1
|
Regmi M, Liu W, Liu S, Dai Y, Xiong Y, Yang J, Yang C. The evolution and integration of technology in spinal neurosurgery: A scoping review. J Clin Neurosci 2024; 129:110853. [PMID: 39348790 DOI: 10.1016/j.jocn.2024.110853] [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: 06/04/2024] [Revised: 09/19/2024] [Accepted: 09/24/2024] [Indexed: 10/02/2024]
Abstract
Spinal disorders pose a significant global health challenge, affecting nearly 5% of the population and incurring substantial socioeconomic costs. Over time, spinal neurosurgery has evolved from basic 19th-century techniques to today's minimally invasive procedures. The recent integration of technologies such as robotic assistance and advanced imaging has not only improved precision but also reshaped treatment paradigms. This review explores key innovations in imaging, biomaterials, and emerging fields such as AI, examining how they address long-standing challenges in spinal care, including enhancing surgical accuracy and promoting tissue regeneration. Are we at the threshold of a new era in healthcare technology, or are these innovations merely enhancements that may not fundamentally advance clinical care? We aim to answer this question by offering a concise introduction to each technology and discussing in depth its status and challenges, providing readers with a clearer understanding of its actual potential to revolutionize surgical practices.
Collapse
Affiliation(s)
- Moksada Regmi
- State Key Laboratory of Vascular Homeostasis and Remodeling, Department of Neurosurgery, Peking University Third Hospital, Peking University, Beijing 100191, China; Center for Precision Neurosurgery and Oncology of Peking University Health Science Center, Peking University, Beijing 100191, China; Peking University Health Science Center, Beijing 100191, China; Henan Academy of Innovations in Medical Science (AIMS), Zhengzhou 450003, China
| | - Weihai Liu
- State Key Laboratory of Vascular Homeostasis and Remodeling, Department of Neurosurgery, Peking University Third Hospital, Peking University, Beijing 100191, China; Center for Precision Neurosurgery and Oncology of Peking University Health Science Center, Peking University, Beijing 100191, China
| | - Shikun Liu
- State Key Laboratory of Vascular Homeostasis and Remodeling, Department of Neurosurgery, Peking University Third Hospital, Peking University, Beijing 100191, China; Center for Precision Neurosurgery and Oncology of Peking University Health Science Center, Peking University, Beijing 100191, China
| | - Yuwei Dai
- State Key Laboratory of Vascular Homeostasis and Remodeling, Department of Neurosurgery, Peking University Third Hospital, Peking University, Beijing 100191, China; Center for Precision Neurosurgery and Oncology of Peking University Health Science Center, Peking University, Beijing 100191, China
| | - Ying Xiong
- State Key Laboratory of Vascular Homeostasis and Remodeling, Department of Neurosurgery, Peking University Third Hospital, Peking University, Beijing 100191, China; Center for Precision Neurosurgery and Oncology of Peking University Health Science Center, Peking University, Beijing 100191, China
| | - Jun Yang
- State Key Laboratory of Vascular Homeostasis and Remodeling, Department of Neurosurgery, Peking University Third Hospital, Peking University, Beijing 100191, China; Center for Precision Neurosurgery and Oncology of Peking University Health Science Center, Peking University, Beijing 100191, China
| | - Chenlong Yang
- State Key Laboratory of Vascular Homeostasis and Remodeling, Department of Neurosurgery, Peking University Third Hospital, Peking University, Beijing 100191, China; Center for Precision Neurosurgery and Oncology of Peking University Health Science Center, Peking University, Beijing 100191, China; Henan Academy of Innovations in Medical Science (AIMS), Zhengzhou 450003, China.
| |
Collapse
|
2
|
Lee A, Ong W, Makmur A, Ting YH, Tan WC, Lim SWD, Low XZ, Tan JJH, Kumar N, Hallinan JTPD. Applications of Artificial Intelligence and Machine Learning in Spine MRI. Bioengineering (Basel) 2024; 11:894. [PMID: 39329636 PMCID: PMC11428307 DOI: 10.3390/bioengineering11090894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2024] [Revised: 09/01/2024] [Accepted: 09/01/2024] [Indexed: 09/28/2024] Open
Abstract
Diagnostic imaging, particularly MRI, plays a key role in the evaluation of many spine pathologies. Recent progress in artificial intelligence and its subset, machine learning, has led to many applications within spine MRI, which we sought to examine in this review. A literature search of the major databases (PubMed, MEDLINE, Web of Science, ClinicalTrials.gov) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The search yielded 1226 results, of which 50 studies were selected for inclusion. Key data from these studies were extracted. Studies were categorized thematically into the following: Image Acquisition and Processing, Segmentation, Diagnosis and Treatment Planning, and Patient Selection and Prognostication. Gaps in the literature and the proposed areas of future research are discussed. Current research demonstrates the ability of artificial intelligence to improve various aspects of this field, from image acquisition to analysis and clinical care. We also acknowledge the limitations of current technology. Future work will require collaborative efforts in order to fully exploit new technologies while addressing the practical challenges of generalizability and implementation. In particular, the use of foundation models and large-language models in spine MRI is a promising area, warranting further research. Studies assessing model performance in real-world clinical settings will also help uncover unintended consequences and maximize the benefits for patient care.
Collapse
Affiliation(s)
- Aric Lee
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
| | - Wilson Ong
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, 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
| | - Yong Han Ting
- 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
| | - Wei Chuan Tan
- 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
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Jonathan Jiong Hao Tan
- National University Spine Institute, Department of Orthopaedic Surgery, National University Health System, 1E Lower Kent Ridge Road, Singapore 119228, Singapore
| | - Naresh Kumar
- National University Spine Institute, Department of Orthopaedic Surgery, National University Health System, 1E Lower Kent Ridge Road, Singapore 119228, Singapore
| | - James T P D 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
| |
Collapse
|
3
|
Awuah WA, Adebusoye FT, Wellington J, David L, Salam A, Weng Yee AL, Lansiaux E, Yarlagadda R, Garg T, Abdul-Rahman T, Kalmanovich J, Miteu GD, Kundu M, Mykolaivna NI. Recent Outcomes and Challenges of Artificial Intelligence, Machine Learning, and Deep Learning in Neurosurgery. World Neurosurg X 2024; 23:100301. [PMID: 38577317 PMCID: PMC10992893 DOI: 10.1016/j.wnsx.2024.100301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 07/23/2023] [Accepted: 02/21/2024] [Indexed: 04/06/2024] Open
Abstract
Neurosurgeons receive extensive technical training, which equips them with the knowledge and skills to specialise in various fields and manage the massive amounts of information and decision-making required throughout the various stages of neurosurgery, including preoperative, intraoperative, and postoperative care and recovery. Over the past few years, artificial intelligence (AI) has become more useful in neurosurgery. AI has the potential to improve patient outcomes by augmenting the capabilities of neurosurgeons and ultimately improving diagnostic and prognostic outcomes as well as decision-making during surgical procedures. By incorporating AI into both interventional and non-interventional therapies, neurosurgeons may provide the best care for their patients. AI, machine learning (ML), and deep learning (DL) have made significant progress in the field of neurosurgery. These cutting-edge methods have enhanced patient outcomes, reduced complications, and improved surgical planning.
Collapse
Affiliation(s)
| | | | - Jack Wellington
- Cardiff University School of Medicine, Cardiff University, Wales, United Kingdom
| | - Lian David
- Norwich Medical School, University of East Anglia, United Kingdom
| | - Abdus Salam
- Department of Surgery, Khyber Teaching Hospital, Peshawar, Pakistan
| | | | | | - Rohan Yarlagadda
- Rowan University School of Osteopathic Medicine, Stratford, NJ, USA
| | - Tulika Garg
- Government Medical College and Hospital Chandigarh, India
| | | | | | | | - Mrinmoy Kundu
- Institute of Medical Sciences and SUM Hospital, Bhubaneswar, India
| | | |
Collapse
|
4
|
Windsor R, Jamaludin A, Kadir T, Zisserman A. Automated detection, labelling and radiological grading of clinical spinal MRIs. Sci Rep 2024; 14:14993. [PMID: 38951574 PMCID: PMC11217300 DOI: 10.1038/s41598-024-64580-w] [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: 11/19/2023] [Accepted: 06/11/2024] [Indexed: 07/03/2024] Open
Abstract
Spinal magnetic resonance (MR) scans are a vital tool for diagnosing the cause of back pain for many diseases and conditions. However, interpreting clinically useful information from these scans can be challenging, time-consuming and hard to reproduce across different radiologists. In this paper, we alleviate these problems by introducing a multi-stage automated pipeline for analysing spinal MR scans. This pipeline first detects and labels vertebral bodies across several commonly used sequences (e.g. T1w, T2w and STIR) and fields of view (e.g. lumbar, cervical, whole spine). Using these detections it then performs automated diagnosis for several spinal disorders, including intervertebral disc degenerative changes in T1w and T2w lumbar scans, and spinal metastases, cord compression and vertebral fractures. To achieve this, we propose a new method of vertebrae detection and labelling, using vector fields to group together detected vertebral landmarks and a language-modelling inspired beam search to determine the corresponding levels of the detections. We also employ a new transformer-based architecture to perform radiological grading which incorporates context from multiple vertebrae and sequences, as a real radiologist would. The performance of each stage of the pipeline is tested in isolation on several clinical datasets, each consisting of 66 to 421 scans. The outputs are compared to manual annotations of expert radiologists, demonstrating accurate vertebrae detection across a range of scan parameters. Similarly, the model's grading predictions for various types of disc degeneration and detection of spinal metastases closely match those of an expert radiologist. To aid future research, our code and trained models are made publicly available.
Collapse
Affiliation(s)
- Rhydian Windsor
- Visual Geometry Group, Department of Engineering Science, University of Oxford, Oxford, UK.
| | - Amir Jamaludin
- Visual Geometry Group, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Timor Kadir
- Visual Geometry Group, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Andrew Zisserman
- Visual Geometry Group, Department of Engineering Science, University of Oxford, Oxford, UK
| |
Collapse
|
5
|
Yüksek M, Yokuş A, Arslan H, Canayaz M, Akdemir Z. The Success of Deep Learning Modalities in Evaluating Modic Changes. World Neurosurg 2024; 184:e354-e359. [PMID: 38296043 DOI: 10.1016/j.wneu.2024.01.129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 01/22/2024] [Accepted: 01/23/2024] [Indexed: 03/01/2024]
Abstract
BACKGROUND Modic changes are pathologies that are common in the population and cause low back pain. The aim of the study is to analyze the modic changes detected in magnetic resonance imaging (MRI) using deep learning modalities. METHODS The sagittal T1, sagittal and axial T2-weighted lumbar MRI images of 307 patients, of which 125 were female and 182 were male, aged 19-86 years, who underwent MRI examination between 2016-2021 were analyzed. Modic changes (MC) were categorized and marked according to signal changes. Our study consists of 2 independent stages: classification and segmentation. The categorized data were first classified using convolutional neural network (CNN) architectures such as DenseNet-121, DenseNet-169, and VGG-19. In the next stage, masks were removed by segmentation using U-Net, which is the CNN architecture, with image processing programs on the marked images. RESULTS During the classification stage, the success rates for modic type 1, type 2, and type 3 changes were 98%, 96%, 100% in DenseNet-121, 100%, 94%, 100% in DenseNet-169, and 98%, 92%, 97% in VGG-19, respectively. At the segmentation phase, the success rate was 71% with the U-Net architecture. CONCLUSIONS Evaluation of MRI findings of MC in the etiology of lower back pain with deep learning architectures can significantly reduce the workload of the radiologist by providing ease of diagnosis.
Collapse
Affiliation(s)
- Mehmet Yüksek
- Department of Radiology, Van Training and Research Hospital, Van, Turkey
| | - Adem Yokuş
- Department of Radiology, Faculty of Medicine, Van Yüzüncü Yıl University, Van, Turkey.
| | - Harun Arslan
- Department of Radiology, Faculty of Medicine, Van Yüzüncü Yıl University, Van, Turkey
| | - Murat Canayaz
- Department of Computer Engineering, Faculty of Engineering, Van Yüzüncü Yıl University, Van, Turkey
| | - Zülküf Akdemir
- Department of Radiology, Faculty of Medicine, Van Yüzüncü Yıl University, Van, Turkey
| |
Collapse
|
6
|
Qu Z, Deng B, Sun W, Yang R, Feng H. A Convolutional Neural Network for Automated Detection of Cervical Ossification of the Posterior Longitudinal Ligament using Magnetic Resonance Imaging. Clin Spine Surg 2024; 37:E106-E112. [PMID: 37941120 DOI: 10.1097/bsd.0000000000001547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 10/03/2023] [Indexed: 11/10/2023]
Abstract
STUDY DESIGN Retrospective cohort study. OBJECTIVE We aimed to develop and validate a convolutional neural network (CNN) model to distinguish between cervical ossification of posterior longitudinal ligament (OPLL) and multilevel degenerative spinal stenosis using Magnetic Resonance Imaging (MRI) and to compare the diagnostic ability with spine surgeons. SUMMARY OF BACKGROUND DATA Some artificial intelligence models have been applied in spinal image analysis and many of promising results were obtained; however, there was still no study attempted to develop a deep learning model in detecting cervical OPLL using MRI images. MATERIALS AND METHODS In this retrospective study, 272 cervical OPLL and 412 degenerative patients underwent surgical treatment were enrolled and divided into the training (513 cases) and test dataset (171 cases). CNN models applying ResNet architecture with 34, 50, and 101 layers of residual blocks were constructed and trained with the sagittal MRI images from the training dataset. To evaluate the performance of CNN, the receiver operating characteristic curves of 3 ResNet models were plotted and the area under the curve were calculated on the test dataset. The accuracy, sensitivity, and specificity of the diagnosis by the CNN were calculated and compared with 3 senior spine surgeons. RESULTS The diagnostic accuracies of our ResNet34, ResNet50, and ResNet101 models were 92.98%, 95.32%, and 97.66%, respectively; the area under the curve of receiver operating characteristic curves of these models were 0.914, 0.942, and 0.971, respectively. The accuracies and specificities of ResNet50 and ResNet101 models were significantly higher than all spine surgeons; for the sensitivity, ResNet101 model achieved better values than that of the 2 surgeons. CONCLUSION The performance of our ResNet model in differentiating cervical OPLL from degenerative spinal stenosis using MRI is promising, better results were achieved with more layers of residual blocks applied.
Collapse
Affiliation(s)
- Zhe Qu
- Department of Spine Surgery, The Affiliated Hospital of Xuzhou Medical University
- Xuzhou Medical University
| | - Bin Deng
- Department of Spine Surgery, The Affiliated Hospital of Xuzhou Medical University
- Xuzhou Medical University
| | - Wei Sun
- Department of Spine Surgery, The Affiliated Hospital of Xuzhou Medical University
- Xuzhou Medical University
| | - Ranran Yang
- Xuzhou Medical University
- Department of Radiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Hu Feng
- Department of Spine Surgery, The Affiliated Hospital of Xuzhou Medical University
- Xuzhou Medical University
| |
Collapse
|
7
|
Payne DL, Xu X, Faraji F, John K, Pradas KF, Bernard VV, Bangiyev L, Prasanna P. Automated Detection of Cervical Spinal Stenosis and Cord Compression via Vision Transformer and Rules-Based Classification. AJNR Am J Neuroradiol 2024; 45:ajnr.A8141. [PMID: 38360785 PMCID: PMC11288556 DOI: 10.3174/ajnr.a8141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Accepted: 12/15/2023] [Indexed: 02/17/2024]
Abstract
BACKGROUND AND PURPOSE Cervical spinal cord compression, defined as spinal cord deformity and severe narrowing of the spinal canal in the cervical region, can lead to severe clinical consequences, including intractable pain, sensory disturbance, paralysis, and even death, and may require emergent intervention to prevent negative outcomes. Despite the critical nature of cord compression, no automated tool is available to alert clinical radiologists to the presence of such findings. This study aims to demonstrate the ability of a vision transformer (ViT) model for the accurate detection of cervical cord compression. MATERIALS AND METHODS A clinically diverse cohort of 142 cervical spine MRIs was identified, 34% of which were normal or had mild stenosis, 31% with moderate stenosis, and 35% with cord compression. Utilizing gradient-echo images, slices were labeled as no cord compression/mild stenosis, moderate stenosis, or severe stenosis/cord compression. Segmentation of the spinal canal was performed and confirmed by neuroradiology faculty. A pretrained ViT model was fine-tuned to predict section-level severity by using a train:validation:test split of 60:20:20. Each examination was assigned an overall severity based on the highest level of section severity, with an examination labeled as positive for cord compression if ≥1 section was predicted in the severe category. Additionally, 2 convolutional neural network (CNN) models (ResNet50, DenseNet121) were tested in the same manner. RESULTS The ViT model outperformed both CNN models at the section level, achieving section-level accuracy of 82%, compared with 72% and 78% for ResNet and DenseNet121, respectively. ViT patient-level classification achieved accuracy of 93%, sensitivity of 0.90, positive predictive value of 0.90, specificity of 0.95, and negative predictive value of 0.95. Receiver operating characteristic area under the curve was greater for ViT than either CNN. CONCLUSIONS This classification approach using a ViT model and rules-based classification accurately detects the presence of cervical spinal cord compression at the patient level. In this study, the ViT model outperformed both conventional CNN approaches at the section and patient levels. If implemented into the clinical setting, such a tool may streamline neuroradiology workflow, improving efficiency and consistency.
Collapse
Affiliation(s)
- David L Payne
- From the Department of Radiology (D.L.P., F.F., K.J., K.F.P., V.V.B., L.B.), Stony Brook University Hospital, Stony Brook, New York
- Department of Biomedical Informatics (D.L.P., X.X., F.F., K.J., P.P.), Stony Brook University, Stony Brook, New York
| | - Xuan Xu
- Department of Biomedical Informatics (D.L.P., X.X., F.F., K.J., P.P.), Stony Brook University, Stony Brook, New York
| | - Farshid Faraji
- From the Department of Radiology (D.L.P., F.F., K.J., K.F.P., V.V.B., L.B.), Stony Brook University Hospital, Stony Brook, New York
- Department of Biomedical Informatics (D.L.P., X.X., F.F., K.J., P.P.), Stony Brook University, Stony Brook, New York
| | - Kevin John
- From the Department of Radiology (D.L.P., F.F., K.J., K.F.P., V.V.B., L.B.), Stony Brook University Hospital, Stony Brook, New York
- Department of Biomedical Informatics (D.L.P., X.X., F.F., K.J., P.P.), Stony Brook University, Stony Brook, New York
| | - Katherine Ferra Pradas
- From the Department of Radiology (D.L.P., F.F., K.J., K.F.P., V.V.B., L.B.), Stony Brook University Hospital, Stony Brook, New York
| | - Vahni Vishala Bernard
- From the Department of Radiology (D.L.P., F.F., K.J., K.F.P., V.V.B., L.B.), Stony Brook University Hospital, Stony Brook, New York
| | - Lev Bangiyev
- From the Department of Radiology (D.L.P., F.F., K.J., K.F.P., V.V.B., L.B.), Stony Brook University Hospital, Stony Brook, New York
| | - Prateek Prasanna
- Department of Biomedical Informatics (D.L.P., X.X., F.F., K.J., P.P.), Stony Brook University, Stony Brook, New York
| |
Collapse
|
8
|
Park J, Cho H, Ji Y, Lee K, Yoon H. Detection of spondylosis deformans in thoracolumbar and lumbar lateral X-ray images of dogs using a deep learning network. Front Vet Sci 2024; 11:1334438. [PMID: 38425836 PMCID: PMC10902442 DOI: 10.3389/fvets.2024.1334438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 01/30/2024] [Indexed: 03/02/2024] Open
Abstract
Introduction Spondylosis deformans is a non-inflammatory osteophytic reaction that develops to re-establish the stability of weakened joints between intervertebral discs. However, assessing these changes using radiography is subjective and difficult. In human medicine, attempts have been made to use artificial intelligence to accurately diagnose difficult and ambiguous diseases in medical imaging. Deep learning, a form of artificial intelligence, is most commonly used in medical imaging data analysis. It is a technique that utilizes neural networks to self-learn and extract features from data to diagnose diseases. However, no deep learning model has been developed to detect vertebral diseases in canine thoracolumbar and lumbar lateral X-ray images. Therefore, this study aimed to establish a segmentation model that automatically recognizes the vertebral body and spondylosis deformans in the thoracolumbar and lumbar lateral radiographs of dogs. Methods A total of 265 thoracolumbar and lumbar lateral radiographic images from 162 dogs were used to develop and evaluate the deep learning model based on the attention U-Net algorithm to segment the vertebral body and detect spondylosis deformans. Results When comparing the ability of the deep learning model and veterinary clinicians to recognize spondylosis deformans in the test dataset, the kappa value was 0.839, indicating an almost perfect agreement. Conclusions The deep learning model developed in this study is expected to automatically detect spondylosis deformans on thoracolumbar and lumbar lateral radiographs of dogs, helping to quickly and accurately identify unstable intervertebral disc space sites. Furthermore, the segmentation model developed in this study is expected to be useful for developing models that automatically recognize various vertebral and disc diseases.
Collapse
Affiliation(s)
- Junseol Park
- Department of Veterinary Medical Imaging, College of Veterinary Medicine, Jeonbuk National University, Iksan, Republic of Korea
- Biosafety Research Institute and College of Veterinary Medicine, Jeonbuk National University, Iksan, Republic of Korea
| | - Hyunwoo Cho
- Department of Electronic Engineering, Sogang University, Seoul, Republic of Korea
| | - Yewon Ji
- Department of Veterinary Medical Imaging, College of Veterinary Medicine, Jeonbuk National University, Iksan, Republic of Korea
| | - Kichang Lee
- Department of Veterinary Medical Imaging, College of Veterinary Medicine, Jeonbuk National University, Iksan, Republic of Korea
| | - Hakyoung Yoon
- Department of Veterinary Medical Imaging, College of Veterinary Medicine, Jeonbuk National University, Iksan, Republic of Korea
- Biosafety Research Institute and College of Veterinary Medicine, Jeonbuk National University, Iksan, Republic of Korea
| |
Collapse
|
9
|
Kim Y, Lim M, Kim SY, Kim TU, Lee SJ, Bok SK, Park S, Han Y, Jung HY, Hyun JK. Integrated Machine Learning Approach for the Early Prediction of Pressure Ulcers in Spinal Cord Injury Patients. J Clin Med 2024; 13:990. [PMID: 38398304 PMCID: PMC10889422 DOI: 10.3390/jcm13040990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 01/19/2024] [Accepted: 02/07/2024] [Indexed: 02/25/2024] Open
Abstract
(1) Background: Pressure ulcers (PUs) substantially impact the quality of life of spinal cord injury (SCI) patients and require prompt intervention. This study used machine learning (ML) techniques to develop advanced predictive models for the occurrence of PUs in patients with SCI. (2) Methods: By analyzing the medical records of 539 patients with SCI, we observed a 35% incidence of PUs during hospitalization. Our analysis included 139 variables, including baseline characteristics, neurological status (International Standards for Neurological Classification of Spinal Cord Injury [ISNCSCI]), functional ability (Korean version of the Modified Barthel Index [K-MBI] and Functional Independence Measure [FIM]), and laboratory data. We used a variety of ML methods-a graph neural network (GNN), a deep neural network (DNN), a linear support vector machine (SVM_linear), a support vector machine with radial basis function kernel (SVM_RBF), K-nearest neighbors (KNN), a random forest (RF), and logistic regression (LR)-focusing on an integrative analysis of laboratory, neurological, and functional data. (3) Results: The SVM_linear algorithm using these composite data showed superior predictive ability (area under the receiver operating characteristic curve (AUC) = 0.904, accuracy = 0.944), as demonstrated by a 5-fold cross-validation. The critical discriminators of PU development were identified based on limb functional status and laboratory markers of inflammation. External validation highlighted the challenges of model generalization and provided a direction for future research. (4) Conclusions: Our study highlights the importance of a comprehensive, multidimensional data approach for the effective prediction of PUs in patients with SCI, especially in the acute and subacute phases. The proposed ML models show potential for the early detection and prevention of PUs, thus contributing substantially to improving patient care in clinical settings.
Collapse
Affiliation(s)
- Yuna Kim
- Department of Rehabilitation Medicine, College of Medicine, Dankook University, Cheonan 31116, Republic of Korea; (Y.K.); (S.Y.K.); (T.U.K.); (S.J.L.)
| | - Myungeun Lim
- Digital Biomedical Research Division, Electronics and Telecommunications Research Institute, Daejeon 34129, Republic of Korea; (M.L.); (S.P.); (Y.H.)
| | - Seo Young Kim
- Department of Rehabilitation Medicine, College of Medicine, Dankook University, Cheonan 31116, Republic of Korea; (Y.K.); (S.Y.K.); (T.U.K.); (S.J.L.)
| | - Tae Uk Kim
- Department of Rehabilitation Medicine, College of Medicine, Dankook University, Cheonan 31116, Republic of Korea; (Y.K.); (S.Y.K.); (T.U.K.); (S.J.L.)
| | - Seong Jae Lee
- Department of Rehabilitation Medicine, College of Medicine, Dankook University, Cheonan 31116, Republic of Korea; (Y.K.); (S.Y.K.); (T.U.K.); (S.J.L.)
| | - Soo-Kyung Bok
- Department of Rehabilitation Medicine, College of Medicine, Chungnam National University, Daejeon 35015, Republic of Korea;
| | - Soojun Park
- Digital Biomedical Research Division, Electronics and Telecommunications Research Institute, Daejeon 34129, Republic of Korea; (M.L.); (S.P.); (Y.H.)
| | - Youngwoong Han
- Digital Biomedical Research Division, Electronics and Telecommunications Research Institute, Daejeon 34129, Republic of Korea; (M.L.); (S.P.); (Y.H.)
| | - Ho-Youl Jung
- Digital Biomedical Research Division, Electronics and Telecommunications Research Institute, Daejeon 34129, Republic of Korea; (M.L.); (S.P.); (Y.H.)
| | - Jung Keun Hyun
- Department of Rehabilitation Medicine, College of Medicine, Dankook University, Cheonan 31116, Republic of Korea; (Y.K.); (S.Y.K.); (T.U.K.); (S.J.L.)
- Department of Nanobiomedical Science and BK21 NBM Global Research Center for Regenerative Medicine, Dankook University, Cheonan 31116, Republic of Korea
- Institute of Tissue Regeneration Engineering, Dankook University, Cheonan 31116, Republic of Korea
| |
Collapse
|
10
|
Hesni S, Baxter D, Saifuddin A. The imaging of cervical spondylotic myeloradiculopathy. Skeletal Radiol 2023; 52:2341-2365. [PMID: 37071191 DOI: 10.1007/s00256-023-04329-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Revised: 03/12/2023] [Accepted: 03/20/2023] [Indexed: 04/19/2023]
Abstract
This review provides a detailed description of the imaging features of cervical spondylotic myelopathy and radiculopathy, with a focus on MRI. Where relevant, we will outline grading systems of vertebral central canal and foraminal stenosis. Whilst post-operative appearances of the cervical spine are outside the scope of this paper, we will touch on imaging features recognised as predictors of clinical outcome and neurological recovery. This paper will serve as a reference for both radiologists and clinicians involved in the care of patients with cervical spondylotic myeloradiculopathy.
Collapse
Affiliation(s)
- Susan Hesni
- Department of Radiology, Royal National Orthopaedic Hospital (RNOH), Stanmore, UK.
| | - David Baxter
- Department of Surgery, Royal National Orthopaedic Hospital (RNOH), Stanmore, UK
| | - Asif Saifuddin
- Department of Radiology, Royal National Orthopaedic Hospital (RNOH), Stanmore, UK
| |
Collapse
|
11
|
Constant C, Aubin CE, Kremers HM, Garcia DVV, Wyles CC, Rouzrokh P, Larson AN. The use of deep learning in medical imaging to improve spine care: A scoping review of current literature and clinical applications. NORTH AMERICAN SPINE SOCIETY JOURNAL 2023; 15:100236. [PMID: 37599816 PMCID: PMC10432249 DOI: 10.1016/j.xnsj.2023.100236] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Accepted: 06/14/2023] [Indexed: 08/22/2023]
Abstract
Background Artificial intelligence is a revolutionary technology that promises to assist clinicians in improving patient care. In radiology, deep learning (DL) is widely used in clinical decision aids due to its ability to analyze complex patterns and images. It allows for rapid, enhanced data, and imaging analysis, from diagnosis to outcome prediction. The purpose of this study was to evaluate the current literature and clinical utilization of DL in spine imaging. Methods This study is a scoping review and utilized the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to review the scientific literature from 2012 to 2021. A search in PubMed, Web of Science, Embased, and IEEE Xplore databases with syntax specific for DL and medical imaging in spine care applications was conducted to collect all original publications on the subject. Specific data was extracted from the available literature, including algorithm application, algorithms tested, database type and size, algorithm training method, and outcome of interest. Results A total of 365 studies (total sample of 232,394 patients) were included and grouped into 4 general applications: diagnostic tools, clinical decision support tools, automated clinical/instrumentation assessment, and clinical outcome prediction. Notable disparities exist in the selected algorithms and the training across multiple disparate databases. The most frequently used algorithms were U-Net and ResNet. A DL model was developed and validated in 92% of included studies, while a pre-existing DL model was investigated in 8%. Of all developed models, only 15% of them have been externally validated. Conclusions Based on this scoping review, DL in spine imaging is used in a broad range of clinical applications, particularly for diagnosing spinal conditions. There is a wide variety of DL algorithms, database characteristics, and training methods. Future studies should focus on external validation of existing models before bringing them into clinical use.
Collapse
Affiliation(s)
- Caroline Constant
- Orthopedic Surgery AI Laboratory, Mayo Clinic, 200 1st St Southwest, Rochester, MN, 55902, United States
- Polytechnique Montreal, 2500 Chem. de Polytechnique, Montréal, QC H3T 1J4, Canada
- AO Research Institute Davos, Clavadelerstrasse 8, CH 7270, Davos, Switzerland
| | - Carl-Eric Aubin
- Polytechnique Montreal, 2500 Chem. de Polytechnique, Montréal, QC H3T 1J4, Canada
| | - Hilal Maradit Kremers
- Orthopedic Surgery AI Laboratory, Mayo Clinic, 200 1st St Southwest, Rochester, MN, 55902, United States
| | - Diana V. Vera Garcia
- Orthopedic Surgery AI Laboratory, Mayo Clinic, 200 1st St Southwest, Rochester, MN, 55902, United States
| | - Cody C. Wyles
- Orthopedic Surgery AI Laboratory, Mayo Clinic, 200 1st St Southwest, Rochester, MN, 55902, United States
- Department of Orthopedic Surgery, Mayo Clinic, 200, 1st St Southwest, Rochester, MN, 55902, United States
| | - Pouria Rouzrokh
- Orthopedic Surgery AI Laboratory, Mayo Clinic, 200 1st St Southwest, Rochester, MN, 55902, United States
- Radiology Informatics Laboratory, Mayo Clinic, 200, 1st St Southwest, Rochester, MN, 55902, United States
| | - Annalise Noelle Larson
- Orthopedic Surgery AI Laboratory, Mayo Clinic, 200 1st St Southwest, Rochester, MN, 55902, United States
- Department of Orthopedic Surgery, Mayo Clinic, 200, 1st St Southwest, Rochester, MN, 55902, United States
| |
Collapse
|
12
|
Pelc M, Vilimkova Kahankova R, Blaszczyszyn M, Mikolajewski D, Konieczny M, Khoma V, Bara G, Zygarlicki J, Martinek R, Gupta MK, Gorzelanczyk EJ, Pawłowski M, Czapiga B, Zygarlicka M, Kawala-Sterniuk A. Initial study on an expert system for spine diseases screening using inertial measurement unit. Sci Rep 2023; 13:10440. [PMID: 37369726 PMCID: PMC10300108 DOI: 10.1038/s41598-023-36798-7] [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: 11/24/2022] [Accepted: 06/12/2023] [Indexed: 06/29/2023] Open
Abstract
In recent times, widely understood spine diseases have advanced to one of the most urgetn problems where quick diagnosis and treatment are needed. To diagnose its specifics (e.g. to decide whether this is a scoliosis or sagittal imbalance) and assess its extend, various kind of imaging diagnostic methods (such as X-Ray, CT, MRI scan or ST) are used. However, despite their common use, some may be regarded as (to a level) invasive methods and there are cases where there are contraindications to using them. Besides, which is even more of a problem, these are very expensive methods and whilst their use for pure diagnostic purposes is absolutely valid, then due to their cost, they cannot rather be considered as tools which would be equally valid for bad posture screening programs purposes. This paper provides an initial evaluation of the alternative approach to the spine diseases diagnostic/screening using inertial measurement unit and we propose policy-based computing as the core for the inference systems. Although the methodology presented herein is potentially applicable to a variety of spine diseases, in the nearest future we will focus specifically on sagittal imbalance detection.
Collapse
Affiliation(s)
- Mariusz Pelc
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758, Opole, Poland.
- School of Computing and Mathematical Sciences, University of Greenwich, London, SE10 9LS, UK.
| | - Radana Vilimkova Kahankova
- Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Ostrava-Poruba, Czech Republic
| | - Monika Blaszczyszyn
- Faculty of Physical Education and Physiotherapy, Opole University of Technology, 45-758, Opole, Poland
| | - Dariusz Mikolajewski
- Faculty of Computer Science, Kazimierz Wielki University, 85-064, Bydgoszcz, Poland
| | - Mariusz Konieczny
- Faculty of Physical Education and Physiotherapy, Opole University of Technology, 45-758, Opole, Poland
| | - Volodymir Khoma
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758, Opole, Poland
- Lviv Polytechnic National University, Institute of Computer Technologies, Automation and Metrology, Lviv, Ukraine
| | - Gregor Bara
- Department of Neurosurgery, University Hospital Bonn, Bonn, Germany
| | - Jaroslaw Zygarlicki
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758, Opole, Poland
| | - Radek Martinek
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758, Opole, Poland
- Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Ostrava-Poruba, Czech Republic
| | - Munish K Gupta
- Faculty of Mechanical Engineering, Opole University of Technology, 45-271, Opole, Poland
- Department of Mechanical Engineering, Graphic Era University, Dehradun, India
| | - Edward Jacek Gorzelanczyk
- Faculty of Philosophy, Kazimierz Wielki University, Bydgoszcz, 85-092, Poland
- Faculty of Mathematics and Computer Science, Adam Mickiewicz University in Poznan, Poznan, 61-614, Poland
- Department of Theoretical Basis of Biomedical Sciences and Medical Informatics, Nicolaus Copernicus University, Collegium Medicum, 85-067, Bydgoszcz, Poland
- The Society for the Substitution Treatment of Addiction "Medically Assisted Recovery", 85-791, Bydgoszcz, Poland
- Psychiatric Department of Children and Adolescents Psychiatric Center in Warta, 98-290, Warta, Poland
| | - Mateusz Pawłowski
- Faculty of Health Sciences, Wroclaw Medical University, Wrocław, Poland
- Department of Neurosurgery, "Vital Medic" Hospital, Kluczbork, Poland
| | - Bogdan Czapiga
- Department of Neurosurgery, 4th Military Hospital in Wrocław, Wrocław, Poland
| | - Malgorzata Zygarlicka
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758, Opole, Poland
| | - Aleksandra Kawala-Sterniuk
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758, Opole, Poland.
| |
Collapse
|
13
|
Tamai K, Terai H, Hoshino M, Tabuchi H, Kato M, Toyoda H, Suzuki A, Takahashi S, Yabu A, Sawada Y, Iwamae M, Oka M, Nakaniwa K, Okada M, Nakamura H. Deep Learning Algorithm for Identifying Cervical Cord Compression Due to Degenerative Canal Stenosis on Radiography. Spine (Phila Pa 1976) 2023; 48:519-525. [PMID: 36763843 DOI: 10.1097/brs.0000000000004595] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 01/06/2023] [Indexed: 02/12/2023]
Abstract
STUDY DESIGN Cross-sectional study. OBJECTIVE Validate the diagnostic accuracy of a deep-learning algorithm for cervical cord compression due to degenerative canal stenosis on radiography. SUMMARY OF BACKGROUND DATA The diagnosis of degenerative cervical myelopathy is often delayed, resulting in improper management. Screening tools for suspected degenerative cervical myelopathy would help identify patients who require detailed physical evaluation. MATERIALS AND METHODS Data from 240 patients (120 with cervical stenosis on magnetic resonance imaging and 120 age and sex-matched controls) were randomly divided into training (n = 198) and test (n = 42) data sets. The deep-learning algorithm, designed to identify the suspected stenosis level on radiography, was constructed using a convolutional neural network model called EfficientNetB2, and radiography and magnetic resonance imaging data from the training data set. The accuracy and area under the curve of the receiver operating characteristic curve were calculated for the independent test data set. Finally, the number of correct diagnoses was compared between the algorithm and 10 physicians using the test cohort. RESULTS The diagnostic accuracy and area under the curve of the deep-learning algorithm were 0.81 and 0.81, respectively, in the independent test data set. The rate of correct responses in the test data set was significantly higher for the algorithm than for the physician's consensus (81.0% vs . 66.2%; P = 0.034). Furthermore, the accuracy of the algorithm was greater than that of each individual physician. CONCLUSIONS We developed a deep-learning algorithm capable of suggesting the presence of cervical spinal cord compression on cervical radiography and highlighting the suspected levels on radiographic imaging when cord compression is identified. The diagnostic accuracy of the algorithm was greater than that of spine physicians. LEVEL OF EVIDENCE Level IV.
Collapse
Affiliation(s)
- Koji Tamai
- Department of Orthopedics, Osaka Metropolitan University Graduate School of Medicine, Osaka, Japan
| | - Hidetomi Terai
- Department of Orthopedics, Osaka Metropolitan University Graduate School of Medicine, Osaka, Japan
| | - Masatoshi Hoshino
- Department of Orthopedics, Osaka Metropolitan University Graduate School of Medicine, Osaka, Japan
| | - Hitoshi Tabuchi
- Department of Technology and Design Thinking for Medicine, Hiroshima University, Hiroshima, Japan
- Department of Ophthalmology, Tsukazaki Hospital, Himeji, Japan
| | - Minori Kato
- Department of Orthopedics, Osaka Metropolitan University Graduate School of Medicine, Osaka, Japan
| | - Hiromitsu Toyoda
- Department of Orthopedics, Osaka Metropolitan University Graduate School of Medicine, Osaka, Japan
| | - Akinobu Suzuki
- Department of Orthopedics, Osaka Metropolitan University Graduate School of Medicine, Osaka, Japan
| | - Shinji Takahashi
- Department of Orthopedics, Osaka Metropolitan University Graduate School of Medicine, Osaka, Japan
| | - Akito Yabu
- Department of Orthopedics, Osaka Metropolitan University Graduate School of Medicine, Osaka, Japan
| | - Yuta Sawada
- Department of Orthopedics, Osaka Metropolitan University Graduate School of Medicine, Osaka, Japan
| | - Masayoshi Iwamae
- Department of Orthopedics, Osaka Metropolitan University Graduate School of Medicine, Osaka, Japan
| | - Makoto Oka
- Department of Orthopedics, Osaka Metropolitan University Graduate School of Medicine, Osaka, Japan
| | - Kazunori Nakaniwa
- Department of Orthopedics, Osaka Metropolitan University Graduate School of Medicine, Osaka, Japan
| | - Mitsuhiro Okada
- Department of Orthopedics, Osaka Metropolitan University Graduate School of Medicine, Osaka, Japan
| | - Hiroaki Nakamura
- Department of Orthopedics, Osaka Metropolitan University Graduate School of Medicine, Osaka, Japan
| |
Collapse
|
14
|
Gupta P, Kingston KA, O’Malley M, Williams RJ, Ramkumar PN. Advancements in Artificial Intelligence for Foot and Ankle Surgery: A Systematic Review. FOOT & ANKLE ORTHOPAEDICS 2023; 8:24730114221151079. [PMID: 36817020 PMCID: PMC9929923 DOI: 10.1177/24730114221151079] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2023] Open
Abstract
Background There has been a rapid increase in research applying artificial intelligence (AI) to various subspecialties of orthopaedic surgery, including foot and ankle surgery. The purpose of this systematic review is to (1) characterize the topics and objectives of studies using AI in foot and ankle surgery, (2) evaluate the performance of their models, and (3) evaluate their validity (internal or external validation). Methods A systematic literature review was conducted using PubMed/MEDLINE and Embase databases in December 2022. All studies that used AI or its subsets machine learning (ML) and deep learning (DL) in the setting of foot and ankle surgery relevant to orthopaedic surgeons were included. Studies were evaluated for their demographics, subject area, outcomes of interest, model(s) tested, model(s)' performance, and validity (internal or external). Results A total of 31 studies met inclusion criteria: 14 studies investigated AI for image interpretation, 13 studies investigated AI for clinical predictions, and 4 studies were grouped as "other." Studies commonly explored AI for ankle fractures, calcaneus fractures, hallux valgus, Achilles tendon pathologies, plantar fasciitis, and sports injuries. For studies reporting the area under the receiver operating characteristic curve (AUC), AUCs ranged from 0.64 (poor) to 0.99 (excellent). Two studies (6.45%) reported external validation. Conclusion Applications of AI in the field of foot and ankle surgery are expanding, particularly for image interpretation and clinical predictions. Current model performances range from poor to excellent, and most studies lack external validation, demonstrating a need for further research prior to deploying AI-based clinical applications. Level of Evidence Level III, retrospective cohort study.
Collapse
Affiliation(s)
- Puneet Gupta
- Department of Orthopaedic Surgery, George Washington University School of Medicine and Health Sciences, Washington, DC, USA
| | | | - Martin O’Malley
- Hospital for Special Surgery, New York, NY, USA,Brooklyn Nets, National Basketball Association (NBA), Brooklyn, NY, USA
| | - Riley J. Williams
- Hospital for Special Surgery, New York, NY, USA,Brooklyn Nets, National Basketball Association (NBA), Brooklyn, NY, USA
| | - Prem N. Ramkumar
- Hospital for Special Surgery, New York, NY, USA,Brooklyn Nets, National Basketball Association (NBA), Brooklyn, NY, USA,Prem N. Ramkumar, MD, MBA, Hospital for Special Surgery, 535 E 70th St, New York, NY 10021-4898, USA.
| |
Collapse
|
15
|
Detection of cervical spondylotic myelopathy based on gait analysis and deterministic learning. Artif Intell Rev 2023. [DOI: 10.1007/s10462-023-10404-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
|
16
|
Thavarajasingam SG, El-Khatib M, Vemulapalli K, Iradukunda HAS, K. SV, Borchert R, Russo S, Eide PK. Radiological predictors of shunt response in the diagnosis and treatment of idiopathic normal pressure hydrocephalus: a systematic review and meta-analysis. Acta Neurochir (Wien) 2023; 165:369-419. [PMID: 36435931 PMCID: PMC9922237 DOI: 10.1007/s00701-022-05402-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 10/24/2022] [Indexed: 11/28/2022]
Abstract
BACKGROUND Patients with the dementia subtype idiopathic normal pressure hydrocephalus (iNPH) may improve clinically following cerebrospinal fluid (CSF) diversion (shunt) surgery, though the predictors of shunt response remain debated. Currently, radiological features play an important role in the diagnosis of iNPH, but it is not well established which radiological markers most precisely predict shunt responsive iNPH. OBJECTIVE To conduct a systematic review and meta-analysis to identify radiological predictors of shunt responsiveness, evaluate their diagnostic effectiveness, and recommend the most predictive radiological features. METHODS Embase, MEDLINE, Scopus, PubMed, Google Scholar, and JSTOR were searched for original studies investigating radiological predictors of shunt response in iNPH patients. Included studies were assessed using the ROBINS-1 tool, and eligible studies were evaluated using a univariate meta-analysis. RESULTS Overall, 301 full-text papers were screened, of which 28 studies were included, and 26 different radiological features were identified, 5 of these met the inclusion criteria for the meta-analysis: disproportionately enlarged subarachnoid space (DESH), callosal angle, periventricular white matter changes, cerebral blood flow (CBF), and computerized tomography cisternography. The meta-analysis showed that only callosal angle and periventricular white matter changes significantly differentiated iNPH shunt responders from non-responders, though both markers had a low diagnostic odds ratio (DOR) of 1.88 and 1.01 respectively. None of the other radiological markers differentiated shunt responsive from shunt non-responsive iNPH. CONCLUSION Callosal angle and periventricular changes are the only diagnostically effective radiological predictors of shunt responsive iNPH patients. However, due to the DORs approximating 1, they are insufficient as sole predictors and are advised to be used only in combination with other diagnostic tests of shunt response. Future research must evaluate the combined use of multiple radiological predictors, as it may yield beneficial additive effects that may allow for more robust radiological shunt response prediction.
Collapse
Affiliation(s)
| | | | | | | | | | - Robin Borchert
- Department of Clinical Neurosciences, Cambridge University Hospital NHS Trust, Cambridge, UK
| | - Salvatore Russo
- Department of Neurosurgery, Imperial College Healthcare NHS Trust, London, UK
| | - Per K. Eide
- Department of Neurosurgery, Oslo University Hospital – Rikshospitalet, Oslo, Norway ,Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| |
Collapse
|
17
|
Roy S, Meena T, Lim SJ. Demystifying Supervised Learning in Healthcare 4.0: A New Reality of Transforming Diagnostic Medicine. Diagnostics (Basel) 2022; 12:2549. [PMID: 36292238 PMCID: PMC9601517 DOI: 10.3390/diagnostics12102549] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 10/17/2022] [Accepted: 10/18/2022] [Indexed: 11/17/2022] Open
Abstract
The global healthcare sector continues to grow rapidly and is reflected as one of the fastest-growing sectors in the fourth industrial revolution (4.0). The majority of the healthcare industry still uses labor-intensive, time-consuming, and error-prone traditional, manual, and manpower-based methods. This review addresses the current paradigm, the potential for new scientific discoveries, the technological state of preparation, the potential for supervised machine learning (SML) prospects in various healthcare sectors, and ethical issues. The effectiveness and potential for innovation of disease diagnosis, personalized medicine, clinical trials, non-invasive image analysis, drug discovery, patient care services, remote patient monitoring, hospital data, and nanotechnology in various learning-based automation in healthcare along with the requirement for explainable artificial intelligence (AI) in healthcare are evaluated. In order to understand the potential architecture of non-invasive treatment, a thorough study of medical imaging analysis from a technical point of view is presented. This study also represents new thinking and developments that will push the boundaries and increase the opportunity for healthcare through AI and SML in the near future. Nowadays, SML-based applications require a lot of data quality awareness as healthcare is data-heavy, and knowledge management is paramount. Nowadays, SML in biomedical and healthcare developments needs skills, quality data consciousness for data-intensive study, and a knowledge-centric health management system. As a result, the merits, demerits, and precautions need to take ethics and the other effects of AI and SML into consideration. The overall insight in this paper will help researchers in academia and industry to understand and address the future research that needs to be discussed on SML in the healthcare and biomedical sectors.
Collapse
Affiliation(s)
- Sudipta Roy
- Artificial Intelligence & Data Science, Jio Institute, Navi Mumbai 410206, India
| | - Tanushree Meena
- Artificial Intelligence & Data Science, Jio Institute, Navi Mumbai 410206, India
| | - Se-Jung Lim
- Division of Convergence, Honam University, 120, Honamdae-gil, Gwangsan-gu, Gwangju 62399, Korea
| |
Collapse
|
18
|
Deep Learning Model for Grading Metastatic Epidural Spinal Cord Compression on Staging CT. Cancers (Basel) 2022; 14:cancers14133219. [PMID: 35804990 PMCID: PMC9264856 DOI: 10.3390/cancers14133219] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 06/21/2022] [Accepted: 06/24/2022] [Indexed: 02/02/2023] Open
Abstract
Background: Metastatic epidural spinal cord compression (MESCC) is a disastrous complication of advanced malignancy. Deep learning (DL) models for automatic MESCC classification on staging CT were developed to aid earlier diagnosis. Methods: This retrospective study included 444 CT staging studies from 185 patients with suspected MESCC who underwent MRI spine studies within 60 days of the CT studies. The DL model training/validation dataset consisted of 316/358 (88%) and the test set of 42/358 (12%) CT studies. Training/validation and test datasets were labeled in consensus by two subspecialized radiologists (6 and 11-years-experience) using the MRI studies as the reference standard. Test sets were labeled by the developed DL models and four radiologists (2−7 years of experience) for comparison. Results: DL models showed almost-perfect interobserver agreement for classification of CT spine images into normal, low, and high-grade MESCC, with kappas ranging from 0.873−0.911 (p < 0.001). The DL models (lowest κ = 0.873, 95% CI 0.858−0.887) also showed superior interobserver agreement compared to two of the four radiologists for three-class classification, including a specialist (κ = 0.820, 95% CI 0.803−0.837) and general radiologist (κ = 0.726, 95% CI 0.706−0.747), both p < 0.001. Conclusion: DL models for the MESCC classification on a CT showed comparable to superior interobserver agreement to radiologists and could be used to aid earlier diagnosis.
Collapse
|
19
|
Zhou Y, Shi J. Brain Structural and Functional Dissociated Patterns in Degenerative Cervical Myelopathy: A Case-Controlled Retrospective Resting-State fMRI Study. Front Neurol 2022; 13:895348. [PMID: 35785340 PMCID: PMC9240811 DOI: 10.3389/fneur.2022.895348] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Accepted: 05/03/2022] [Indexed: 11/23/2022] Open
Abstract
Background Previous studies have shown the whole-brain global functional connectivity density (gFCD) and gray matter volume (GMV) alterations in patients with degenerative cervical myelopathy (DCM). However, no study aimed to investigate the associations between the spatial patterns of GMV and gFCD alterations in patients with DCM. Methods Structural data and resting-state functional MRI data of 35 DCM patients and 35 matched healthy controls were collected to assess their gFCD and GMV and investigate gFCD and GMV alterations in patients with DCM and their spatial pattern associations. Results In our current study, significant gFCD and GMV differences were observed in some regions of the visual system, sensorimotor cortices, and cerebellum between patients with DCM and healthy controls. In our findings, decreased gFCD was found in areas primarily located at the sensorimotor cortices, while increased gFCD was observed primarily within areas located at the visual system and cerebellum. Decreased GMV was seen in the left thalamus, bilateral supplementary motor area (SMA), and left inferior occipital cortices in patients with DCM, while increased GMV was observed in the cerebellum. Conclusion Our findings suggest that structural and functional alterations independently contributed to the neuropathology of DCM. However, longitudinal studies are still needed to further illustrate the associations between structural deficits and functional alterations underlying the onset of brain abnormalities as DCM develops.
Collapse
|
20
|
Gao KT, Tibrewala R, Hess M, Bharadwaj UU, Inamdar G, Link TM, Chin CT, Pedoia V, Majumdar S. Automatic detection and voxel-wise mapping of lumbar spine Modic changes with deep learning. JOR Spine 2022; 5:e1204. [PMID: 35783915 PMCID: PMC9238279 DOI: 10.1002/jsp2.1204] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 05/01/2022] [Accepted: 05/05/2022] [Indexed: 11/14/2022] Open
Abstract
Background Modic changes (MCs) are the most prevalent classification system for describing magnetic resonance imaging (MRI) signal intensity changes in the vertebrae. However, there is a growing need for novel quantitative and standardized methods of characterizing these anomalies, particularly for lesions of transitional or mixed nature, due to the lack of conclusive evidence of their associations with low back pain. This retrospective imaging study aims to develop an interpretable deep learning-based detection tool for voxel-wise mapping of MCs. Methods Seventy-five lumbar spine MRI exams that presented with acute-to-chronic low back pain, radiculopathy, and other symptoms of the lumbar spine were enrolled. The pipeline consists of two deep convolutional neural networks to generate an interpretable voxel-wise Modic map. First, an autoencoder was trained to segment vertebral bodies from T1-weighted sagittal lumbar spine images. Next, two radiologists segmented and labeled MCs from a combined T1- and T2-weighted assessment to serve as ground truth for training a second autoencoder that performs segmentation of MCs. The voxels in the detected regions were then categorized to the appropriate Modic type using a rule-based signal intensity algorithm. Post hoc, three radiologists independently graded a second dataset with the aid of the model predictions in an artificial (AI)-assisted experiment. Results The model successfully identified the presence of changes in 85.7% of samples in the unseen test set with a sensitivity of 0.71 (±0.072), specificity of 0.95 (±0.022), and Cohen's kappa score of 0.63. In the AI-assisted experiment, the agreement between the junior radiologist and the senior neuroradiologist significantly improved from Cohen's kappa score of 0.52 to 0.58 (p < 0.05). Conclusions This deep learning-based approach demonstrates substantial agreement with radiologists and may serve as a tool to improve inter-rater reliability in the assessment of MCs.
Collapse
Affiliation(s)
- Kenneth T. Gao
- Department of Radiology and Biomedical ImagingUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
- Department of BioengineeringUniversity of California Berkeley–University of California San Francisco Graduate Program in BioengineeringBerkeleyCaliforniaUSA
| | - Radhika Tibrewala
- Department of Radiology and Biomedical ImagingUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - Madeline Hess
- Department of Radiology and Biomedical ImagingUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - Upasana U. Bharadwaj
- Department of Radiology and Biomedical ImagingUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - Gaurav Inamdar
- Department of Radiology and Biomedical ImagingUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - Thomas M. Link
- Department of Radiology and Biomedical ImagingUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - Cynthia T. Chin
- Department of Radiology and Biomedical ImagingUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - Valentina Pedoia
- Department of Radiology and Biomedical ImagingUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - Sharmila Majumdar
- Department of Radiology and Biomedical ImagingUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| |
Collapse
|
21
|
Ogawa T, Yoshii T, Oyama J, Sugimura N, Akada T, Sugino T, Hashimoto M, Morishita S, Takahashi T, Motoyoshi T, Oyaizu T, Yamada T, Onuma H, Hirai T, Inose H, Nakajima Y, Okawa A. Detecting ossification of the posterior longitudinal ligament on plain radiographs using a deep convolutional neural network: a pilot study. Spine J 2022; 22:934-940. [PMID: 35017056 DOI: 10.1016/j.spinee.2022.01.004] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 12/01/2021] [Accepted: 01/03/2022] [Indexed: 02/03/2023]
Abstract
BACKGROUND CONTEXT Its rare prevalence and subtle radiological changes often lead to difficulties in diagnosing cervical ossification of the posterior longitudinal ligament (OPLL) on plain radiographs. However, OPLL progression may lead to trauma-induced spinal cord injury, resulting in severe paralysis. To address the difficulties in diagnosis, a deep learning approach using a convolutional neural network (CNN) was applied. PURPOSE The aim of our research was to evaluate the performance of a CNN model for diagnosing cervical OPLL. STUDY DESIGN AND SETTING Diagnostic image study. PATIENT SAMPLE This study included 50 patients with cervical OPLL, and 50 control patients with plain radiographs. OUTCOME MEASURES For the CNN model performance evaluation, we calculated the area under the receiver operating characteristic curve (AUC). We also compared the sensitivity, specificity, and accuracy of the diagnosis by the CNN with those of general orthopedic surgeons and spine specialists. METHODS Computed tomography was used as the gold standard for diagnosis. Radiographs of the cervical spine in neutral, flexion, and extension positions were used for training and validation of the CNN model. We used the deep learning PyTorch framework to construct the CNN architecture. RESULTS The accuracy of the CNN model was 90% (18/20), with a sensitivity and specificity of 80% and 100%, respectively. In contrast, the mean accuracy of orthopedic surgeons was 70%, with a sensitivity and specificity of 73% (SD: 0.12) and 67% (SD: 0.17), respectively. The mean accuracy of the spine surgeons was 75%, with a sensitivity and specificity of 80% (SD: 0.08) and 70% (SD: 0.08), respectively. The AUC of the CNN model based on the radiographs was 0.924. CONCLUSIONS The CNN model had successful diagnostic accuracy and sufficient specificity in the diagnosis of OPLL.
Collapse
Affiliation(s)
- Takahisa Ogawa
- Department of Orthopaedic and Spine Surgery, Tokyo Medical and Dental University Graduate School of Medicine, Tokyo, Japan
| | - Toshitaka Yoshii
- Department of Orthopaedic and Spine Surgery, Tokyo Medical and Dental University Graduate School of Medicine, Tokyo, Japan.
| | - Jun Oyama
- Department of Radiology, Tokyo Medical and Dental University Graduate School of Medicine, Tokyo, Japan
| | - Nobuhiro Sugimura
- Tokyo Medical and Dental University School of Medicine, Tokyo, Japan
| | - Takashi Akada
- Tokyo Medical and Dental University School of Medicine, Tokyo, Japan
| | - Takaaki Sugino
- Department of Biomedical Information, Institute of Biomaterials and Bioengineering, Tokyo Medical and Dental University, Tokyo, Japan
| | - Motonori Hashimoto
- Department of Orthopaedic and Spine Surgery, Tokyo Medical and Dental University Graduate School of Medicine, Tokyo, Japan
| | - Shingo Morishita
- Department of Orthopaedic and Spine Surgery, Tokyo Medical and Dental University Graduate School of Medicine, Tokyo, Japan
| | - Takuya Takahashi
- Department of Orthopaedic and Spine Surgery, Tokyo Medical and Dental University Graduate School of Medicine, Tokyo, Japan
| | - Takayuki Motoyoshi
- Department of Orthopaedic and Spine Surgery, Tokyo Medical and Dental University Graduate School of Medicine, Tokyo, Japan
| | - Takuya Oyaizu
- Department of Orthopaedic and Spine Surgery, Tokyo Medical and Dental University Graduate School of Medicine, Tokyo, Japan
| | - Tsuyoshi Yamada
- Department of Orthopaedic and Spine Surgery, Tokyo Medical and Dental University Graduate School of Medicine, Tokyo, Japan
| | - Hiroaki Onuma
- Department of Orthopaedic and Spine Surgery, Tokyo Medical and Dental University Graduate School of Medicine, Tokyo, Japan
| | - Takashi Hirai
- Department of Orthopaedic and Spine Surgery, Tokyo Medical and Dental University Graduate School of Medicine, Tokyo, Japan
| | - Hiroyuki Inose
- Department of Orthopaedic and Spine Surgery, Tokyo Medical and Dental University Graduate School of Medicine, Tokyo, Japan
| | - Yoshikazu Nakajima
- Department of Biomedical Information, Institute of Biomaterials and Bioengineering, Tokyo Medical and Dental University, Tokyo, Japan
| | - Atsushi Okawa
- Department of Orthopaedic and Spine Surgery, Tokyo Medical and Dental University Graduate School of Medicine, Tokyo, Japan
| |
Collapse
|
22
|
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] [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.
Collapse
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
| |
Collapse
|
23
|
Deep Learning Approaches for Video Compression: A Bibliometric Analysis. BIG DATA AND COGNITIVE COMPUTING 2022. [DOI: 10.3390/bdcc6020044] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Every data and kind of data need a physical drive to store it. There has been an explosion in the volume of images, video, and other similar data types circulated over the internet. Users using the internet expect intelligible data, even under the pressure of multiple resource constraints such as bandwidth bottleneck and noisy channels. Therefore, data compression is becoming a fundamental problem in wider engineering communities. There has been some related work on data compression using neural networks. Various machine learning approaches are currently applied in data compression techniques and tested to obtain better lossy and lossless compression results. A very efficient and variety of research is already available for image compression. However, this is not the case for video compression. Because of the explosion of big data and the excess use of cameras in various places globally, around 82% of the data generated involve videos. Proposed approaches have used Deep Neural Networks (DNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs), and various variants of Autoencoders (AEs) are used in their approaches. All newly proposed methods aim to increase performance (reducing bitrate up to 50% at the same data quality and complexity). This paper presents a bibliometric analysis and literature survey of all Deep Learning (DL) methods used in video compression in recent years. Scopus and Web of Science are well-known research databases. The results retrieved from them are used for this analytical study. Two types of analysis are performed on the extracted documents. They include quantitative and qualitative results. In quantitative analysis, records are analyzed based on their citations, keywords, source of publication, and country of publication. The qualitative analysis provides information on DL-based approaches for video compression, as well as the advantages, disadvantages, and challenges of using them.
Collapse
|
24
|
Study on Automatic Multi-Classification of Spine Based on Deep Learning and Postoperative Infection Screening. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:2779686. [PMID: 35360477 PMCID: PMC8964172 DOI: 10.1155/2022/2779686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 12/15/2021] [Accepted: 01/05/2022] [Indexed: 11/17/2022]
Abstract
The preoperative qualitative and hierarchical diagnosis of intervertebral foramen stenosis is very important for clinicians to explore the effect of multimodal analgesia nursing on pain control after spinal fusion and to formulate treatment strategies and patients' health recovery. However, there are still many problems in this aspect, and there is a lack of relevant research and effective methods to assist clinicians in diagnosis. Therefore, to improve the accuracy of computer-aided diagnosis of intervertebral foramen stenosis and the work efficiency of doctors, a deep learning automatic grading algorithm of intervertebral foramen stenosis image is proposed in this study. The image of intervertebral foramen was extracted from the MRI image of sagittal spine, and the image was preprocessed. 86 patients with spinal fusion treated in our hospital, specifically from May 2018 to May 2020, were randomly divided into the control group (routine analgesic nursing) and the multimodal group (multimodal analgesic nursing), with 43 cases in each group. The pain control effect and satisfaction of the two groups were observed. The results after multimodal analgesia nursing show that the VASs of the multimodal group at different time points were significantly lower than those of the control group (P < 0.05); the satisfaction score of pain control in the multimodal group was significantly higher than that in the control group (P < 0.05). Multimodal analgesia nursing for patients undergoing spinal fusion can effectively reduce the degree of postoperative pain and improve the effect of pain control and satisfaction with pain control, which is worthy of promotion.
Collapse
|
25
|
Doerr SA, Weber-Levine C, Hersh AM, Awosika T, Judy B, Jin Y, Raj D, Liu A, Lubelski D, Jones CK, Sair HI, Theodore N. Automated prediction of the Thoracolumbar Injury Classification and Severity Score from CT using a novel deep learning algorithm. Neurosurg Focus 2022; 52:E5. [PMID: 35364582 DOI: 10.3171/2022.1.focus21745] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 01/18/2022] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Damage to the thoracolumbar spine can confer significant morbidity and mortality. The Thoracolumbar Injury Classification and Severity Score (TLICS) is used to categorize injuries and determine patients at risk of spinal instability for whom surgical intervention is warranted. However, calculating this score can constitute a bottleneck in triaging and treating patients, as it relies on multiple imaging studies and a neurological examination. Therefore, the authors sought to develop and validate a deep learning model that can automatically categorize vertebral morphology and determine posterior ligamentous complex (PLC) integrity, two critical features of TLICS, using only CT scans. METHODS All patients who underwent neurosurgical consultation for traumatic spine injury or degenerative pathology resulting in spine injury at a single tertiary center from January 2018 to December 2019 were retrospectively evaluated for inclusion. The morphology of injury and integrity of the PLC were categorized on CT scans. A state-of-the-art object detection region-based convolutional neural network (R-CNN), Faster R-CNN, was leveraged to predict both vertebral locations and the corresponding TLICS. The network was trained with patient CT scans, manually labeled vertebral bounding boxes, TLICS morphology, and PLC annotations, thus allowing the model to output the location of vertebrae, categorize their morphology, and determine the status of PLC integrity. RESULTS A total of 111 patients were included (mean ± SD age 62 ± 20 years) with a total of 129 separate injury classifications. Vertebral localization and PLC integrity classification achieved Dice scores of 0.92 and 0.88, respectively. Binary classification between noninjured and injured morphological scores demonstrated 95.1% accuracy. TLICS morphology accuracy, the true positive rate, and positive injury mismatch classification rate were 86.3%, 76.2%, and 22.7%, respectively. Classification accuracy between no injury and suspected PLC injury was 86.8%, while true positive, false negative, and false positive rates were 90.0%, 10.0%, and 21.8%, respectively. CONCLUSIONS In this study, the authors demonstrate a novel deep learning method to automatically predict injury morphology and PLC disruption with high accuracy. This model may streamline and improve diagnostic decision support for patients with thoracolumbar spinal trauma.
Collapse
Affiliation(s)
- Sophia A Doerr
- 1Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore
| | - Carly Weber-Levine
- 1Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore
| | - Andrew M Hersh
- 1Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore
| | - Tolulope Awosika
- 1Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore
| | - Brendan Judy
- 1Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore
| | - Yike Jin
- 1Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore
| | - Divyaansh Raj
- 1Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore
| | - Ann Liu
- 1Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore
| | - Daniel Lubelski
- 1Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore
| | - Craig K Jones
- 2Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore; and
| | - Haris I Sair
- 3Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Nicholas Theodore
- 1Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore
| |
Collapse
|
26
|
Ouyang H, Meng F, Liu J, Song X, Li Y, Yuan Y, Wang C, Lang N, Tian S, Yao M, Liu X, Yuan H, Jiang S, Jiang L. Evaluation of Deep Learning-Based Automated Detection of Primary Spine Tumors on MRI Using the Turing Test. Front Oncol 2022; 12:814667. [PMID: 35359400 PMCID: PMC8962659 DOI: 10.3389/fonc.2022.814667] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Accepted: 02/16/2022] [Indexed: 01/04/2023] Open
Abstract
BackgroundRecently, the Turing test has been used to investigate whether machines have intelligence similar to humans. Our study aimed to assess the ability of an artificial intelligence (AI) system for spine tumor detection using the Turing test.MethodsOur retrospective study data included 12179 images from 321 patients for developing AI detection systems and 6635 images from 187 patients for the Turing test. We utilized a deep learning-based tumor detection system with Faster R-CNN architecture, which generates region proposals by Region Proposal Network in the first stage and corrects the position and the size of the bounding box of the lesion area in the second stage. Each choice question featured four bounding boxes enclosing an identical tumor. Three were detected by the proposed deep learning model, whereas the other was annotated by a doctor; the results were shown to six doctors as respondents. If the respondent did not correctly identify the image annotated by a human, his answer was considered a misclassification. If all misclassification rates were >30%, the respondents were considered unable to distinguish the AI-detected tumor from the human-annotated one, which indicated that the AI system passed the Turing test.ResultsThe average misclassification rates in the Turing test were 51.2% (95% CI: 45.7%–57.5%) in the axial view (maximum of 62%, minimum of 44%) and 44.5% (95% CI: 38.2%–51.8%) in the sagittal view (maximum of 59%, minimum of 36%). The misclassification rates of all six respondents were >30%; therefore, our AI system passed the Turing test.ConclusionOur proposed intelligent spine tumor detection system has a similar detection ability to annotation doctors and may be an efficient tool to assist radiologists or orthopedists in primary spine tumor detection.
Collapse
Affiliation(s)
- Hanqiang Ouyang
- Department of Orthopaedics, Peking University Third Hospital, Beijing, China
- Engineering Research Center of Bone and Joint Precision Medicine, Beijing, China
- Beijing Key Laboratory of Spinal Disease Research, Beijing, China
| | - Fanyu Meng
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Jianfang Liu
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Xinhang Song
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
| | - Yuan Li
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Yuan Yuan
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Chunjie Wang
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Ning Lang
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Shuai Tian
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Meiyi Yao
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Xiaoguang Liu
- Department of Orthopaedics, Peking University Third Hospital, Beijing, China
- Engineering Research Center of Bone and Joint Precision Medicine, Beijing, China
- Beijing Key Laboratory of Spinal Disease Research, Beijing, China
| | - Huishu Yuan
- Department of Radiology, Peking University Third Hospital, Beijing, China
- *Correspondence: Huishu Yuan, ; Shuqiang Jiang, ; Liang Jiang,
| | - Shuqiang Jiang
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
- *Correspondence: Huishu Yuan, ; Shuqiang Jiang, ; Liang Jiang,
| | - Liang Jiang
- Department of Orthopaedics, Peking University Third Hospital, Beijing, China
- Engineering Research Center of Bone and Joint Precision Medicine, Beijing, China
- Beijing Key Laboratory of Spinal Disease Research, Beijing, China
- *Correspondence: Huishu Yuan, ; Shuqiang Jiang, ; Liang Jiang,
| |
Collapse
|
27
|
Min Kim H, Ko T, Young Choi I, Myong JP. Asbestosis diagnosis algorithm combining the lung segmentation method and deep learning model in computed tomography image. Int J Med Inform 2021; 158:104667. [PMID: 34952282 DOI: 10.1016/j.ijmedinf.2021.104667] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 12/02/2021] [Accepted: 12/15/2021] [Indexed: 12/23/2022]
Abstract
BACKGROUND Early detection of asbestosis is important; hence, quick and accurate diagnostic tools are essential. This study aimed to develop an algorithm that combines lung segmentation and deep learning models that can be utilized as a clinical decision support system (CDSS) for diagnosing patients with asbestosis in segmented computed tomography (CT) images. METHODS We accurately segmented the lungs in CT images of patients examined at Seoul St. Mary's Hospital using a threshold-based method. Lungs with asbestosis and normal lungs were classified by applying the segmented image to the long-term recurrent convolutional network deep learning model. Performance was evaluated using the area under the receiver operating characteristic curve (AUROC) and F1 score from the test data. RESULTS The algorithm developed using the DenseNet201pre-trained model showed excellent performance, with a sensitivity of 0.962, specificity of 0.975, accuracy of 0.970, AUROC of 0.968, and F1 score of 0.961. CONCLUSIONS We developed an algorithm with significantly better diagnostic accuracy than a radiologist (0.970 vs. 0.73-0.79). Our developed algorithm is expected to be an excellent support tool if used as a CDSS to diagnose asbestosis using CT images.
Collapse
Affiliation(s)
- Hyung Min Kim
- Department of Medical Informatics, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea; Department of Biomedicine and Health Sciences, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea
| | - Taehoon Ko
- Department of Medical Informatics, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea
| | - In Young Choi
- Department of Medical Informatics, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea; Department of Biomedicine and Health Sciences, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea.
| | - Jun-Pyo Myong
- Department of Occupational and Environment Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea.
| |
Collapse
|