1
|
Fan G, Li Y, Wang D, Zhang J, Du X, Liu H, Liao X. Automatic segmentation of dura for quantitative analysis of lumbar stenosis: A deep learning study with 518 CT myelograms. J Appl Clin Med Phys 2024; 25:e14378. [PMID: 38729652 PMCID: PMC11244674 DOI: 10.1002/acm2.14378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 04/01/2024] [Accepted: 04/18/2024] [Indexed: 05/12/2024] Open
Abstract
BACKGROUND The diagnosis of lumbar spinal stenosis (LSS) can be challenging because radicular pain is not often present in the culprit-level localization. Accurate segmentation and quantitative analysis of the lumbar dura on radiographic images are key to the accurate differential diagnosis of LSS. The aim of this study is to develop an automatic dura-contouring tool for radiographic quantification on computed tomography myelogram (CTM) for patients with LSS. METHODS A total of 518 CTM cases with or without lumbar stenosis were included in this study. A deep learning (DL) segmentation algorithm 3-dimensional (3D) U-Net was deployed. A total of 210 labeled cases were used to develop the dura-contouring tool, with the ratio of the training, independent testing, and external validation datasets being 150:30:30. The Dice score (DCS) was the primary measure to evaluate the segmentation performance of the 3D U-Net, which was subsequently developed as the dura-contouring tool to segment another unlabeled 308 CTM cases with LSS. Automatic masks of 446 slices on the stenotic levels were then meticulously reviewed and revised by human experts, and the cross-sectional area (CSA) of the dura was compared. RESULTS The mean DCS of the 3D U-Net were 0.905 ± 0.080, 0.933 ± 0.018, and 0.928 ± 0.034 in the five-fold cross-validation, the independent testing, and the external validation datasets, respectively. The segmentation performance of the dura-contouring tool was also comparable to that of the second observer (the human expert). With the dura-contouring tool, only 59.0% (263/446) of the automatic masks of the stenotic slices needed to be revised. In the revised cases, there were no significant differences in the dura CSA between automatic masks and corresponding revised masks (p = 0.652). Additionally, a strong correlation of dura CSA was found between the automatic masks and corresponding revised masks (r = 0.805). CONCLUSIONS A dura-contouring tool was developed that could automatically segment the dural sac on CTM, and it demonstrated high accuracy and generalization ability. Additionally, the dura-contouring tool has the potential to be applied in patients with LSS because it facilitates the quantification of the dural CSA on stenotic slices.
Collapse
Affiliation(s)
- Guoxin Fan
- Department of Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
- Department of Spine Surgery, Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Yufeng Li
- Department of Sports Medicine, Eighth Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China
| | - Dongdong Wang
- Department of Orthopaedics, Putuo People's Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Jianjin Zhang
- Department of Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
| | - Xiaokang Du
- Department of Orthopedics, The People's Hospital of Wenshang County, Wenshang, Shandong, China
| | - Huaqing Liu
- Artificial Intelligence Innovation Center, Research Institute of Tsinghua PearlRiverDelta, Guangzhou, China
| | - Xiang Liao
- Department of Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
| |
Collapse
|
2
|
Bu J, Lei Y, Wang Y, Zhao J, Huang S, Liang J, Wang Z, Xu L, He B, Dong M, Liu G, Niu R, Ma C, Liu G. A Multi-Element Identification System Based on Deep Learning for the Visual Field of Percutaneous Endoscopic Spine Surgery. Indian J Orthop 2024; 58:587-597. [PMID: 38694692 PMCID: PMC11058141 DOI: 10.1007/s43465-024-01134-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Accepted: 03/10/2024] [Indexed: 05/04/2024]
Abstract
Background Lumbar disc herniation is a common degenerative lumbar disease with an increasing incidence. Percutaneous endoscopic lumbar discectomy can treat lumbar disc herniation safely and effectively with a minimally invasive procedure. However, the learning curve of this technology is steep, which means that initial learners are often not sufficiently proficient in endoscopic operations, which can easily lead to iatrogenic damage. At present, the application of computer deep learning technology to clinical diagnosis, treatment, and surgical navigation has achieved satisfactory results. Purpose The objective of our team is to develop a multi-element identification system for the visual field of endoscopic spine surgery using deep learning algorithms and to evaluate the feasibility of this system. Method We established an image database by collecting surgical videos of 48 patients diagnosed with lumbar disc herniation, which was labeled by two spinal surgeons. We selected 6000 images of the visual field of percutaneous endoscopic spine surgery (including various tissue structures and surgical instruments), divided into the training data, validation data, and test data according to 2:1:2. We developed convolutional neural network models based on instance segmentation-Solov2, CondInst, Mask R-CNN and Yolact, and set the four network model backbone as ResNet101 and ResNet50 respectively. Mean average precision (mAP) and frames per second (FPS) were used to measure the performance of each model for classification, localization and recognition in real time, and AP (average) is used to evaluate how easily an element is detected by neural networks based on computer deep learning. Result Comprehensively comparing mAP and FSP of each model for bounding box test and segmentation task for the test set of images, we found that Solov2 (ResNet101) (mAP = 73.5%, FPS = 28.9), Mask R-CNN (ResNet101) (mAP = 72.8%, FPS = 28.5) models are the most stable, with higher precision and faster image processing speed. Combining the average precision of the elements in the bounding box test and segmentation tasks in each network, the AP(average) was highest for tool 3 (bbox-0.85, segm-0.89) and lowest for tool 5 (bbox-0.63, segm-0.72) in the instrumentation, whereas in the anatomical tissue elements, the fibrosus annulus (bbox-0.68, segm-0.69) and ligamentum flavum (bbox-0.65, segm-0.62) had higher AP(average),while extra-dural fat (bbox-0.42, segm-0.44) was lowest. Conclusion Our team has developed a multi-element identification system for the visual field of percutaneous endoscopic spine surgery adapted to the interlaminar and foraminal approaches, which can identify and track anatomical tissue (nerve, ligamentum flavum, nucleus pulposus, etc.) and surgical instruments (endoscopic forceps, an high-speed diamond burr, etc.), which can be used in the future as a virtual educational tool or applied to the intraoperative real-time assistance system for spinal endoscopic operation.
Collapse
Affiliation(s)
- Jinhui Bu
- Affiliated Xuzhou Clinical College of Xuzhou Medical University, Xuzhou, 221009 China
| | - Yan Lei
- Affiliated Xuzhou Clinical College of Xuzhou Medical University, Xuzhou, 221009 China
| | - Yari Wang
- School of Computer Science, China University of Mining and Technology, Xuzhou, 221116 China
| | - Jiaqi Zhao
- School of Computer Science, China University of Mining and Technology, Xuzhou, 221116 China
| | - Sen Huang
- Affiliated Xuzhou Clinical College of Xuzhou Medical University, Xuzhou, 221009 China
| | - Jun Liang
- Department of Orthopedic Surgery, Xuzhou Central Hospital, Xuzhou Central Hospital Affiliated to Nanjing University of Chinese Medicine, The Xuzhou School of Clinical Medicine of Nanjing Medical University, Xuzhou Central Hospital Affiliated to Medical School of Southeast University, Xuzhou, 221009 China
| | - Zhenfei Wang
- Department of Orthopedic Surgery, Xuzhou Central Hospital, Xuzhou Central Hospital Affiliated to Nanjing University of Chinese Medicine, The Xuzhou School of Clinical Medicine of Nanjing Medical University, Xuzhou Central Hospital Affiliated to Medical School of Southeast University, Xuzhou, 221009 China
| | - Long Xu
- Bengbu Medical College, Bengbu, 233000 China
| | - Bo He
- Affiliated Xuzhou Clinical College of Xuzhou Medical University, Xuzhou, 221009 China
| | - Minghui Dong
- Affiliated Xuzhou Clinical College of Xuzhou Medical University, Xuzhou, 221009 China
| | - Guangpu Liu
- Department of Orthopedic Surgery, Xuzhou Central Hospital, Xuzhou Central Hospital Affiliated to Nanjing University of Chinese Medicine, The Xuzhou School of Clinical Medicine of Nanjing Medical University, Xuzhou Central Hospital Affiliated to Medical School of Southeast University, Xuzhou, 221009 China
| | - Ru Niu
- Department of Orthopedic Surgery, Xuzhou Central Hospital, Xuzhou Central Hospital Affiliated to Nanjing University of Chinese Medicine, The Xuzhou School of Clinical Medicine of Nanjing Medical University, Xuzhou Central Hospital Affiliated to Medical School of Southeast University, Xuzhou, 221009 China
| | - Chao Ma
- Affiliated Xuzhou Clinical College of Xuzhou Medical University, Xuzhou, 221009 China
- Department of Orthopedic Surgery, Xuzhou Central Hospital, Xuzhou Central Hospital Affiliated to Nanjing University of Chinese Medicine, The Xuzhou School of Clinical Medicine of Nanjing Medical University, Xuzhou Central Hospital Affiliated to Medical School of Southeast University, Xuzhou, 221009 China
| | - Guangwang Liu
- Affiliated Xuzhou Clinical College of Xuzhou Medical University, Xuzhou, 221009 China
- Department of Orthopedic Surgery, Xuzhou Central Hospital, Xuzhou Central Hospital Affiliated to Nanjing University of Chinese Medicine, The Xuzhou School of Clinical Medicine of Nanjing Medical University, Xuzhou Central Hospital Affiliated to Medical School of Southeast University, Xuzhou, 221009 China
| |
Collapse
|
3
|
Bardeesi A, Tabarestani TQ, Bergin SM, Huang CC, Shaffrey CI, Wiggins WF, Abd-El-Barr MM. Using Augmented Reality Technology to Optimize Transfacet Lumbar Interbody Fusion: A Case Report. J Clin Med 2024; 13:1513. [PMID: 38592365 PMCID: PMC10934424 DOI: 10.3390/jcm13051513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 02/23/2024] [Accepted: 02/29/2024] [Indexed: 04/10/2024] Open
Abstract
The transfacet minimally invasive transforaminal lumbar interbody fusion (MIS-TLIF) is a novel approach available for the management of lumbar spondylolisthesis. It avoids the need to manipulate either of the exiting or traversing nerve roots, both protected by the bony boundaries of the approach. With the advancement in operative technologies such as navigation, mapping, segmentation, and augmented reality (AR), surgeons are prompted to utilize these technologies to enhance their surgical outcomes. A 36-year-old male patient was complaining of chronic progressive lower back pain. He was found to have grade 2 L4/5 spondylolisthesis. We studied the feasibility of a trans-Kambin or a transfacet MIS-TLIF, and decided to proceed with the latter given the wider corridor it provides. Preoperative trajectory planning and level segmentation in addition to intraoperative navigation and image merging were all utilized to provide an AR model to guide us through the surgery. The use of AR can build on the safety and learning of novel surgical approaches to spine pathologies. However, larger high-quality studies are needed to further objectively analyze its impact on surgical outcomes and to expand on its application.
Collapse
Affiliation(s)
- Anas Bardeesi
- Department of Neurosurgery, Duke University Hospital, Durham, NC 27710, USA
| | | | - Stephen M. Bergin
- Department of Neurosurgery, Duke University Hospital, Durham, NC 27710, USA
| | - Chuan-Ching Huang
- Department of Neurosurgery, Duke University Hospital, Durham, NC 27710, USA
| | | | - Walter F. Wiggins
- Department of Radiology, Duke University Hospital, Durham, NC 27710, USA
| | | |
Collapse
|
4
|
Zhao J, Sun L, Sun Z, Zhou X, Si H, Zhang D. MSEF-Net: Multi-scale edge fusion network for lumbosacral plexus segmentation with MR image. Artif Intell Med 2024; 148:102771. [PMID: 38325928 DOI: 10.1016/j.artmed.2024.102771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Revised: 12/08/2023] [Accepted: 01/14/2024] [Indexed: 02/09/2024]
Abstract
Nerve damage of spine areas is a common cause of disability and paralysis. The lumbosacral plexus segmentation from magnetic resonance imaging (MRI) scans plays an important role in many computer-aided diagnoses and surgery of spinal nerve lesions. Due to the complex structure and low contrast of the lumbosacral plexus, it is difficult to delineate the regions of edges accurately. To address this issue, we propose a Multi-Scale Edge Fusion Network (MSEF-Net) to fully enhance the edge feature in the encoder and adaptively fuse multi-scale features in the decoder. Specifically, to highlight the edge structure feature, we propose an edge feature fusion module (EFFM) by combining the Sobel operator edge detection and the edge-guided attention module (EAM), respectively. To adaptively fuse the multi-scale feature map in the decoder, we introduce an adaptive multi-scale fusion module (AMSF). Our proposed MSEF-Net method was evaluated on the collected spinal MRI dataset with 89 patients (a total of 2848 MR images). Experimental results demonstrate that our MSEF-Net is effective for lumbosacral plexus segmentation with MR images, when compared with several state-of-the-art segmentation methods.
Collapse
Affiliation(s)
- Junyong Zhao
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, the Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing 211106, China
| | - Liang Sun
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, the Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing 211106, China; Nanjing University of Aeronautics and Astronautics Shenzhen Research Institute, Shenzhen 518063, China.
| | - Zhi Sun
- Department of Medical Imaging, Shandong Provincial Hospital, Jinan 250021, China
| | - Xin Zhou
- Department of Orthopedics, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Haipeng Si
- Department of Orthopedics, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan 250012, China.
| | - Daoqiang Zhang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, the Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing 211106, China; Nanjing University of Aeronautics and Astronautics Shenzhen Research Institute, Shenzhen 518063, China.
| |
Collapse
|
5
|
Shan ZM, Ren XS, Shi H, Zheng SJ, Zhang C, Zhuang SY, Wu XT, Xie XH. Machine Learning Prediction Model and Risk Factor Analysis of Reoperation in Recurrent Lumbar Disc Herniation Patients After Percutaneous Endoscopic Lumbar Discectomy. Global Spine J 2023:21925682231173353. [PMID: 37161730 DOI: 10.1177/21925682231173353] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/11/2023] Open
Abstract
OBJECTIVE To investigate the risk factors of reoperation after percutaneous endoscopic lumbar discectomy (PELD) due to recurrent lumbar disc herniation (rLDH) and to establish a set of individualized prediction models. METHODS Patients who underwent PELD successfully from January 2016 to February 2022 in a single institution were enrolled in this study. Six methods of machine learning (ML) were used to establish an individualized prediction model for reoperation in rLDH patients after PELD, and these models were compared with logistics regression model to select optimal model. RESULTS A total of 2603 patients were enrolled in this study. 57 patients had repeated operation due to rLDH and 114 patients were selected from the remaining 2546 nonrecurrent patients as matched controls. Multivariate logistic regression analysis showed that disc herniation type (P < .001), Modic changes (type II) (P = .003), sagittal range of motion (sROM) (P = .022), facet orientation (FO) (P = .028) and fat infiltration (FI) (P = .001) were independent risk factors for reoperation in rLDH patients after PELD. The XGBoost AUC was of 90.71%, accuracy was approximately 88.87%, sensitivity was 70.81%, specificity was 97.19%. The traditional logistic regression AUC was 77.4%, accuracy was about 77.73%, sensitivity was 47.15%, specificity was 92.12%. CONCLUSION This study showed that disc herniation type (extrusion, sequestration), Modic changes (type II), a large sROM, a large FO and high FI were independent risk factors for reoperation in LDH patients after PELD. The prediction efficiency of XGBoost model was higher than traditional Logistic regression analysis model.
Collapse
Affiliation(s)
- Zheng-Ming Shan
- Department of Spine Surgery, ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Xue-Song Ren
- School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Hang Shi
- Department of Spine Surgery, ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Shi-Jie Zheng
- Department of Spine Surgery, ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Cong Zhang
- Department of Spine Surgery, ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Su-Yang Zhuang
- Department of Spine Surgery, ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Xiao-Tao Wu
- Department of Spine Surgery, ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Xin-Hui Xie
- Department of Spine Surgery, ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| |
Collapse
|
6
|
Benzakour A, Altsitzioglou P, Lemée JM, Ahmad A, Mavrogenis AF, Benzakour T. Artificial intelligence in spine surgery. INTERNATIONAL ORTHOPAEDICS 2023; 47:457-465. [PMID: 35902390 DOI: 10.1007/s00264-022-05517-8] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 07/11/2022] [Indexed: 01/28/2023]
Abstract
The continuous progress of research and clinical trials has offered a wide variety of information concerning the spine and the treatment of the different spinal pathologies that may occur. Planning the best therapy for each patient could be a very difficult and challenging task as it often requires thorough processing of the patient's history and individual characteristics by the clinician. Clinicians and researchers also face problems when it comes to data availability due to patients' personal information protection policies. Artificial intelligence refers to the reproduction of human intelligence via special programs and computers that are trained in a way that simulates human cognitive functions. Artificial intelligence implementations to daily clinical practice such as surgical robots that facilitate spine surgery and reduce radiation dosage to medical staff, special algorithms that can predict the possible outcomes of conservative versus surgical treatment in patients with low back pain and disk herniations, and systems that create artificial populations with great resemblance and similar characteristics to real patients are considered to be a novel breakthrough in modern medicine. To enhance the body of the related literature and inform the readers on the clinical applications of artificial intelligence, we performed this review to discuss the contribution of artificial intelligence in spine surgery and pathology.
Collapse
Affiliation(s)
- Ahmed Benzakour
- Centre Orléanais du Dos - Pôle Santé Oréliance, Saran, France
| | - Pavlos Altsitzioglou
- First Department of Orthopaedics, National and Kapodistrian University of Athens, School of Medicine, Athens, Greece
| | - Jean Michel Lemée
- Department of Neurosurgery, University Hospital of Angers, Angers, France
| | | | - Andreas F Mavrogenis
- First Department of Orthopaedics, National and Kapodistrian University of Athens, School of Medicine, Athens, Greece.
| | | |
Collapse
|
7
|
Waguia Kouam R, Tabarestani TQ, Sykes DAW, Gupta N, Futch BG, Kakmou E, Goodwin CR, Foster NA, Than KD, Wiggins WF, Abd-El-Barr MM. How dimensions can guide surgical planning and training: a systematic review of Kambin's triangle. Neurosurg Focus 2023; 54:E6. [PMID: 36587400 DOI: 10.3171/2022.10.focus22606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 10/17/2022] [Indexed: 01/02/2023]
Abstract
OBJECTIVE The authors sought to analyze the current literature to determine dimensional trends across the lumbar levels of Kambin's triangle, clarify the role of imaging techniques for preoperative planning, and understand the effect of inclusion of the superior articular process (SAP). This compiled knowledge of the triangle is needed to perform successful procedures, reduce nerve root injuries, and help guide surgeons in training. METHODS The authors performed a search of multiple databases using combinations of keywords: Kambin's triangle, size, measurement, safe triangle, and bony triangle. Articles were included if their main findings included measurement of Kambin's triangle. The PubMed, Scopus, Ovid, Cochrane, Embase, and Medline databases were systematically searched for English-language articles with no time frame restrictions through July 2022. RESULTS Eight studies comprising 132 patients or cadavers were included in the study. The mean ± SD age was 66.69 ± 9.6 years, and 53% of patients were male. Overall, the size of Kambin's triangle increased in area moving down vertebral levels, with L5-S1 being the largest (133.59 ± 4.36 mm2). This trend followed a linear regression model when SAP was kept (p = 0.008) and removed (p = 0.003). There was also a considerable increase in the size of Kambin's triangle if the SAP was removed. CONCLUSIONS Here, the authors have provided the first reported systematic review of the literature of Kambin's triangle, its measurements at each lumbar level, and key areas of debate related to the definition of the working safe zone. These findings indicate that CT is heavily utilized for imaging of the safe zone, the area of Kambin's triangle tends to increase caudally, and variation exists between patients. Future studies should focus on using advanced imaging techniques for preoperative planning and establishing guidelines for surgeons.
Collapse
Affiliation(s)
| | | | | | - Nithin Gupta
- 1Campbell University School of Osteopathic Medicine, Lillington
| | | | - Elisabeth Kakmou
- 3Department of Neurosurgery, Duke University Hospital, Durham, North Carolina
| | - C Rory Goodwin
- 3Department of Neurosurgery, Duke University Hospital, Durham, North Carolina
| | - Norah A Foster
- 4Department of Orthopedic Surgery, Miami Valley Hospital, Centerville, Ohio; and
| | - Khoi D Than
- 3Department of Neurosurgery, Duke University Hospital, Durham, North Carolina
| | - Walter F Wiggins
- 5Department of Radiology, Duke University Hospital, Durham, North Carolina
| | | |
Collapse
|
8
|
Zhao J, Sun L, Zhou X, Huang S, Si H, Zhang D. Residual-atrous attention network for lumbosacral plexus segmentation with MR image. Comput Med Imaging Graph 2022; 100:102109. [DOI: 10.1016/j.compmedimag.2022.102109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 07/12/2022] [Accepted: 07/28/2022] [Indexed: 10/15/2022]
|
9
|
Qu B, Cao J, Qian C, Wu J, Lin J, Wang L, Ou-Yang L, Chen Y, Yan L, Hong Q, Zheng G, Qu X. Current development and prospects of deep learning in spine image analysis: a literature review. Quant Imaging Med Surg 2022; 12:3454-3479. [PMID: 35655825 PMCID: PMC9131328 DOI: 10.21037/qims-21-939] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 03/04/2022] [Indexed: 10/07/2023]
Abstract
BACKGROUND AND OBJECTIVE As the spine is pivotal in the support and protection of human bodies, much attention is given to the understanding of spinal diseases. Quick, accurate, and automatic analysis of a spine image greatly enhances the efficiency with which spine conditions can be diagnosed. Deep learning (DL) is a representative artificial intelligence technology that has made encouraging progress in the last 6 years. However, it is still difficult for clinicians and technicians to fully understand this rapidly evolving field due to the diversity of applications, network structures, and evaluation criteria. This study aimed to provide clinicians and technicians with a comprehensive understanding of the development and prospects of DL spine image analysis by reviewing published literature. METHODS A systematic literature search was conducted in the PubMed and Web of Science databases using the keywords "deep learning" and "spine". Date ranges used to conduct the search were from 1 January, 2015 to 20 March, 2021. A total of 79 English articles were reviewed. KEY CONTENT AND FINDINGS The DL technology has been applied extensively to the segmentation, detection, diagnosis, and quantitative evaluation of spine images. It uses static or dynamic image information, as well as local or non-local information. The high accuracy of analysis is comparable to that achieved manually by doctors. However, further exploration is needed in terms of data sharing, functional information, and network interpretability. CONCLUSIONS The DL technique is a powerful method for spine image analysis. We believe that, with the joint efforts of researchers and clinicians, intelligent, interpretable, and reliable DL spine analysis methods will be widely applied in clinical practice in the future.
Collapse
Affiliation(s)
- Biao Qu
- Department of Instrumental and Electrical Engineering, Xiamen University, Xiamen, China
| | - Jianpeng Cao
- Department of Electronic Science, Biomedical Intelligent Cloud R&D Center, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | - Chen Qian
- Department of Electronic Science, Biomedical Intelligent Cloud R&D Center, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | - Jinyu Wu
- Department of Electronic Science, Biomedical Intelligent Cloud R&D Center, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | - Jianzhong Lin
- Department of Radiology, Zhongshan Hospital of Xiamen University, Xiamen, China
| | - Liansheng Wang
- Department of Computer Science, School of Informatics, Xiamen University, Xiamen, China
| | - Lin Ou-Yang
- Department of Medical Imaging of Southeast Hospital, Medical College of Xiamen University, Zhangzhou, China
| | - Yongfa Chen
- Department of Pediatric Orthopedic Surgery, The First Affiliated Hospital of Xiamen University, Xiamen, China
| | - Liyue Yan
- Department of Information & Computational Mathematics, Xiamen University, Xiamen, China
| | - Qing Hong
- Biomedical Intelligent Cloud R&D Center, China Mobile Group, Xiamen, China
| | - Gaofeng Zheng
- Department of Instrumental and Electrical Engineering, Xiamen University, Xiamen, China
| | - Xiaobo Qu
- Department of Electronic Science, Biomedical Intelligent Cloud R&D Center, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| |
Collapse
|
10
|
Stumpo V, Kernbach JM, van Niftrik CHB, Sebök M, Fierstra J, Regli L, Serra C, Staartjes VE. Machine Learning Algorithms in Neuroimaging: An Overview. ACTA NEUROCHIRURGICA. SUPPLEMENT 2021; 134:125-138. [PMID: 34862537 DOI: 10.1007/978-3-030-85292-4_17] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Machine learning (ML) and artificial intelligence (AI) applications in the field of neuroimaging have been on the rise in recent years, and their clinical adoption is increasing worldwide. Deep learning (DL) is a field of ML that can be defined as a set of algorithms enabling a computer to be fed with raw data and progressively discover-through multiple layers of representation-more complex and abstract patterns in large data sets. The combination of ML and radiomics, namely the extraction of features from medical images, has proven valuable, too: Radiomic information can be used for enhanced image characterization and prognosis or outcome prediction. This chapter summarizes the basic concepts underlying ML application for neuroimaging and discusses technical aspects of the most promising algorithms, with a specific focus on Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs), in order to provide the readership with the fundamental theoretical tools to better understand ML in neuroimaging. Applications are highlighted from a practical standpoint in the last section of the chapter, including: image reconstruction and restoration, image synthesis and super-resolution, registration, segmentation, classification, and outcome prediction.
Collapse
Affiliation(s)
- Vittorio Stumpo
- Machine Intelligence in Clinical Neuroscience (MICN) Lab, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Julius M Kernbach
- Neurosurgical Artificial Intelligence Lab Aachen (NAILA), Department of Neurosurgery, RWTH University Hospital, Aachen, Germany
- Department of Neurosurgery, Faculty of Medicine, RWTH Aachen University, Aachen, Germany
| | - Christiaan H B van Niftrik
- Machine Intelligence in Clinical Neuroscience (MICN) Lab, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Martina Sebök
- Machine Intelligence in Clinical Neuroscience (MICN) Lab, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Jorn Fierstra
- Machine Intelligence in Clinical Neuroscience (MICN) Lab, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Luca Regli
- Machine Intelligence in Clinical Neuroscience (MICN) Lab, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Carlo Serra
- Machine Intelligence in Clinical Neuroscience (MICN) Lab, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Victor E Staartjes
- Machine Intelligence in Clinical Neuroscience (MICN) Lab, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
| |
Collapse
|
11
|
D’Antoni F, Russo F, Ambrosio L, Vollero L, Vadalà G, Merone M, Papalia R, Denaro V. Artificial Intelligence and Computer Vision in Low Back Pain: A Systematic Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph182010909. [PMID: 34682647 PMCID: PMC8535895 DOI: 10.3390/ijerph182010909] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 10/04/2021] [Accepted: 10/09/2021] [Indexed: 12/16/2022]
Abstract
Chronic Low Back Pain (LBP) is a symptom that may be caused by several diseases, and it is currently the leading cause of disability worldwide. The increased amount of digital images in orthopaedics has led to the development of methods related to artificial intelligence, and to computer vision in particular, which aim to improve diagnosis and treatment of LBP. In this manuscript, we have systematically reviewed the available literature on the use of computer vision in the diagnosis and treatment of LBP. A systematic research of PubMed electronic database was performed. The search strategy was set as the combinations of the following keywords: "Artificial Intelligence", "Feature Extraction", "Segmentation", "Computer Vision", "Machine Learning", "Deep Learning", "Neural Network", "Low Back Pain", "Lumbar". Results: The search returned a total of 558 articles. After careful evaluation of the abstracts, 358 were excluded, whereas 124 papers were excluded after full-text examination, taking the number of eligible articles to 76. The main applications of computer vision in LBP include feature extraction and segmentation, which are usually followed by further tasks. Most recent methods use deep learning models rather than digital image processing techniques. The best performing methods for segmentation of vertebrae, intervertebral discs, spinal canal and lumbar muscles achieve Sørensen-Dice scores greater than 90%, whereas studies focusing on localization and identification of structures collectively showed an accuracy greater than 80%. Future advances in artificial intelligence are expected to increase systems' autonomy and reliability, thus providing even more effective tools for the diagnosis and treatment of LBP.
Collapse
Affiliation(s)
- Federico D’Antoni
- Unit of Computer Systems and Bioinformatics, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo 21, 00128 Rome, Italy; (F.D.); (L.V.)
| | - Fabrizio Russo
- Department of Orthopaedic Surgery, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo 200, 00128 Rome, Italy; (L.A.); (G.V.); (R.P.); (V.D.)
- Correspondence: (F.R.); (M.M.)
| | - Luca Ambrosio
- Department of Orthopaedic Surgery, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo 200, 00128 Rome, Italy; (L.A.); (G.V.); (R.P.); (V.D.)
| | - Luca Vollero
- Unit of Computer Systems and Bioinformatics, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo 21, 00128 Rome, Italy; (F.D.); (L.V.)
| | - Gianluca Vadalà
- Department of Orthopaedic Surgery, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo 200, 00128 Rome, Italy; (L.A.); (G.V.); (R.P.); (V.D.)
| | - Mario Merone
- Unit of Computer Systems and Bioinformatics, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo 21, 00128 Rome, Italy; (F.D.); (L.V.)
- Correspondence: (F.R.); (M.M.)
| | - Rocco Papalia
- Department of Orthopaedic Surgery, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo 200, 00128 Rome, Italy; (L.A.); (G.V.); (R.P.); (V.D.)
| | - Vincenzo Denaro
- Department of Orthopaedic Surgery, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo 200, 00128 Rome, Italy; (L.A.); (G.V.); (R.P.); (V.D.)
| |
Collapse
|
12
|
Kedia S, Pahwa B, Bali O, Goyal S. Applications of Machine Learning in Pediatric Hydrocephalus: A Systematic Review. Neurol India 2021; 69:S380-S389. [DOI: 10.4103/0028-3886.332287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
|