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Yang K, Musio F, Ma Y, Juchler N, Paetzold JC, Al-Maskari R, Höher L, Li HB, Hamamci IE, Sekuboyina A, Shit S, Huang H, Waldmannstetter D, Kofler F, Navarro F, Menten M, Ezhov I, Rueckert D, Vos I, Ruigrok Y, Velthuis B, Kuijf H, Hämmerli J, Wurster C, Bijlenga P, Westphal L, Bisschop J, Colombo E, Baazaoui H, Makmur A, Hallinan J, Wiestler B, Kirschke JS, Wiest R, Montagnon E, Letourneau-Guillon L, Galdran A, Galati F, Falcetta D, Zuluaga MA, Lin C, Zhao H, Zhang Z, Ra S, Hwang J, Park H, Chen J, Wodzinski M, Müller H, Shi P, Liu W, Ma T, Yalçin C, Hamadache RE, Salvi J, Llado X, Maria Lal-Trehan Estrada U, Abramova V, Giancardo L, Oliver A, Liu J, Huang H, Cui Y, Lin Z, Liu Y, Zhu S, Patel TR, Tutino VM, Orouskhani M, Wang H, Mossa-Basha M, Zhu C, Rokuss MR, Kirchhoff Y, Disch N, Holzschuh J, Isensee F, Maier-Hein K, Sato Y, Hirsch S, Wegener S, Menze B. TopCoW: Benchmarking Topology-Aware Anatomical Segmentation of the Circle of Willis (CoW) for CTA and MRA. ArXiv 2024:arXiv:2312.17670v2. [PMID: 38235066 PMCID: PMC10793481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
The Circle of Willis (CoW) is an important network of arteries connecting major circulations of the brain. Its vascular architecture is believed to affect the risk, severity, and clinical outcome of serious neuro-vascular diseases. However, characterizing the highly variable CoW anatomy is still a manual and time-consuming expert task. The CoW is usually imaged by two angiographic imaging modalities, magnetic resonance angiography (MRA) and computed tomography angiography (CTA), but there exist limited public datasets with annotations on CoW anatomy, especially for CTA. Therefore we organized the TopCoW Challenge in 2023 with the release of an annotated CoW dataset. The TopCoW dataset was the first public dataset with voxel-level annotations for thirteen possible CoW vessel components, enabled by virtual-reality (VR) technology. It was also the first large dataset with paired MRA and CTA from the same patients. TopCoW challenge formalized the CoW characterization problem as a multiclass anatomical segmentation task with an emphasis on topological metrics. We invited submissions worldwide for the CoW segmentation task, which attracted over 140 registered participants from four continents. The top performing teams managed to segment many CoW components to Dice scores around 90%, but with lower scores for communicating arteries and rare variants. There were also topological mistakes for predictions with high Dice scores. Additional topological analysis revealed further areas for improvement in detecting certain CoW components and matching CoW variant topology accurately. TopCoW represented a first attempt at benchmarking the CoW anatomical segmentation task for MRA and CTA, both morphologically and topologically.
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Affiliation(s)
- Kaiyuan Yang
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Fabio Musio
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Center for Computational Health, Zurich University of Applied Sciences, Zurich, Switzerland
| | - Yihui Ma
- Department of Neuroradiology, University Hospital of Zurich, Zurich, Switzerland
- Department of Neurosurgery, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Norman Juchler
- Center for Computational Health, Zurich University of Applied Sciences, Zurich, Switzerland
| | - Johannes C Paetzold
- Department of Computing, Imperial College London, London, UK
- Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Center, Munich, Germany
| | - Rami Al-Maskari
- Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Center, Munich, Germany
| | - Luciano Höher
- Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Center, Munich, Germany
| | - Hongwei Bran Li
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Boston, USA
| | | | - Anjany Sekuboyina
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Suprosanna Shit
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- School of Medicine, Technical University of Munich, Munich, Germany
| | - Houjing Huang
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Diana Waldmannstetter
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Department of Informatics, Technical University of Munich, Munich, Germany
| | - Florian Kofler
- School of Medicine, Technical University of Munich, Munich, Germany
- Department of Informatics, Technical University of Munich, Munich, Germany
- Helmholtz AI, Helmholtz Munich, Munich, Germany
| | - Fernando Navarro
- School of Medicine, Technical University of Munich, Munich, Germany
- Department of Informatics, Technical University of Munich, Munich, Germany
| | - Martin Menten
- Department of Informatics, Technical University of Munich, Munich, Germany
- Department of Computing, Imperial College London, London, UK
| | - Ivan Ezhov
- Department of Informatics, Technical University of Munich, Munich, Germany
| | - Daniel Rueckert
- Department of Informatics, Technical University of Munich, Munich, Germany
- Department of Computing, Imperial College London, London, UK
| | - Iris Vos
- Image Sciences Institute, UMC Utrecht, Utrecht, the Netherlands
| | - Ynte Ruigrok
- Department of Neurology, UMC Utrecht, Utrecht, the Netherlands
| | | | - Hugo Kuijf
- Image Sciences Institute, UMC Utrecht, Utrecht, the Netherlands
| | - Julien Hämmerli
- Department of Clinical Neurosciences, Division of Neurosurgery, Geneva University Hospitals, Geneva, Switzerland
| | - Catherine Wurster
- Department of Clinical Neurosciences, Division of Neurosurgery, Geneva University Hospitals, Geneva, Switzerland
| | - Philippe Bijlenga
- Department of Clinical Neurosciences, Division of Neurosurgery, Geneva University Hospitals, Geneva, Switzerland
| | - Laura Westphal
- Department of Neurology, University Hospital of Zurich, Zurich, Switzerland
| | - Jeroen Bisschop
- Department of Physiology, University of Toronto, Toronto, Canada
| | - Elisa Colombo
- Department of Neurosurgery, University Hospital of Zurich, Zurich, Switzerland
| | - Hakim Baazaoui
- Department of Neurology, University Hospital of Zurich, Zurich, Switzerland
| | - Andrew Makmur
- Department of Diagnostic Imaging, National University Hospital, Singapore
| | - James Hallinan
- Department of Diagnostic Imaging, National University Hospital, Singapore
| | - Bene Wiestler
- School of Medicine, Technical University of Munich, Munich, Germany
| | - Jan S Kirschke
- School of Medicine, Technical University of Munich, Munich, Germany
| | - Roland Wiest
- Department of Diagnostic and Interventional Neuroradiology, University Hospital Berne and University of Berne, Berne, Switzerland
| | - Emmanuel Montagnon
- Centre de Recherche du Centre Hospitalier de l'Université de Montreal (CRCHUM), Montreal, Canada
| | | | | | | | | | | | - Chaolong Lin
- Institute of Medical Technology, Peking University Health Science Center, Beijing, China
| | - Haoran Zhao
- Institute of Medical Technology, Peking University Health Science Center, Beijing, China
| | - Zehan Zhang
- Hangzhou Genlight Medtech Co. Ltd., Hangzhou, China
| | - Sinyoung Ra
- Department of Artificial Intelligence, Sungkyunkwan University, Seoul, Korea
| | - Jongyun Hwang
- Department of Artificial Intelligence, Sungkyunkwan University, Seoul, Korea
| | - Hyunjin Park
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Seoul, Korea
| | - Junqiang Chen
- Shanghai MediWorks Precision Instruments Co., Ltd, Shanghai, China
| | - Marek Wodzinski
- Institute of Informatics, HES-SO Valais-Wallis, Switzerland
- Department of Measurement and Electronics, AGH University of Krakow, Poland
| | - Henning Müller
- Institute of Informatics, HES-SO Valais-Wallis, Switzerland
| | - Pengcheng Shi
- Electronic & Information Engineering School, Harbin Institute of Technology (Shenzhen), Shenzhen, China
| | - Wei Liu
- Electronic & Information Engineering School, Harbin Institute of Technology (Shenzhen), Shenzhen, China
| | - Ting Ma
- Electronic & Information Engineering School, Harbin Institute of Technology (Shenzhen), Shenzhen, China
- Peng Cheng Laboratory, Shenzhen, China
| | - Cansu Yalçin
- Research Institute of Computer Vision and Robotics (ViCOROB), Universitat de Girona, Catalonia, Spain
| | - Rachika E Hamadache
- Research Institute of Computer Vision and Robotics (ViCOROB), Universitat de Girona, Catalonia, Spain
| | - Joaquim Salvi
- Research Institute of Computer Vision and Robotics (ViCOROB), Universitat de Girona, Catalonia, Spain
| | - Xavier Llado
- Research Institute of Computer Vision and Robotics (ViCOROB), Universitat de Girona, Catalonia, Spain
| | | | - Valeriia Abramova
- Research Institute of Computer Vision and Robotics (ViCOROB), Universitat de Girona, Catalonia, Spain
| | - Luca Giancardo
- Center for Precision Health, McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, USA
| | - Arnau Oliver
- Research Institute of Computer Vision and Robotics (ViCOROB), Universitat de Girona, Catalonia, Spain
| | - Jialu Liu
- Laboratory of Brain Atlas and Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Haibin Huang
- Laboratory of Brain Atlas and Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Yue Cui
- Laboratory of Brain Atlas and Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Zehang Lin
- School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China
| | - Yusheng Liu
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China
| | - Shunzhi Zhu
- School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China
| | - Tatsat R Patel
- Canon Stroke and Vascular Research Center, Buffalo, USA
- Department of Pathology and Anatomical Sciences, University at Buffalo, Buffalo, USA
- Department of Neurosurgery, University at Buffalo, Buffalo, USA
| | - Vincent M Tutino
- Canon Stroke and Vascular Research Center, Buffalo, USA
- Department of Pathology and Anatomical Sciences, University at Buffalo, Buffalo, USA
- Department of Neurosurgery, University at Buffalo, Buffalo, USA
- Department of Biomedical Engineering, University at Buffalo, Buffalo, USA
| | | | - Huayu Wang
- Department of Radiology, University of Washington, Seattle, USA
| | | | - Chengcheng Zhu
- Department of Radiology, University of Washington, Seattle, USA
| | - Maximilian R Rokuss
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Germany
- Faculty of Mathematics and Computer Science, Heidelberg University, Germany
| | - Yannick Kirchhoff
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Germany
- Faculty of Mathematics and Computer Science, Heidelberg University, Germany
- HIDSS4Health - Helmholtz Information and Data Science School for Health, Karlsruhe/Heidelberg, Germany
| | - Nico Disch
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Germany
- Faculty of Mathematics and Computer Science, Heidelberg University, Germany
- HIDSS4Health - Helmholtz Information and Data Science School for Health, Karlsruhe/Heidelberg, Germany
| | - Julius Holzschuh
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Germany
| | - Fabian Isensee
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Germany
- Helmholtz Imaging, German Cancer Research Center, Heidelberg, Germany
| | - Klaus Maier-Hein
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Germany
- Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital
| | | | - Sven Hirsch
- Center for Computational Health, Zurich University of Applied Sciences, Zurich, Switzerland
| | - Susanne Wegener
- Department of Neurology, University Hospital of Zurich, Zurich, Switzerland
| | - Bjoern Menze
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
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Lim GZ, Lai JY, Seet CYH, Tham CH, Venketasubramanian N, Tan BYQ, Jing M, Yeo JYP, Myint MZ, Sia CH, Teoh HL, Sharma VK, Chan BPL, Yang C, Makmur A, Ong SJ, Yeo LLL. Revolutionizing the Management of Large-Core Ischaemic Strokes: Decoding the Success of Endovascular Therapy in the Recent Stroke Trials. J Cardiovasc Dev Dis 2023; 10:499. [PMID: 38132666 PMCID: PMC10743836 DOI: 10.3390/jcdd10120499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 12/02/2023] [Accepted: 12/15/2023] [Indexed: 12/23/2023] Open
Abstract
Endovascular therapy (EVT) has revolutionized the management of acute ischaemic strokes with large vessel occlusion, with emerging evidence suggesting its benefit also in large infarct core volume strokes. In the last two years, four randomised controlled trials have been published on this topic-RESCUE-Japan LIMIT, ANGEL-ASPECT, SELECT2 and TENSION, with overall results showing that EVT improves functional and neurological outcomes compared to medical management alone. This review aims to summarise the recent evidence presented by these four trials and highlight some of the limitations in our current understanding of this topic.
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Affiliation(s)
- Gareth Zigui Lim
- Department of Neurology, National Neuroscience Institute, Tan Tock Seng Hospital, Singapore 308433, Singapore
| | - Jonathan Yexian Lai
- Department of Neurology, National Neuroscience Institute, Tan Tock Seng Hospital, Singapore 308433, Singapore
| | - Christopher Ying Hao Seet
- Department of Neurology, National Neuroscience Institute, Tan Tock Seng Hospital, Singapore 308433, Singapore
| | - Carol Huilian Tham
- Department of Neurology, National Neuroscience Institute, Tan Tock Seng Hospital, Singapore 308433, Singapore
| | | | - Benjamin Yong Qiang Tan
- Division of Neurology, Department of Medicine, National University Health System, Singapore 119228, Singapore (V.K.S.); (B.P.L.C.)
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119077, Singapore
| | - Mingxue Jing
- Division of Neurology, Department of Medicine, National University Health System, Singapore 119228, Singapore (V.K.S.); (B.P.L.C.)
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119077, Singapore
| | - Joshua Yee Peng Yeo
- Division of Neurology, Department of Medicine, National University Health System, Singapore 119228, Singapore (V.K.S.); (B.P.L.C.)
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119077, Singapore
| | - May Zin Myint
- Division of Neurology, Department of Medicine, National University Health System, Singapore 119228, Singapore (V.K.S.); (B.P.L.C.)
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119077, Singapore
| | - Ching-Hui Sia
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119077, Singapore
- Department of Cardiology, National University Heart Center, Singapore 119228, Singapore
| | - Hock Luen Teoh
- Division of Neurology, Department of Medicine, National University Health System, Singapore 119228, Singapore (V.K.S.); (B.P.L.C.)
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119077, Singapore
| | - Vijay Kumar Sharma
- Division of Neurology, Department of Medicine, National University Health System, Singapore 119228, Singapore (V.K.S.); (B.P.L.C.)
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119077, Singapore
| | - Bernard Poon Lap Chan
- Division of Neurology, Department of Medicine, National University Health System, Singapore 119228, Singapore (V.K.S.); (B.P.L.C.)
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119077, Singapore
| | - Cunli Yang
- Division of Neurology, Department of Medicine, National University Health System, Singapore 119228, Singapore (V.K.S.); (B.P.L.C.)
- Department of Diagnostic Imaging, National University Health System, Singapore 119228, Singapore
| | - Andrew Makmur
- Division of Neurology, Department of Medicine, National University Health System, Singapore 119228, Singapore (V.K.S.); (B.P.L.C.)
- Department of Diagnostic Imaging, National University Health System, Singapore 119228, Singapore
| | - Shao Jin Ong
- Division of Neurology, Department of Medicine, National University Health System, Singapore 119228, Singapore (V.K.S.); (B.P.L.C.)
- Department of Diagnostic Imaging, National University Health System, Singapore 119228, Singapore
| | - Leonard Leong Litt Yeo
- Division of Neurology, Department of Medicine, National University Health System, Singapore 119228, Singapore (V.K.S.); (B.P.L.C.)
- Department of Diagnostic Imaging, National University Health System, Singapore 119228, Singapore
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Ong W, Liu RW, Makmur A, Low XZ, Sng WJ, Tan JH, Kumar N, Hallinan JTPD. Artificial Intelligence Applications for Osteoporosis Classification Using Computed Tomography. Bioengineering (Basel) 2023; 10:1364. [PMID: 38135954 PMCID: PMC10741220 DOI: 10.3390/bioengineering10121364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 11/21/2023] [Accepted: 11/23/2023] [Indexed: 12/24/2023] Open
Abstract
Osteoporosis, marked by low bone mineral density (BMD) and a high fracture risk, is a major health issue. Recent progress in medical imaging, especially CT scans, offers new ways of diagnosing and assessing osteoporosis. This review examines the use of AI analysis of CT scans to stratify BMD and diagnose osteoporosis. By summarizing the relevant studies, we aimed to assess the effectiveness, constraints, and potential impact of AI-based osteoporosis classification (severity) via CT. A systematic search of electronic databases (PubMed, MEDLINE, Web of Science, ClinicalTrials.gov) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 39 articles were retrieved from the databases, and the key findings were compiled and summarized, including the regions analyzed, the type of CT imaging, and their efficacy in predicting BMD compared with conventional DXA studies. Important considerations and limitations are also discussed. The overall reported accuracy, sensitivity, and specificity of AI in classifying osteoporosis using CT images ranged from 61.8% to 99.4%, 41.0% to 100.0%, and 31.0% to 100.0% respectively, with areas under the curve (AUCs) ranging from 0.582 to 0.994. While additional research is necessary to validate the clinical efficacy and reproducibility of these AI tools before incorporating them into routine clinical practice, these studies demonstrate the promising potential of using CT to opportunistically predict and classify osteoporosis without the need for DEXA.
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Affiliation(s)
- Wilson Ong
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore (A.M.); (X.Z.L.); (W.J.S.); (J.T.P.D.H.)
| | - Ren Wei Liu
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore (A.M.); (X.Z.L.); (W.J.S.); (J.T.P.D.H.)
| | - Andrew Makmur
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore (A.M.); (X.Z.L.); (W.J.S.); (J.T.P.D.H.)
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Xi Zhen Low
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore (A.M.); (X.Z.L.); (W.J.S.); (J.T.P.D.H.)
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Weizhong Jonathan Sng
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore (A.M.); (X.Z.L.); (W.J.S.); (J.T.P.D.H.)
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Jiong Hao Tan
- University Spine Centre, Department of Orthopaedic Surgery, National University Health System, 1E Lower Kent Ridge Road, Singapore 119228, Singapore; (J.H.T.); (N.K.)
| | - Naresh Kumar
- University Spine Centre, Department of Orthopaedic Surgery, National University Health System, 1E Lower Kent Ridge Road, Singapore 119228, Singapore; (J.H.T.); (N.K.)
| | - James Thomas Patrick Decourcy Hallinan
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore (A.M.); (X.Z.L.); (W.J.S.); (J.T.P.D.H.)
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
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Hallinan JTPD, Zhu L, Tan HWN, Hui SJ, Lim X, Ong BWL, Ong HY, Eide SE, Cheng AJL, Ge S, Kuah T, Lim SWD, Low XZ, Teo EC, Yap QV, Chan YH, Kumar N, Vellayappan BA, Ooi BC, Quek ST, Makmur A, Tan JH. A deep learning-based technique for the diagnosis of epidural spinal cord compression on thoracolumbar CT. Eur Spine J 2023; 32:3815-3824. [PMID: 37093263 DOI: 10.1007/s00586-023-07706-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 03/12/2023] [Accepted: 04/06/2023] [Indexed: 04/25/2023]
Abstract
PURPOSE To develop a deep learning (DL) model for epidural spinal cord compression (ESCC) on CT, which will aid earlier ESCC diagnosis for less experienced clinicians. METHODS We retrospectively collected CT and MRI data from adult patients with suspected ESCC at a tertiary referral institute from 2007 till 2020. A total of 183 patients were used for training/validation of the DL model. A separate test set of 40 patients was used for DL model evaluation and comprised 60 staging CT and matched MRI scans performed with an interval of up to 2 months. DL model performance was compared to eight readers: one musculoskeletal radiologist, two body radiologists, one spine surgeon, and four trainee spine surgeons. Diagnostic performance was evaluated using inter-rater agreement, sensitivity, specificity and AUC. RESULTS Overall, 3115 axial CT slices were assessed. The DL model showed high kappa of 0.872 for normal, low and high-grade ESCC (trichotomous), which was superior compared to a body radiologist (R4, κ = 0.667) and all four trainee spine surgeons (κ range = 0.625-0.838)(all p < 0.001). In addition, for dichotomous normal versus any grade of ESCC detection, the DL model showed high kappa (κ = 0.879), sensitivity (91.82), specificity (92.01) and AUC (0.919), with the latter AUC superior to all readers (AUC range = 0.732-0.859, all p < 0.001). CONCLUSION A deep learning model for the objective assessment of ESCC on CT had comparable or superior performance to radiologists and spine surgeons. Earlier diagnosis of ESCC on CT could reduce treatment delays, which are associated with poor outcomes, increased costs, and reduced survival.
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Affiliation(s)
- James Thomas Patrick Decourcy Hallinan
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore.
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore, 117597, Singapore.
| | - Lei Zhu
- Department of Computer Science, School of Computing, National University of Singapore, 13 Computing Drive, Singapore, 117417, Singapore
| | - Hui Wen Natalie Tan
- Department of Orthopaedic Surgery, University Spine Centre, National University Health System, 1E, Lower Kent Ridge Road, Singapore, 119228, Singapore
| | - Si Jian Hui
- Department of Orthopaedic Surgery, University Spine Centre, National University Health System, 1E, Lower Kent Ridge Road, Singapore, 119228, Singapore
| | - Xinyi Lim
- Orthopaedic Centre, Alexandra Hospital, 378 Alexandra Road, Singapore, 159964, Singapore
| | - Bryan Wei Loong Ong
- Department of Orthopaedic Surgery, University Spine Centre, National University Health System, 1E, Lower Kent Ridge Road, Singapore, 119228, Singapore
| | - Han Yang Ong
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore, 117597, Singapore
| | - Sterling Ellis Eide
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore, 117597, Singapore
| | - Amanda J L Cheng
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore, 117597, Singapore
| | - Shuliang Ge
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore
| | - Tricia Kuah
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore
| | - Shi Wei Desmond Lim
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore
| | - Xi Zhen Low
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore
| | - Ee Chin Teo
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore
| | - Qai Ven Yap
- Biostatistics Unit, Yong Loo Lin School of Medicine, 10 Medical Drive, Singapore, 117597, Singapore
| | - Yiong Huak Chan
- Biostatistics Unit, Yong Loo Lin School of Medicine, 10 Medical Drive, Singapore, 117597, Singapore
| | - Naresh Kumar
- Department of Orthopaedic Surgery, University Spine Centre, National University Health System, 1E, Lower Kent Ridge Road, Singapore, 119228, Singapore
| | - Balamurugan A Vellayappan
- Department of Radiation Oncology, National University Cancer Institute Singapore, National University Hospital, Singapore, Singapore
| | - Beng Chin Ooi
- Department of Computer Science, School of Computing, National University of Singapore, 13 Computing Drive, Singapore, 117417, Singapore
| | - Swee Tian Quek
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore, 117597, Singapore
| | - Andrew Makmur
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore, 117597, Singapore
| | - Jiong Hao Tan
- Department of Orthopaedic Surgery, University Spine Centre, National University Health System, 1E, Lower Kent Ridge Road, Singapore, 119228, Singapore
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Goh Y, Lee XY, Chin AXY, Jing M, Makmur A, Quek AML. Shapiro syndrome: a cause of episodic hyperhidrosis, hypothermia and altered mental status. QJM 2023; 116:861-863. [PMID: 37338563 DOI: 10.1093/qjmed/hcad145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Indexed: 06/21/2023] Open
Affiliation(s)
- Y Goh
- Division of Neurology, Department of Medicine, National University Hospital, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - X Y Lee
- Division of Advanced Internal Medicine, Department of Medicine, National University Hospital, Singapore
| | - A X Y Chin
- Division of Neurology, Department of Medicine, National University Hospital, Singapore
| | - M Jing
- Division of Neurology, Department of Medicine, National University Hospital, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - A Makmur
- Department of Diagnostic Imaging, National University Hospital, Singapore
| | - A M L Quek
- Division of Neurology, Department of Medicine, National University Hospital, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
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Lau SCX, Lim LZ, Hallinan JTPD, Makmur A. Incidental findings involving the temporomandibular joint on computed tomography and magnetic resonance imaging. Singapore Med J 2023; 64:262-270. [PMID: 37006089 PMCID: PMC10144453 DOI: 10.4103/singaporemedj.smj-2021-068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2023]
Abstract
The temporomandibular joint (TMJ) is frequently imaged in head and neck computed tomography (CT) and magnetic resonance imaging (MRI) studies. Depending on the indication for the study, an abnormality of the TMJ may be an incidental finding. These findings encompass both intra- and extra-articular disorders. They may also be related to local, regional or systemic conditions. Familiarity with these findings along with pertinent clinical information helps narrow the list of differential diagnoses. While definitive diagnosis may not be immediately apparent, a systematic approach contributes to improved discussions between clinicians and radiologists and better patient management.
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Affiliation(s)
| | - Li Zhen Lim
- Discipline of Oral and Maxillofacial Surgery, Faculty of Dentistry, National University of Singapore, Singapore
| | | | - Andrew Makmur
- Department of Diagnostic Imaging, National University Health System, Singapore
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7
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Ong W, Zhu L, Tan YL, Teo EC, Tan JH, Kumar N, Vellayappan BA, Ooi BC, Quek ST, Makmur A, Hallinan JTPD. Application of Machine Learning for Differentiating Bone Malignancy on Imaging: A Systematic Review. Cancers (Basel) 2023; 15:cancers15061837. [PMID: 36980722 PMCID: PMC10047175 DOI: 10.3390/cancers15061837] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 03/07/2023] [Accepted: 03/16/2023] [Indexed: 03/22/2023] Open
Abstract
An accurate diagnosis of bone tumours on imaging is crucial for appropriate and successful treatment. The advent of Artificial intelligence (AI) and machine learning methods to characterize and assess bone tumours on various imaging modalities may assist in the diagnostic workflow. The purpose of this review article is to summarise the most recent evidence for AI techniques using imaging for differentiating benign from malignant lesions, the characterization of various malignant bone lesions, and their potential clinical application. A systematic search through electronic databases (PubMed, MEDLINE, Web of Science, and clinicaltrials.gov) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 34 articles were retrieved from the databases and the key findings were compiled and summarised. A total of 34 articles reported the use of AI techniques to distinguish between benign vs. malignant bone lesions, of which 12 (35.3%) focused on radiographs, 12 (35.3%) on MRI, 5 (14.7%) on CT and 5 (14.7%) on PET/CT. The overall reported accuracy, sensitivity, and specificity of AI in distinguishing between benign vs. malignant bone lesions ranges from 0.44–0.99, 0.63–1.00, and 0.73–0.96, respectively, with AUCs of 0.73–0.96. In conclusion, the use of AI to discriminate bone lesions on imaging has achieved a relatively good performance in various imaging modalities, with high sensitivity, specificity, and accuracy for distinguishing between benign vs. malignant lesions in several cohort studies. However, further research is necessary to test the clinical performance of these algorithms before they can be facilitated and integrated into routine clinical practice.
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Affiliation(s)
- Wilson Ong
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
- Correspondence: ; Tel.: +65-67725207
| | - Lei Zhu
- Department of Computer Science, School of Computing, National University of Singapore, 13 Computing Drive, Singapore 117417, Singapore
| | - Yi Liang Tan
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
| | - Ee Chin Teo
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
| | - Jiong Hao Tan
- University Spine Centre, Department of Orthopaedic Surgery, National University Health System, 1E, Lower Kent Ridge Road, Singapore 119228, Singapore
| | - Naresh Kumar
- University Spine Centre, Department of Orthopaedic Surgery, National University Health System, 1E, Lower Kent Ridge Road, Singapore 119228, Singapore
| | - Balamurugan A. Vellayappan
- Department of Radiation Oncology, National University Cancer Institute Singapore, National University Hospital, 5 Lower Kent Ridge Road, Singapore 119074, Singapore
| | - Beng Chin Ooi
- Department of Computer Science, School of Computing, National University of Singapore, 13 Computing Drive, Singapore 117417, Singapore
| | - Swee Tian Quek
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Andrew Makmur
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - James Thomas Patrick Decourcy Hallinan
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
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8
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Teo YN, Sia CH, Tan BYQ, Mingxue J, Chan B, Sharma VK, Makmur A, Gopinathan A, Yang C, Loh S, Ng S, Ong SJ, Teoh HL, Rathakrishnan R, Andersson T, Arnberg F, Gontu VK, Lee TH, Maus V, Meyer L, Bhogal P, Spooner O, Li TY, Soh RY, Yeo LL. Combined balloon guide catheter, aspiration catheter, and stent retriever technique versus balloon guide catheter and stent retriever alone technique: a systematic review and meta-analysis. J Neurointerv Surg 2023; 15:127-132. [PMID: 35101960 DOI: 10.1136/neurintsurg-2021-018406] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 12/31/2021] [Indexed: 01/14/2023]
Abstract
BACKGROUND The use of a combination of balloon guide catheter (BGC), aspiration catheter, and stent retriever in acute ischemic stroke thrombectomy has not been shown to be better than a stent retriever and BGC alone, but this may be due to a lack of power in these studies. We therefore performed a meta-analysis on this subject. METHODS A systematic literature search was performed on PubMed, Scopus, Embase/Ovid, and the Cochrane Library from inception to October 20, 2021. Our primary outcomes were the rate of successful final reperfusion (Treatment in Cerebral Ischemia (TICI) 2c-3) and first pass effect (FPE, defined as TICI 2c-3 in a single pass). Secondary outcomes were 3 month functional independence (modified Rankin Scale score of 0-2), mortality, procedural complications, embolic complications, and symptomatic intracranial hemorrhage (SICH). A meta-analysis was performed using RevMan 5,4, and heterogeneity was assessed using the I2 test. RESULTS Of 1629 studies identified, five articles with 2091 patients were included. For the primary outcomes, FPE (44.9% vs 45.4%, OR 1.04 (95% CI 0.90 to 1.22), I2=57%) or final successful reperfusion (64.5% vs 68.6%, OR 0.98 (95% CI 0.81% to 1.20%), I2=85%) was similar between the combination technique and stent retriever only groups. However, the combination technique had significantly less rescue treatment (18.8% vs 26.9%; OR 0.70 (95% CI 0.54 to 0.91), I2=0%). This did not translate into significant differences in secondary outcomes in functional outcomes, mortality, emboli, complications, or SICH. CONCLUSION There was no significant difference in successful reperfusion and FPE between the combined techniques and the stent retriever and BGC alone groups. Neither was there any difference in functional outcomes, complications, or mortality.
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Affiliation(s)
| | - Ching-Hui Sia
- National University of Singapore, Singapore.,Department of Cardiology, National University Heart Centre, Singapore
| | - Benjamin Y Q Tan
- National University of Singapore, Singapore .,National University Health System, Singapore
| | - Jing Mingxue
- National University of Singapore, Singapore.,National University Health System, Singapore
| | - Bernard Chan
- National University of Singapore, Singapore.,National University Health System, Singapore
| | - Vijay Kumar Sharma
- National University of Singapore, Singapore.,National University Health System, Singapore
| | - Andrew Makmur
- National University of Singapore, Singapore.,Diagnostic Imaging, National University Hospital, Singapore
| | - Anil Gopinathan
- National University of Singapore, Singapore.,National University Health System, Singapore
| | - Cunli Yang
- National University of Singapore, Singapore.,National University Health System, Singapore
| | - Stanley Loh
- National University of Singapore, Singapore.,Department of Diagnostic Imaging, National University Health System, Singapore
| | - Sheldon Ng
- National University of Singapore, Singapore.,Diagnostic Imaging, National University Health System, Singapore
| | - Shao Jin Ong
- National University of Singapore, Singapore.,Department of Diagnostic Imaging, National University Health System, Singapore
| | - Hock-Luen Teoh
- National University of Singapore, Singapore.,National University Health System, Singapore
| | - Rahul Rathakrishnan
- National University of Singapore, Singapore.,National University Health System, Singapore
| | - Tommy Andersson
- Departments of Radiology and Neurology, AZ Groeninge, Kortrijk, Belgium.,Department of Neuroradiology and Department of Clinical Neuroscience, Karolinska University Hospital and Karolinska Institutet, Stockholm, Sweden
| | | | | | - Tsong-Hai Lee
- Stroke Center and Department of Neurology, Chang Gung Memorial Hospital Linkou Branch, Gueishan, Taoyuan, Taiwan
| | - Volker Maus
- Knappschaftskrankenhaus Bochum Langendeer, Bochum, Germany
| | - Lukas Meyer
- Diagnostic and Interventional Neuroradiology, Universitatsklinikum Hamburg Eppendorf Klinik und Poliklinik fur Neuroradiologische Diagnostik und Intervention, Hamburg, Germany
| | | | - Oliver Spooner
- Department of Interventional Neuroradiology, Royal London Hospital, London, London, UK
| | - Tony Yw Li
- National University of Singapore, Singapore.,Department of Cardiology, National University Heart Centre, Singapore
| | - Rodney Yh Soh
- National University of Singapore, Singapore.,Department of Cardiology, National University Heart Centre, Singapore
| | - Leonard Ll Yeo
- National University of Singapore, Singapore.,National University Health System, Singapore
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Hallinan JTPD, Zhu L, Zhang W, Ge S, Muhamat Nor FE, Ong HY, Eide SE, Cheng AJL, Kuah T, Lim DSW, Low XZ, Yeong KY, AlMuhaish MI, Alsooreti A, Kumarakulasinghe NB, Teo EC, Yap QV, Chan YH, Lin S, Tan JH, Kumar N, Vellayappan BA, Ooi BC, Quek ST, Makmur A. Deep learning assessment compared to radiologist reporting for metastatic spinal cord compression on CT. Front Oncol 2023; 13:1151073. [PMID: 37213273 PMCID: PMC10193838 DOI: 10.3389/fonc.2023.1151073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 03/16/2023] [Indexed: 05/23/2023] Open
Abstract
Introduction Metastatic spinal cord compression (MSCC) is a disastrous complication of advanced malignancy. A deep learning (DL) algorithm for MSCC classification on CT could expedite timely diagnosis. In this study, we externally test a DL algorithm for MSCC classification on CT and compare with radiologist assessment. Methods Retrospective collection of CT and corresponding MRI from patients with suspected MSCC was conducted from September 2007 to September 2020. Exclusion criteria were scans with instrumentation, no intravenous contrast, motion artefacts and non-thoracic coverage. Internal CT dataset split was 84% for training/validation and 16% for testing. An external test set was also utilised. Internal training/validation sets were labelled by radiologists with spine imaging specialization (6 and 11-years post-board certification) and were used to further develop a DL algorithm for MSCC classification. The spine imaging specialist (11-years expertise) labelled the test sets (reference standard). For evaluation of DL algorithm performance, internal and external test data were independently reviewed by four radiologists: two spine specialists (Rad1 and Rad2, 7 and 5-years post-board certification, respectively) and two oncological imaging specialists (Rad3 and Rad4, 3 and 5-years post-board certification, respectively). DL model performance was also compared against the CT report issued by the radiologist in a real clinical setting. Inter-rater agreement (Gwet's kappa) and sensitivity/specificity/AUCs were calculated. Results Overall, 420 CT scans were evaluated (225 patients, mean age=60 ± 11.9[SD]); 354(84%) CTs for training/validation and 66(16%) CTs for internal testing. The DL algorithm showed high inter-rater agreement for three-class MSCC grading with kappas of 0.872 (p<0.001) and 0.844 (p<0.001) on internal and external testing, respectively. On internal testing DL algorithm inter-rater agreement (κ=0.872) was superior to Rad 2 (κ=0.795) and Rad 3 (κ=0.724) (both p<0.001). DL algorithm kappa of 0.844 on external testing was superior to Rad 3 (κ=0.721) (p<0.001). CT report classification of high-grade MSCC disease was poor with only slight inter-rater agreement (κ=0.027) and low sensitivity (44.0), relative to the DL algorithm with almost-perfect inter-rater agreement (κ=0.813) and high sensitivity (94.0) (p<0.001). Conclusion Deep learning algorithm for metastatic spinal cord compression on CT showed superior performance to the CT report issued by experienced radiologists and could aid earlier diagnosis.
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Affiliation(s)
- James Thomas Patrick Decourcy Hallinan
- Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- *Correspondence: James Thomas Patrick Decourcy Hallinan,
| | - Lei Zhu
- Department of Computer Science, School of Computing, National University of Singapore, Singapore, Singapore
| | - Wenqiao Zhang
- Department of Computer Science, School of Computing, National University of Singapore, Singapore, Singapore
| | - Shuliang Ge
- Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore
| | - Faimee Erwan Muhamat Nor
- Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Han Yang Ong
- Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Sterling Ellis Eide
- Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Amanda J. L. Cheng
- Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Tricia Kuah
- Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore
| | - Desmond Shi Wei Lim
- Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore
| | - Xi Zhen Low
- Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore
| | - Kuan Yuen Yeong
- Department of Radiology, Ng Teng Fong General Hospital, Singapore, Singapore
| | - Mona I. AlMuhaish
- Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore
- Department of Radiology, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - Ahmed Mohamed Alsooreti
- Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore
- Department of Diagnostic Imaging, Salmaniya Medical Complex, Manama, Bahrain
| | | | - Ee Chin Teo
- Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore
| | - Qai Ven Yap
- Biostatistics Unit, Yong Loo Lin School of Medicine, Singapore, Singapore
| | - Yiong Huak Chan
- Biostatistics Unit, Yong Loo Lin School of Medicine, Singapore, Singapore
| | - Shuxun Lin
- Division of Spine Surgery, Department of Orthopaedic Surgery, Ng Teng Fong General Hospital, Singapore, Singapore
| | - Jiong Hao Tan
- University Spine Centre, Department of Orthopaedic Surgery, National University Health System, Singapore, Singapore
| | - Naresh Kumar
- University Spine Centre, Department of Orthopaedic Surgery, National University Health System, Singapore, Singapore
| | - Balamurugan A. Vellayappan
- Department of Radiation Oncology, National University Cancer Institute Singapore, National University Hospital, Singapore, Singapore
| | - Beng Chin Ooi
- Department of Computer Science, School of Computing, National University of Singapore, Singapore, Singapore
| | - Swee Tian Quek
- Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Andrew Makmur
- Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
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10
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Lim MJR, Zheng Y, Babla Singbal S, Makmur A, Yeo TT, Kumar N. Clinical and radiological characteristics of spinal epidural lipomatosis: A retrospective review of 90 consecutive patients. J Clin Orthop Trauma 2022; 32:101988. [PMID: 36035782 PMCID: PMC9413947 DOI: 10.1016/j.jcot.2022.101988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Revised: 06/07/2022] [Accepted: 08/09/2022] [Indexed: 11/18/2022] Open
Abstract
Background The epidemiology and clinical characteristics of spinal epidural lipomatosis (SEL) have been well-reported in the literature. However, few studies investigated the concomitant spinal pathologies that were present in patients with SEL. Therefore, we aimed to summarize the clinical and radiological characteristics of patients with SEL diagnosed on spinal imaging. Methods Patients who were diagnosed with SEL on magnetic resonance imaging from January 2018 to October 2020 at our institution were included in the study. Clinical data was collected using a standardized data collection form. SEL was graded using a modified version of the Borré grading system. Factors associated with moderate or severe SEL were determined using multiple logistic regression. Results A total of 90 patients were included in the analysis. The mean (±SD) age was 59.3 (±17.1) years, and 62 patients (68.9%) were male. 61 patients (67.8%) had moderate or severe SEL. Most patients were overweight or obese (57 patients, 63.3%). The most common presenting symptoms was back pain (57 patients, 63.3%). SEL was diagnosed incidentally in 42 patients (46.7%). The lumbar spine was the most common site of SEL (35 patients, 38.9%). The most common concomitant spinal pathologies were disc bulge (83 patients, 92.2%) and flavum hypertrophy (60 patients, 66.7%). Moderate or severe SEL was associated with WHO Obesity Class, back pain or radicular leg pain at first presentation, and SEL that was worst at the lumbar or lumbosacral spinal level. Conclusions Moderate or severe SEL were independently associated with WHO Obesity Class, back pain, radicular leg pain, and SEL that was worst at the lumbar or lumbosacral spinal level. Future studies should prospectively evaluate whether weight loss therapy is warranted in patients with SEL.
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Affiliation(s)
| | - Yilong Zheng
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Salil Babla Singbal
- Department of Diagnostic Imaging, National University Health System, Singapore
| | - Andrew Makmur
- Department of Diagnostic Imaging, National University Health System, Singapore
| | - Tseng Tsai Yeo
- Division of Neurosurgery, National University Health System, Singapore
| | - Naresh Kumar
- Department of Orthopedic Surgery, National University Health System, Singapore
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11
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Hallinan JTPD, Ge S, Zhu L, Zhang W, Lim YT, Thian YL, Jagmohan P, Kuah T, Lim DSW, Low XZ, Teo EC, Barr Kumarakulasinghe N, Yap QV, Chan YH, Tan JH, Kumar N, Vellayappan BA, Ooi BC, Quek ST, Makmur A. Diagnostic Accuracy of CT for Metastatic Epidural Spinal Cord Compression. Cancers (Basel) 2022; 14:cancers14174231. [PMID: 36077767 PMCID: PMC9454807 DOI: 10.3390/cancers14174231] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 08/23/2022] [Accepted: 08/25/2022] [Indexed: 11/16/2022] Open
Abstract
Background: Early diagnosis of metastatic epidural spinal cord compression (MESCC) is vital to expedite therapy and prevent paralysis. Staging CT is performed routinely in cancer patients and presents an opportunity for earlier diagnosis. Methods: This retrospective study included 123 CT scans from 101 patients who underwent spine MRI within 30 days, excluding 549 CT scans from 216 patients due to CT performed post-MRI, non-contrast CT, or a gap greater than 30 days between modalities. Reference standard MESCC gradings on CT were provided in consensus via two spine radiologists (11 and 7 years of experience) analyzing the MRI scans. CT scans were labeled using the original reports and by three radiologists (3, 13, and 14 years of experience) using dedicated CT windowing. Results: For normal/none versus low/high-grade MESCC per CT scan, all radiologists demonstrated almost perfect agreement with kappa values ranging from 0.866 (95% CI 0.787–0.945) to 0.947 (95% CI 0.899–0.995), compared to slight agreement for the reports (kappa = 0.095, 95%CI −0.098–0.287). Radiologists also showed high sensitivities ranging from 91.51 (95% CI 84.49–96.04) to 98.11 (95% CI 93.35–99.77), compared to 44.34 (95% CI 34.69–54.31) for the reports. Conclusion: Dedicated radiologist review for MESCC on CT showed high interobserver agreement and sensitivity compared to the current standard of care.
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Affiliation(s)
- James Thomas Patrick Decourcy Hallinan
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
- Correspondence:
| | - Shuliang Ge
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Lei Zhu
- Department of Computer Science, School of Computing, National University of Singapore, 13 Computing Drive, Singapore 117417, Singapore
| | - Wenqiao Zhang
- Department of Computer Science, School of Computing, National University of Singapore, 13 Computing Drive, Singapore 117417, Singapore
| | - Yi Ting Lim
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Yee Liang Thian
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Pooja Jagmohan
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Tricia Kuah
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
| | - Desmond Shi Wei Lim
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
| | - Xi Zhen Low
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
| | - Ee Chin Teo
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
| | - Nesaretnam Barr Kumarakulasinghe
- National University Cancer Institute, NUH Medical Centre (NUHMC), Levels 8–10, 5 Lower Kent Ridge Road, Singapore 119074, Singapore
| | - Qai Ven Yap
- Biostatistics Unit, Yong Loo Lin School of Medicine, 10 Medical Drive, Singapore 117597, Singapore
| | - Yiong Huak Chan
- Biostatistics Unit, Yong Loo Lin School of Medicine, 10 Medical Drive, Singapore 117597, Singapore
| | - Jiong Hao Tan
- University Spine Centre, Department of Orthopaedic Surgery, National University Health System, 1E, Lower Kent Ridge Road, Singapore 119228, Singapore
| | - Naresh Kumar
- University Spine Centre, Department of Orthopaedic Surgery, National University Health System, 1E, Lower Kent Ridge Road, Singapore 119228, Singapore
| | - Balamurugan A. Vellayappan
- Department of Radiation Oncology, National University Cancer Institute Singapore, National University Hospital, Singapore 119074, Singapore
| | - Beng Chin Ooi
- Department of Computer Science, School of Computing, National University of Singapore, 13 Computing Drive, Singapore 117417, Singapore
| | - Swee Tian Quek
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Andrew Makmur
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
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12
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Ong W, Zhu L, Zhang W, Kuah T, Lim DSW, Low XZ, Thian YL, Teo EC, Tan JH, Kumar N, Vellayappan BA, Ooi BC, Quek ST, Makmur A, Hallinan JTPD. Application of Artificial Intelligence Methods for Imaging of Spinal Metastasis. Cancers (Basel) 2022; 14:4025. [PMID: 36011018 PMCID: PMC9406500 DOI: 10.3390/cancers14164025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 08/10/2022] [Accepted: 08/15/2022] [Indexed: 11/16/2022] Open
Abstract
Spinal metastasis is the most common malignant disease of the spine. Recently, major advances in machine learning and artificial intelligence technology have led to their increased use in oncological imaging. The purpose of this study is to review and summarise the present evidence for artificial intelligence applications in the detection, classification and management of spinal metastasis, along with their potential integration into clinical practice. A systematic, detailed search of the main electronic medical databases was undertaken in concordance with the PRISMA guidelines. A total of 30 articles were retrieved from the database and reviewed. Key findings of current AI applications were compiled and summarised. The main clinical applications of AI techniques include image processing, diagnosis, decision support, treatment assistance and prognostic outcomes. In the realm of spinal oncology, artificial intelligence technologies have achieved relatively good performance and hold immense potential to aid clinicians, including enhancing work efficiency and reducing adverse events. Further research is required to validate the clinical performance of the AI tools and facilitate their integration into routine clinical practice.
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Affiliation(s)
- Wilson Ong
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd., Singapore 119074, Singapore
| | - Lei Zhu
- Department of Computer Science, School of Computing, National University of Singapore, 13 Computing Drive, Singapore 117417, Singapore
| | - Wenqiao Zhang
- Department of Computer Science, School of Computing, National University of Singapore, 13 Computing Drive, Singapore 117417, Singapore
| | - Tricia Kuah
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd., Singapore 119074, Singapore
| | - Desmond Shi Wei Lim
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd., Singapore 119074, Singapore
| | - Xi Zhen Low
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd., Singapore 119074, Singapore
| | - Yee Liang Thian
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd., Singapore 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Ee Chin Teo
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd., Singapore 119074, Singapore
| | - Jiong Hao Tan
- University Spine Centre, Department of Orthopaedic Surgery, National University Health System, 1E, Lower Kent Ridge Road, Singapore 119228, Singapore
| | - Naresh Kumar
- University Spine Centre, Department of Orthopaedic Surgery, National University Health System, 1E, Lower Kent Ridge Road, Singapore 119228, Singapore
| | - Balamurugan A. Vellayappan
- Department of Radiation Oncology, National University Cancer Institute Singapore, National University Hospital, Singapore 119074, Singapore
| | - Beng Chin Ooi
- Department of Computer Science, School of Computing, National University of Singapore, 13 Computing Drive, Singapore 117417, Singapore
| | - Swee Tian Quek
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd., Singapore 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Andrew Makmur
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd., Singapore 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - James Thomas Patrick Decourcy Hallinan
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd., Singapore 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
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Kuah T, Vellayappan BA, Makmur A, Nair S, Song J, Tan JH, Kumar N, Quek ST, Hallinan JTPD. State-of-the-Art Imaging Techniques in Metastatic Spinal Cord Compression. Cancers (Basel) 2022; 14:cancers14133289. [PMID: 35805059 PMCID: PMC9265325 DOI: 10.3390/cancers14133289] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 06/24/2022] [Accepted: 06/28/2022] [Indexed: 12/23/2022] Open
Abstract
Metastatic Spinal Cord Compression (MSCC) is a debilitating complication in oncology patients. This narrative review discusses the strengths and limitations of various imaging modalities in diagnosing MSCC, the role of imaging in stereotactic body radiotherapy (SBRT) for MSCC treatment, and recent advances in deep learning (DL) tools for MSCC diagnosis. PubMed and Google Scholar databases were searched using targeted keywords. Studies were reviewed in consensus among the co-authors for their suitability before inclusion. MRI is the gold standard of imaging to diagnose MSCC with reported sensitivity and specificity of 93% and 97% respectively. CT Myelogram appears to have comparable sensitivity and specificity to contrast-enhanced MRI. Conventional CT has a lower diagnostic accuracy than MRI in MSCC diagnosis, but is helpful in emergent situations with limited access to MRI. Metal artifact reduction techniques for MRI and CT are continually being researched for patients with spinal implants. Imaging is crucial for SBRT treatment planning and three-dimensional positional verification of the treatment isocentre prior to SBRT delivery. Structural and functional MRI may be helpful in post-treatment surveillance. DL tools may improve detection of vertebral metastasis and reduce time to MSCC diagnosis. This enables earlier institution of definitive therapy for better outcomes.
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Affiliation(s)
- Tricia Kuah
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore; (A.M.); (S.N.); (J.S.); (S.T.Q.); (J.T.P.D.H.)
- Correspondence: ; Tel.: +65-6779-5555
| | - Balamurugan A. Vellayappan
- Department of Radiation Oncology, National University Cancer Institute Singapore, National University Hospital, Singapore 119074, Singapore;
| | - Andrew Makmur
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore; (A.M.); (S.N.); (J.S.); (S.T.Q.); (J.T.P.D.H.)
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Shalini Nair
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore; (A.M.); (S.N.); (J.S.); (S.T.Q.); (J.T.P.D.H.)
| | - Junda Song
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore; (A.M.); (S.N.); (J.S.); (S.T.Q.); (J.T.P.D.H.)
| | - Jiong Hao Tan
- University Spine Centre, Department of Orthopaedic Surgery, National University Health System, 1E Lower Kent Ridge Road, Singapore 119228, Singapore; (J.H.T.); (N.K.)
| | - Naresh Kumar
- University Spine Centre, Department of Orthopaedic Surgery, National University Health System, 1E Lower Kent Ridge Road, Singapore 119228, Singapore; (J.H.T.); (N.K.)
| | - Swee Tian Quek
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore; (A.M.); (S.N.); (J.S.); (S.T.Q.); (J.T.P.D.H.)
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - James Thomas Patrick Decourcy Hallinan
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore; (A.M.); (S.N.); (J.S.); (S.T.Q.); (J.T.P.D.H.)
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
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Lim DSW, Makmur A, Zhu L, Zhang W, Cheng AJL, Sia DSY, Eide SE, Ong HY, Jagmohan P, Tan WC, Khoo VM, Wong YM, Thian YL, Baskar S, Teo EC, Algazwi DAR, Yap QV, Chan YH, Tan JH, Kumar N, Ooi BC, Yoshioka H, Quek ST, Hallinan JTPD. Improved Productivity Using Deep Learning-assisted Reporting for Lumbar Spine MRI. Radiology 2022; 305:160-166. [PMID: 35699577 DOI: 10.1148/radiol.220076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Background Lumbar spine MRI studies are widely used for back pain assessment. Interpretation involves grading lumbar spinal stenosis, which is repetitive and time consuming. Deep learning (DL) could provide faster and more consistent interpretation. Purpose To assess the speed and interobserver agreement of radiologists for reporting lumbar spinal stenosis with and without DL assistance. Materials and Methods In this retrospective study, a DL model designed to assist radiologists in the interpretation of spinal canal, lateral recess, and neural foraminal stenoses on lumbar spine MRI scans was used. Randomly selected lumbar spine MRI studies obtained in patients with back pain who were 18 years and older over a 3-year period, from September 2015 to September 2018, were included in an internal test data set. Studies with instrumentation and scoliosis were excluded. Eight radiologists, each with 2-13 years of experience in spine MRI interpretation, reviewed studies with and without DL model assistance with a 1-month washout period. Time to diagnosis (in seconds) and interobserver agreement (using Gwet κ) were assessed for stenosis grading for each radiologist with and without the DL model and compared with test data set labels provided by an external musculoskeletal radiologist (with 32 years of experience) as the reference standard. Results Overall, 444 images in 25 patients (mean age, 51 years ± 20 [SD]; 14 women) were evaluated in a test data set. DL-assisted radiologists had a reduced interpretation time per spine MRI study, from a mean of 124-274 seconds (SD, 25-88 seconds) to 47-71 seconds (SD, 24-29 seconds) (P < .001). DL-assisted radiologists had either superior or equivalent interobserver agreement for all stenosis gradings compared with unassisted radiologists. DL-assisted general and in-training radiologists improved their interobserver agreement for four-class neural foraminal stenosis, with κ values of 0.71 and 0.70 (with DL) versus 0.39 and 0.39 (without DL), respectively (both P < .001). Conclusion Radiologists who were assisted by deep learning for interpretation of lumbar spinal stenosis on MRI scans showed a marked reduction in reporting time and superior or equivalent interobserver agreement for all stenosis gradings compared with radiologists who were unassisted by deep learning. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Hayashi in this issue.
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Affiliation(s)
- Desmond Shi Wei Lim
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Andrew Makmur
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Lei Zhu
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Wenqiao Zhang
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Amanda J L Cheng
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - David Soon Yiew Sia
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Sterling Ellis Eide
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Han Yang Ong
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Pooja Jagmohan
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Wei Chuan Tan
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Vanessa Meihui Khoo
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Ying Mei Wong
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Yee Liang Thian
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Sangeetha Baskar
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Ee Chin Teo
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Diyaa Abdul Rauf Algazwi
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Qai Ven Yap
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Yiong Huak Chan
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Jiong Hao Tan
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Naresh Kumar
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Beng Chin Ooi
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Hiroshi Yoshioka
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Swee Tian Quek
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - James Thomas Patrick Decourcy Hallinan
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
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Dashraath P, Sidek NA, Kalaichelvan V, Makmur A, Lim DGS, Low JJH, Ng JSY. Malignant perivascular epithelioid cell tumor (PEComa) of uterus. Ultrasound Obstet Gynecol 2022; 59:826-828. [PMID: 34605089 DOI: 10.1002/uog.24794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 09/09/2021] [Accepted: 09/24/2021] [Indexed: 06/13/2023]
Affiliation(s)
- P Dashraath
- Department of Obstetrics and Gynecology, National University Hospital, Singapore
| | - N A Sidek
- Department of Obstetrics and Gynecology, National University Hospital, Singapore
| | - V Kalaichelvan
- Department of Obstetrics and Gynecology, National University Hospital, Singapore
| | - A Makmur
- Department of Diagnostic Imaging, National University Hospital, Singapore
| | - D G S Lim
- Department of Pathology, National University Hospital, Singapore
| | - J J H Low
- Department of Obstetrics and Gynecology, National University Hospital, Singapore
| | - J S Y Ng
- Department of Obstetrics and Gynecology, National University Hospital, Singapore
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16
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Hallinan JTPD, Zhu L, Zhang W, Lim DSW, Baskar S, Low XZ, Yeong KY, Teo EC, Kumarakulasinghe NB, Yap QV, Chan YH, Lin S, Tan JH, Kumar N, Vellayappan BA, Ooi BC, Quek ST, Makmur A. Deep Learning Model for Classifying Metastatic Epidural Spinal Cord Compression on MRI. Front Oncol 2022; 12:849447. [PMID: 35600347 PMCID: PMC9114468 DOI: 10.3389/fonc.2022.849447] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 03/18/2022] [Indexed: 11/13/2022] Open
Abstract
Background Metastatic epidural spinal cord compression (MESCC) is a devastating complication of advanced cancer. A deep learning (DL) model for automated MESCC classification on MRI could aid earlier diagnosis and referral. Purpose To develop a DL model for automated classification of MESCC on MRI. Materials and Methods Patients with known MESCC diagnosed on MRI between September 2007 and September 2017 were eligible. MRI studies with instrumentation, suboptimal image quality, and non-thoracic regions were excluded. Axial T2-weighted images were utilized. The internal dataset split was 82% and 18% for training/validation and test sets, respectively. External testing was also performed. Internal training/validation data were labeled using the Bilsky MESCC classification by a musculoskeletal radiologist (10-year experience) and a neuroradiologist (5-year experience). These labels were used to train a DL model utilizing a prototypical convolutional neural network. Internal and external test sets were labeled by the musculoskeletal radiologist as the reference standard. For assessment of DL model performance and interobserver variability, test sets were labeled independently by the neuroradiologist (5-year experience), a spine surgeon (5-year experience), and a radiation oncologist (11-year experience). Inter-rater agreement (Gwet’s kappa) and sensitivity/specificity were calculated. Results Overall, 215 MRI spine studies were analyzed [164 patients, mean age = 62 ± 12(SD)] with 177 (82%) for training/validation and 38 (18%) for internal testing. For internal testing, the DL model and specialists all showed almost perfect agreement (kappas = 0.92–0.98, p < 0.001) for dichotomous Bilsky classification (low versus high grade) compared to the reference standard. Similar performance was seen for external testing on a set of 32 MRI spines with the DL model and specialists all showing almost perfect agreement (kappas = 0.94–0.95, p < 0.001) compared to the reference standard. Conclusion A DL model showed comparable agreement to a subspecialist radiologist and clinical specialists for the classification of malignant epidural spinal cord compression and could optimize earlier diagnosis and surgical referral.
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Affiliation(s)
- James Thomas Patrick Decourcy Hallinan
- Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore.,Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Lei Zhu
- NUS Graduate School, Integrative Sciences and Engineering Programme, National University of Singapore, Singapore, Singapore
| | - Wenqiao Zhang
- Department of Computer Science, School of Computing, National University of Singapore, Singapore, Singapore
| | - Desmond Shi Wei Lim
- Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore.,Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Sangeetha Baskar
- Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore
| | - Xi Zhen Low
- Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore.,Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Kuan Yuen Yeong
- Department of Radiology, Ng Teng Fong General Hospital, Singapore, Singapore
| | - Ee Chin Teo
- Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore
| | | | - Qai Ven Yap
- Biostatistics Unit, Yong Loo Lin School of Medicine, Singapore, Singapore
| | - Yiong Huak Chan
- Biostatistics Unit, Yong Loo Lin School of Medicine, Singapore, Singapore
| | - Shuxun Lin
- Division of Spine Surgery, Department of Orthopaedic Surgery, Ng Teng Fong General Hospital, Singapore, Singapore
| | - Jiong Hao Tan
- University Spine Centre, Department of Orthopaedic Surgery, National University Health System, Singapore, Singapore
| | - Naresh Kumar
- University Spine Centre, Department of Orthopaedic Surgery, National University Health System, Singapore, Singapore
| | - Balamurugan A Vellayappan
- Department of Radiation Oncology, National University Cancer Institute Singapore, National University Hospital, Singapore, Singapore
| | - Beng Chin Ooi
- Department of Computer Science, School of Computing, National University of Singapore, Singapore, Singapore
| | - Swee Tian Quek
- Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore.,Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Andrew Makmur
- Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore.,Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
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17
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Koh JS, Goh Y, Tan BYQ, Hui ACF, Hoe RHM, Makmur A, Kei PL, Vijayan J, Ng KWP, Quek AML, Thirugnanm U. Neuralgic amyotrophy following COVID-19 mRNA vaccination. QJM 2021; 114:503-505. [PMID: 34347105 DOI: 10.1093/qjmed/hcab216] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Indexed: 11/13/2022] Open
Affiliation(s)
- J S Koh
- From the Department of Neurology, National Neuroscience Institute (Tan Tock Seng Hospital Campus), 11 Jalan Tan Tock Seng, Singapore 308433 Singapore
| | - Y Goh
- Division of Neurology, Department of Medicine, National University Health System, 1E Kent Ridge Road, Singapore 119228
| | - B Y-Q Tan
- Division of Neurology, Department of Medicine, National University Health System, 1E Kent Ridge Road, Singapore 119228
| | - A C-F Hui
- Division of Neurology, Department of Medicine, Ng Teng Fong General Hospital: 1 Jurong East Street 21, Singapore 609606
| | - R H M Hoe
- From the Department of Neurology, National Neuroscience Institute (Tan Tock Seng Hospital Campus), 11 Jalan Tan Tock Seng, Singapore 308433 Singapore
| | - A Makmur
- Department of Diagnostic Imaging, National University Health System, 1E Kent Ridge Road, Singapore 119228
| | - P L Kei
- Department of Radiology, Ng Teng Fong General Hospital: 1 Jurong East Street 21, Singapore 609606
| | - J Vijayan
- Division of Neurology, Department of Medicine, National University Health System, 1E Kent Ridge Road, Singapore 119228
| | - K W P Ng
- Division of Neurology, Department of Medicine, National University Health System, 1E Kent Ridge Road, Singapore 119228
| | - A M L Quek
- Division of Neurology, Department of Medicine, National University Health System, 1E Kent Ridge Road, Singapore 119228
| | - U Thirugnanm
- Division of Neurology, Department of Medicine, National University Health System, 1E Kent Ridge Road, Singapore 119228
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18
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Hallinan JTPD, Feng M, Ng D, Sia SY, Tiong VTY, Jagmohan P, Makmur A, Thian YL. Detection of Pneumothorax with Deep Learning Models: Learning From Radiologist Labels vs Natural Language Processing Model Generated Labels. Acad Radiol 2021; 29:1350-1358. [PMID: 34649780 DOI: 10.1016/j.acra.2021.09.013] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Revised: 08/25/2021] [Accepted: 09/05/2021] [Indexed: 11/19/2022]
Abstract
RATIONALE AND OBJECTIVES To compare the performance of pneumothorax deep learning detection models trained with radiologist versus natural language processing (NLP) labels on the NIH ChestX-ray14 dataset. MATERIALS AND METHODS The ChestX-ray14 dataset consisted of 112,120 frontal chest radiographs with 5302 positive and 106, 818 negative labels for pneumothorax using NLP (dataset A). All 112,120 radiographs were also inspected by 4 radiologists leaving a visually confirmed set of 5,138 positive and 104,751 negative for pneumothorax (dataset B). Datasets A and B were used independently to train 3 convolutional neural network (CNN) architectures (ResNet-50, DenseNet-121 and EfficientNetB3). All models' area under the receiver operating characteristic curve (AUC) were evaluated with the official NIH test set and an external test set of 525 chest radiographs from our emergency department. RESULTS There were significantly higher AUCs on the NIH internal test set for CNN models trained with radiologist vs NLP labels across all architectures. AUCs for the NLP/radiologist-label models were 0.838 (95%CI:0.830, 0.846)/0.881 (95%CI:0.873,0.887) for ResNet-50 (p = 0.034), 0.839 (95%CI:0.831,0.847)/0.880 (95%CI:0.873,0.887) for DenseNet-121, and 0.869 (95%CI: 0.863,0.876)/0.943 (95%CI: 0.939,0.946) for EfficientNetB3 (p ≤0.001). Evaluation with the external test set also showed higher AUCs (p <0.001) for the CNN models trained with radiologist versus NLP labels across all architectures. The AUCs for the NLP/radiologist-label models were 0.686 (95%CI:0.632,0.740)/0.806 (95%CI:0.758,0.854) for ResNet-50, 0.736 (95%CI:0.686, 0.787)/0.871 (95%CI:0.830,0.912) for DenseNet-121, and 0.822 (95%CI: 0.775,0.868)/0.915 (95%CI: 0.882,0.948) for EfficientNetB3. CONCLUSION We demonstrated improved performance and generalizability of pneumothorax detection deep learning models trained with radiologist labels compared to models trained with NLP labels.
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Affiliation(s)
| | - Mengling Feng
- Saw Swee Hock School of Public Health, Institute of Data Science, Yong Loo Lin School of Medicine, National University Health System, National University of Singapore, Singapore
| | - Dianwen Ng
- Department of Diagnostic Imaging, National University Hospital, Singapore; Saw Swee Hock School of Public Health, Institute of Data Science, Yong Loo Lin School of Medicine, National University Health System, National University of Singapore, Singapore
| | - Soon Yiew Sia
- Department of Diagnostic Imaging, National University Hospital, Singapore
| | | | - Pooja Jagmohan
- Department of Diagnostic Imaging, National University Hospital, Singapore
| | - Andrew Makmur
- Department of Diagnostic Imaging, National University Hospital, Singapore
| | - Yee Liang Thian
- Department of Diagnostic Imaging, National University Hospital, Singapore
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19
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Myint MZ, Yeo LL, Tan BYQ, The EZ, Lim MC, Sia CH, Teoh HL, Sharma VK, Chan B, Ahmad A, Paliwal P, Gopinathan A, Yang C, Makmur A, Andersson T, Arnberg F, Holmin S. Internal cerebral vein asymmetry is an independent predictor of poor functional outcome in endovascular thrombectomy. J Neurointerv Surg 2021; 14:683-687. [PMID: 34353888 DOI: 10.1136/neurintsurg-2021-017684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 07/26/2021] [Indexed: 11/03/2022]
Abstract
BACKGROUND Endovascular thrombectomy (EVT) in large vessel occlusion (LVO) in anterior circulation acute ischaemic stroke (AIS) results in good functional outcomes in only approximately 60% of the patients. Internal cerebral veins (ICVs) are easily visible, with a consistent midline location, and are linked to stroke outcomes. We hypothesize that ICV asymmetry on multiphasic CT angiogram (mCTA) can be an adjunctive predictor for poor functional outcomes. METHODS We studied consecutive AIS patients from 2017 to 2019 with anterior circulation LVO treated with EVT regardless of intravenous thrombolysis. Asymmetrical ICV was defined as the presence of hypodensity (less opacification) on the ipsilateral occlusion side as compared with the contralateral side. The primary outcome was modified Rankin Score (mRS) score at 3 months. Secondary outcomes were good recanalization (modified Thrombolysis In Cerebral Infarction (mTICI) 2b-3), symptomatic hemorrhage, and mortality. RESULTS A total of 185 patients were included with a median age of 70 years (IQR 59-77); 87 patients (47%) were female. 82 patients (44.3%) achieved good functional outcomes (mRS 0-2) at 3 months. On multivariate analysis, National Institutes of Health Stroke Scale (NIHSS) (OR 1.076, 95% CI 1.015 to 1.140; p<0.013), poor collateral score (OR 0.285, 95% CI 0.162 to 0.501; p<0.001), asymmetrical ICV on the peak venous phase (OR 2.47, 95% CI 1.115 to 5.471; p<0.026), and late venous phase of the mCTA (OR 2.642, 95% CI 1.161 to 6.016; p<0.021) were independent risks factors of poor outcomes. CONCLUSION ICV asymmetry is a novel radiological sign which is independently associated with poor functional outcomes in EVT, even after correction for collateral circulation. Further studies are needed to validate this finding.
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Affiliation(s)
- May Zin Myint
- Division of Neurology, Department of Medicine, National University Health System, Singapore
| | - Leonard Ll Yeo
- Division of Neurology, Department of Medicine, National University Health System, Singapore .,Yong Loo Lin School of Medicine, National University of Singapore, Singapore.,Department of Clinical Neuroscience, Karolinska Institutet and Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden
| | - Benjamin Y Q Tan
- Division of Neurology, Department of Medicine, National University Health System, Singapore.,Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Ei Zune The
- Division of Neurology, Department of Medicine, National University Health System, Singapore
| | - Mei Chin Lim
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore.,Department of Diagnostic Imaging, National University Health System, Singapore
| | - Ching-Hui Sia
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore.,Department of Cardiology, National University Heart Center, Singapore
| | - Hock-Luen Teoh
- Division of Neurology, Department of Medicine, National University Health System, Singapore.,Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Vijay Kumar Sharma
- Division of Neurology, Department of Medicine, National University Health System, Singapore.,Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Bernard Chan
- Division of Neurology, Department of Medicine, National University Health System, Singapore.,Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Aftab Ahmad
- Division of Neurology, Ng Teng Fong General Hospital, Singapore
| | - Prakash Paliwal
- Division of Neurology, Department of Medicine, National University Health System, Singapore.,Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Anil Gopinathan
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore.,Department of Diagnostic Imaging, National University Health System, Singapore
| | - Cunli Yang
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore.,Department of Diagnostic Imaging, National University Health System, Singapore
| | - Andrew Makmur
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore.,Department of Diagnostic Imaging, National University Health System, Singapore
| | - Tommy Andersson
- Department of Clinical Neuroscience, Karolinska Institutet and Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden.,Department of Medical Imaging, AZ Groeninge, Kortrijk, Belgium
| | - Fabian Arnberg
- Department of Clinical Neuroscience, Karolinska Institutet and Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden
| | - Staffan Holmin
- Department of Clinical Neuroscience, Karolinska Institutet and Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden
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20
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Hallinan JTPD, Zhu L, Yang K, Makmur A, Algazwi DAR, Thian YL, Lau S, Choo YS, Eide SE, Yap QV, Chan YH, Tan JH, Kumar N, Ooi BC, Yoshioka H, Quek ST. Deep Learning Model for Automated Detection and Classification of Central Canal, Lateral Recess, and Neural Foraminal Stenosis at Lumbar Spine MRI. Radiology 2021; 300:130-138. [PMID: 33973835 DOI: 10.1148/radiol.2021204289] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Background Assessment of lumbar spinal stenosis at MRI is repetitive and time consuming. Deep learning (DL) could improve -productivity and the consistency of reporting. Purpose To develop a DL model for automated detection and classification of lumbar central canal, lateral recess, and neural -foraminal stenosis. Materials and Methods In this retrospective study, lumbar spine MRI scans obtained from September 2015 to September 2018 were included. Studies of patients with spinal instrumentation or studies with suboptimal image quality, as well as postgadolinium studies and studies of patients with scoliosis, were excluded. Axial T2-weighted and sagittal T1-weighted images were used. Studies were split into an internal training set (80%), validation set (9%), and test set (11%). Training data were labeled by four radiologists using predefined gradings (normal, mild, moderate, and severe). A two-component DL model was developed. First, a convolutional neural network (CNN) was trained to detect the region of interest (ROI), with a second CNN for classification. An internal test set was labeled by a musculoskeletal radiologist with 31 years of experience (reference standard) and two subspecialist radiologists (radiologist 1: A.M., 5 years of experience; radiologist 2: J.T.P.D.H., 9 years of experience). DL model performance on an external test set was evaluated. Detection recall (in percentage), interrater agreement (Gwet κ), sensitivity, and specificity were calculated. Results Overall, 446 MRI lumbar spine studies were analyzed (446 patients; mean age ± standard deviation, 52 years ± 19; 240 women), with 396 patients in the training (80%) and validation (9%) sets and 50 (11%) in the internal test set. For internal testing, DL model and radiologist central canal recall were greater than 99%, with reduced neural foramina recall for the DL model (84.5%) and radiologist 1 (83.9%) compared with radiologist 2 (97.1%) (P < .001). For internal testing, dichotomous classification (normal or mild vs moderate or severe) showed almost-perfect agreement for both radiologists and the DL model, with respective κ values of 0.98, 0.98, and 0.96 for the central canal; 0.92, 0.95, and 0.92 for lateral recesses; and 0.94, 0.95, and 0.89 for neural foramina (P < .001). External testing with 100 MRI scans of lumbar spines showed almost perfect agreement for the DL model for dichotomous classification of all ROIs (κ, 0.95-0.96; P < .001). Conclusion A deep learning model showed comparable agreement with subspecialist radiologists for detection and classification of central canal and lateral recess stenosis, with slightly lower agreement for neural foraminal stenosis at lumbar spine MRI. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Hayashi in this issue.
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Affiliation(s)
- James Thomas Patrick Decourcy Hallinan
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (J.T.P.D.H., A.M., Y.L.T., S.L., Y.S.C., S.E.E., S.T.Q.); Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore (J.T.P.D.H., A.M., Y.L.T., S.L., Y.S.C., S.E.E., S.T.Q.); NUS Graduate School, Integrative Sciences and Engineering Programme, National University of Singapore, Singapore (L.Z.); Department of Computer Science, School of Computing, National University of Singapore, Singapore (K.Y., B.C.O.); Department of Radiology, Dammam Medical Complex, Dammam, Saudi Arabia (D.A.R.A.); Biostatistics Unit, Yong Loo Lin School of Medicine, Singapore (Q.V.Y., Y.H.C.); University Spine Centre, Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Lei Zhu
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (J.T.P.D.H., A.M., Y.L.T., S.L., Y.S.C., S.E.E., S.T.Q.); Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore (J.T.P.D.H., A.M., Y.L.T., S.L., Y.S.C., S.E.E., S.T.Q.); NUS Graduate School, Integrative Sciences and Engineering Programme, National University of Singapore, Singapore (L.Z.); Department of Computer Science, School of Computing, National University of Singapore, Singapore (K.Y., B.C.O.); Department of Radiology, Dammam Medical Complex, Dammam, Saudi Arabia (D.A.R.A.); Biostatistics Unit, Yong Loo Lin School of Medicine, Singapore (Q.V.Y., Y.H.C.); University Spine Centre, Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Kaiyuan Yang
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (J.T.P.D.H., A.M., Y.L.T., S.L., Y.S.C., S.E.E., S.T.Q.); Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore (J.T.P.D.H., A.M., Y.L.T., S.L., Y.S.C., S.E.E., S.T.Q.); NUS Graduate School, Integrative Sciences and Engineering Programme, National University of Singapore, Singapore (L.Z.); Department of Computer Science, School of Computing, National University of Singapore, Singapore (K.Y., B.C.O.); Department of Radiology, Dammam Medical Complex, Dammam, Saudi Arabia (D.A.R.A.); Biostatistics Unit, Yong Loo Lin School of Medicine, Singapore (Q.V.Y., Y.H.C.); University Spine Centre, Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Andrew Makmur
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (J.T.P.D.H., A.M., Y.L.T., S.L., Y.S.C., S.E.E., S.T.Q.); Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore (J.T.P.D.H., A.M., Y.L.T., S.L., Y.S.C., S.E.E., S.T.Q.); NUS Graduate School, Integrative Sciences and Engineering Programme, National University of Singapore, Singapore (L.Z.); Department of Computer Science, School of Computing, National University of Singapore, Singapore (K.Y., B.C.O.); Department of Radiology, Dammam Medical Complex, Dammam, Saudi Arabia (D.A.R.A.); Biostatistics Unit, Yong Loo Lin School of Medicine, Singapore (Q.V.Y., Y.H.C.); University Spine Centre, Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Diyaa Abdul Rauf Algazwi
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (J.T.P.D.H., A.M., Y.L.T., S.L., Y.S.C., S.E.E., S.T.Q.); Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore (J.T.P.D.H., A.M., Y.L.T., S.L., Y.S.C., S.E.E., S.T.Q.); NUS Graduate School, Integrative Sciences and Engineering Programme, National University of Singapore, Singapore (L.Z.); Department of Computer Science, School of Computing, National University of Singapore, Singapore (K.Y., B.C.O.); Department of Radiology, Dammam Medical Complex, Dammam, Saudi Arabia (D.A.R.A.); Biostatistics Unit, Yong Loo Lin School of Medicine, Singapore (Q.V.Y., Y.H.C.); University Spine Centre, Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Yee Liang Thian
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (J.T.P.D.H., A.M., Y.L.T., S.L., Y.S.C., S.E.E., S.T.Q.); Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore (J.T.P.D.H., A.M., Y.L.T., S.L., Y.S.C., S.E.E., S.T.Q.); NUS Graduate School, Integrative Sciences and Engineering Programme, National University of Singapore, Singapore (L.Z.); Department of Computer Science, School of Computing, National University of Singapore, Singapore (K.Y., B.C.O.); Department of Radiology, Dammam Medical Complex, Dammam, Saudi Arabia (D.A.R.A.); Biostatistics Unit, Yong Loo Lin School of Medicine, Singapore (Q.V.Y., Y.H.C.); University Spine Centre, Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Samuel Lau
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (J.T.P.D.H., A.M., Y.L.T., S.L., Y.S.C., S.E.E., S.T.Q.); Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore (J.T.P.D.H., A.M., Y.L.T., S.L., Y.S.C., S.E.E., S.T.Q.); NUS Graduate School, Integrative Sciences and Engineering Programme, National University of Singapore, Singapore (L.Z.); Department of Computer Science, School of Computing, National University of Singapore, Singapore (K.Y., B.C.O.); Department of Radiology, Dammam Medical Complex, Dammam, Saudi Arabia (D.A.R.A.); Biostatistics Unit, Yong Loo Lin School of Medicine, Singapore (Q.V.Y., Y.H.C.); University Spine Centre, Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Yun Song Choo
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (J.T.P.D.H., A.M., Y.L.T., S.L., Y.S.C., S.E.E., S.T.Q.); Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore (J.T.P.D.H., A.M., Y.L.T., S.L., Y.S.C., S.E.E., S.T.Q.); NUS Graduate School, Integrative Sciences and Engineering Programme, National University of Singapore, Singapore (L.Z.); Department of Computer Science, School of Computing, National University of Singapore, Singapore (K.Y., B.C.O.); Department of Radiology, Dammam Medical Complex, Dammam, Saudi Arabia (D.A.R.A.); Biostatistics Unit, Yong Loo Lin School of Medicine, Singapore (Q.V.Y., Y.H.C.); University Spine Centre, Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Sterling Ellis Eide
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (J.T.P.D.H., A.M., Y.L.T., S.L., Y.S.C., S.E.E., S.T.Q.); Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore (J.T.P.D.H., A.M., Y.L.T., S.L., Y.S.C., S.E.E., S.T.Q.); NUS Graduate School, Integrative Sciences and Engineering Programme, National University of Singapore, Singapore (L.Z.); Department of Computer Science, School of Computing, National University of Singapore, Singapore (K.Y., B.C.O.); Department of Radiology, Dammam Medical Complex, Dammam, Saudi Arabia (D.A.R.A.); Biostatistics Unit, Yong Loo Lin School of Medicine, Singapore (Q.V.Y., Y.H.C.); University Spine Centre, Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Qai Ven Yap
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (J.T.P.D.H., A.M., Y.L.T., S.L., Y.S.C., S.E.E., S.T.Q.); Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore (J.T.P.D.H., A.M., Y.L.T., S.L., Y.S.C., S.E.E., S.T.Q.); NUS Graduate School, Integrative Sciences and Engineering Programme, National University of Singapore, Singapore (L.Z.); Department of Computer Science, School of Computing, National University of Singapore, Singapore (K.Y., B.C.O.); Department of Radiology, Dammam Medical Complex, Dammam, Saudi Arabia (D.A.R.A.); Biostatistics Unit, Yong Loo Lin School of Medicine, Singapore (Q.V.Y., Y.H.C.); University Spine Centre, Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Yiong Huak Chan
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (J.T.P.D.H., A.M., Y.L.T., S.L., Y.S.C., S.E.E., S.T.Q.); Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore (J.T.P.D.H., A.M., Y.L.T., S.L., Y.S.C., S.E.E., S.T.Q.); NUS Graduate School, Integrative Sciences and Engineering Programme, National University of Singapore, Singapore (L.Z.); Department of Computer Science, School of Computing, National University of Singapore, Singapore (K.Y., B.C.O.); Department of Radiology, Dammam Medical Complex, Dammam, Saudi Arabia (D.A.R.A.); Biostatistics Unit, Yong Loo Lin School of Medicine, Singapore (Q.V.Y., Y.H.C.); University Spine Centre, Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Jiong Hao Tan
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (J.T.P.D.H., A.M., Y.L.T., S.L., Y.S.C., S.E.E., S.T.Q.); Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore (J.T.P.D.H., A.M., Y.L.T., S.L., Y.S.C., S.E.E., S.T.Q.); NUS Graduate School, Integrative Sciences and Engineering Programme, National University of Singapore, Singapore (L.Z.); Department of Computer Science, School of Computing, National University of Singapore, Singapore (K.Y., B.C.O.); Department of Radiology, Dammam Medical Complex, Dammam, Saudi Arabia (D.A.R.A.); Biostatistics Unit, Yong Loo Lin School of Medicine, Singapore (Q.V.Y., Y.H.C.); University Spine Centre, Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Naresh Kumar
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (J.T.P.D.H., A.M., Y.L.T., S.L., Y.S.C., S.E.E., S.T.Q.); Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore (J.T.P.D.H., A.M., Y.L.T., S.L., Y.S.C., S.E.E., S.T.Q.); NUS Graduate School, Integrative Sciences and Engineering Programme, National University of Singapore, Singapore (L.Z.); Department of Computer Science, School of Computing, National University of Singapore, Singapore (K.Y., B.C.O.); Department of Radiology, Dammam Medical Complex, Dammam, Saudi Arabia (D.A.R.A.); Biostatistics Unit, Yong Loo Lin School of Medicine, Singapore (Q.V.Y., Y.H.C.); University Spine Centre, Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Beng Chin Ooi
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (J.T.P.D.H., A.M., Y.L.T., S.L., Y.S.C., S.E.E., S.T.Q.); Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore (J.T.P.D.H., A.M., Y.L.T., S.L., Y.S.C., S.E.E., S.T.Q.); NUS Graduate School, Integrative Sciences and Engineering Programme, National University of Singapore, Singapore (L.Z.); Department of Computer Science, School of Computing, National University of Singapore, Singapore (K.Y., B.C.O.); Department of Radiology, Dammam Medical Complex, Dammam, Saudi Arabia (D.A.R.A.); Biostatistics Unit, Yong Loo Lin School of Medicine, Singapore (Q.V.Y., Y.H.C.); University Spine Centre, Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Hiroshi Yoshioka
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (J.T.P.D.H., A.M., Y.L.T., S.L., Y.S.C., S.E.E., S.T.Q.); Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore (J.T.P.D.H., A.M., Y.L.T., S.L., Y.S.C., S.E.E., S.T.Q.); NUS Graduate School, Integrative Sciences and Engineering Programme, National University of Singapore, Singapore (L.Z.); Department of Computer Science, School of Computing, National University of Singapore, Singapore (K.Y., B.C.O.); Department of Radiology, Dammam Medical Complex, Dammam, Saudi Arabia (D.A.R.A.); Biostatistics Unit, Yong Loo Lin School of Medicine, Singapore (Q.V.Y., Y.H.C.); University Spine Centre, Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Swee Tian Quek
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (J.T.P.D.H., A.M., Y.L.T., S.L., Y.S.C., S.E.E., S.T.Q.); Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore (J.T.P.D.H., A.M., Y.L.T., S.L., Y.S.C., S.E.E., S.T.Q.); NUS Graduate School, Integrative Sciences and Engineering Programme, National University of Singapore, Singapore (L.Z.); Department of Computer Science, School of Computing, National University of Singapore, Singapore (K.Y., B.C.O.); Department of Radiology, Dammam Medical Complex, Dammam, Saudi Arabia (D.A.R.A.); Biostatistics Unit, Yong Loo Lin School of Medicine, Singapore (Q.V.Y., Y.H.C.); University Spine Centre, Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
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Xi Zhen L, Algazwi DAR, Makmur A, Salada BM, Hallinan JTPD. Symphysis Pubis Diastasis Due to Parvimonas micra Infection; an Unusual Suspect. J Clin Rheumatol 2021; 27:e98-e99. [PMID: 31985727 DOI: 10.1097/rhu.0000000000001272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Affiliation(s)
- Low Xi Zhen
- From the Department of Diagnostic Imaging, National University Health System, Singapore
| | | | - Andrew Makmur
- From the Department of Diagnostic Imaging, National University Health System, Singapore
| | - Brenda Mae Salada
- Division of Infectious Diseases, University Medicine Cluster, National University Health System, Singapore
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Affiliation(s)
- Yihui Goh
- From the Divisions of Neurology (Y.G., A.C.Y.C.) and Infectious Diseases (D.L.L.B., J.S.), Department of Medicine, National University Health System; and Department of Diagnostic Imaging (A.M.), National University Hospital, Singapore
| | - Darius L L Beh
- From the Divisions of Neurology (Y.G., A.C.Y.C.) and Infectious Diseases (D.L.L.B., J.S.), Department of Medicine, National University Health System; and Department of Diagnostic Imaging (A.M.), National University Hospital, Singapore
| | - Andrew Makmur
- From the Divisions of Neurology (Y.G., A.C.Y.C.) and Infectious Diseases (D.L.L.B., J.S.), Department of Medicine, National University Health System; and Department of Diagnostic Imaging (A.M.), National University Hospital, Singapore
| | - Jyoti Somani
- From the Divisions of Neurology (Y.G., A.C.Y.C.) and Infectious Diseases (D.L.L.B., J.S.), Department of Medicine, National University Health System; and Department of Diagnostic Imaging (A.M.), National University Hospital, Singapore
| | - Amanda C Y Chan
- From the Divisions of Neurology (Y.G., A.C.Y.C.) and Infectious Diseases (D.L.L.B., J.S.), Department of Medicine, National University Health System; and Department of Diagnostic Imaging (A.M.), National University Hospital, Singapore.
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Tiong TYV, Sundar G, Young SM, Makmur A, Yong HRC, Wong YLJ, Lang SS, Tan AP. A Novel Method of CT Exophthalmometry in Patients With Thyroid Eye Disease. Asia Pac J Ophthalmol (Phila) 2020; 9:39-43. [PMID: 31990744 PMCID: PMC7004459 DOI: 10.1097/01.apo.0000617908.29733.84] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2019] [Accepted: 08/01/2019] [Indexed: 11/29/2022] Open
Abstract
PURPOSE Conventional computed tomography (CT) exophthalmometry requires an intact lateral orbital wall and is therefore not feasible in patients who have undergone any form of lateral orbital wall surgery where the normal bony landmark may be lost or displaced. The purpose of our study is to validate an alternative method of CT exophthalmometry utilizing the posterior clinoid (PC) process as a new reference point that will allow for reproducible comparison of the anterior-posterior globe position in the preoperative and postoperative settings. DESIGN Cohort study. METHODS This is a retrospective study of 48 patients with clinically diagnosed thyroid eye disease who had undergone cross-sectional CT imaging in the pre- or postoperative settings. CT exophthalmometry was performed using both the conventional interzygomatic method and our proposed PC process method on all pre- and postoperative CT imaging by two independent observers. Interobserver variability analysis was performed with intraclass correlation coefficient. Correlation and agreement between the two methods were analyzed with Pearson correlation coefficient and linear regression method. All analyses were conducted at 5% level of significance with Stata MP V14. RESULTS Interobserver variability analysis showed an intraclass correlation coefficient of >0.9 for both interzygomatic and PC methods. There is good correlation between the two different measurements observed in both the pre- and postoperative groups (r = 0.68 and r = 0.72, respectively, P < 0.001). Linear regression showed good agreement between the two different measurements with most of the points lying within the 95% limits. CONCLUSIONS Our new method agrees well with the conventional method and has the added benefit of being able to reliably assess the anterior-posterior globe position in patients who do not have intact lateral orbital walls after decompressive surgery.
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Affiliation(s)
| | - Gangadhara Sundar
- Department of Ophthalmology, National University Hospital Singapore, Singapore
| | - Stephanie M. Young
- Department of Ophthalmology, National University Hospital Singapore, Singapore
| | - Andrew Makmur
- Department of Diagnostic Imaging, National University Hospital Singapore, Singapore
| | | | | | | | - Ai Peng Tan
- Department of Diagnostic Imaging, National University Hospital Singapore, Singapore
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Ooi SKG, Makmur A, Soon AYQ, Fook-Chong S, Liew C, Sia SY, Ting YH, Lim CY. Attitudes toward artificial intelligence in radiology with learner needs assessment within radiology residency programmes: a national multi-programme survey. Singapore Med J 2019; 62:126-134. [PMID: 31680181 DOI: 10.11622/smedj.2019141] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
INTRODUCTION We aimed to assess the attitudes and learner needs of radiology residents and faculty radiologists regarding artificial intelligence (AI) and machine learning (ML) in radiology. METHODS A web-based questionnaire, designed using SurveyMonkey, was sent out to residents and faculty radiologists in all three radiology residency programmes in Singapore. The questionnaire comprised four sections and aimed to evaluate respondents' current experience, attempts at self-learning, perceptions of career prospects and expectations of an AI/ML curriculum in their residency programme. Respondents' anonymity was ensured. RESULTS A total of 125 respondents (86 male, 39 female; 70 residents, 55 faculty radiologists) completed the questionnaire. The majority agreed that AI/ML will drastically change radiology practice (88.8%) and makes radiology more exciting (76.0%), and most would still choose to specialise in radiology if given a choice (80.0%). 64.8% viewed themselves as novices in their understanding of AI/ML, 76.0% planned to further advance their AI/ML knowledge and 67.2% were keen to get involved in an AI/ML research project. An overwhelming majority (84.8%) believed that AI/ML knowledge should be taught during residency, and most opined that this was as important as imaging physics and clinical skills/knowledge curricula (80.0% and 72.8%, respectively). More than half thought that their residency programme had not adequately implemented AI/ML teaching (59.2%). In subgroup analyses, male and tech-savvy respondents were more involved in AI/ML activities, leading to better technical understanding. CONCLUSION A growing optimism towards radiology undergoing technological transformation and AI/ML implementation has led to a strong demand for an AI/ML curriculum in residency education.
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Affiliation(s)
- Su Kai Gideon Ooi
- Department of Nuclear Medicine and Molecular Imaging, Division of Radiological Sciences, Singapore General Hospital, Singapore
| | - Andrew Makmur
- Department of Diagnostic Imaging, National University Hospital, Singapore
| | | | | | - Charlene Liew
- Department of Diagnostic Radiology, Changi General Hospital, Singapore
| | - Soon Yiew Sia
- Department of Diagnostic Imaging, National University Hospital, Singapore
| | - Yong Han Ting
- Department of Diagnostic Radiology, Tan Tock Seng Hospital, Singapore
| | - Chee Yeong Lim
- Department of Diagnostic Radiology, Division of Radiological Sciences, Singapore General Hospital, Singapore
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Yong CW, Lum JL, Makmur A, Seet JE, Lim AAT. Unusual metastatic presentations of a primary right parapharyngeal acinic cell adenocarcinoma. Int J Oral Maxillofac Surg 2019; 49:564-568. [PMID: 31668783 DOI: 10.1016/j.ijom.2019.09.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2019] [Revised: 07/30/2019] [Accepted: 09/17/2019] [Indexed: 11/27/2022]
Abstract
Acinic cell adenocarcinoma (ACA) is a malignant epithelial neoplasm of the salivary glands. The patient reported herein presented with an unusual case of a metastatic ACA originating from the right parapharyngeal region, which eventually metastasized to the ipsilateral cavernous sinus and the contralateral mandibular ramus. The trigeminal nerve may have served as a channel for the spread of the cancer from the right parapharyngeal region to the cavernous sinus, and subsequently to the left mandibular ramus. The widening of the left inferior alveolar nerve canal was an early sign of metastasis in this case.
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Affiliation(s)
- C W Yong
- Discipline of Oral Maxillofacial Surgery, Faculty of Dentistry, National University Centre for Oral Health, Singapore
| | - J L Lum
- Discipline of Oral Maxillofacial Surgery, Faculty of Dentistry, National University Centre for Oral Health, Singapore
| | - A Makmur
- Department of Diagnostic Imaging, National University Hospital, Singapore
| | - J E Seet
- Department of Pathology, National University Hospital, Singapore
| | - A A T Lim
- Discipline of Oral Maxillofacial Surgery, Faculty of Dentistry, National University Centre for Oral Health, Singapore.
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Hallinan JTPD, Venkatesh SK, Peter L, Makmur A, Yong WP, So JBY. CT volumetry for gastric carcinoma: association with TNM stage. Eur Radiol 2014; 24:3105-14. [PMID: 25038858 DOI: 10.1007/s00330-014-3316-5] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2014] [Revised: 06/10/2014] [Accepted: 07/04/2014] [Indexed: 12/26/2022]
Abstract
OBJECTIVES We evaluated the feasibility of performing CT volumetry of gastric carcinoma (GC) and its correlation with TNM stage. METHODS This institutional review board-approved retrospective study was performed on 153 patients who underwent a staging CT study for histologically confirmed GC. CT volumetry was performed by drawing regions of interest including abnormal thickening of the stomach wall. Reproducibility of tumour volume (Tvol) between two readers was assessed. Correlation between Tvol and TNM/peritoneal staging derived from histology/surgical findings was evaluated using ROC analysis and compared with CT evaluation of TNM/peritoneal staging. RESULTS Tvol was successfully performed in all patients. Reproducibility among readers was excellent (r = 0.97; P = 0.0001). The median Tvol of GC showed an incremental trend with T-stage (T1 = 27 ml; T2 = 32 ml; T3 = 53 ml and T4 = 121 ml, P < 0.01). Tvol predicted with good accuracy T-stage (≥T2:0.95; ≥T3:0.89 and T4:0.83, P = 0.0001), M-stage (0.87, P = 0.0001), peritoneal metastases (0.87, P = 0.0001) and final stage (≥stage 2:0.89; ≥stage 3:0.86 and stage 4:0.87, P = 0.0001), with moderate accuracy for N-stage (≥N1:0.75; ≥N2:0.74 and N3:0.75, P = 0.0001). Tvol was significantly (P < 0.05) more accurate than standard CT staging for prediction of T-stage, N3-stage, M-stage and peritoneal metastases. CONCLUSION CT volumetry may provide useful adjunct information for preoperative staging of GC. KEY POINTS CT volumetry of gastric carcinoma is feasible and reproducible. Tumour volume <19.4 ml predicts T1-stage gastric cancer with 91% sensitivity and 100% specificity (P = 0.0001). Tumour volume >95.7 ml predicts metastatic gastric cancer with 87% sensitivity and 78.5% specificity (P = 0.0001). CT volumetry may be a useful adjunct for staging gastric carcinoma.
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Affiliation(s)
- James T P D Hallinan
- Diagnostic Radiology, National University Health System (NUHS), Singapore, Singapore
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