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Kojima A, Tomita T, Tsuji S, Kadono Y, Tada K, Nozaki T, Tamaki M, Koyama Y, Dobashi H, Okano T, Kawaai S, Atsumi T, Tamura N, Matsumoto Y, Goto H, Taniguchi Y, Ueki Y, Takagi M, Matsui K, Hagimori K, Shimizu A. Bone marrow edema detection for diagnostic support of axial spondyloarthritis using MRI. Int J Comput Assist Radiol Surg 2024; 19:1699-1711. [PMID: 39088129 DOI: 10.1007/s11548-024-03228-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Accepted: 06/28/2024] [Indexed: 08/02/2024]
Abstract
PURPOSE This study proposes a process for detecting slices with bone marrow edema (BME), a typical finding of axSpA, using MRI scans as the input. This process does not require manual input of ROIs and provides the results of the judgment of the presence or absence of BME on a slice and the location of edema as the rationale for the judgment. METHODS First, the signal intensity of the MRI scans of the sacroiliac joint was normalized to reduce the variation in signal values between scans. Next, slices containing synovial joints were extracted using a slice selection network. Finally, the BME slice detection network determines the presence or absence of the BME in each slice and outputs the location of the BME. RESULTS The proposed method was applied to 86 MRI scans collected from 15 hospitals in Japan. The results showed that the average absolute error of the slice selection process was 1.49 slices for the misalignment between the upper and lower slices of the synovial joint range. The accuracy, sensitivity, and specificity of the BME slice detection network were 0.905, 0.532, and 0.974, respectively. CONCLUSION This paper proposes a process to detect the slice with BME and its location as the rationale of the judgment from an MRI scan and shows its effectiveness using 86 MRI scans. In the future, we plan to develop a process for detecting other findings such as bone erosion from MR scans, followed by the development of a diagnostic support system.
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Affiliation(s)
- Akira Kojima
- Institute of Engineering, Tokyo University of Agriculture and Technology, Tokyo, Japan
| | - Tetsuya Tomita
- Graduate School of Health Sciences, Morinomiya University of Medical Sciences, Osaka, Japan
| | - Shigeyoshi Tsuji
- Department of Rehabilitation, Orthopedic Surgery, Psoriasis Center, Public Interest Incorporated Foundation of the Nippon Life Saiseikai Foundation, Nippon Life Hospital, Osaka, Japan
| | - Yuho Kadono
- Orthopedic Surgery, Saitama Medical University Hospital, Saitama, Japan
| | - Kurisu Tada
- Department of Internal Medicine and Rheumatology, Juntendo University School of Medicine, Tokyo, Japan
| | - Taiki Nozaki
- Department of Radiology, Keio University School of Medicine, Tokyo, Japan
| | - Masashi Tamaki
- Department of Orthopedics, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Yoshinobu Koyama
- Rheumatology, Center for Autoimmune Diseases, Okayama Red Cross Hospital, Japanese Red Cross Society, Okayama, Japan
| | - Hiroaki Dobashi
- Division of Rheumatology, Department of Internal Medicine, Kagawa University Hospital, Kagawa, Japan
| | - Tadashi Okano
- Center for Senile Degenerative Disorders (CSDD), Osaka Metropolitan University Graduate School of Medicine, Osaka, Japan
| | - Satoshi Kawaai
- Immuno-Rheumatology Center, St. Luke's International Hospital, Tokyo, Japan
| | - Tatsuya Atsumi
- Department of Rheumatology, Endocrinology and Nephrology, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Hokkaido, Japan
| | - Naoto Tamura
- Department of Internal Medicine and Rheumatology, Juntendo University School of Medicine, Tokyo, Japan
| | | | - Hitoshi Goto
- Internal Medicine, Osaka City General Hospital, Osaka, Japan
| | - Yoshinori Taniguchi
- Department of Endocrinology, Metabolism, Nephrology and Rheumatology, Kochi Medical School Hospital, Kochi, Japan
| | - Yukitaka Ueki
- Rheumatic Disease Center, Sasebo Chuo Hospital, Hakujyujikai Foundation, Nagasaki, Japan
| | - Michiaki Takagi
- Department of Orthopedic Surgery, Yamagata University Faculty of Medicine, Yamagata, Japan
| | - Kiyoshi Matsui
- Department of Diabetes, Endoclinology and Clinical Immunology, School of Medicine, Hyogo Medical University, Hyogo, Japan
| | | | - Akinobu Shimizu
- Institute of Engineering, Tokyo University of Agriculture and Technology, Tokyo, Japan.
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Fu Q, Yuan X, Han X, Wang W, Zhang J, Yuan X. Enhancing the Diagnostic Accuracy of Sacroiliitis: A Machine Learning Approach Applied to Computed Tomography Imaging. Br J Hosp Med (Lond) 2024; 85:1-13. [PMID: 39212564 DOI: 10.12968/hmed.2024.0228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/04/2024]
Abstract
Aims/Background Sacroiliitis is a challenging condition to diagnose accurately due to the subtle nature of its presentation in imaging studies. This study aims to improve the diagnostic accuracy of sacroiliitis by applying advanced machine learning techniques to computed tomography (CT) images. Methods We employed five convolutional neural network (CNN) models-Visual Geometry Group 16-layer Network (VGG16), ResNet101, DenseNet, Inception-v4, and ResNeXt-50-to analyze a dataset of 830 CT images, including both sacroiliitis and non-sacroiliitis cases. Each model's performance was evaluated using metrics such as accuracy, precision, recall, F1 score, Receiver Operating Characteristic (ROC), and Area Under the Curve (AUC). The interpretability of the models' decisions was enhanced using Gradient-weighted Class Activation Mapping (Grad-CAM) visualization. Results The ResNeXt-50 and Inception-v4 models demonstrated superior performance, achieving the highest accuracy and F1 scores among the tested models. Grad-CAM visualizations offered insights into the decision-making processes, highlighting the models' focus on relevant anatomical features critical for accurate diagnosis. Conclusion The use of CNN models, particularly ResNeXt-50 and Inception-v4, significantly improves the diagnosis of sacroiliitis from CT images. These models not only provide high diagnostic accuracy but also offer transparency in their decision-making processes, aiding clinicians in understanding and trusting Artificial Intelligence (AI)-driven diagnostics.
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Affiliation(s)
- Qingsong Fu
- Department of Orthopaedics, Ningbo No. 2 Hospital, Ningbo, Zhejiang, China
| | - Xueru Yuan
- Department of Geography & Spatial Information Techniques, Ningbo University, Ningbo, Zhejiang, China
| | - Xinyou Han
- Department of Orthopaedics, Ningbo No. 2 Hospital, Ningbo, Zhejiang, China
| | - Weibin Wang
- Department of Orthopaedics, Ningbo No. 2 Hospital, Ningbo, Zhejiang, China
| | - Jiakai Zhang
- Department of Orthopaedics, Ningbo No. 2 Hospital, Ningbo, Zhejiang, China
| | - Xinhua Yuan
- Department of Orthopaedics, Ningbo No. 2 Hospital, Ningbo, Zhejiang, China
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Lu Z, Zou Q, Wang M, Han X, Shi X, Wu S, Xie Z, Ye Q, Song L, He Y, Feng Q, Zhao Y. Artificial intelligence improves the diagnosis of human leukocyte antigen (HLA)-B27-negative axial spondyloarthritis based on multi-sequence magnetic resonance imaging and clinical features. Quant Imaging Med Surg 2024; 14:5845-5860. [PMID: 39144059 PMCID: PMC11320510 DOI: 10.21037/qims-24-729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Accepted: 07/05/2024] [Indexed: 08/16/2024]
Abstract
Background Axial spondyloarthritis (axSpA) is frequently diagnosed late, particularly in human leukocyte antigen (HLA)-B27-negative patients, resulting in a missed opportunity for optimal treatment. This study aimed to develop an artificial intelligence (AI) tool, termed NegSpA-AI, using sacroiliac joint (SIJ) magnetic resonance imaging (MRI) and clinical SpA features to improve the diagnosis of axSpA in HLA-B27-negative patients. Methods We retrospectively included 454 HLA-B27-negative patients with rheumatologist-diagnosed axSpA or other diseases (non-axSpA) from the Third Affiliated Hospital of Southern Medical University and Nanhai Hospital between January 2010 and August 2021. They were divided into a training set (n=328) for 5-fold cross-validation, an internal test set (n=72), and an independent external test set (n=54). To construct a prospective test set, we further enrolled 87 patients between September 2021 and August 2023 from the Third Affiliated Hospital of Southern Medical University. MRI techniques employed included T1-weighted (T1W), T2-weighted (T2W), and fat-suppressed (FS) sequences. We developed NegSpA-AI using a deep learning (DL) network to differentiate between axSpA and non-axSpA at admission. Furthermore, we conducted a reader study involving 4 radiologists and 2 rheumatologists to evaluate and compare the performance of independent and AI-assisted clinicians. Results NegSpA-AI demonstrated superior performance compared to the independent junior rheumatologist (≤5 years of experience), achieving areas under the curve (AUCs) of 0.878 [95% confidence interval (CI): 0.786-0.971], 0.870 (95% CI: 0.771-0.970), and 0.815 (95% CI: 0.714-0.915) on the internal, external, and prospective test sets, respectively. The assistance of NegSpA-AI promoted discriminating accuracy, sensitivity, and specificity of independent junior radiologists by 7.4-11.5%, 1.0-13.3%, and 7.4-20.6% across the 3 test sets (all P<0.05). On the prospective test set, AI assistance also improved the diagnostic accuracy, sensitivity, and specificity of independent junior rheumatologists by 7.7%, 7.7%, and 6.9%, respectively (all P<0.01). Conclusions The proposed NegSpA-AI effectively improves radiologists' interpretations of SIJ MRI and rheumatologists' diagnoses of HLA-B27-negative axSpA.
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Affiliation(s)
- Zixiao Lu
- Department of Radiology, The Third Affiliated Hospital of Southern Medical University (Academy of Orthopedics, Guangdong Province), Guangzhou, China
| | - Qingqing Zou
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China
| | - Menghong Wang
- Department of Radiology, The Third Affiliated Hospital of Southern Medical University (Academy of Orthopedics, Guangdong Province), Guangzhou, China
| | - Xinai Han
- Department of Rheumatology and Immunology, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China
| | - Xingliang Shi
- Department of Rheumatology and Immunology, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China
| | - Shufan Wu
- Department of Hematology and Rheumatology, The Second Affiliated Hospital of Xiamen Medical College, Xiamen, China
| | - Zhuoyao Xie
- Department of Radiology, The Third Affiliated Hospital of Southern Medical University (Academy of Orthopedics, Guangdong Province), Guangzhou, China
| | - Qiang Ye
- Department of Radiology, The Third Affiliated Hospital of Southern Medical University (Academy of Orthopedics, Guangdong Province), Guangzhou, China
| | - Liwen Song
- Department of Radiology, The Third Affiliated Hospital of Southern Medical University (Academy of Orthopedics, Guangdong Province), Guangzhou, China
| | - Yi He
- Department of Rheumatology and Immunology, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China
| | - Qianjin Feng
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China
| | - Yinghua Zhao
- Department of Radiology, The Third Affiliated Hospital of Southern Medical University (Academy of Orthopedics, Guangdong Province), Guangzhou, China
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Adams LC, Bressem KK, Poddubnyy D. Artificial intelligence and machine learning in axial spondyloarthritis. Curr Opin Rheumatol 2024; 36:267-273. [PMID: 38533807 DOI: 10.1097/bor.0000000000001015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/28/2024]
Abstract
PURPOSE OF REVIEW To evaluate the current applications and prospects of artificial intelligence and machine learning in diagnosing and managing axial spondyloarthritis (axSpA), focusing on their role in medical imaging, predictive modelling, and patient monitoring. RECENT FINDINGS Artificial intelligence, particularly deep learning, is showing promise in diagnosing axSpA assisting with X-ray, computed tomography (CT) and MRI analyses, with some models matching or outperforming radiologists in detecting sacroiliitis and markers. Moreover, it is increasingly being used in predictive modelling of disease progression and personalized treatment, and could aid risk assessment, treatment response and clinical subtype identification. Variable study designs, sample sizes and the predominance of retrospective, single-centre studies still limit the generalizability of results. SUMMARY Artificial intelligence technologies have significant potential to advance the diagnosis and treatment of axSpA, providing more accurate, efficient and personalized healthcare solutions. However, their integration into clinical practice requires rigorous validation, ethical and legal considerations, and comprehensive training for healthcare professionals. Future advances in artificial intelligence could complement clinical expertise and improve patient care through improved diagnostic accuracy and tailored therapeutic strategies, but the challenge remains to ensure that these technologies are validated in prospective multicentre trials and ethically integrated into patient care.
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Affiliation(s)
- Lisa C Adams
- Department of Diagnostic and Interventional Radiology, Faculty of Medicine
| | - Keno K Bressem
- Institute for Radiology and Nuclear Medicine, German Heart Centre Munich, Technical University of Munich, Munich
| | - Denis Poddubnyy
- Department of Gastroenterology, Infectiology and Rheumatology (including Nutrition Medicine), Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin
- Epidemiology Unit, German Rheumatism Research Centre, Berlin, Germany
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Adams LC, Bressem KK, Ziegeler K, Vahldiek JL, Poddubnyy D. Artificial intelligence to analyze magnetic resonance imaging in rheumatology. Joint Bone Spine 2024; 91:105651. [PMID: 37797827 DOI: 10.1016/j.jbspin.2023.105651] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Revised: 08/29/2023] [Accepted: 09/26/2023] [Indexed: 10/07/2023]
Abstract
Rheumatic disorders present a global health challenge, marked by inflammation and damage to joints, bones, and connective tissues. Accurate, timely diagnosis and appropriate management are crucial for favorable patient outcomes. Magnetic resonance imaging (MRI) has become indispensable in rheumatology, but interpretation remains laborious and variable. Artificial intelligence (AI), including machine learning (ML) and deep learning (DL), offers a means to improve and advance MRI analysis. This review examines current AI applications in rheumatology MRI analysis, addressing diagnostic support, disease classification, activity assessment, and progression monitoring. AI demonstrates promise, with high sensitivity, specificity, and accuracy, achieving or surpassing expert performance. The review also discusses clinical implementation challenges and future research directions to enhance rheumatic disease diagnosis and management.
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Affiliation(s)
- Lisa C Adams
- Department of Diagnostic and Interventional Radiology, Faculty of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany.
| | - Keno K Bressem
- Department of Radiology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Hindenburgdamm 30, 12203 Berlin, Germany; Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Katharina Ziegeler
- Department of Hematology, Oncology , and Cancer Immunology, Campus Charité Mitte, Charité Universitätsmedizin Berlin, Germany; Evidia Radiologie am Rheumazentrum Ruhrgebiet, Germany
| | - Janis L Vahldiek
- Department of Radiology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Hindenburgdamm 30, 12203 Berlin, Germany
| | - Denis Poddubnyy
- Department of Gastroenterology, Infectious Diseases and Rheumatology (including Nutrition Medicine), Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Hindenburgdamm 30, 12203 Berlin, Germany
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Li X, Lin Y, Xie Z, Lu Z, Song L, Ye Q, Wang M, Fang X, He Y, Chen H, Zhao Y. Automatic segmentation of fat metaplasia on sacroiliac joint MRI using deep learning. Insights Imaging 2024; 15:93. [PMID: 38530554 DOI: 10.1186/s13244-024-01659-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 02/25/2024] [Indexed: 03/28/2024] Open
Abstract
OBJECTIVE To develop a deep learning (DL) model for segmenting fat metaplasia (FM) on sacroiliac joint (SIJ) MRI and further develop a DL model for classifying axial spondyloarthritis (axSpA) and non-axSpA. MATERIALS AND METHODS This study retrospectively collected 706 patients with FM who underwent SIJ MRI from center 1 (462 axSpA and 186 non-axSpA) and center 2 (37 axSpA and 21 non-axSpA). Patients from center 1 were divided into the training, validation, and internal test sets (n = 455, 64, and 129). Patients from center 2 were used as the external test set. We developed a UNet-based model to segment FM. Based on segmentation results, a classification model was built to distinguish axSpA and non-axSpA. Dice Similarity Coefficients (DSC) and area under the curve (AUC) were used for model evaluation. Radiologists' performance without and with model assistance was compared to assess the clinical utility of the models. RESULTS Our segmentation model achieved satisfactory DSC of 81.86% ± 1.55% and 85.44% ± 6.09% on the internal cross-validation and external test sets. The classification model yielded AUCs of 0.876 (95% CI: 0.811-0.942) and 0.799 (95% CI: 0.696-0.902) on the internal and external test sets, respectively. With model assistance, segmentation performance was improved for the radiological resident (DSC, 75.70% vs. 82.87%, p < 0.05) and expert radiologist (DSC, 85.03% vs. 85.74%, p > 0.05). CONCLUSIONS DL is a novel method for automatic and accurate segmentation of FM on SIJ MRI and can effectively increase radiologist's performance, which might assist in improving diagnosis and progression of axSpA. CRITICAL RELEVANCE STATEMENT DL models allowed automatic and accurate segmentation of FM on sacroiliac joint MRI, which might facilitate quantitative analysis of FM and have the potential to improve diagnosis and prognosis of axSpA. KEY POINTS • Deep learning was used for automatic segmentation of fat metaplasia on MRI. • UNet-based models achieved automatic and accurate segmentation of fat metaplasia. • Automatic segmentation facilitates quantitative analysis of fat metaplasia to improve diagnosis and prognosis of axial spondyloarthritis.
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Affiliation(s)
- Xin Li
- Department of Radiology, The Third Affiliated Hospital of Southern Medical University (Academy of Orthopedics, Guangdong Province), Guangzhou, 510630, Guangdong, China
| | - Yi Lin
- Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, 999077, China
| | - Zhuoyao Xie
- Department of Radiology, The Third Affiliated Hospital of Southern Medical University (Academy of Orthopedics, Guangdong Province), Guangzhou, 510630, Guangdong, China
| | - Zixiao Lu
- Department of Radiology, The Third Affiliated Hospital of Southern Medical University (Academy of Orthopedics, Guangdong Province), Guangzhou, 510630, Guangdong, China
| | - Liwen Song
- Department of Radiology, The Third Affiliated Hospital of Southern Medical University (Academy of Orthopedics, Guangdong Province), Guangzhou, 510630, Guangdong, China
| | - Qiang Ye
- Department of Radiology, The Third Affiliated Hospital of Southern Medical University (Academy of Orthopedics, Guangdong Province), Guangzhou, 510630, Guangdong, China
| | - Menghong Wang
- Department of Radiology, The Third Affiliated Hospital of Southern Medical University (Academy of Orthopedics, Guangdong Province), Guangzhou, 510630, Guangdong, China
| | - Xiao Fang
- Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, 999077, China
| | - Yi He
- Department of Rheumatology and Immunology, The Third Affiliated Hospital of Southern Medical University (Academy of Orthopedics, Guangdong Province), Guangzhou, 510630, China
| | - Hao Chen
- Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, 999077, China
| | - Yinghua Zhao
- Department of Radiology, The Third Affiliated Hospital of Southern Medical University (Academy of Orthopedics, Guangdong Province), Guangzhou, 510630, Guangdong, China.
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Stoel BC, Staring M, Reijnierse M, van der Helm-van Mil AHM. Deep learning in rheumatological image interpretation. Nat Rev Rheumatol 2024; 20:182-195. [PMID: 38332242 DOI: 10.1038/s41584-023-01074-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/20/2023] [Indexed: 02/10/2024]
Abstract
Artificial intelligence techniques, specifically deep learning, have already affected daily life in a wide range of areas. Likewise, initial applications have been explored in rheumatology. Deep learning might not easily surpass the accuracy of classic techniques when performing classification or regression on low-dimensional numerical data. With images as input, however, deep learning has become so successful that it has already outperformed the majority of conventional image-processing techniques developed during the past 50 years. As with any new imaging technology, rheumatologists and radiologists need to consider adapting their arsenal of diagnostic, prognostic and monitoring tools, and even their clinical role and collaborations. This adaptation requires a basic understanding of the technical background of deep learning, to efficiently utilize its benefits but also to recognize its drawbacks and pitfalls, as blindly relying on deep learning might be at odds with its capabilities. To facilitate such an understanding, it is necessary to provide an overview of deep-learning techniques for automatic image analysis in detecting, quantifying, predicting and monitoring rheumatic diseases, and of currently published deep-learning applications in radiological imaging for rheumatology, with critical assessment of possible limitations, errors and confounders, and conceivable consequences for rheumatologists and radiologists in clinical practice.
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Affiliation(s)
- Berend C Stoel
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands.
| | - Marius Staring
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Monique Reijnierse
- Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
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Zhang K, Liu C, Pan J, Zhu Y, Li X, Zheng J, Zhan Y, Li W, Li S, Luo G, Hong G. Use of MRI-based deep learning radiomics to diagnose sacroiliitis related to axial spondyloarthritis. Eur J Radiol 2024; 172:111347. [PMID: 38325189 DOI: 10.1016/j.ejrad.2024.111347] [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: 10/30/2023] [Revised: 01/13/2024] [Accepted: 01/25/2024] [Indexed: 02/09/2024]
Abstract
OBJECTIVES This study aimed to evaluate the performance of a deep learning radiomics (DLR) model, which integrates multimodal MRI features and clinical information, in diagnosing sacroiliitis related to axial spondyloarthritis (axSpA). MATERIAL & METHODS A total of 485 patients diagnosed with sacroiliitis related to axSpA (n = 288) or non-sacroiliitis (n = 197) by sacroiliac joint (SIJ) MRI between May 2018 and October 2022 were retrospectively included in this study. The patients were randomly divided into training (n = 388) and testing (n = 97) cohorts. Data were collected using three MRI scanners. We applied a convolutional neural network (CNN) called 3D U-Net for automated SIJ segmentation. Additionally, three CNNs (ResNet50, ResNet101, and DenseNet121) were used to diagnose axSpA-related sacroiliitis using a single modality. The prediction results of all the CNN models across different modalities were integrated using a stacking method based on different algorithms to construct ensemble models, and the optimal ensemble model was used as DLR signature. A combined model incorporating DLR signature with clinical factors was developed using multivariable logistic regression. The performance of the models was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). RESULTS Automated deep learning-based segmentation and manual delineation showed good correlation. ResNet50, as the optimal basic model, achieved an area under the curve (AUC) and accuracy of 0.839 and 0.804, respectively. The combined model yielded the highest performance in diagnosing axSpA-related sacroiliitis (AUC: 0.910; accuracy: 0.856) and outperformed the best ensemble model (AUC: 0.868; accuracy: 0.825) (all P < 0.05). Moreover, the DCA showed good clinical utility in the combined model. CONCLUSION We developed a diagnostic model for axSpA-related sacroiliitis by combining the DLR signature with clinical factors, which resulted in excellent diagnostic performance.
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Affiliation(s)
- Ke Zhang
- Department of Radiology, the Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai 519000, China
| | - Chaoran Liu
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, 510280, China
| | - Jielin Pan
- Department of Radiology, Zhuhai People's Hospital, Zhuhai Hospital affiliated with Jinan University, Zhuhai, 519000, China
| | - Yunfei Zhu
- Department of Radiology, the Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai 519000, China
| | - Ximeng Li
- Department of Radiology, the Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai 519000, China
| | - Jing Zheng
- Department of rheumatology, the Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai 519000, China
| | - Yingying Zhan
- Department of Radiology, the Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai 519000, China
| | - Wenjuan Li
- Department of Radiology, the Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai 519000, China
| | - Shaolin Li
- Department of Radiology, the Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai 519000, China.
| | - Guibo Luo
- Shenzhen Graduate School, Peking University, Xili, Nanshan District, Shenzhen 518055, China.
| | - Guobin Hong
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, 510280, China.
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Lin Y, Chan SCW, Chung HY, Lee KH, Cao P. A deep neural network for MRI spinal inflammation in axial spondyloarthritis. EUROPEAN SPINE JOURNAL : OFFICIAL PUBLICATION OF THE EUROPEAN SPINE SOCIETY, THE EUROPEAN SPINAL DEFORMITY SOCIETY, AND THE EUROPEAN SECTION OF THE CERVICAL SPINE RESEARCH SOCIETY 2024:10.1007/s00586-023-08099-0. [PMID: 38190004 DOI: 10.1007/s00586-023-08099-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 11/03/2023] [Accepted: 12/09/2023] [Indexed: 01/09/2024]
Abstract
OBJECTIVE To develop a deep neural network for the detection of inflammatory spine in short tau inversion recovery (STIR) sequence of magnetic resonance imaging (MRI) on patients with axial spondyloarthritis (axSpA). METHODS A total 330 patients with axSpA were recruited. STIR MRI of the whole spine and clinical data were obtained. Regions of interests (ROIs) were drawn outlining the active inflammatory lesion consisting of bone marrow edema (BME). Spinal inflammation was defined by the presence of an active inflammatory lesion on the STIR sequence. The 'fake-color' images were constructed. Images from 270 and 60 patients were randomly separated into the training/validation and testing sets, respectively. Deep neural network was developed using attention UNet. The neural network performance was compared to the image interpretation by a radiologist blinded to the ground truth. RESULTS Active inflammatory lesions were identified in 2891 MR images and were absent in 14,590 MR images. The sensitivity and specificity of the derived deep neural network were 0.80 ± 0.03 and 0.88 ± 0.02, respectively. The Dice coefficient of the true positive lesions was 0.55 ± 0.02. The area under the curve of the receiver operating characteristic (AUC-ROC) curve of the deep neural network was 0.87 ± 0.02. The performance of the developed deep neural network was comparable to the interpretation of a radiologist with similar sensitivity and specificity. CONCLUSION The developed deep neural network showed similar sensitivity and specificity to a radiologist with four years of experience. The results indicated that the network can provide a reliable and straightforward way of interpreting spinal MRI. The use of this deep neural network has the potential to expand the use of spinal MRI in managing axSpA.
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Affiliation(s)
- Yingying Lin
- Department of Diagnostic Radiology, The University of Hong Kong, LG3 Sassoon Road No. 5, Pok Fu Lam, Hong Kong
| | - Shirley Chiu Wai Chan
- Division of Rheumatology and Clinical Immunology, Department of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong
| | - Ho Yin Chung
- Division of Rheumatology and Clinical Immunology, Department of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong
- Chiron Medical, Central, Hong Kong
| | - Kam Ho Lee
- Department of Radiology, Queen Mary Hospital, Pok Fu Lam, Hong Kong
| | - Peng Cao
- Department of Diagnostic Radiology, The University of Hong Kong, LG3 Sassoon Road No. 5, Pok Fu Lam, Hong Kong.
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Lin Y, Cao P, Chan SCW, Lee KH, Lau VWH, Chung HY. Deep Learning Algorithm of the SPARCC Scoring System in SI Joint MRI. J Magn Reson Imaging 2024. [PMID: 38168061 DOI: 10.1002/jmri.29211] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 12/14/2023] [Accepted: 12/17/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND The Spondyloarthritis Research Consortium of Canada (SPARCC) scoring system is a sacroiliitis grading system. PURPOSE To develop a deep learning-based pipeline for grading sacroiliitis using the SPARCC scoring system. STUDY TYPE Prospective. POPULATION The study included 389 participants (42.2-year-old, 44.6% female, 317/35/37 for training/validation/testing). A pretrained algorithm was used to differentiate image with/without sacroiliitis. FIELD STRENGTH/SEQUENCE 3-T, short tau inversion recovery (STIR) sequence, fast spine echo. ASSESSMENT The regions of interest as ground truth for models' training were identified by a rheumatologist (HYC, 10-year-experience) and a radiologist (KHL, 6-year-experience) using the Assessment of Spondyloarthritis International Society definition of MRI sacroiliitis independently. Another radiologist (YYL, 4.5-year-experience) solved the discrepancies. The bone marrow edema (BME) and sacroiliac region models were for segmentation. Frangi-filter detected vessels used as intense reference. Deep learning pipeline scored using SPARCC scoring system evaluating presence and features of BMEs. A rheumatologist (SCWC, 6-year-experience) and a radiologist (VWHL, 14-year-experience) scored using the SPARCC scoring system once. The radiologist (YYL) scored twice with 5-day interval. STATISTICAL TESTS Independent samples t-tests and Chi-squared tests were used. Interobserver and intraobserver reliability by intraclass correlation coefficient (ICC) and Pearson coefficient evaluated consistency between readers and the deep learning pipeline. We evaluated the performance using sensitivity, accuracy, positive predictive value, and Dice coefficient. A P-value <0.05 was considered statistically significant. RESULTS The ICC and the Pearson coefficient between the SPARCC scores from three readers and the deep learning pipeline were 0.83 and 0.86, respectively. The sensitivity in identifying BME and accuracy of identifying SI joints and blood vessels was 0.83, 0.90, and 0.88, respectively. The dice coefficients were 0.82 (sacrum) and 0.80 (ilium). DATA CONCLUSION The high consistency with human readers indicated that deep learning pipeline may provide a SPARCC-informed deep learning approach for scoring of STIR images in spondyloarthritis. EVIDENCE LEVEL 1 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Yingying Lin
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, China
| | - Peng Cao
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, China
| | - Shirley Chiu Wai Chan
- Division of Rheumatology and Clinical Immunology, The University of Hong Kong, Hong Kong, China
- Department of Medicine, The University of Hong Kong, Hong Kong, China
| | - Kam Ho Lee
- Department of diagnostic radiology, Queen Mary Hospital, Hong Kong, China
| | | | - Ho Yin Chung
- Division of Rheumatology and Clinical Immunology, The University of Hong Kong, Hong Kong, China
- Department of Medicine, The University of Hong Kong, Hong Kong, China
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Nicoara AI, Sas LM, Bita CE, Dinescu SC, Vreju FA. Implementation of artificial intelligence models in magnetic resonance imaging with focus on diagnosis of rheumatoid arthritis and axial spondyloarthritis: narrative review. Front Med (Lausanne) 2023; 10:1280266. [PMID: 38173943 PMCID: PMC10761482 DOI: 10.3389/fmed.2023.1280266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Accepted: 12/04/2023] [Indexed: 01/05/2024] Open
Abstract
Early diagnosis in rheumatoid arthritis (RA) and axial spondyloarthritis (axSpA) is essential to initiate timely interventions, such as medication and lifestyle changes, preventing irreversible joint damage, reducing symptoms, and improving long-term outcomes for patients. Since magnetic resonance imaging (MRI) of the wrist and hand, in case of RA and MRI of the sacroiliac joints (SIJ) in case of axSpA can identify inflammation before it is clinically discernible, this modality may be crucial for early diagnosis. Artificial intelligence (AI) techniques, together with machine learning (ML) and deep learning (DL) have quickly evolved in the medical field, having an important role in improving diagnosis, prognosis, in evaluating the effectiveness of treatment and monitoring the activity of rheumatic diseases through MRI. The improvements of AI techniques in the last years regarding imaging interpretation have demonstrated that a computer-based analysis can equal and even exceed the human eye. The studies in the field of AI have investigated how specific algorithms could distinguish between tissues, diagnose rheumatic pathology and grade different signs of early inflammation, all of them being crucial for tracking disease activity. The aim of this paper is to highlight the implementation of AI models in MRI with focus on diagnosis of RA and axSpA through a literature review.
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Affiliation(s)
| | - Lorena-Mihaela Sas
- Radiology and Medical Imaging Laboratory, Craiova Emergency County Clinical Hospital, Craiova, Romania
- Department of Human Anatomy, University of Medicine and Pharmacy of Craiova, Craiova, Romania
| | - Cristina Elena Bita
- Department of Rheumatology, University of Medicine and Pharmacy of Craiova, Craiova, Romania
| | - Stefan Cristian Dinescu
- Department of Rheumatology, University of Medicine and Pharmacy of Craiova, Craiova, Romania
| | - Florentin Ananu Vreju
- Department of Rheumatology, University of Medicine and Pharmacy of Craiova, Craiova, Romania
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12
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Lee KH, Lee RW, Lee KH, Park W, Kwon SR, Lim MJ. The Development and Validation of an AI Diagnostic Model for Sacroiliitis: A Deep-Learning Approach. Diagnostics (Basel) 2023; 13:3643. [PMID: 38132228 PMCID: PMC10743277 DOI: 10.3390/diagnostics13243643] [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: 10/13/2023] [Revised: 12/07/2023] [Accepted: 12/11/2023] [Indexed: 12/23/2023] Open
Abstract
PURPOSE Sacroiliitis refers to the inflammatory condition of the sacroiliac joints, frequently causing lower back pain. It is often associated with systemic conditions. However, its signs on radiographic images can be subtle, which may result in it being overlooked or underdiagnosed. This study aims to utilize artificial intelligence (AI) to create a diagnostic tool for more accurate sacroiliitis detection in radiological images, with the goal of optimizing treatment plans and improving patient outcomes. MATERIALS AND METHOD The study included 492 patients who visited our hospital. Right sacroiliac joint films were independently evaluated by two musculoskeletal radiologists using the Modified New York criteria (Normal, Grades 1-4). A consensus reading resolved disagreements. The images were preprocessed with Z-score standardization and histogram equalization. The DenseNet121 algorithm, a convolutional neural network with 201 layers, was used for learning and classification. All steps were performed on the DEEP:PHI platform. RESULT The AI model exhibited high accuracy across different grades: 94.53% (Grade 1), 95.83% (Grade 2), 98.44% (Grade 3), 96.88% (Grade 4), and 96.09% (Normal cases). Sensitivity peaked at Grade 3 and Normal cases (100%), while Grade 4 achieved perfect specificity (100%). PPVs ranged from 82.61% (Grade 1) to 100% (Grade 4), and NPVs peaked at 100% for Grade 3 and Normal cases. The F1 scores ranged from 64.41% (Grade 1) to 95.38% (Grade 3). CONCLUSIONS The AI diagnostic model showcased a robust performance in detecting and grading sacroiliitis, reflecting its potential to enhance diagnostic accuracy in clinical settings. By facilitating earlier and more accurate diagnoses, this model could substantially impact treatment strategies and patient outcomes.
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Affiliation(s)
- Kyu-Hong Lee
- Department of Radiology, College of Medicine, Inha University, Incheon 22212, Republic of Korea; (K.-H.L.)
| | - Ro-Woon Lee
- Department of Radiology, College of Medicine, Inha University, Incheon 22212, Republic of Korea; (K.-H.L.)
| | - Kyung-Hee Lee
- Department of Radiology, College of Medicine, Inha University, Incheon 22212, Republic of Korea; (K.-H.L.)
| | - Won Park
- Division of Rheumatology, Department of Internal Medicine, College of Medicine, Inha University, Incheon 22212, Republic of Korea; (W.P.)
| | - Seong-Ryul Kwon
- Division of Rheumatology, Department of Internal Medicine, College of Medicine, Inha University, Incheon 22212, Republic of Korea; (W.P.)
| | - Mie-Jin Lim
- Division of Rheumatology, Department of Internal Medicine, College of Medicine, Inha University, Incheon 22212, Republic of Korea; (W.P.)
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Roels J, De Craemer AS, Renson T, de Hooge M, Gevaert A, Van Den Berghe T, Jans L, Herregods N, Carron P, Van den Bosch F, Saeys Y, Elewaut D. Machine Learning Pipeline for Predicting Bone Marrow Edema Along the Sacroiliac Joints on Magnetic Resonance Imaging. Arthritis Rheumatol 2023; 75:2169-2177. [PMID: 37410803 DOI: 10.1002/art.42650] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 06/22/2023] [Accepted: 06/26/2023] [Indexed: 07/08/2023]
Abstract
OBJECTIVE We aimed to develop and validate a fully automated machine learning (ML) algorithm that predicts bone marrow edema (BME) on a quadrant level in sacroiliac (SI) joint magnetic resonance imaging (MRI). METHODS A computer vision workflow automatically locates the SI joints, segments regions of interest (ilium and sacrum), performs objective quadrant extraction, and predicts presence of BME, suggestive of inflammatory lesions, on a quadrant level in semicoronal slices of T1/T2-weighted MRI scans. Ground truth was determined by consensus among human readers. The inflammation classifier was trained using a ResNet18 backbone and five-fold cross-validated on scans of patients with spondyloarthritis (SpA) (n = 279), postpartum individuals (n = 71), and healthy subjects (n = 114). Independent SpA patient MRI scans (n = 243) served as test data set. Patient-level predictions were derived from aggregating quadrant-level predictions, ie, at least one positive quadrant. RESULTS The algorithm automatically detects the SI joints with a precision of 98.4% and segments ilium/sacrum with an intersection over union of 85.6% and 67.9%, respectively. The inflammation classifier performed well in cross-validation: area under the curve (AUC) 94.5%, balanced accuracy (B-ACC) 80.5%, and F1 score 64.1%. In the test data set, AUC was 88.2%, B-ACC 72.1%, and F1 score 50.8%. On a patient level, the model achieved a B-ACC of 81.6% and 81.4% in the cross-validation and test data set, respectively. CONCLUSION We propose a fully automated ML pipeline that enables objective and standardized evaluation of BME along the SI joints on MRI. This method has the potential to screen large numbers of patients with (suspected) SpA and is a step closer towards artificial intelligence-assisted diagnosis and follow-up.
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Affiliation(s)
- Joris Roels
- Vlaams Instituut voor Biotechnologie - Universiteit Gent (VIB-UGent), Ghent-Zwijnaarde, and Ghent University, Ghent, Belgium
| | - Ann-Sophie De Craemer
- Vlaams Instituut voor Biotechnologie - Universiteit Gent (VIB-UGent), Ghent-Zwijnaarde, and Ghent University Hospital, Ghent, Belgium
| | - Thomas Renson
- Vlaams Instituut voor Biotechnologie - Universiteit Gent (VIB-UGent), Ghent-Zwijnaarde, and Ghent University Hospital, Ghent, Belgium
| | - Manouk de Hooge
- Vlaams Instituut voor Biotechnologie - Universiteit Gent (VIB-UGent), Ghent-Zwijnaarde, and Ghent University Hospital, Ghent, Belgium
| | - Arne Gevaert
- Vlaams Instituut voor Biotechnologie - Universiteit Gent (VIB-UGent), Ghent-Zwijnaarde, and Ghent University, Ghent, Belgium
| | | | | | | | - Philippe Carron
- Vlaams Instituut voor Biotechnologie - Universiteit Gent (VIB-UGent), Ghent-Zwijnaarde, and Ghent University Hospital, Ghent, Belgium
| | - Filip Van den Bosch
- Vlaams Instituut voor Biotechnologie - Universiteit Gent (VIB-UGent), Ghent-Zwijnaarde, and Ghent University Hospital, Ghent, Belgium
| | - Yvan Saeys
- Vlaams Instituut voor Biotechnologie - Universiteit Gent (VIB-UGent), Ghent-Zwijnaarde, and Ghent University, Ghent, Belgium
| | - Dirk Elewaut
- Vlaams Instituut voor Biotechnologie - Universiteit Gent (VIB-UGent), Ghent-Zwijnaarde, and Ghent University Hospital, Ghent, Belgium
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Moon SJ, Lee S, Hwang J, Lee J, Kang S, Cha HS. Performances of machine learning algorithms in discriminating sacroiliitis features on MRI: a systematic review. RMD Open 2023; 9:e003783. [PMID: 37996126 PMCID: PMC10668284 DOI: 10.1136/rmdopen-2023-003783] [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: 10/03/2023] [Accepted: 11/08/2023] [Indexed: 11/25/2023] Open
Abstract
OBJECTIVES Summarise the evidence of the performance of the machine learning algorithm in discriminating sacroiliitis features on MRI and compare it with the accuracy of human physicians. METHODS MEDLINE, EMBASE, CIHNAL, Web of Science, IEEE, American College of Rheumatology and European Alliance of Associations for Rheumatology abstract archives were searched for studies published between 2008 and 4 June 2023. Two authors independently screened and extracted the variables, and the results are presented using tables and forest plots. RESULTS Ten studies were selected from 2381. Over half of the studies used deep learning models, using Assessment of Spondyloarthritis International Society sacroiliitis criteria as the ground truth, and manually extracted the regions of interest. All studies reported the area under the curve as a performance index, ranging from 0.76 to 0.99. Sensitivity and specificity were the second-most commonly reported indices, with sensitivity ranging from 0.56 to 1.00 and specificity ranging from 0.67 to 1.00; these results are comparable to a radiologist's sensitivity of 0.67-1.00 and specificity of 0.78-1.00 in the same cohort. More than half of the studies showed a high risk of bias in the analysis domain of quality appraisal owing to the small sample size or overfitting issues. CONCLUSION The performance of machine learning algorithms in discriminating sacroiliitis features on MRI varied owing to the high heterogeneity between studies and the small sample sizes, overfitting, and under-reporting issues of individual studies. Further well-designed and transparent studies are required.
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Affiliation(s)
- Sun Jae Moon
- Department of Medicine, Santa Marie 24 Clinic, Seongnam-si, Korea (the Republic of)
| | - Seulkee Lee
- Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea (the Republic of)
| | - Jinseub Hwang
- Department of Data Science, Daegu University, Gyeongsan-si, Korea (the Republic of)
| | - Jaejoon Lee
- Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea (the Republic of)
| | - Seonyoung Kang
- Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea (the Republic of)
| | - Hoon-Suk Cha
- Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea (the Republic of)
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邹 青, 王 梦, 陆 紫, 赵 英, 冯 前. [Multi-sequence relation attention network for diagnosing HLA-B27-negative axial spondyloarthritis]. NAN FANG YI KE DA XUE XUE BAO = JOURNAL OF SOUTHERN MEDICAL UNIVERSITY 2023; 43:1955-1964. [PMID: 38081615 PMCID: PMC10713466 DOI: 10.12122/j.issn.1673-4254.2023.11.17] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Indexed: 12/18/2023]
Abstract
OBJECTIVE To develop a new 3D multi-sequence relation attention network for exploring the complementary and correlation information of different magnetic resonance imaging (MRI) modalities and improving the diagnostic performance of HLA-B27-negative axial spondyloarthropathy (axSpA). METHODS We retrospectively collected T1-weighted imaging (T1WI) and fat suppuration MRI (FS-MRI) data and clinical data of 375 HLA-B27-negative patients from the Third Affiliated Hospital of Southern Medical University (including 164 axSpA and 211 non-axSpA patients) and 49 patients from Nanhai Hospital (including 27 axSpA and 22 non-axSpA patients) between January, 2010 and August, 2021.A 3D relation attention network MSFANet based on multi-sequence MRI was used for automatic diagnosis of axSpA against non-axSpA in these patients.MSFANet consisted of a shallow shared feature learning module and a class-aware feature learning module, and latter module used a 3D multi-sequence relation attention mechanism to refine and fuse multi-sequence MRI features.A hybrid loss function was used to enhance the recognition ability of MSFANet by learning the loss weight coefficients of different branches to improve the classification performance. RESULTS The experimental results demonstrated that MSFANet outperformed several state-of-the-art fusion algorithms (P<0.05) with AUC, accuracy, sensitivity, and specificity of 0.840, 77.93%, 83.70%, and 70.29% in the internal validation set, and of 0.783, 74.47%, 82.43% and 70.40% in the independent external validation set, respectively.The ablation studies showed that under the same architecture, the fusion model was superior to single-sequence models, which confirmed the effectiveness and necessity of fusing multi-sequence MRI.The visualization results demonstrated that MSFANet could focus on learning information from abnormal areas on MRI during the classification. CONCLUSION We successfully constructed a 3D deep neural network based on multi-sequence MRI for differential diagnosis of HLA-B27-negative axSpA against nonaxSpA and verified the effectiveness of the multisequence relation attention mechanism for promoting classification performance of the network.
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Affiliation(s)
- 青清 邹
- 南方医科大学生物医学工程学院//广东省医学图像处理重点实验室,广东 广州 510515School of Biomedical Engineering, Southern Medical University//Guangdong Provincial Key Laboratory of Medical Image Processing, Guangzhou 510515, China
| | - 梦虹 王
- 南方医科大学第三附属医院(广东省骨科医院)放射科,广东 广州 510630Academy of Orthopedics, Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, China
| | - 紫箫 陆
- 南方医科大学第三附属医院(广东省骨科医院)放射科,广东 广州 510630Academy of Orthopedics, Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, China
| | - 英华 赵
- 南方医科大学第三附属医院(广东省骨科医院)放射科,广东 广州 510630Academy of Orthopedics, Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, China
| | - 前进 冯
- 南方医科大学生物医学工程学院//广东省医学图像处理重点实验室,广东 广州 510515School of Biomedical Engineering, Southern Medical University//Guangdong Provincial Key Laboratory of Medical Image Processing, Guangzhou 510515, China
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Lee S, Jeon U, Lee JH, Kang S, Kim H, Lee J, Chung MJ, Cha HS. Artificial intelligence for the detection of sacroiliitis on magnetic resonance imaging in patients with axial spondyloarthritis. Front Immunol 2023; 14:1278247. [PMID: 38022576 PMCID: PMC10676202 DOI: 10.3389/fimmu.2023.1278247] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 10/25/2023] [Indexed: 12/01/2023] Open
Abstract
Background Magnetic resonance imaging (MRI) is important for the early detection of axial spondyloarthritis (axSpA). We developed an artificial intelligence (AI) model for detecting sacroiliitis in patients with axSpA using MRI. Methods This study included MRI examinations of patients who underwent semi-coronal MRI scans of the sacroiliac joints owing to chronic back pain with short tau inversion recovery (STIR) sequences between January 2010 and December 2021. Sacroiliitis was defined as a positive MRI finding according to the ASAS classification criteria for axSpA. We developed a two-stage framework. First, the Faster R-CNN network extracted regions of interest (ROIs) to localize the sacroiliac joints. Maximum intensity projection (MIP) of three consecutive slices was used to mimic the reading of two adjacent slices. Second, the VGG-19 network determined the presence of sacroiliitis in localized ROIs. We augmented the positive dataset six-fold. The sacroiliitis classification performance was measured using the sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC). The prediction models were evaluated using three-round three-fold cross-validation. Results A total of 296 participants with 4,746 MRI slices were included in the study. Sacroiliitis was identified in 864 MRI slices of 119 participants. The mean sensitivity, specificity, and AUROC for the detection of sacroiliitis were 0.725 (95% CI, 0.705-0.745), 0.936 (95% CI, 0.924-0.947), and 0.830 (95%CI, 0.792-0.868), respectively, at the image level and 0.947 (95% CI, 0.912-0.982), 0.691 (95% CI, 0.603-0.779), and 0.816 (95% CI, 0.776-0.856), respectively, at the patient level. In the original model, without using MIP and dataset augmentation, the mean sensitivity, specificity, and AUROC were 0.517 (95% CI, 0.493-0.780), 0.944 (95% CI, 0.933-0.955), and 0.731 (95% CI, 0.681-0.780), respectively, at the image level and 0.806 (95% CI, 0.729-0.883), 0.617 (95% CI, 0.523-0.711), and 0.711 (95% CI, 0.660-0.763), respectively, at the patient level. The performance was improved by MIP techniques and data augmentation. Conclusion An AI model was developed for the detection of sacroiliitis using MRI, compatible with the ASAS criteria for axSpA, with the potential to aid MRI application in a wider clinical setting.
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Affiliation(s)
- Seulkee Lee
- Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Uju Jeon
- Medical AI Research Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Ji Hyun Lee
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Seonyoung Kang
- Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Hyungjin Kim
- Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Jaejoon Lee
- Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Myung Jin Chung
- Medical AI Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Data Convergence and Future Medicine, Sungkyunkwan University School of Medicine, Suwon, Republic of Korea
| | - Hoon-Suk Cha
- Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
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Zheng M, Zhu G, Chen D, Xiao Q, Lei T, Ye C, Pan C, Miao S, Ye L. T1-weighted images-based radiomics for structural lesions evaluation in patients with suspected axial spondyloarthritis. LA RADIOLOGIA MEDICA 2023; 128:1398-1406. [PMID: 37731149 DOI: 10.1007/s11547-023-01717-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Accepted: 09/01/2023] [Indexed: 09/22/2023]
Abstract
PURPOSE The aim of this study was to investigate the feasibility of radiomics based on T1-weighted images (T1WI) for assessing sacroiliac joint (SIJ) structural lesions in patients with suspected axial spondyloarthritis (axSpA). MATERIALS AND METHODS A total of 266 patients with clinical suspicion of axSpA between December 2016 and January 2022 were enrolled. Structural lesions were assessed on low-dose CT (ldCT) and MRI, respectively. Radiomic features, extracted from SIJ T1WI, were included to generate the radiomics model. The performance of the radiomics model was evaluated using receiver operating characteristic (ROC) curve. Furthermore, point-biserial correlation analysis was used to interpret the associations between the radiomic feature and structural lesions. RESULTS Using ldCT as the reference standard, the radiomics model showed favorable performance for detecting positive global structural lesions in the training cohort (AUC, 0.82 [95% CI: 0.76, 0.88]) and validation cohort (AUC, 0.82 [95% CI: 0.72, 0.91]. Experienced MRI raters yielded predictive AUCs of 0.73 (95% CI: 0.67, 0.79), and 0.74 (95% CI: 0.66, 0.83) in the training and validation cohort, respectively. The seven radiomic features included in the radiomics model showed significant correlation with different kinds of structural lesions (P all < 0.05). Among them, Wavelet.LHL_firstorder_90Percentile showed the strongest association with fat lesion (r = 0.48, P < 0.05). CONCLUSION The radiomics analysis with T1WI could effectively detect SIJ structural lesions and achieved expert-level performance. Each radiomic feature was correlated with different structural lesions significantly, which might inform radiomic-based applications for axSpA intelligent diagnosis.
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Affiliation(s)
- Mo Zheng
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325015, Zhejiang, China
| | - Guanxia Zhu
- Department of Radiology, Longgang People's Hospital, Wenzhou, 325802, Zhejiang, China
| | - Dan Chen
- Department of Rheumatology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325015, Zhejiang, China
| | - Qinqin Xiao
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325015, Zhejiang, China
| | - Tao Lei
- Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Chenhao Ye
- Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Chenqiang Pan
- Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Shouliang Miao
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325015, Zhejiang, China.
| | - Lusi Ye
- Department of Rheumatology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325015, Zhejiang, China.
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Bordner A, Aouad T, Medina CL, Yang S, Molto A, Talbot H, Dougados M, Feydy A. A deep learning model for the diagnosis of sacroiliitis according to Assessment of SpondyloArthritis International Society classification criteria with magnetic resonance imaging. Diagn Interv Imaging 2023:S2211-5684(23)00056-6. [PMID: 37012131 DOI: 10.1016/j.diii.2023.03.008] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 02/28/2023] [Accepted: 03/17/2023] [Indexed: 04/05/2023]
Abstract
PURPOSE The purpose of this study was to develop and evaluate a deep learning model to detect bone marrow edema (BME) in sacroiliac joints and predict the MRI Assessment of SpondyloArthritis International Society (ASAS) definition of active sacroiliitis in patients with chronic inflammatory back pain. MATERIALS AND METHODS MRI examinations of patients from the French prospective multicenter DESIR cohort (DEvenir des Spondyloarthropathies Indifférenciées Récentes) were used for training, validation and testing. Patients with inflammatory back pain lasting three months to three years were recruited. Test datasets were from MRI follow-ups at five years and ten years. The model was evaluated using an external test dataset from the ASAS cohort. A neuronal network classifier (mask-RCNN) was trained and evaluated for sacroiliac joints detection and BME classification. Diagnostic capabilities of the model to predict ASAS MRI active sacroiliitis (BME in at least two half-slices) were assessed using Matthews correlation coefficient (MCC), sensitivity, specificity, accuracy and AUC. The gold standard was experts' majority decision. RESULTS A total of 256 patients with 362 MRI examinations from the DESIR cohort were included, with 27% meeting the ASAS definition for experts. A total of 178 MRI examinations were used for the training set, 25 for the validation set and 159 for the evaluation set. MCCs for DESIR baseline, 5-years, and 10-years follow-up were 0.90 (n = 53), 0.64 (n = 70), and 0.61 (n = 36), respectively. AUCs for predicting ASAS MRI were 0.98 (95% CI: 0.93-1), 0.90 (95% CI: 0.79-1), and 0.80 (95% CI: 0.62-1), respectively. The ASAS external validation cohort included 47 patients (mean age 36 ± 10 [SD] years; women, 51%) with 19% meeting the ASAS definition. MCC was 0.62, sensitivity 56% (95% CI: 42-70), specificity 100% (95% CI: 100-100) and AUC 0.76 (95% CI: 0.57-0.95). CONCLUSION The deep learning model achieves performance close to those of experts for BME detection in sacroiliac joints and determination of active sacroiliitis according to the ASAS definition.
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Affiliation(s)
- Adrien Bordner
- Sorbonne Médecine Université, 75013 Paris, France; Department of Radiology, Hôpital Cochin, APHP, 75014 Paris, France.
| | - Théodore Aouad
- CentraleSupélec, Université Paris-Saclay, Inria, 91190 Gif-sur-Yvette, France
| | - Clementina Lopez Medina
- Department of Rheumatology, Reina Sofia University Hospital, IMIBIC, University of Cordoba, 14004 Cordoba, Spain
| | - Sisi Yang
- Department of Radiology, Hôpital Cochin, APHP, 75014 Paris, France; Université Paris Cité, 75006 Paris, France
| | - Anna Molto
- Department of Rheumatology, Hôpital Cochin, APHP, 75014 Paris, France; INSERM U1153, Clinical Epidemiology and Biostatistics, PRES Sorbonne Paris-Cité, 75004 Paris, France
| | - Hugues Talbot
- CentraleSupélec, Université Paris-Saclay, Inria, 91190 Gif-sur-Yvette, France
| | - Maxime Dougados
- Université Paris Cité, 75006 Paris, France; Department of Rheumatology, Hôpital Cochin, APHP, 75014 Paris, France; INSERM U1153, Clinical Epidemiology and Biostatistics, PRES Sorbonne Paris-Cité, 75004 Paris, France
| | - Antoine Feydy
- Department of Radiology, Hôpital Cochin, APHP, 75014 Paris, France; Université Paris Cité, 75006 Paris, France; INSERM U1153, Clinical Epidemiology and Biostatistics, PRES Sorbonne Paris-Cité, 75004 Paris, France
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Song C, Zhu S, Liu Y, Zhang W, Wang Z, Li W, Sun Z, Zhao P, Tian S. DCNAS-Net: deformation convolution and neural architecture search detection network for bone marrow oedema. BMC Med Imaging 2023; 23:45. [PMID: 36978011 PMCID: PMC10045610 DOI: 10.1186/s12880-023-01003-8] [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: 09/10/2022] [Accepted: 03/21/2023] [Indexed: 03/30/2023] Open
Abstract
Background Lumbago is a global disease that affects more than 500 million people worldwide. Bone marrow oedema is one of the main causes of the condition and clinical diagnosis is mainly made by radiologists manually reviewing MRI images to determine whether oedema is present. However, the number of patients with Lumbago has risen dramatically in recent years, which has brought a huge workload to radiologists. In order to improve the efficiency of diagnosis, this paper is devoted to developing and evaluating a neural network for detecting bone marrow edema in MRI images. Related work Inspired by the development of deep learning and image processing techniques, we design a deep learning detection algorithm specifically for the detection of bone marrow oedema from lumbar MRI images. We introduce deformable convolution, feature pyramid networks and neural architecture search modules, and redesign the existing neural networks. We explain in detail the construction of the network and illustrate the setting of the network hyperparameters. Results and discussion The detection accuracy of our algorithm is excellent. And its accuracy of detecting bone marrow oedema reached up to 90.6\documentclass[12pt]{minimal}
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\begin{document}$$\%$$\end{document}%. And our algorithm is fast in detecting it, taking only 0.144 s per image. Conclusion Extensive experiments have demonstrated that deformable convolution and aggregated feature pyramid structures are conducive for the detection of bone marrow oedema. Our algorithm has better detection accuracy and good detection speed compared to other algorithms.
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Affiliation(s)
- Chengyu Song
- grid.33763.320000 0004 1761 2484Tianjin University, Tianjin, China
| | - Shan Zhu
- grid.33763.320000 0004 1761 2484Tianjin Hospital, Tianjin University, Tianjin, China
| | - Yanyan Liu
- grid.216938.70000 0000 9878 7032Nankai University, Tianjin, China
| | - Wei Zhang
- grid.33763.320000 0004 1761 2484Tianjin University, Tianjin, China
| | - Zhi Wang
- grid.33763.320000 0004 1761 2484Tianjin Hospital, Tianjin University, Tianjin, China
| | - Wangxiao Li
- grid.33763.320000 0004 1761 2484Tianjin University, Tianjin, China
| | - Zhenye Sun
- grid.33763.320000 0004 1761 2484Tianjin Hospital, Tianjin University, Tianjin, China
| | - Peng Zhao
- grid.33763.320000 0004 1761 2484Tianjin Hospital, Tianjin University, Tianjin, China
| | - Shengzhang Tian
- grid.33763.320000 0004 1761 2484Tianjin Hospital, Tianjin University, Tianjin, China
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Can radiomics replace the SPARCC scoring system in evaluating bone marrow edema of sacroiliac joints in patients with axial spondyloarthritis? Clin Rheumatol 2023; 42:1675-1682. [PMID: 36795334 DOI: 10.1007/s10067-023-06543-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 02/03/2023] [Accepted: 02/06/2023] [Indexed: 02/17/2023]
Abstract
OBJECTIVES To develop an objective and efficient method based on radiomics to evaluate bone marrow edema (BMO) of sacroiliac joints (SIJs) by magnetic resonance imaging (MRI) in patients with axial spondyloarthritis (axSpA) and to compare with the Spondyloarthritis Research Consortium of Canada (SPARCC) scoring system. METHODS From September 2013 to March 2022, patients with axSpA who underwent 3.0T SIJ-MRI were included and were randomly divided into training and validation cohorts at a ratio of 7:3. The optimal radiomics features selected from the SIJ-MRI in the training cohort were included to generate the radiomics model. The performance of the model was evaluated by ROC analysis and decision curve analysis (DCA). Rad scores were calculated using the radiomics model. The responsiveness was compared for Rad scores and SPARCC scores. We also assessed the correlation between the Rad score and SPARCC score. RESULTS A total of 558 patients were finally included. The radiomics model showed favorable discrimination of a SPARCC score <2 or ≥2 both in the training (AUC, 0.90; 95% CI: 0.87-0.93) and validation cohorts (AUC, 0.90; 95% CI, 0.86-0.95). DCA confirmed that the model was clinically useful. Rad score showed higher responsiveness to treatment-related change than SPARCC score. Furthermore, a significant correlation was noted between the Rad score and SPARCC score when scoring the status of BMO (rs=0.80, P < 0.001), and a strong correlation was noted when scoring the change in BMO (r=0.70, P < 0.001). CONCLUSION The study proposed a radiomics model to accurately quantify the BMO of SIJs in patients with axSpA, providing an alternative to the SPARCC scoring system. Key Points • The Rad score is an index with high validity for the objective and quantitative evaluation of bone marrow edema (BMO) of the sacroiliac joints in axial spondyloarthritis. • The Rad score is a promising tool to monitor the change of BMO upon treatment.
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Li H, Tao X, Liang T, Jiang J, Zhu J, Wu S, Chen L, Zhang Z, Zhou C, Sun X, Huang S, Chen J, Chen T, Ye Z, Chen W, Guo H, Yao Y, Liao S, Yu C, Fan B, Liu Y, Lu C, Hu J, Xie Q, Wei X, Fang C, Liu H, Huang C, Pan S, Zhan X, Liu C. Comprehensive AI-assisted tool for ankylosing spondylitis based on multicenter research outperforms human experts. Front Public Health 2023; 11:1063633. [PMID: 36844823 PMCID: PMC9947660 DOI: 10.3389/fpubh.2023.1063633] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Accepted: 01/18/2023] [Indexed: 02/11/2023] Open
Abstract
Introduction The diagnosis and treatment of ankylosing spondylitis (AS) is a difficult task, especially in less developed countries without access to experts. To address this issue, a comprehensive artificial intelligence (AI) tool was created to help diagnose and predict the course of AS. Methods In this retrospective study, a dataset of 5389 pelvic radiographs (PXRs) from patients treated at a single medical center between March 2014 and April 2022 was used to create an ensemble deep learning (DL) model for diagnosing AS. The model was then tested on an additional 583 images from three other medical centers, and its performance was evaluated using the area under the receiver operating characteristic curve analysis, accuracy, precision, recall, and F1 scores. Furthermore, clinical prediction models for identifying high-risk patients and triaging patients were developed and validated using clinical data from 356 patients. Results The ensemble DL model demonstrated impressive performance in a multicenter external test set, with precision, recall, and area under the receiver operating characteristic curve values of 0.90, 0.89, and 0.96, respectively. This performance surpassed that of human experts, and the model also significantly improved the experts' diagnostic accuracy. Furthermore, the model's diagnosis results based on smartphone-captured images were comparable to those of human experts. Additionally, a clinical prediction model was established that accurately categorizes patients with AS into high-and low-risk groups with distinct clinical trajectories. This provides a strong foundation for individualized care. Discussion In this study, an exceptionally comprehensive AI tool was developed for the diagnosis and management of AS in complex clinical scenarios, especially in underdeveloped or rural areas that lack access to experts. This tool is highly beneficial in providing an efficient and effective system of diagnosis and management.
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Affiliation(s)
- Hao Li
- The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Xiang Tao
- The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Tuo Liang
- The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Jie Jiang
- The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Jichong Zhu
- The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Shaofeng Wu
- The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Liyi Chen
- The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Zide Zhang
- The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Chenxing Zhou
- The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Xuhua Sun
- The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Shengsheng Huang
- The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Jiarui Chen
- The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Tianyou Chen
- The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Zhen Ye
- The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Wuhua Chen
- The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Hao Guo
- The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Yuanlin Yao
- The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Shian Liao
- The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Chaojie Yu
- The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Binguang Fan
- The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Yihong Liu
- Guangxi Medical University, Nanning, Guangxi, China
| | - Chunai Lu
- Guangxi Medical University, Nanning, Guangxi, China
| | - Junnan Hu
- Guangxi Medical University, Nanning, Guangxi, China
| | - Qinghong Xie
- Guangxi Medical University, Nanning, Guangxi, China
| | - Xiao Wei
- Guangxi Medical University, Nanning, Guangxi, China
| | - Cairen Fang
- Guangxi Medical University, Nanning, Guangxi, China
| | - Huijiang Liu
- Orthopaedics of The First People's Hospital of Nanning, Nanning, Guangxi, China
| | - Chengqian Huang
- Orthopaedics of People's Hospital of Baise, Baise, Guangxi, China
| | - Shixin Pan
- Orthopaedics of Wuzhou Red Cross Hospital, Wuzhou, Guangxi, China
| | - Xinli Zhan
- The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Chong Liu
- The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China,*Correspondence: Chong Liu ✉
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Lin KYY, Cao P, Lee KH, Chan SCW, Chung HY. Comment on: Deep learning algorithms for magnetic resonance imaging of inflammatory sacroiliitis in axial spondyloarthritis: reply. Rheumatology (Oxford) 2022; 61:e318-e319. [PMID: 35416943 DOI: 10.1093/rheumatology/keac216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 04/03/2022] [Indexed: 11/13/2022] Open
Affiliation(s)
| | - Peng Cao
- Department of diagnostic radiology, The University of Hong Kong
| | - Kam Ho Lee
- Department of radiology, Queen Mary Hospital, Hong Kong
| | - Shirley Chiu Wai Chan
- Division of rheumatology and clinical immunology, department of medicine, The University of Hong Kong
| | - Ho Yin Chung
- Division of rheumatology and clinical immunology, department of medicine, The University of Hong Kong.,Chiron Medical
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