 0.572 to 0.656) than that of FRAX in women. CONCLUSION DeepSurv, a CNN-based prediction algorithm using baseline image and clinical information, outperformed the FRAX and CoxPH models in predicting osteoporotic fracture from spine radiographs in a longitudinal cohort.
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Affiliation(s)
- Sung Hye Kong
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
| | | | | | | | | | - Jung Hee Kim
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
- Corresponding author: Jung Hee Kim. Department of Internal Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea Tel: +82-2-2072-4839, Fax: +82-2-2072-7246, E-mail:
| | - Chan Soo Shin
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
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20
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Monchka BA, Schousboe JT, Davidson MJ, Kimelman D, Hans D, Raina P, Leslie WD. Development of a manufacturer-independent convolutional neural network for the automated identification of vertebral compression fractures in vertebral fracture assessment images using active learning. Bone 2022; 161:116427. [PMID: 35489707 DOI: 10.1016/j.bone.2022.116427] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 04/20/2022] [Accepted: 04/20/2022] [Indexed: 11/18/2022]
Abstract
BACKGROUND Convolutional neural networks (CNNs) can identify vertebral compression fractures in GE vertebral fracture assessment (VFA) images with high balanced accuracy, but performance against Hologic VFAs is unknown. To obtain good classification performance, supervised machine learning requires balanced and labeled training data. Active learning is an iterative data annotation process with the ability to reduce the cost of labeling medical image data and reduce class imbalance. PURPOSE To train CNNs to identify vertebral fractures in Hologic VFAs using an active learning approach, and evaluate the ability of CNNs to generalize to both Hologic and GE VFA images. METHODS VFAs were obtained from the OsteoLaus Study (labeled Hologic Discovery A, n = 2726), the Manitoba Bone Mineral Density Program (labeled GE Prodigy and iDXA, n = 12,742), and the Canadian Longitudinal Study on Aging (CLSA, unlabeled Hologic Discovery A, n = 17,190). Unlabeled CLSA VFAs were split into five equal-sized partitions (n = 3438) and reviewed sequentially using active learning. Based on predicted fracture probability, 17.6% (n = 3032) of the unlabeled VFAs were selected for expert review using the modified algorithm-based qualitative (mABQ) method. CNNs were simultaneously trained on Hologic, GE dual-energy and GE single-energy VFAs. Two ensemble CNNs were constructed using the maximum and mean predicted probability from six separately trained CNNs that differed due to stochastic variation. CNNs were evaluated against the OsteoLaus validation set (n = 408) during the active learning process; ensemble performance was measured against the OsteoLaus test set (n = 819). RESULTS The baseline CNN, prior to active learning, achieved 55.0% sensitivity, 97.9% specificity, 57.9% positive predictive value (PPV), F1-score 56.4%. Through active learning, 2942 CLSA Hologic VFAs (492 fractures) were added to the training data-increasing the proportion of Hologic VFAs with fractures from 4.2% to 12.5%. With active learning, CNN performance improved to 80.0% sensitivity, 99.7% specificity, 94.1% PPV, F1-score 86.5%. The CNN maximum ensemble achieved 91.9% sensitivity (100% for grade 3 and 95.5% for grade 2 fractures), 99.0% specificity, 81.0% PPV, F1-score 86.1%. CONCLUSION Simultaneously training on a composite dataset consisting of both Hologic and GE VFAs allowed for the development of a single manufacturer-independent CNN that generalized to both scanner types with good classification performance. Active learning can reduce class imbalance and produce an effective medical image classifier while only labeling a subset of available unlabeled image data-thereby reducing the time and cost required to train a machine learning model.
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Affiliation(s)
| | | | | | | | - Didier Hans
- Lausanne University Hospital, Lausanne, Switzerland
| | - Parminder Raina
- Department of Health Evidence & Impact, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada; McMaster Institute for Research on Aging, Hamilton, Ontario, Canada
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21
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Xiao BH, Zhu MSY, Du EZ, Liu WH, Ma JB, Huang H, Gong JS, Diacinti D, Zhang K, Gao B, Liu H, Jiang RF, Ji ZY, Xiong XB, He LC, Wu L, Xu CJ, Du MM, Wang XR, Chen LM, Wu KY, Yang L, Xu MS, Diacinti D, Dou Q, Kwok TYC, Wáng YXJ. A software program for automated compressive vertebral fracture detection on elderly women's lateral chest radiograph: Ofeye 1.0. Quant Imaging Med Surg 2022; 12:4259-4271. [PMID: 35919046 PMCID: PMC9338385 DOI: 10.21037/qims-22-433] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 05/25/2022] [Indexed: 11/17/2022]
Abstract
Background Because osteoporotic vertebral fracture (OVF) on chest radiographs is commonly missed in radiological reports, we aimed to develop a software program which offers automated detection of compressive vertebral fracture (CVF) on lateral chest radiographs, and which emphasizes CVF detection specificity with a low false positivity rate. Methods For model training, we retrieved 3,991 spine radiograph cases and 1,979 chest radiograph cases from 16 sources, with among them in total 1,404 cases had OVF. For model testing, we retrieved 542 chest radiograph cases and 162 spine radiograph cases from four independent clinics, with among them 215 cases had OVF. All cases were female subjects, and except for 31 training data cases which were spine trauma cases, all the remaining cases were post-menopausal women. Image data included DICOM (Digital Imaging and Communications in Medicine) format, hard film scanned PNG (Portable Network Graphics) format, DICOM exported PNG format, and PACS (Picture Archiving and Communication System) downloaded resolution reduced DICOM format. OVF classification included: minimal and mild grades with <20% or ≥20-25% vertebral height loss respectively, moderate grade with ≥25-40% vertebral height loss, severe grade with ≥40%-2/3 vertebral height loss, and collapsed grade with ≥2/3 vertebral height loss. The CVF detection base model was mainly composed of convolution layers that include convolution kernels of different sizes, pooling layers, up-sampling layers, feature merging layers, and residual modules. When the model loss function could not be further decreased with additional training, the model was considered to be optimal and termed 'base-model 1.0'. A user-friendly interface was also developed, with the synthesized software termed 'Ofeye 1.0'. Results Counting cases and with minimal and mild OVFs included, base-model 1.0 demonstrated a specificity of 97.1%, a sensitivity of 86%, and an accuracy of 93.9% for the 704 testing cases. In total, 33 OVFs in 30 cases had a false negative reading, which constituted a false negative rate of 14.0% (30/215) by counting all OVF cases. Eighteen OVFs in 15 cases had OVFs of ≥ moderate grades missed, which constituted a false negative rate of 7.0% (15/215, i.e., sensitivity 93%) if only counting cases with ≥ moderate grade OVFs missed. False positive reading was recorded in 13 vertebrae in 13 cases (one vertebra in each case), which constituted a false positivity rate of 2.7% (13/489). These vertebrae with false positivity labeling could be readily differentiated from a true OVF by a human reader. The software Ofeye 1.0 allows 'batch processing', for example, 100 radiographs can be processed in a single operation. This software can be integrated into hospital PACS, or installed in a standalone personal computer. Conclusions A user-friendly software program was developed for CVF detection on elderly women's lateral chest radiographs. It has an overall low false positivity rate, and for moderate and severe CVFs an acceptably low false negativity rate. The integration of this software into radiological practice is expected to improve osteoporosis management for elderly women.
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Affiliation(s)
- Ben-Heng Xiao
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | | | - Er-Zhu Du
- Department of Radiology, Dongguan Traditional Chinese Medicine Hospital, Dongguan, China
| | - Wei-Hong Liu
- Department of Radiology, General Hospital of China Resources & Wuhan Iron and Steel Corporation, Wuhan, China
| | - Jian-Bing Ma
- Department of Radiology, the First Hospital of Jiaxing, The Affiliated Hospital of Jiaxing University, Jiaxing, China
| | - Hua Huang
- Department of Radiology, The Third People’s Hospital of Shenzhen, The Second Affiliated Hospital of Southern University of Science and Technology, National Clinical Research Center for Infectious Diseases, Shenzhen, China
| | - Jing-Shan Gong
- Department of Radiology, Shenzhen People’s Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, China
| | - Davide Diacinti
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome, Sapienza University of Rome, Rome, Italy
- Department of Diagnostic and Molecular Imaging, Radiology and Radiotherapy, University Foundation Hospital Tor Vergata, Rome, Italy
| | - Kun Zhang
- Department of Radiology, First Affiliated Hospital of Hunan University of Chinese Medicine, Changsha, China
| | - Bo Gao
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Heng Liu
- Department of Radiology, the Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Ri-Feng Jiang
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Zhong-You Ji
- PET-CT Center, Fujian Medical University Union Hospital, Fuzhou, China
| | - Xiao-Bao Xiong
- Department of Radiology, Zhejiang Provincial Tongde Hospital, Hangzhou, China
| | - Lai-Chang He
- Department of Radiology, the First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Lei Wu
- Department of Radiology, the First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Chuan-Jun Xu
- Department of Radiology, The Second Hospital of Nanjing, Nanjing University of Chinese Medicine, Nanjing, China
| | - Mei-Mei Du
- Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital, Wenzhou Medical University, Wenzhou, China
| | - Xiao-Rong Wang
- Department of Radiology, Ningbo First Hospital, Ningbo, China
| | - Li-Mei Chen
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Kong-Yang Wu
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
- College of Electrical and Information Engineering, Jinan University, Guangzhou, China
| | - Liu Yang
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Mao-Sheng Xu
- Department of Radiology, the First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Daniele Diacinti
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome, Sapienza University of Rome, Rome, Italy
| | - Qi Dou
- Department of Computer Science and Engineering, Faculty of Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Timothy Y. C. Kwok
- JC Centre for Osteoporosis Care and Control, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Yì Xiáng J. Wáng
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
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22
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Zhou X, Wang H, Feng C, Xu R, He Y, Li L, Tu C. Emerging Applications of Deep Learning in Bone Tumors: Current Advances and Challenges. Front Oncol 2022; 12:908873. [PMID: 35928860 PMCID: PMC9345628 DOI: 10.3389/fonc.2022.908873] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 06/15/2022] [Indexed: 12/12/2022] Open
Abstract
Deep learning is a subfield of state-of-the-art artificial intelligence (AI) technology, and multiple deep learning-based AI models have been applied to musculoskeletal diseases. Deep learning has shown the capability to assist clinical diagnosis and prognosis prediction in a spectrum of musculoskeletal disorders, including fracture detection, cartilage and spinal lesions identification, and osteoarthritis severity assessment. Meanwhile, deep learning has also been extensively explored in diverse tumors such as prostate, breast, and lung cancers. Recently, the application of deep learning emerges in bone tumors. A growing number of deep learning models have demonstrated good performance in detection, segmentation, classification, volume calculation, grading, and assessment of tumor necrosis rate in primary and metastatic bone tumors based on both radiological (such as X-ray, CT, MRI, SPECT) and pathological images, implicating a potential for diagnosis assistance and prognosis prediction of deep learning in bone tumors. In this review, we first summarized the workflows of deep learning methods in medical images and the current applications of deep learning-based AI for diagnosis and prognosis prediction in bone tumors. Moreover, the current challenges in the implementation of the deep learning method and future perspectives in this field were extensively discussed.
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Affiliation(s)
- Xiaowen Zhou
- Department of Orthopaedics, The Second Xiangya Hospital, Central South University, Changsha, China
- Xiangya School of Medicine, Central South University, Changsha, China
| | - Hua Wang
- Xiangya School of Medicine, Central South University, Changsha, China
| | - Chengyao Feng
- Department of Orthopaedics, The Second Xiangya Hospital, Central South University, Changsha, China
- Hunan Key Laboratory of Tumor Models and Individualized Medicine, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Ruilin Xu
- Department of Orthopaedics, The Second Xiangya Hospital, Central South University, Changsha, China
- Hunan Key Laboratory of Tumor Models and Individualized Medicine, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Yu He
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Lan Li
- Department of Pathology, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Chao Tu
- Department of Orthopaedics, The Second Xiangya Hospital, Central South University, Changsha, China
- Hunan Key Laboratory of Tumor Models and Individualized Medicine, The Second Xiangya Hospital, Central South University, Changsha, China
- *Correspondence: Chao Tu,
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23
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Zhang X, Yang Y, Shen YW, Zhang KR, Jiang ZK, Ma LT, Ding C, Wang BY, Meng Y, Liu H. Diagnostic accuracy and potential covariates of artificial intelligence for diagnosing orthopedic fractures: a systematic literature review and meta-analysis. Eur Radiol 2022; 32:7196-7216. [PMID: 35754091 DOI: 10.1007/s00330-022-08956-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 05/07/2022] [Accepted: 06/08/2022] [Indexed: 02/05/2023]
Abstract
OBJECTIVES To systematically quantify the diagnostic accuracy and identify potential covariates affecting the performance of artificial intelligence (AI) in diagnosing orthopedic fractures. METHODS PubMed, Embase, Web of Science, and Cochrane Library were systematically searched for studies on AI applications in diagnosing orthopedic fractures from inception to September 29, 2021. Pooled sensitivity and specificity and the area under the receiver operating characteristic curves (AUC) were obtained. This study was registered in the PROSPERO database prior to initiation (CRD 42021254618). RESULTS Thirty-nine were eligible for quantitative analysis. The overall pooled AUC, sensitivity, and specificity were 0.96 (95% CI 0.94-0.98), 90% (95% CI 87-92%), and 92% (95% CI 90-94%), respectively. In subgroup analyses, multicenter designed studies yielded higher sensitivity (92% vs. 88%) and specificity (94% vs. 91%) than single-center studies. AI demonstrated higher sensitivity with transfer learning (with vs. without: 92% vs. 87%) or data augmentation (with vs. without: 92% vs. 87%), compared to those without. Utilizing plain X-rays as input images for AI achieved results comparable to CT (AUC 0.96 vs. 0.96). Moreover, AI achieved comparable results to humans (AUC 0.97 vs. 0.97) and better results than non-expert human readers (AUC 0.98 vs. 0.96; sensitivity 95% vs. 88%). CONCLUSIONS AI demonstrated high accuracy in diagnosing orthopedic fractures from medical images. Larger-scale studies with higher design quality are needed to validate our findings. KEY POINTS • Multicenter study design, application of transfer learning, and data augmentation are closely related to improving the performance of artificial intelligence models in diagnosing orthopedic fractures. • Utilizing plain X-rays as input images for AI to diagnose fractures achieved results comparable to CT (AUC 0.96 vs. 0.96). • AI achieved comparable results to humans (AUC 0.97 vs. 0.97) but was superior to non-expert human readers (AUC 0.98 vs. 0.96, sensitivity 95% vs. 88%) in diagnosing fractures.
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Affiliation(s)
- Xiang Zhang
- Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, No. 37 Guo Xue Rd, Chengdu, 610041, China
| | - Yi Yang
- Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, No. 37 Guo Xue Rd, Chengdu, 610041, China
| | - Yi-Wei Shen
- Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, No. 37 Guo Xue Rd, Chengdu, 610041, China
| | - Ke-Rui Zhang
- Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, No. 37 Guo Xue Rd, Chengdu, 610041, China
| | - Ze-Kun Jiang
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610000, China
| | - Li-Tai Ma
- Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, No. 37 Guo Xue Rd, Chengdu, 610041, China
| | - Chen Ding
- Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, No. 37 Guo Xue Rd, Chengdu, 610041, China
| | - Bei-Yu Wang
- Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, No. 37 Guo Xue Rd, Chengdu, 610041, China
| | - Yang Meng
- Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, No. 37 Guo Xue Rd, Chengdu, 610041, China
| | - Hao Liu
- Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, No. 37 Guo Xue Rd, Chengdu, 610041, China.
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24
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Chae HD, Hong SH, Yeoh HJ, Kang YR, Lee SM, Kim M, Koh SY, Lee Y, Park MS, Choi JY, Yoo HJ. Improved diagnostic performance of plain radiography for cervical ossification of the posterior longitudinal ligament using deep learning. PLoS One 2022; 17:e0267643. [PMID: 35476649 PMCID: PMC9045646 DOI: 10.1371/journal.pone.0267643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 04/12/2022] [Indexed: 11/18/2022] Open
Abstract
Background A high false-negative rate has been reported for the diagnosis of ossification of the posterior longitudinal ligament (OPLL) using plain radiography. We investigated whether deep learning (DL) can improve the diagnostic performance of radiologists for cervical OPLL using plain radiographs. Materials and methods The training set consisted of 915 radiographs from 207 patients diagnosed with OPLL. For the test set, we used 200 lateral cervical radiographs from 100 patients with cervical OPLL and 100 patients without OPLL. An observer performance study was conducted over two reading sessions. In the first session, we compared the diagnostic performance of the DL-model and the six observers. The diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC) at the vertebra and patient level. The sensitivity and specificity of the DL model and average observers were calculated in per-patient analysis. Subgroup analysis was performed according to the morphologic classification of OPLL. In the second session, observers evaluated the radiographs by referring to the results of the DL-model. Results In the vertebra-level analysis, the DL-model showed an AUC of 0.854, which was higher than the average AUC of observers (0.826), but the difference was not significant (p = 0.292). In the patient-level analysis, the performance of the DL-model had an AUC of 0.851, and the average AUC of observers was 0.841 (p = 0.739). The patient-level sensitivity and specificity were 91% and 69% in the DL model, and 83% and 68% for the average observers, respectively. Both the DL-model and observers showed decreases in overall performance in the segmental and circumscribed types. With knowledge of the results of the DL-model, the average AUC of observers increased to 0.893 (p = 0.001) at the vertebra level and 0.911 (p < 0.001) at the patient level. In the subgroup analysis, the improvement was largest in segmental-type (AUC difference 0.087; p = 0.002). Conclusions The DL-based OPLL detection model can significantly improve the diagnostic performance of radiologists on cervical radiographs.
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Affiliation(s)
- Hee-Dong Chae
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Sung Hwan Hong
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea
- * E-mail:
| | - Hyun Jung Yeoh
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Yeo Ryang Kang
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Su Min Lee
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Minyoung Kim
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Seok Young Koh
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | | | | | - Ja-Young Choi
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea
| | - Hye Jin Yoo
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea
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Kuo RYL, Harrison C, Curran TA, Jones B, Freethy A, Cussons D, Stewart M, Collins GS, Furniss D. Artificial Intelligence in Fracture Detection: A Systematic Review and Meta-Analysis. Radiology 2022; 304:50-62. [PMID: 35348381 DOI: 10.1148/radiol.211785] [Citation(s) in RCA: 67] [Impact Index Per Article: 33.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Background Patients with fractures are a common emergency presentation and may be misdiagnosed at radiologic imaging. An increasing number of studies apply artificial intelligence (AI) techniques to fracture detection as an adjunct to clinician diagnosis. Purpose To perform a systematic review and meta-analysis comparing the diagnostic performance in fracture detection between AI and clinicians in peer-reviewed publications and the gray literature (ie, articles published on preprint repositories). Materials and Methods A search of multiple electronic databases between January 2018 and July 2020 (updated June 2021) was performed that included any primary research studies that developed and/or validated AI for the purposes of fracture detection at any imaging modality and excluded studies that evaluated image segmentation algorithms. Meta-analysis with a hierarchical model to calculate pooled sensitivity and specificity was used. Risk of bias was assessed by using a modified Prediction Model Study Risk of Bias Assessment Tool, or PROBAST, checklist. Results Included for analysis were 42 studies, with 115 contingency tables extracted from 32 studies (55 061 images). Thirty-seven studies identified fractures on radiographs and five studies identified fractures on CT images. For internal validation test sets, the pooled sensitivity was 92% (95% CI: 88, 93) for AI and 91% (95% CI: 85, 95) for clinicians, and the pooled specificity was 91% (95% CI: 88, 93) for AI and 92% (95% CI: 89, 92) for clinicians. For external validation test sets, the pooled sensitivity was 91% (95% CI: 84, 95) for AI and 94% (95% CI: 90, 96) for clinicians, and the pooled specificity was 91% (95% CI: 81, 95) for AI and 94% (95% CI: 91, 95) for clinicians. There were no statistically significant differences between clinician and AI performance. There were 22 of 42 (52%) studies that were judged to have high risk of bias. Meta-regression identified multiple sources of heterogeneity in the data, including risk of bias and fracture type. Conclusion Artificial intelligence (AI) and clinicians had comparable reported diagnostic performance in fracture detection, suggesting that AI technology holds promise as a diagnostic adjunct in future clinical practice. Clinical trial registration no. CRD42020186641 © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Cohen and McInnes in this issue.
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Affiliation(s)
- Rachel Y L Kuo
- From the Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, Old Road Headington, Oxford OX3 7LD, UK (R.Y.L.K., C.H., M.S., G.S.C., D.F.); Department of Plastic Surgery, John Radcliffe Hospital, Oxford, UK (T.A.C., A.F.); Department of Vascular Surgery, Royal Berkshire Hospital, Reading, UK (B.J.); Department of Plastic Surgery, Stoke Mandeville Hospital, Aylesbury, Buckinghamshire UK (D.C.); and UK EQUATOR Center, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford Centre for Statistics in Medicine, Oxford UK (G.S.C.)
| | - Conrad Harrison
- From the Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, Old Road Headington, Oxford OX3 7LD, UK (R.Y.L.K., C.H., M.S., G.S.C., D.F.); Department of Plastic Surgery, John Radcliffe Hospital, Oxford, UK (T.A.C., A.F.); Department of Vascular Surgery, Royal Berkshire Hospital, Reading, UK (B.J.); Department of Plastic Surgery, Stoke Mandeville Hospital, Aylesbury, Buckinghamshire UK (D.C.); and UK EQUATOR Center, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford Centre for Statistics in Medicine, Oxford UK (G.S.C.)
| | - Terry-Ann Curran
- From the Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, Old Road Headington, Oxford OX3 7LD, UK (R.Y.L.K., C.H., M.S., G.S.C., D.F.); Department of Plastic Surgery, John Radcliffe Hospital, Oxford, UK (T.A.C., A.F.); Department of Vascular Surgery, Royal Berkshire Hospital, Reading, UK (B.J.); Department of Plastic Surgery, Stoke Mandeville Hospital, Aylesbury, Buckinghamshire UK (D.C.); and UK EQUATOR Center, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford Centre for Statistics in Medicine, Oxford UK (G.S.C.)
| | - Benjamin Jones
- From the Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, Old Road Headington, Oxford OX3 7LD, UK (R.Y.L.K., C.H., M.S., G.S.C., D.F.); Department of Plastic Surgery, John Radcliffe Hospital, Oxford, UK (T.A.C., A.F.); Department of Vascular Surgery, Royal Berkshire Hospital, Reading, UK (B.J.); Department of Plastic Surgery, Stoke Mandeville Hospital, Aylesbury, Buckinghamshire UK (D.C.); and UK EQUATOR Center, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford Centre for Statistics in Medicine, Oxford UK (G.S.C.)
| | - Alexander Freethy
- From the Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, Old Road Headington, Oxford OX3 7LD, UK (R.Y.L.K., C.H., M.S., G.S.C., D.F.); Department of Plastic Surgery, John Radcliffe Hospital, Oxford, UK (T.A.C., A.F.); Department of Vascular Surgery, Royal Berkshire Hospital, Reading, UK (B.J.); Department of Plastic Surgery, Stoke Mandeville Hospital, Aylesbury, Buckinghamshire UK (D.C.); and UK EQUATOR Center, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford Centre for Statistics in Medicine, Oxford UK (G.S.C.)
| | - David Cussons
- From the Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, Old Road Headington, Oxford OX3 7LD, UK (R.Y.L.K., C.H., M.S., G.S.C., D.F.); Department of Plastic Surgery, John Radcliffe Hospital, Oxford, UK (T.A.C., A.F.); Department of Vascular Surgery, Royal Berkshire Hospital, Reading, UK (B.J.); Department of Plastic Surgery, Stoke Mandeville Hospital, Aylesbury, Buckinghamshire UK (D.C.); and UK EQUATOR Center, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford Centre for Statistics in Medicine, Oxford UK (G.S.C.)
| | - Max Stewart
- From the Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, Old Road Headington, Oxford OX3 7LD, UK (R.Y.L.K., C.H., M.S., G.S.C., D.F.); Department of Plastic Surgery, John Radcliffe Hospital, Oxford, UK (T.A.C., A.F.); Department of Vascular Surgery, Royal Berkshire Hospital, Reading, UK (B.J.); Department of Plastic Surgery, Stoke Mandeville Hospital, Aylesbury, Buckinghamshire UK (D.C.); and UK EQUATOR Center, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford Centre for Statistics in Medicine, Oxford UK (G.S.C.)
| | - Gary S Collins
- From the Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, Old Road Headington, Oxford OX3 7LD, UK (R.Y.L.K., C.H., M.S., G.S.C., D.F.); Department of Plastic Surgery, John Radcliffe Hospital, Oxford, UK (T.A.C., A.F.); Department of Vascular Surgery, Royal Berkshire Hospital, Reading, UK (B.J.); Department of Plastic Surgery, Stoke Mandeville Hospital, Aylesbury, Buckinghamshire UK (D.C.); and UK EQUATOR Center, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford Centre for Statistics in Medicine, Oxford UK (G.S.C.)
| | - Dominic Furniss
- From the Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, Old Road Headington, Oxford OX3 7LD, UK (R.Y.L.K., C.H., M.S., G.S.C., D.F.); Department of Plastic Surgery, John Radcliffe Hospital, Oxford, UK (T.A.C., A.F.); Department of Vascular Surgery, Royal Berkshire Hospital, Reading, UK (B.J.); Department of Plastic Surgery, Stoke Mandeville Hospital, Aylesbury, Buckinghamshire UK (D.C.); and UK EQUATOR Center, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford Centre for Statistics in Medicine, Oxford UK (G.S.C.)
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AIM in Endocrinology. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2022]
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Mao L, Xia Z, Pan L, Chen J, Liu X, Li Z, Yan Z, Lin G, Wen H, Liu B. Deep learning for screening primary osteopenia and osteoporosis using spine radiographs and patient clinical covariates in a Chinese population. Front Endocrinol (Lausanne) 2022; 13:971877. [PMID: 36176468 PMCID: PMC9513384 DOI: 10.3389/fendo.2022.971877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 08/23/2022] [Indexed: 11/17/2022] Open
Abstract
PURPOSE Many high-risk osteopenia and osteoporosis patients remain undiagnosed. We proposed to construct a convolutional neural network model for screening primary osteopenia and osteoporosis based on the lumbar radiographs, and to compare the diagnostic performance of the CNN model adding the clinical covariates with the image model alone. METHODS A total of 6,908 participants were collected for analysis, including postmenopausal women and men aged 50-95 years, who performed conventional lumbar x-ray examinations and dual-energy x-ray absorptiometry (DXA) examinations within 3 months. All participants were divided into a training set, a validation set, test set 1, and test set 2 at a ratio of 8:1:1:1. The bone mineral density (BMD) values derived from DXA were applied as the reference standard. A three-class CNN model was developed to classify the patients into normal BMD, osteopenia, and osteoporosis. Moreover, we developed the models integrating the images with clinical covariates (age, gender, and BMI), and explored whether adding clinical data improves diagnostic performance over the image mode alone. The receiver operating characteristic curve analysis was performed for assessing the model performance. RESULTS As for classifying osteoporosis, the model based on the anteroposterior+lateral channel performed best, with the area under the curve (AUC) range from 0.909 to 0.937 in three test cohorts. The models with images alone achieved moderate sensitivity in classifying osteopenia, in which the highest AUC achieved 0.785. The performance of models integrating images with clinical data shows a slight improvement over models with anteroposterior or lateral images input alone for diagnosing osteoporosis, in which the AUC increased about 2%-4%. Regarding categorizing osteopenia and the normal BMD, the proposed models integrating images with clinical data also outperformed the models with images solely. CONCLUSION The deep learning-based approach could screen osteoporosis and osteopenia based on lumbar radiographs.
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Affiliation(s)
- Liting Mao
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Ziqiang Xia
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Liang Pan
- Department of AI Research Lab, Guangzhou YLZ Ruitu Information Technology Co, Ltd, Guangzhou, China
| | - Jun Chen
- Department of Radiology, ZHUHAI Branch of Guangdong Hospital of Chinese Medicine, Zhuhai, China
| | - Xian Liu
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Zhiqiang Li
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Zhaoxian Yan
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Gengbin Lin
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Huisen Wen
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Bo Liu
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
- *Correspondence: Bo Liu,
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Oliveira E Carmo L, van den Merkhof A, Olczak J, Gordon M, Jutte PC, Jaarsma RL, IJpma FFA, Doornberg JN, Prijs J. An increasing number of convolutional neural networks for fracture recognition and classification in orthopaedics : are these externally validated and ready for clinical application? Bone Jt Open 2021; 2:879-885. [PMID: 34669518 PMCID: PMC8558452 DOI: 10.1302/2633-1462.210.bjo-2021-0133] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
Aims The number of convolutional neural networks (CNN) available for fracture detection and classification is rapidly increasing. External validation of a CNN on a temporally separate (separated by time) or geographically separate (separated by location) dataset is crucial to assess generalizability of the CNN before application to clinical practice in other institutions. We aimed to answer the following questions: are current CNNs for fracture recognition externally valid?; which methods are applied for external validation (EV)?; and, what are reported performances of the EV sets compared to the internal validation (IV) sets of these CNNs? Methods The PubMed and Embase databases were systematically searched from January 2010 to October 2020 according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. The type of EV, characteristics of the external dataset, and diagnostic performance characteristics on the IV and EV datasets were collected and compared. Quality assessment was conducted using a seven-item checklist based on a modified Methodologic Index for NOn-Randomized Studies instrument (MINORS). Results Out of 1,349 studies, 36 reported development of a CNN for fracture detection and/or classification. Of these, only four (11%) reported a form of EV. One study used temporal EV, one conducted both temporal and geographical EV, and two used geographical EV. When comparing the CNN’s performance on the IV set versus the EV set, the following were found: AUCs of 0.967 (IV) versus 0.975 (EV), 0.976 (IV) versus 0.985 to 0.992 (EV), 0.93 to 0.96 (IV) versus 0.80 to 0.89 (EV), and F1-scores of 0.856 to 0.863 (IV) versus 0.757 to 0.840 (EV). Conclusion The number of externally validated CNNs in orthopaedic trauma for fracture recognition is still scarce. This greatly limits the potential for transfer of these CNNs from the developing institute to another hospital to achieve similar diagnostic performance. We recommend the use of geographical EV and statements such as the Consolidated Standards of Reporting Trials–Artificial Intelligence (CONSORT-AI), the Standard Protocol Items: Recommendations for Interventional Trials–Artificial Intelligence (SPIRIT-AI) and the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis–Machine Learning (TRIPOD-ML) to critically appraise performance of CNNs and improve methodological rigor, quality of future models, and facilitate eventual implementation in clinical practice. Cite this article: Bone Jt Open 2021;2(10):879–885.
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Affiliation(s)
- Luisa Oliveira E Carmo
- Department of Orthopaedic Surgery, University Medical Centre, University of Groningen, Groningen, Groningen, Netherlands
| | - Anke van den Merkhof
- Department of Orthopaedic Surgery, Flinders Medical Centre, Bedford Park, Adelaide, South Australia, Australia.,Flinders University, Bedford Park, Adelaide, South Australia, Australia
| | - Jakub Olczak
- Institute of Clinical Sciences, Danderyd University Hospital, Karolinska Institute, Stockholm, Sweden
| | - Max Gordon
- Institute of Clinical Sciences, Danderyd University Hospital, Karolinska Institute, Stockholm, Sweden
| | - Paul C Jutte
- Department of Orthopaedic Surgery, University Medical Centre, University of Groningen, Groningen, Groningen, Netherlands
| | - Ruurd L Jaarsma
- Department of Orthopaedic Surgery, Flinders Medical Centre, Bedford Park, Adelaide, South Australia, Australia.,Flinders University, Bedford Park, Adelaide, South Australia, Australia
| | - Frank F A IJpma
- Department of Trauma Surgery, University Medical Centre Groningen, University of Groningen, Groningen, Groningen, Netherlands
| | - Job N Doornberg
- Department of Orthopaedic Surgery, University Medical Centre, University of Groningen, Groningen, Groningen, Netherlands.,Department of Orthopaedic Surgery, Flinders Medical Centre, Bedford Park, Adelaide, South Australia, Australia.,Flinders University, Bedford Park, Adelaide, South Australia, Australia.,Department of Trauma Surgery, University Medical Centre Groningen, University of Groningen, Groningen, Groningen, Netherlands
| | - Jasper Prijs
- Department of Orthopaedic Surgery, University Medical Centre, University of Groningen, Groningen, Groningen, Netherlands.,Department of Orthopaedic Surgery, Flinders Medical Centre, Bedford Park, Adelaide, South Australia, Australia.,Flinders University, Bedford Park, Adelaide, South Australia, Australia.,Department of Trauma Surgery, University Medical Centre Groningen, University of Groningen, Groningen, Groningen, Netherlands
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- Machine Learning Consortium
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Ho CS, Chen YP, Fan TY, Kuo CF, Yen TY, Liu YC, Pei YC. Application of deep learning neural network in predicting bone mineral density from plain X-ray radiography. Arch Osteoporos 2021; 16:153. [PMID: 34626252 DOI: 10.1007/s11657-021-00985-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Accepted: 08/02/2021] [Indexed: 02/03/2023]
Abstract
UNLABELLED DeepDXA is a deep learning model designed to infer bone mineral density data from plain pelvis X-ray, and it can achieve good predicted value for clinical use. PURPOSE Osteoporosis is defined as a systemic disease of the bone characterized by a decrease in bone strength and deterioration of bone structure at the microscopic level, leading to bone fragility and increased risk of fracture. Bone mineral density (BMD) is the preferred method for the diagnosis of osteoporosis, and dual-energy x-ray absorptiometry (DXA) is the gold standard for diagnosing osteoporosis. Conventional radiography is more suited for the screening of osteoporosis rather than diagnosis, and osteoporosis can be detected on radiographs by experienced physicians only. This study explored the possibility of predicting BMD relative to DXA using patient radiographs. METHODS A deep learning algorithm of convolutional neural network (CNN) was used for the purpose. The method includes image segmentation, CNN learning, and a convolution-based regression model (DeepDXA) that links the isolated images of the femur bone to predict BMD value. Data were obtained in a single medical center from 2006 to 2018, with a total amount of 3472 pairs of pelvis X-ray and DXA examination within 1 year. RESULTS The proposed workflow successfully predicted BMD values of the femur bone with the correlation coefficient (R) of 0.85 (P < 0.001) and the accuracy of 0.88 for prediction osteoporosis, a finding that could be reliably ready for further clinical use. CONCLUSION When suspicious osteoporosis is seen on plain films using the deep learning method we developed, further referral to DXA for the definite diagnosis of osteoporosis is indicated.
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Affiliation(s)
- Chan-Shien Ho
- Department of Physical Medicine and Rehabilitation, Chang Gung Memorial Hospital at Linkou, No. 5, Fuxing St., Guishan Dist., Taoyuan City, 333, Taiwan
| | - Yueh-Peng Chen
- Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital at Linkou, No. 5, Fuxing St., Guishan Dist., Taoyuan City, 333, Taiwan.
- Department of Industrial Design, College of Management, Chang Gung University, Taoyuan, Taiwan.
| | - Tzuo-Yau Fan
- Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital at Linkou, No. 5, Fuxing St., Guishan Dist., Taoyuan City, 333, Taiwan
| | - Chang-Fu Kuo
- Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital at Linkou, No. 5, Fuxing St., Guishan Dist., Taoyuan City, 333, Taiwan
- Division of Rheumatology, Allergy and Immunology, Chang Gung Memorial Hospital at Linkou, No. 5, Fuxing St., Guishan Dist., Taoyuan City, 333, Taiwan
- Division of Rheumatology, Orthopaedics and Dermatology, School of Medicine, University of Nottingham, Nottingham, NG7 2UH, UK
| | - Tzu-Yun Yen
- School of Medicine, Chang Gung University, No. 259, Wenhua 1st Rd., Guishan Dist., Taoyuan City, 33302, Taiwan
- Department of Education, Chang Gung Memorial Hospital at Linkou, No.5, Fuxing St., Guishan Dist., Taoyuan City, 333, Taiwan
| | - Yuan-Chang Liu
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Linkou, No. 5, Fuxing St., Guishan Dist., Taoyuan City, 333, Taiwan
- College of Medicine, Institute for Radiologic Research, Chang Gung University, No. 259, Wenhua 1st Rd., Guishan Dist., Taoyuan City, 33302, Taiwan
| | - Yu-Cheng Pei
- Department of Physical Medicine and Rehabilitation, Chang Gung Memorial Hospital at Linkou, No. 5, Fuxing St., Guishan Dist., Taoyuan City, 333, Taiwan.
- School of Medicine, Chang Gung University, No. 259, Wenhua 1st Rd., Guishan Dist., Taoyuan City, 33302, Taiwan.
- Center of Vascularized Tissue Allograft, Gung Memorial Hospital at Linkou, No. 5, Fuxing St., Guishan Dist., Taoyuan City, 333, Taiwan.
- Healthy Aging Research Center, Chang Gung University, No. 259, Wenhua 1st Rd., Guishan Dist., Taoyuan City, 33302, Taiwan.
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Fiani B, Pasko KBD, Sarhadi K, Covarrubias C. Current uses, emerging applications, and clinical integration of artificial intelligence in neuroradiology. Rev Neurosci 2021; 33:383-395. [PMID: 34506699 DOI: 10.1515/revneuro-2021-0101] [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: 07/30/2021] [Accepted: 08/18/2021] [Indexed: 11/15/2022]
Abstract
Artificial intelligence (AI) is a branch of computer science with a variety of subfields and techniques, exploited to serve as a deductive tool that performs tasks originally requiring human cognition. AI tools and its subdomains are being incorporated into healthcare delivery for the improvement of medical data interpretation encompassing clinical management, diagnostics, and prognostic outcomes. In the field of neuroradiology, AI manifested through deep machine learning and connected neural networks (CNNs) has demonstrated incredible accuracy in identifying pathology and aiding in diagnosis and prognostication in several areas of neurology and neurosurgery. In this literature review, we survey the available clinical data highlighting the utilization of AI in the field of neuroradiology across multiple neurological and neurosurgical subspecialties. In addition, we discuss the emerging role of AI in neuroradiology, its strengths and limitations, as well as future needs in strengthening its role in clinical practice. Our review evaluated data across several subspecialties of neurology and neurosurgery including vascular neurology, spinal pathology, traumatic brain injury (TBI), neuro-oncology, multiple sclerosis, Alzheimer's disease, and epilepsy. AI has established a strong presence within the realm of neuroradiology as a successful and largely supportive technology aiding in the interpretation, diagnosis, and even prognostication of various pathologies. More research is warranted to establish its full scientific validity and determine its maximum potential to aid in optimizing and providing the most accurate imaging interpretation.
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Affiliation(s)
- Brian Fiani
- Department of Neurosurgery, Desert Regional Medical Center, 1150 N Indian Canyon Dr, Palm Springs, CA, 92262, USA
| | - Kory B Dylan Pasko
- School of Medicine, Georgetown University, 3900 Reservoir Rd NW, Washington, DC, 20007, USA
| | - Kasra Sarhadi
- Department of Neurology, University of Washington, Main Hospital, 325 9th Ave, Seattle, WA, 98104, USA
| | - Claudia Covarrubias
- School of Medicine, Universidad Anáhuac Querétaro, Cto. Universidades I, Fracción 2, 76246 Qro., Querétaro, Mexico
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Monchka BA, Kimelman D, Lix LM, Leslie WD. Feasibility of a generalized convolutional neural network for automated identification of vertebral compression fractures: The Manitoba Bone Mineral Density Registry. Bone 2021; 150:116017. [PMID: 34020078 DOI: 10.1016/j.bone.2021.116017] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 05/03/2021] [Accepted: 05/16/2021] [Indexed: 11/15/2022]
Abstract
BACKGROUND Vertebral fracture assessment (VFA) images are acquired in dual-energy (DE) or single-energy (SE) scan modes. Automated identification of vertebral compression fractures, from VFA images acquired using GE Healthcare scanners in DE mode, has achieved high accuracy through the use of convolutional neural networks (CNNs). Due to differences between DE and SE images, it is uncertain whether CNNs trained on one scan mode will generalize to the other. PURPOSE To evaluate the ability of CNNs to generalize between GE DE and GE SE VFA scan modes. METHODS 12,742 GE VFA images from the Manitoba Bone Mineral Density Program, obtained between 2010 and 2017, were exported in both DE and SE modes. VFAs were classified by imaging specialists as fracture present or absent using the modified algorithm-based qualitative (mABQ) method. VFA scans were randomly divided into independent training (60%), validation (10%), and test (30%) sets. Three CNN models were constructed by training separately on DE only, SE only, and a composite dataset comprised of both SE and DE VFAs. All three trained CNN models were separately evaluated against both SE and DE test datasets. RESULTS Good performance was seen for CNNs trained and evaluated on the same scan mode. DE scans used for both training and evaluation (DE/DE) achieved 87.9% sensitivity, 87.4% specificity, and an area under the receiver operating characteristic curve (AUC) of 0.94. SE scans used for both training and evaluation (SE/SE) achieved 78.6% sensitivity, 90.6% specificity, AUC = 0.92. Conversely, CNNs performed poorly when evaluated on scan modes that differed from their training sets (AUC = 0.58). However, a composite CNN trained simultaneously on both SE and DE VFAs gave performance comparable to DE/DE (82.4% sensitivity, 94.3% specificity, AUC = 0.95); and provided improved performance over SE/SE (82.2% sensitivity, 92.3% specificity, AUC = 0.94). Positive predictive value was higher with the composite CNN compared with models trained solely on DE (74.5% vs. 58.7%) or SE VFAs (68.6% vs. 62.9%). CONCLUSION CNNs for vertebral fracture identification are highly sensitive to scan mode. Training CNNs on a composite dataset, comprised of both GE DE and GE SE VFAs, allows CNNs to generalize to both scan modes and may facilitate the development of manufacturer-independent machine learning models for vertebral fracture detection.
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Affiliation(s)
- Barret A Monchka
- Department of Community Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Douglas Kimelman
- Department of Radiology, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Lisa M Lix
- Department of Community Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
| | - William D Leslie
- Department of Radiology, University of Manitoba, Winnipeg, Manitoba, Canada; Department of Internal Medicine, University of Manitoba, Winnipeg, Manitoba, Canada.
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Ananda A, Ngan KH, Karabağ C, Ter-Sarkisov A, Alonso E, Reyes-Aldasoro CC. Classification and Visualisation of Normal and Abnormal Radiographs; A Comparison between Eleven Convolutional Neural Network Architectures. SENSORS (BASEL, SWITZERLAND) 2021; 21:5381. [PMID: 34450821 PMCID: PMC8400172 DOI: 10.3390/s21165381] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 07/20/2021] [Accepted: 08/01/2021] [Indexed: 02/03/2023]
Abstract
This paper investigates the classification of radiographic images with eleven convolutional neural network (CNN) architectures (GoogleNet, VGG-19, AlexNet, SqueezeNet, ResNet-18, Inception-v3, ResNet-50, VGG-16, ResNet-101, DenseNet-201 and Inception-ResNet-v2). The CNNs were used to classify a series of wrist radiographs from the Stanford Musculoskeletal Radiographs (MURA) dataset into two classes-normal and abnormal. The architectures were compared for different hyper-parameters against accuracy and Cohen's kappa coefficient. The best two results were then explored with data augmentation. Without the use of augmentation, the best results were provided by Inception-ResNet-v2 (Mean accuracy = 0.723, Mean kappa = 0.506). These were significantly improved with augmentation to Inception-ResNet-v2 (Mean accuracy = 0.857, Mean kappa = 0.703). Finally, Class Activation Mapping was applied to interpret activation of the network against the location of an anomaly in the radiographs.
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Affiliation(s)
- Ananda Ananda
- giCentre, Department of Computer Science, School of Mathematics, Computer Science and Engineering, City, University of London, London EC1V 0HB, UK; (K.H.N.); (C.K.)
| | - Kwun Ho Ngan
- giCentre, Department of Computer Science, School of Mathematics, Computer Science and Engineering, City, University of London, London EC1V 0HB, UK; (K.H.N.); (C.K.)
| | - Cefa Karabağ
- giCentre, Department of Computer Science, School of Mathematics, Computer Science and Engineering, City, University of London, London EC1V 0HB, UK; (K.H.N.); (C.K.)
| | - Aram Ter-Sarkisov
- CitAI Research Centre, Department of Computer Science, School of Mathematics, Computer Science and Engineering, City, University of London, London EC1V 0HB, UK; (A.T.-S.); (E.A.)
| | - Eduardo Alonso
- CitAI Research Centre, Department of Computer Science, School of Mathematics, Computer Science and Engineering, City, University of London, London EC1V 0HB, UK; (A.T.-S.); (E.A.)
| | - Constantino Carlos Reyes-Aldasoro
- giCentre, Department of Computer Science, School of Mathematics, Computer Science and Engineering, City, University of London, London EC1V 0HB, UK; (K.H.N.); (C.K.)
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Cancellous bone structure assessment using a new trabecular connectivity. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Reid S, Schousboe JT, Kimelman D, Monchka BA, Jafari Jozani M, Leslie WD. Machine learning for automated abdominal aortic calcification scoring of DXA vertebral fracture assessment images: A pilot study. Bone 2021; 148:115943. [PMID: 33836309 DOI: 10.1016/j.bone.2021.115943] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Revised: 03/28/2021] [Accepted: 03/30/2021] [Indexed: 01/20/2023]
Abstract
BACKGROUND Abdominal aortic calcification (AAC) identified on dual-energy x-ray absorptiometry (DXA) vertebral fracture assessment (VFA) lateral spine images is predictive of cardiovascular outcomes, but is time-consuming to perform manually. Whether this procedure can be automated using convolutional neural networks (CNNs), a class of machine learning algorithms used for image processing, has not been widely investigated. METHODS Using the Province of Manitoba Bone Density Program DXA database, we selected a random sample of 1100 VFA images from individuals qualifying for VFA as part of their osteoporosis assessment. For each scan, AAC was manually scored using the 24-point semi-quantitative scale and categorized as low (score < 2), moderate (score 2 to <6), or high (score ≥ 6). An ensemble consisting of two CNNs was developed, by training and evaluating separately on single-energy and dual-energy images. AAC prediction was performed using the mean AAC score of the two models. RESULTS Mean (SD) age of the cohort was 75.5 (6.7) years, 95.5% were female. Training (N = 770, 70%), validation (N = 110, 10%) and test sets (N = 220, 20%) were well-balanced with respect to baseline characteristics and AAC scores. For the test set, the Pearson correlation between the CNN-predicted and human-labelled scores was 0.93 with intraclass correlation coefficient for absolute agreement 0.91 (95% CI 0.89-0.93). Kappa for AAC category agreement (prevalence- and bias-adjusted, ordinal scale) was 0.71 (95% CI 0.65-0.78). There was complete separation of the low and high categories, without any low AAC score scans predicted to be high and vice versa. CONCLUSIONS CNNs are capable of detecting AAC in VFA images, with high correlation between the human and predicted scores. These preliminary results suggest CNNs are a promising method for automatically detecting and quantifying AAC.
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Affiliation(s)
| | - John T Schousboe
- Park Nicollet Clinic and HealthPartners Institute, Bloomington, MN, USA; University of Minnesota, Minneapolis, MN, USA.
| | - Douglas Kimelman
- University of Manitoba, Winnipeg, Canada; St. Boniface Hospital Albrechtsen Research Centre, Winnipeg, Manitoba, Canada
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Nissinen T, Suoranta S, Saavalainen T, Sund R, Hurskainen O, Rikkonen T, Kröger H, Lähivaara T, Väänänen SP. Detecting pathological features and predicting fracture risk from dual-energy X-ray absorptiometry images using deep learning. Bone Rep 2021; 14:101070. [PMID: 33997147 PMCID: PMC8102403 DOI: 10.1016/j.bonr.2021.101070] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Accepted: 04/14/2021] [Indexed: 11/08/2022] Open
Abstract
Dual-energy X-ray absorptiometry (DXA) is the gold standard imaging method for diagnosing osteoporosis in clinical practice. The DXA images are commonly used to estimate a numerical value for bone mineral density (BMD), which decreases in osteoporosis. Low BMD is a known risk factor for osteoporotic fractures. In this study, we used deep learning to identify lumbar scoliosis and structural abnormalities that potentially affect BMD but are often neglected in lumbar spine DXA analysis. In addition, we tested the approach's ability to predict fractures using only DXA images. A dataset of 2949 images gathered by Kuopio Osteoporosis Risk Factor and Prevention Study was used to train a convolutional neural network (CNN) for classification. The model was able to classify scoliosis with an AUC of 0.96 and structural abnormalities causing unreliable BMD measurement with an AUC of 0.91. It predicted fractures occurring within 5 years from the lumbar spine DXA scan with an AUC of 0.63, meeting the predictive performance of combined BMD measurements from the lumbar spine and hip. In an independent test set of 574 clinical patients, AUC for lumbar scoliosis was 0.93 and AUC for unreliable BMD measurements was 0.94. In each classification task, neural network visualizations indicated the model's predictive strategy. We conclude that deep learning could complement the well established DXA method for osteoporosis diagnostics by analyzing incidental findings and image reliability, and improve its predictive ability in the future.
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Affiliation(s)
- Tomi Nissinen
- Department of Applied Physics, University of Eastern Finland, POB1627, 70211 Kuopio, Finland
- Department of Orthopaedics, Traumatology and Hand Surgery, Kuopio University Hospital, POB1777, 70211 Kuopio, Finland
| | - Sanna Suoranta
- Department of Clinical Radiology, Kuopio University Hospital, POB1777, 70211 Kuopio, Finland
| | - Taavi Saavalainen
- Department of Clinical Radiology, Kuopio University Hospital, POB1777, 70211 Kuopio, Finland
| | - Reijo Sund
- Institute of Clinical Medicine, University of Eastern Finland, POB1627, 70211 Kuopio, Finland
| | - Ossi Hurskainen
- Department of Clinical Radiology, Kuopio University Hospital, POB1777, 70211 Kuopio, Finland
| | - Toni Rikkonen
- Institute of Clinical Medicine, University of Eastern Finland, POB1627, 70211 Kuopio, Finland
| | - Heikki Kröger
- Department of Orthopaedics, Traumatology and Hand Surgery, Kuopio University Hospital, POB1777, 70211 Kuopio, Finland
| | - Timo Lähivaara
- Department of Applied Physics, University of Eastern Finland, POB1627, 70211 Kuopio, Finland
| | - Sami P. Väänänen
- Department of Applied Physics, University of Eastern Finland, POB1627, 70211 Kuopio, Finland
- Department of Clinical Radiology, Kuopio University Hospital, POB1777, 70211 Kuopio, Finland
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Schousboe JT, Langsetmo L, Szulc P, Lewis JR, Taylor BC, Kats AM, Vo TN, Ensrud KE. Joint Associations of Prevalent Radiographic Vertebral Fracture and Abdominal Aortic Calcification With Incident Hip, Major Osteoporotic, and Clinical Vertebral Fractures. J Bone Miner Res 2021; 36:892-900. [PMID: 33729640 PMCID: PMC8131243 DOI: 10.1002/jbmr.4257] [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: 11/11/2020] [Revised: 01/12/2021] [Accepted: 01/20/2021] [Indexed: 01/11/2023]
Abstract
Prevalent vertebral fractures (PVFx) and abdominal aortic calcification (AAC) are both associated with incident fractures and can be ascertained on the same lateral spine images, but their joint association with incident fractures is unclear. Our objective was to estimate the individual and joint associations of PVFx and AAC with incident major osteoporotic, hip, and clinical vertebral fractures in 5365 older men enrolled in the Osteoporotic Fractures in Men (MrOS) Study, using Cox proportional hazards and Fine and Gray subdistribution hazards models to account for competing mortality. PVFx (Genant SQ grade 2 or 3) and 24-point AAC score were ascertained on baseline lateral spine radiographs. Self-reports of incident fractures were solicited every 4 months and confirmed by review of clinical radiographic reports. Compared with men without PVFx and AAC-24 score 0 or 1, the subdistribution hazard ratio (SHR) for incident major osteoporotic fracture was 1.38 (95% confidence interval [CI] 1.13-1.69) among men with AAC-24 score ≥2 alone, 1.71 (95% CI 1.37-2.14) for men with PVFx alone, and 2.35 (95% CI 1.75-3.16) for men with both risk factors, after accounting for conventional risk factors and competing mortality. Wald statistics showed improved prediction model performance by including both risk factors compared with including only AAC (chi-square = 17.3, p < .001) or including only PVFx (chi-square = 8.5, p = .036). Older men with both PVFx and a high level of AAC are at higher risk of incident major osteoporotic fracture than men with either risk factor alone. Assessing prevalent radiographic vertebral fracture and AAC on the same lateral spine images may improve prediction of older men who will have an incident major osteoporotic fracture, even after accounting for traditional fracture risk factors and competing mortality. © 2021 American Society for Bone and Mineral Research (ASBMR).
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Affiliation(s)
- John T Schousboe
- Park Nicollet Clinic and HealthPartners Institute, Bloomington, MN, USA.,University of Minnesota, Minneapolis, MN, USA
| | | | - Pawel Szulc
- INSERM UMR 1033, University of Lyon, Lyon, France
| | - Joshua R Lewis
- Edith Cowan University, Perth, Australia.,University of Western Australia, Perth, Australia
| | - Brent C Taylor
- University of Minnesota, Minneapolis, MN, USA.,Center for Care Delivery & Outcomes Research, VA Health Care System, Minneapolis, MN, USA
| | | | - Tien N Vo
- University of Minnesota, Minneapolis, MN, USA
| | - Kristine E Ensrud
- University of Minnesota, Minneapolis, MN, USA.,Center for Care Delivery & Outcomes Research, VA Health Care System, Minneapolis, MN, USA
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Smets J, Shevroja E, Hügle T, Leslie WD, Hans D. Machine Learning Solutions for Osteoporosis-A Review. J Bone Miner Res 2021; 36:833-851. [PMID: 33751686 DOI: 10.1002/jbmr.4292] [Citation(s) in RCA: 69] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 02/04/2021] [Accepted: 03/16/2021] [Indexed: 12/11/2022]
Abstract
Osteoporosis and its clinical consequence, bone fracture, is a multifactorial disease that has been the object of extensive research. Recent advances in machine learning (ML) have enabled the field of artificial intelligence (AI) to make impressive breakthroughs in complex data environments where human capacity to identify high-dimensional relationships is limited. The field of osteoporosis is one such domain, notwithstanding technical and clinical concerns regarding the application of ML methods. This qualitative review is intended to outline some of these concerns and to inform stakeholders interested in applying AI for improved management of osteoporosis. A systemic search in PubMed and Web of Science resulted in 89 studies for inclusion in the review. These covered one or more of four main areas in osteoporosis management: bone properties assessment (n = 13), osteoporosis classification (n = 34), fracture detection (n = 32), and risk prediction (n = 14). Reporting and methodological quality was determined by means of a 12-point checklist. In general, the studies were of moderate quality with a wide range (mode score 6, range 2 to 11). Major limitations were identified in a significant number of studies. Incomplete reporting, especially over model selection, inadequate splitting of data, and the low proportion of studies with external validation were among the most frequent problems. However, the use of images for opportunistic osteoporosis diagnosis or fracture detection emerged as a promising approach and one of the main contributions that ML could bring to the osteoporosis field. Efforts to develop ML-based models for identifying novel fracture risk factors and improving fracture prediction are additional promising lines of research. Some studies also offered insights into the potential for model-based decision-making. Finally, to avoid some of the common pitfalls, the use of standardized checklists in developing and sharing the results of ML models should be encouraged. © 2021 American Society for Bone and Mineral Research (ASBMR).
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Affiliation(s)
- Julien Smets
- Center of Bone Diseases, Bone and Joint Department, Lausanne University Hospital, Lausanne, Switzerland
| | - Enisa Shevroja
- Center of Bone Diseases, Bone and Joint Department, Lausanne University Hospital, Lausanne, Switzerland
| | - Thomas Hügle
- Department of Rheumatology, Lausanne University Hospital, Lausanne, Switzerland
| | | | - Didier Hans
- Center of Bone Diseases, Bone and Joint Department, Lausanne University Hospital, Lausanne, Switzerland
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Small JE, Osler P, Paul AB, Kunst M. CT Cervical Spine Fracture Detection Using a Convolutional Neural Network. AJNR Am J Neuroradiol 2021; 42:1341-1347. [PMID: 34255730 DOI: 10.3174/ajnr.a7094] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 01/25/2021] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Multidetector CT has emerged as the standard of care imaging technique to evaluate cervical spine trauma. Our aim was to evaluate the performance of a convolutional neural network in the detection of cervical spine fractures on CT. MATERIALS AND METHODS We evaluated C-spine, an FDA-approved convolutional neural network developed by Aidoc to detect cervical spine fractures on CT. A total of 665 examinations were included in our analysis. Ground truth was established by retrospective visualization of a fracture on CT by using all available CT, MR imaging, and convolutional neural network output information. The ĸ coefficients, sensitivity, specificity, and positive and negative predictive values were calculated with 95% CIs comparing diagnostic accuracy and agreement of the convolutional neural network and radiologist ratings, respectively, compared with ground truth. RESULTS Convolutional neural network accuracy in cervical spine fracture detection was 92% (95% CI, 90%-94%), with 76% (95% CI, 68%-83%) sensitivity and 97% (95% CI, 95%-98%) specificity. The radiologist accuracy was 95% (95% CI, 94%-97%), with 93% (95% CI, 88%-97%) sensitivity and 96% (95% CI, 94%-98%) specificity. Fractures missed by the convolutional neural network and by radiologists were similar by level and location and included fractured anterior osteophytes, transverse processes, and spinous processes, as well as lower cervical spine fractures that are often obscured by CT beam attenuation. CONCLUSIONS The convolutional neural network holds promise at both worklist prioritization and assisting radiologists in cervical spine fracture detection on CT. Understanding the strengths and weaknesses of the convolutional neural network is essential before its successful incorporation into clinical practice. Further refinements in sensitivity will improve convolutional neural network diagnostic utility.
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Affiliation(s)
- J E Small
- From the Departments of Neuroradiology (J.E.S., A.B.P., M.K.)
| | - P Osler
- Radiology (P.O), Lahey Hospital and Medical Center, Burlington, Massachusetts
| | - A B Paul
- From the Departments of Neuroradiology (J.E.S., A.B.P., M.K.)
| | - M Kunst
- From the Departments of Neuroradiology (J.E.S., A.B.P., M.K.)
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Lems WF, Paccou J, Zhang J, Fuggle NR, Chandran M, Harvey NC, Cooper C, Javaid K, Ferrari S, Akesson KE. Vertebral fracture: epidemiology, impact and use of DXA vertebral fracture assessment in fracture liaison services. Osteoporos Int 2021; 32:399-411. [PMID: 33475820 PMCID: PMC7929949 DOI: 10.1007/s00198-020-05804-3] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Accepted: 12/16/2020] [Indexed: 02/07/2023]
Abstract
Vertebral fractures are independent risk factors for vertebral and nonvertebral fractures. Since vertebral fractures are often missed, the relatively new introduction of vertebral fracture assessment (VFA) for imaging of the lateral spine during DXA-measurement of the spine and hips may contribute to detect vertebral fractures. We advocate performing a VFA in all patients with a recent fracture visiting a fracture liaison service (FLS). Fracture liaison services (FLS) are important service models for delivering secondary fracture prevention for older adults presenting with a fragility fracture. While commonly age, clinical risk factors (including fracture site and number of prior fracture) and BMD play a crucial role in determining fracture risk and indications for treatment with antiosteoporosis medications, prevalent vertebral fractures usually remain undetected. However, vertebral fractures are important independent risk factors for future vertebral and nonvertebral fractures. A development of the DXA technology, vertebral fracture assessment (VFA), allows for assessment of the lateral spine during the regular DXA bone mineral density measurement of the lumbar spine and hips. Recent approaches to the stratification of antiosteoporosis medication type according to baseline fracture risk, and differences by age in the indication for treatment by prior fracture mean that additional information from VFA may influence initiation and type of treatment. Furthermore, knowledge of baseline vertebral fractures allows reliable definition of incident vertebral fracture events during treatment, which may modify the approach to therapy. In this manuscript, we will discuss the epidemiology and clinical significance of vertebral fractures, the different methods of detecting vertebral fractures, and the rationale for, and implications of, use of VFA routinely in FLS. • Vertebral fracture assessment is a tool available on modern DXA instruments and has proven ability to detect vertebral fractures, the majority of which occur without a fall and without the signs and symptoms of an acute fracture. • Most osteoporosis guidelines internationally suggest that treatment with antiosteoporosis medications should be considered for older individuals (e.g., 65 years +) with a recent low trauma fracture without the need for DXA. • Younger individuals postfracture may be risk-assessed on the basis of FRAX® probability including DXA and associated treatment thresholds. • Future fracture risk is markedly influenced by both site, number, severity, and recency of prior fracture; awareness of baseline vertebral fractures facilitates definition of true incident vertebral fracture events occurring during antiosteoporosis treatment. • Detection of previously clinically silent vertebral fractures, defining site of prior fracture, might alter treatment decisions in younger or older FLS patients, consistent with recent IOF-ESCEO guidance on baseline-risk-stratified therapy, and provides a reliable baseline from which to define new, potentially therapy-altering, vertebral fracture events.
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Affiliation(s)
- W F Lems
- Amsterdam UMC, VU University Medical Center, Amsterdam, The Netherlands.
| | - J Paccou
- Department of Rheumatology, Univ. Lille, CHU Lille, MABLab ULR 4490, 59000, Lille, France
| | - J Zhang
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK
| | - N R Fuggle
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK
| | - M Chandran
- Osteoporosis and Bone Metabolism Unit, Department of Endocrinology, Singapore General Hospital, Singapore, Singapore
| | - N C Harvey
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK
| | - C Cooper
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK
- Nuffield Department of Orthopaedics, Rheumatology and Orthopaedic Sciences, University of Oxford, Oxford, UK
| | - K Javaid
- Nuffield Department of Orthopaedics, Rheumatology and Orthopaedic Sciences, University of Oxford, Oxford, UK
| | - S Ferrari
- Clinical Service and Research Laboratory of Bone Diseases, Hôpitaux Universitaires de Genève, Geneva, Switzerland
| | - K E Akesson
- Department of Clinical Sciences and Department of Orthopaedics, Skane University Hospital, Lund University, Malmö, Sweden
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Hong N, Park Y, You SC, Rhee Y. AIM in Endocrinology. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_328-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2022]
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41
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Petraikin AV, Belaya ZE, Kiseleva AN, Artyukova ZR, Belyaev MG, Kondratenko VA, Pisov ME, Solovev AV, Smorchkova AK, Abuladze LR, Kieva IN, Fedanov VA, Iassin LR, Semenov DS, Kudryavtsev ND, Shchelykalina SP, Zinchenko VV, Akhmad ES, Sergunova KA, Gombolevsky VA, Nisovstova LA, Vladzymyrskyy AV, Morozov SP. [Artificial intelligence for diagnosis of vertebral compression fractures using a morphometric analysis model, based on convolutional neural networks]. ACTA ACUST UNITED AC 2020; 66:48-60. [PMID: 33369372 DOI: 10.14341/probl12605] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Revised: 09/03/2020] [Accepted: 09/21/2020] [Indexed: 11/06/2022]
Abstract
BACKGROUND Pathological low-energy (LE) vertebral compression fractures (VFs) are common complications of osteoporosis and predictors of subsequent LE fractures. In 84% of cases, VFs are not reported on chest CT (CCT), which calls for the development of an artificial intelligence-based (AI) assistant that would help radiology specialists to improve the diagnosis of osteoporosis complications and prevent new LE fractures. AIMS To develop an AI model for automated diagnosis of compression fractures of the thoracic spine based on chest CT images. MATERIALS AND METHODS Between September 2019 and May 2020 the authors performed a retrospective sampling study of ССТ images. The 160 of results were selected and anonymized. The data was labeled by seven readers. Using the morphometric analysis, the investigators received the following metric data: ventral, medial and dorsal dimensions. This was followed by a semiquantitative assessment of VFs degree. The data was used to develop the Comprise-G AI mode based on CNN, which subsequently measured the size of the vertebral bodies and then calculates the compression degree. The model was evaluated with the ROC curve analysis and by calculating sensitivity and specificity values. RESULTS Formed data consist of 160 patients (a training group - 100 patients; a test group - 60 patients). The total of 2,066 vertebrae was annotated. When detecting Grade 2 and 3 maximum VFs in patients the Comprise-G model demonstrated sensitivity - 90,7%, specificity - 90,7%, AUC ROC - 0.974 on the 5-FOLD cross-validation data of the training dataset; on the test data - sensitivity - 83,2%, specificity - 90,0%, AUC ROC - 0.956; in vertebrae demonstrated sensitivity - 91,5%, specificity - 95,2%, AUC ROC - 0.981 on the cross-validation data; for the test data sensitivity - 79,3%, specificity - 98,7%, AUC ROC - 0.978. CONCLUSIONS The Comprise-G model demonstrated high diagnostic capabilities in detecting the VFs on CCT images and can be recommended for further validation.
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Affiliation(s)
- A V Petraikin
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies
| | | | | | - Z R Artyukova
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies
| | - M G Belyaev
- Skolkovo Institute of Science and Technology
| | | | - M E Pisov
- Skolkovo Institute of Science and Technology; Kharkevich Institute for Information Transmission Problems
| | - A V Solovev
- Sklifosovsky Clinical and Research Institute of Emergency Medicine
| | - A K Smorchkova
- Central State Medical Academy of the Presidential Administration of the Russian Federation
| | | | - I N Kieva
- Peoples' Friendship University of Russia
| | - V A Fedanov
- Central State Medical Academy of the Presidential Administration of the Russian Federation
| | | | - D S Semenov
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies
| | | | | | - V V Zinchenko
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies
| | - E S Akhmad
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies
| | - K A Sergunova
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies
| | - V A Gombolevsky
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies
| | - L A Nisovstova
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies
| | - A V Vladzymyrskyy
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies
| | - S P Morozov
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies
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Kaka H, Zhang E, Khan N. Artificial Intelligence and Deep Learning in Neuroradiology: Exploring the New Frontier. Can Assoc Radiol J 2020; 72:35-44. [PMID: 32946272 DOI: 10.1177/0846537120954293] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
There have been many recently published studies exploring machine learning (ML) and deep learning applications within neuroradiology. The improvement in performance of these techniques has resulted in an ever-increasing number of commercially available tools for the neuroradiologist. In this narrative review, recent publications exploring ML in neuroradiology are assessed with a focus on several key clinical domains. In particular, major advances are reviewed in the context of: (1) intracranial hemorrhage detection, (2) stroke imaging, (3) intracranial aneurysm screening, (4) multiple sclerosis imaging, (5) neuro-oncology, (6) head and tumor imaging, and (7) spine imaging.
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Affiliation(s)
- Hussam Kaka
- Department of Radiology, 3710McMaster University, Hamilton, Ontario, Canada
| | - Euan Zhang
- Department of Radiology, 3710McMaster University, Hamilton General Hospital, Hamilton, Ontario, Canada
| | - Nazir Khan
- Department of Radiology, 3710McMaster University, Hamilton General Hospital, Hamilton, Ontario, Canada
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43
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Wani IM, Arora S. Computer-aided diagnosis systems for osteoporosis detection: a comprehensive survey. Med Biol Eng Comput 2020; 58:1873-1917. [PMID: 32583141 DOI: 10.1007/s11517-020-02171-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Accepted: 03/26/2020] [Indexed: 12/18/2022]
Abstract
Computer-aided diagnosis (CAD) has revolutionized the field of medical diagnosis. They assist in improving the treatment potentials and intensify the survival frequency by early diagnosing the diseases in an efficient, timely, and cost-effective way. The automatic segmentation has led the radiologist to successfully segment the region of interest to improve the diagnosis of diseases from medical images which is not so efficiently possible by manual segmentation. The aim of this paper is to survey the vision-based CAD systems especially focusing on the segmentation techniques for the pathological bone disease known as osteoporosis. Osteoporosis is the state of the bones where the mineral density of bones decreases and they become porous, making the bones easily susceptible to fractures by small injury or a fall. The article covers the image acquisition techniques for acquiring the medical images for osteoporosis diagnosis. The article also discusses the advanced machine learning paradigms employed in segmentation for osteoporosis disease. Other image processing steps in osteoporosis like feature extraction and classification are also briefly described. Finally, the paper gives the future directions to improve the osteoporosis diagnosis and presents the proposed architecture. Graphical abstract.
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Affiliation(s)
- Insha Majeed Wani
- School of Computer Science and Engineering, SMVDU, Katra, J&K, India
| | - Sakshi Arora
- School of Computer Science and Engineering, SMVDU, Katra, J&K, India.
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Yasaka K, Akai H, Kunimatsu A, Kiryu S, Abe O. Prediction of bone mineral density from computed tomography: application of deep learning with a convolutional neural network. Eur Radiol 2020; 30:3549-3557. [PMID: 32060712 DOI: 10.1007/s00330-020-06677-0] [Citation(s) in RCA: 59] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Revised: 01/01/2020] [Accepted: 01/27/2020] [Indexed: 02/07/2023]
Abstract
OBJECTIVES To investigate whether a deep learning model can predict the bone mineral density (BMD) of lumbar vertebrae from unenhanced abdominal computed tomography (CT) images. METHODS In this Institutional Review Board-approved retrospective study, patients who received both unenhanced CT examinations and dual-energy X-ray absorptiometry (DXA) of the lumbar vertebrae, in two institutions (1 and 2), were included. Supervised deep learning was employed to obtain a convolutional neural network (CNN) model using axial CT images, including the lumbar vertebrae as input data and BMD values obtained with DXA as reference data. For this purpose, 1665 CT images from 183 patients in institution 1, which were augmented to 99,900 (= 1665 × 60) images (noise adding, parallel shift and rotation were performed), were used. Internal (by using data of 45 other patients in institution 1) and external validations (by using data of 50 patients in institution 2) were performed to evaluate the performance of the trained CNN model. Correlations and diagnostic performances were evaluated with Pearson's correlation coefficient (r) and area under the receiver operating characteristic curve (AUC), respectively. RESULTS The estimated BMD values, according to the CNN model (BMDCNN), were significantly correlated with the BMD values obtained with DXA (r = 0.852 (p < 0.001) and 0.840 (p < 0.001) for the internal and external validation datasets, respectively). Using BMDCNN, osteoporosis was diagnosed with AUCs of 0.965 and 0.970 for the internal and external validation datasets, respectively. CONCLUSIONS Using deep learning, the BMD of lumbar vertebrae could be predicted from unenhanced abdominal CT images. KEY POINTS • By applying a deep learning technique, the bone mineral density (BMD) of lumbar vertebrae can be estimated from unenhanced abdominal CT images. • A strong correlation was observed between the estimated BMD and the BMD obtained with DXA. • By using the estimated BMD, osteoporosis could be diagnosed with high performance.
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Affiliation(s)
- Koichiro Yasaka
- Department of Radiology, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan.
| | - Hiroyuki Akai
- Department of Radiology, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan
| | - Akira Kunimatsu
- Department of Radiology, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan
| | - Shigeru Kiryu
- Department of Radiology, Graduate School of Medical Sciences, International University of Health and Welfare, 537-3 Iguchi, Nasushiobara, Tochigi, 329-2763, Japan
| | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
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