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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.
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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
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Xu Z, Wang YH, Cheng ZY, Feng YZ, Li XC, Zhou Q, Cai XR. Combined radiomics nomogram of different machine learning models for preoperative distinguishing intraspinal schwannomas and meningiomas: a multicenter and comparative study. Clin Radiol 2024; 79:e1108-e1116. [PMID: 38849236 DOI: 10.1016/j.crad.2024.05.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2024] [Revised: 05/12/2024] [Accepted: 05/14/2024] [Indexed: 06/09/2024]
Abstract
AIMS The objective of our study was to establish and verify a novel combined model based on multiparameter magnetic resonance imaging (MRI) radiomics and clinical features to distinguish intraspinal schwannomas from meningiomas. MATERIALS AND METHODS This research analyzed the preoperative magnetic resonance (MR) images and clinical characteristics of 209 patients with intraspinal tumors who received tumor resection at three institutions. 159 individuals from institutions 1 and 2 were randomly assigned into a training group (n=111) and a test group (n=48) in a 7-3 ratio. A nomogram was constructed using the training cohort and was internally and externally verified in the test cohort and an independent validation cohort (n=50). Model performance was assessed utilizing the area under the curve (AUC) of receiver operating characteristics (ROC), decision curve analysis (DCA), and calibration curves. RESULTS The nomogram exhibited superior predictive efficacy in distinguishing between spinal schwannomas and meningiomas when compared to both the radiomics model and the clinical model. The nomogram yielded AUCs of 0.994, 0.962, and 0.949 in the training, test, and external validation cohorts, respectively, indicating its exceptional differentiating ability. The DCAs demonstrated that the nomogram yielded the best net benefit. The calibration curves indicated that the nomogram got good agreement between the predicted and the actual observation. CONCLUSION This research suggests that the nomogram incorporating clinical and radiomic features may be an effective auxiliary tool for distinguishing between intraspinal schwannomas and meningiomas, and has important clinical significance for clinical decision-making and prognosis prediction.
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Affiliation(s)
- Z Xu
- Medical Imaging Center, First Affiliated Hospital of Jinan University, No.613 West Huangpu Avenue, Tianhe District, Guangzhou 510630, Guangdong, China
| | - Y-H Wang
- Department of Radiology, Third Affiliated Hospital of Southern Medical University, 183 Zhongshan Avenue West, Tianhe District, Guangzhou 510630, China; Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, No. 151 Yanjiangxi Road, Guangzhou, Guangdong 510120, China
| | - Z-Y Cheng
- Medical Imaging Center, First Affiliated Hospital of Jinan University, No.613 West Huangpu Avenue, Tianhe District, Guangzhou 510630, Guangdong, China
| | - Y-Z Feng
- Medical Imaging Center, First Affiliated Hospital of Jinan University, No.613 West Huangpu Avenue, Tianhe District, Guangzhou 510630, Guangdong, China
| | - X-C Li
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, No. 151 Yanjiangxi Road, Guangzhou, Guangdong 510120, China.
| | - Q Zhou
- Department of Radiology, Third Affiliated Hospital of Southern Medical University, 183 Zhongshan Avenue West, Tianhe District, Guangzhou 510630, China.
| | - X-R Cai
- Medical Imaging Center, First Affiliated Hospital of Jinan University, No.613 West Huangpu Avenue, Tianhe District, Guangzhou 510630, Guangdong, China.
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Nakamae T, Kamei N, Tamura T, Maruyama T, Nakao K, Farid F, Fukui H, Adachi N. Differentiation of the Intradural Extramedullary Spinal Tumors, Schwannomas, and Meningiomas Utilizing the Contrast Ratio as a Quantitative Magnetic Resonance Imaging Method. World Neurosurg 2024; 188:e320-e325. [PMID: 38797281 DOI: 10.1016/j.wneu.2024.05.106] [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: 05/07/2024] [Accepted: 05/16/2024] [Indexed: 05/29/2024]
Abstract
BACKGROUND Schwannomas and meningiomas are the most common intradural extramedullary spinal tumors; however, differentiating between them using magnetic resonance imaging (MRI) is a frequent challenge. In this study, we aimed to investigate the use of the contrast ratio (CR) as a quantitative MRI method in the differentiation of schwannomas and meningiomas. METHODS We analyzed the data of patients with intradural extramedullary spinal tumors who underwent surgery and were diagnosed with either schwannomas or meningiomas by histopathological analysis. Regions of interest were set for the entire spinal tumor on T2-weighted sagittal MRI. To obtain the CR values of spinal tumors (CRtumor), we used the signal intensity (SI) values of the tumor (SItumor) and spinal cord (SIcord) according to the following formula: [CRtumor = (SItumor-SIcord)/(SItumor+SIcord)]. RESULTS The study included 50 patients (23 males and 27 females) with a mean age of 61.5 years old (11-85 years old). Histopathological analysis revealed that 33 and 17 patients were diagnosed with schwannomas and meningiomas, respectively. The mean CR values of the schwannomas and meningiomas were 0.3040 ± 0.1386 and 0.0173 ± 0.1929, respectively. The CR value of the schwannomas was statistically significantly higher than that of meningiomas (P < 0.01). The cutoff CR value obtained from the receiver operating characteristic curve was 0.143, with a specificity and sensitivity of 90.9% and 88.2%, respectively. Furthermore, the value for the area under the receiver operating characteristic curve was 0.925 (95% confidence interval: 0.852-0.998). CONCLUSIONS The evaluation of CRs by using MRI to distinguish between schwannomas and meningiomas is a beneficial quantitative tool.
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Affiliation(s)
- Toshio Nakamae
- Department of Orthopaedic Surgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.
| | - Naosuke Kamei
- Department of Orthopaedic Surgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Takayuki Tamura
- Department of Clinical Support, Hiroshima University Hospital, Hiroshima, Japan
| | - Toshiaki Maruyama
- Department of Orthopaedic Surgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Kazuto Nakao
- Department of Orthopaedic Surgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Fadlyansyah Farid
- Department of Orthopaedic Surgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan; Departement of Orthopaedic and Traumatology, Faculty of Medicine, Hasanuddin University, Makassar, Indonesia
| | - Hiroki Fukui
- Department of Orthopaedic Surgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Nobuo Adachi
- Department of Orthopaedic Surgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
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Suzuki H, Kokabu T, Yamada K, Ishikawa Y, Yabu A, Yanagihashi Y, Hyakumachi T, Tachi H, Shimizu T, Endo T, Ohnishi T, Ukeba D, Nagahama K, Takahata M, Sudo H, Iwasaki N. Deep learning-based detection of lumbar spinal canal stenosis using convolutional neural networks. Spine J 2024:S1529-9430(24)00299-7. [PMID: 38909909 DOI: 10.1016/j.spinee.2024.06.009] [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: 02/28/2024] [Revised: 06/13/2024] [Accepted: 06/14/2024] [Indexed: 06/25/2024]
Abstract
BACKGROUND CONTEXT Lumbar spinal canal stenosis (LSCS) is the most common spinal degenerative disorder in elderly people and usually first seen by primary care physicians or orthopedic surgeons who are not spine surgery specialists. Magnetic resonance imaging (MRI) is useful in the diagnosis of LSCS, but the equipment is often not available or difficult to read. LSCS patients with progressive neurologic deficits have difficulty with recovery if surgical treatment is delayed. So, early diagnosis and determination of appropriate surgical indications are crucial in the treatment of LSCS. Convolutional neural networks (CNNs), a type of deep learning, offers significant advantages for image recognition and classification, and work well with radiographs, which can be easily taken at any facility. PURPOSE Our purpose was to develop an algorithm to diagnose the presence or absence of LSCS requiring surgery from plain radiographs using CNNs. STUDY DESIGN Retrospective analysis of consecutive, nonrandomized series of patients at a single institution. PATIENT SAMPLE Data of 150 patients who underwent surgery for LSCS, including degenerative spondylolisthesis, at a single institution from January 2022 to August 2022, were collected. Additionally, 25 patients who underwent surgery at 2 other hospitals were included for extra external validation. OUTCOME MEASURES In annotation 1, the area under the curve (AUC) computed from the receiver operating characteristic (ROC) curve, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, positive likelihood ratio (PLR), and negative likelihood ratio (NLR) were calculated. In annotation 2, correlation coefficients were used. METHODS Four intervertebral levels from L1/2 to L4/5 were extracted as region of interest from lateral plain lumbar spine radiographs totaling 600 images were obtained. Based on the date of surgery, 500 images derived from the first 125 cases were used for internal validation, and 100 images from the subsequent 25 cases used for external validation. Additionally, 100 images from other hospitals were used for extra external validation. In annotation 1, binary classification of operative and nonoperative levels was used, and in annotation 2, the spinal canal area measured on axial MRI was labeled as the output layer. For internal validation, the 500 images were divided into each 5 dataset on per-patient basis and 5-fold cross-validation was performed. Five trained models were registered in the external validation prediction performance. Grad-CAM was used to visualize area with the high features extracted by CNNs. RESULTS In internal validation, the AUC and accuracy for annotation 1 ranged between 0.85-0.89 and 79-83%, respectively, and the correlation coefficients for annotation 2 ranged between 0.53 and 0.64 (all p<.01). In external validation, the AUC and accuracy for annotation 1 were 0.90 and 82%, respectively, and the correlation coefficient for annotation 2 was 0.69, using 5 trained CNN models. In the extra external validation, the AUC and accuracy for annotation 1 were 0.89 and 84%, respectively, and the correlation coefficient for annotation 2 was 0.56. Grad-CAM showed high feature density in the intervertebral joints and posterior intervertebral discs. CONCLUSIONS This technology automatically detects LSCS from plain lumbar spine radiographs, making it possible for medical facilities without MRI or nonspecialists to diagnose LSCS, suggesting the possibility of eliminating delays in the diagnosis and treatment of LSCS that require early treatment.
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Affiliation(s)
- Hisataka Suzuki
- Department of Orthopaedic Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, N15W7, Sapporo, Hokkaido 060-8638, Japan; Department of Orthopaedic Surgery, Eniwa Hospital, 2-1-1 Kogane Chuo, Eniwa, Hokkaido 061-1449, Japan
| | - Terufumi Kokabu
- Department of Orthopaedic Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, N15W7, Sapporo, Hokkaido 060-8638, Japan; Department of Orthopaedic Surgery, Eniwa Hospital, 2-1-1 Kogane Chuo, Eniwa, Hokkaido 061-1449, Japan
| | - Katsuhisa Yamada
- Department of Orthopaedic Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, N15W7, Sapporo, Hokkaido 060-8638, Japan.
| | - Yoko Ishikawa
- Department of Orthopaedic Surgery, Eniwa Hospital, 2-1-1 Kogane Chuo, Eniwa, Hokkaido 061-1449, Japan
| | - Akito Yabu
- Department of Orthopaedic Surgery, Eniwa Hospital, 2-1-1 Kogane Chuo, Eniwa, Hokkaido 061-1449, Japan
| | - Yasushi Yanagihashi
- Department of Orthopaedic Surgery, Eniwa Hospital, 2-1-1 Kogane Chuo, Eniwa, Hokkaido 061-1449, Japan
| | - Takahiko Hyakumachi
- Department of Orthopaedic Surgery, Eniwa Hospital, 2-1-1 Kogane Chuo, Eniwa, Hokkaido 061-1449, Japan
| | - Hiroyuki Tachi
- Department of Orthopaedic Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, N15W7, Sapporo, Hokkaido 060-8638, Japan
| | - Tomohiro Shimizu
- Department of Orthopaedic Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, N15W7, Sapporo, Hokkaido 060-8638, Japan
| | - Tsutomu Endo
- Department of Orthopaedic Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, N15W7, Sapporo, Hokkaido 060-8638, Japan
| | - Takashi Ohnishi
- Department of Orthopaedic Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, N15W7, Sapporo, Hokkaido 060-8638, Japan
| | - Daisuke Ukeba
- Department of Orthopaedic Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, N15W7, Sapporo, Hokkaido 060-8638, Japan
| | - Ken Nagahama
- Department of Orthopaedic Surgery, Sapporo Endoscopic Spine Surgery, N16E16, Sapporo, Hokkaido 065-0016, Japan
| | - Masahiko Takahata
- Department of Orthopaedic Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, N15W7, Sapporo, Hokkaido 060-8638, Japan
| | - Hideki Sudo
- Department of Orthopaedic Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, N15W7, Sapporo, Hokkaido 060-8638, Japan
| | - Norimasa Iwasaki
- Department of Orthopaedic Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, N15W7, Sapporo, Hokkaido 060-8638, Japan
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Wilson SB, Ward J, Munjal V, Lam CSA, Patel M, Zhang P, Xu DS, Chakravarthy VB. Machine Learning in Spine Oncology: A Narrative Review. Global Spine J 2024:21925682241261342. [PMID: 38860699 DOI: 10.1177/21925682241261342] [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] [Indexed: 06/12/2024] Open
Abstract
STUDY DESIGN Narrative Review. OBJECTIVE Machine learning (ML) is one of the latest advancements in artificial intelligence used in medicine and surgery with the potential to significantly impact the way physicians diagnose, prognose, and treat spine tumors. In the realm of spine oncology, ML is utilized to analyze and interpret medical imaging and classify tumors with incredible accuracy. The authors present a narrative review that specifically addresses the use of machine learning in spine oncology. METHODS This study was conducted in accordance with the Preferred Reporting Items of Systematic Reviews and Meta-Analysis (PRISMA) methodology. A systematic review of the literature in the PubMed, EMBASE, Web of Science, Scopus, and Cochrane Library databases since inception was performed to present all clinical studies with the search terms '[[Machine Learning] OR [Artificial Intelligence]] AND [[Spine Oncology] OR [Spine Cancer]]'. Data included studies that were extracted and included algorithms, training and test size, outcomes reported. Studies were separated based on the type of tumor investigated using the machine learning algorithms into primary, metastatic, both, and intradural. A minimum of 2 independent reviewers conducted the study appraisal, data abstraction, and quality assessments of the studies. RESULTS Forty-five studies met inclusion criteria out of 480 references screened from the initial search results. Studies were grouped by metastatic, primary, and intradural tumors. The majority of ML studies relevant to spine oncology focused on utilizing a mixture of clinical and imaging features to risk stratify mortality and frailty. Overall, these studies showed that ML is a helpful tool in tumor detection, differentiation, segmentation, predicting survival, predicting readmission rates of patients with either primary, metastatic, or intradural spine tumors. CONCLUSION Specialized neural networks and deep learning algorithms have shown to be highly effective at predicting malignant probability and aid in diagnosis. ML algorithms can predict the risk of tumor recurrence or progression based on imaging and clinical features. Additionally, ML can optimize treatment planning, such as predicting radiotherapy dose distribution to the tumor and surrounding normal tissue or in surgical resection planning. It has the potential to significantly enhance the accuracy and efficiency of health care delivery, leading to improved patient outcomes.
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Affiliation(s)
- Seth B Wilson
- Department of Neurosurgery, The Ohio State University, Columbus, OH, USA
| | - Jacob Ward
- Department of Neurosurgery, The Ohio State University, Columbus, OH, USA
| | - Vikas Munjal
- Department of Neurosurgery, The Ohio State University, Columbus, OH, USA
| | | | - Mayur Patel
- Department of Neurosurgery, The Ohio State University, Columbus, OH, USA
| | - Ping Zhang
- Department of Computer Science and Engineering, The Ohio State University College of Engineering, Columbus, OH, USA
- Department of Biomedical Informatics, The Ohio State University College of Medicine, Columbus, OH, USA
| | - David S Xu
- Department of Neurosurgery, The Ohio State University, Columbus, OH, USA
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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.
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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
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Gu Z, Dai W, Chen J, Jiang Q, Lin W, Wang Q, Chen J, Gu C, Li J, Ying G, Zhu Y. Convolutional neural network-based magnetic resonance image differentiation of filum terminale ependymomas from schwannomas. BMC Cancer 2024; 24:350. [PMID: 38504164 PMCID: PMC10949807 DOI: 10.1186/s12885-024-12023-0] [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: 06/18/2023] [Accepted: 02/20/2024] [Indexed: 03/21/2024] Open
Abstract
PURPOSE Preoperative diagnosis of filum terminale ependymomas (FTEs) versus schwannomas is difficult but essential for surgical planning and prognostic assessment. With the advancement of deep-learning approaches based on convolutional neural networks (CNNs), the aim of this study was to determine whether CNN-based interpretation of magnetic resonance (MR) images of these two tumours could be achieved. METHODS Contrast-enhanced MRI data from 50 patients with primary FTE and 50 schwannomas in the lumbosacral spinal canal were retrospectively collected and used as training and internal validation datasets. The diagnostic accuracy of MRI was determined by consistency with postoperative histopathological examination. T1-weighted (T1-WI), T2-weighted (T2-WI) and contrast-enhanced T1-weighted (CE-T1) MR images of the sagittal plane containing the tumour mass were selected for analysis. For each sequence, patient MRI data were randomly allocated to 5 groups that further underwent fivefold cross-validation to evaluate the diagnostic efficacy of the CNN models. An additional 34 pairs of cases were used as an external test dataset to validate the CNN classifiers. RESULTS After comparing multiple backbone CNN models, we developed a diagnostic system using Inception-v3. In the external test dataset, the per-examination combined sensitivities were 0.78 (0.71-0.84, 95% CI) based on T1-weighted images, 0.79 (0.72-0.84, 95% CI) for T2-weighted images, 0.88 (0.83-0.92, 95% CI) for CE-T1 images, and 0.88 (0.83-0.92, 95% CI) for all weighted images. The combined specificities were 0.72 based on T1-WI (0.66-0.78, 95% CI), 0.84 (0.78-0.89, 95% CI) based on T2-WI, 0.74 (0.67-0.80, 95% CI) for CE-T1, and 0.81 (0.76-0.86, 95% CI) for all weighted images. After all three MRI modalities were merged, the receiver operating characteristic (ROC) curve was calculated, and the area under the curve (AUC) was 0.93, with an accuracy of 0.87. CONCLUSIONS CNN based MRI analysis has the potential to accurately differentiate ependymomas from schwannomas in the lumbar segment.
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Affiliation(s)
- Zhaowen Gu
- Department of Neurosurgery, The Second Affiliated Hospital, School of Medicine, Zhejiang University, 88, Jiefang Road, Hangzhou, China
| | - Wenli Dai
- Zhejiang University School of Mathematical Sciences, Hangzhou, Zhejiang, China
| | - Jiarui Chen
- Department of Neurosurgery, The Second Affiliated Hospital, School of Medicine, Zhejiang University, 88, Jiefang Road, Hangzhou, China
| | - Qixuan Jiang
- Department of Neurosurgery, The Second Affiliated Hospital, School of Medicine, Zhejiang University, 88, Jiefang Road, Hangzhou, China
| | - Weiwei Lin
- Department of Neurosurgery, The Second Affiliated Hospital, School of Medicine, Zhejiang University, 88, Jiefang Road, Hangzhou, China
| | - Qiangwei Wang
- Department of Neurosurgery, The Second Affiliated Hospital, School of Medicine, Zhejiang University, 88, Jiefang Road, Hangzhou, China
| | - Jingyin Chen
- Department of Neurosurgery, The Second Affiliated Hospital, School of Medicine, Zhejiang University, 88, Jiefang Road, Hangzhou, China
| | - Chi Gu
- Department of Neurosurgery, The Second Affiliated Hospital, School of Medicine, Zhejiang University, 88, Jiefang Road, Hangzhou, China
| | - Jia Li
- Ningbo Medical Center Lihuili Hospital, Department of Neurosurgery, Ningbo University, 1111, Jiangnan Road, Ningbo, Zhejiang, China.
| | - Guangyu Ying
- Department of Neurosurgery, The Second Affiliated Hospital, School of Medicine, Zhejiang University, 88, Jiefang Road, Hangzhou, China.
| | - Yongjian Zhu
- Department of Neurosurgery, The Second Affiliated Hospital, School of Medicine, Zhejiang University, 88, Jiefang Road, Hangzhou, China.
- Clinical Research Center for Neurological Diseases of Zhejiang Province, Hangzhou, China.
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Haim O, Agur A, Gabay S, Azolai L, Shutan I, Chitayat M, Katirai M, Sadon S, Artzi M, Lidar Z. Differentiating spinal pathologies by deep learning approach. Spine J 2024; 24:297-303. [PMID: 37797840 DOI: 10.1016/j.spinee.2023.09.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 09/19/2023] [Accepted: 09/26/2023] [Indexed: 10/07/2023]
Abstract
BACKGROUND CONTEXT Spinal pathologies are diverse in nature and, excluding trauma and degenerative diseases, includes infectious, neoplastic (either extradural or intradural), and inflammatory conditions. The preoperative diagnosis is made with clinical judgment incorporating lab findings and radiological studies. When the diagnosis is uncertain, a biopsy is almost always mandatory since the treatment is dictated by the type of pathology. This is an invasive, timely, and costly process. PURPOSE The aim of this study was to develop a deep learning (DL) algorithm, based on preoperative MRI and post-operative pathological results, to differentiate between leading spinal pathologies. STUDY DESIGN We retrospectively collected and analyzed clinical, radiological, and pathological data of patients who underwent spinal surgery or biopsy for various spinal pathologies between 2008 and 2022 at a tertiary center. The patients were stratified according to their pathological reports (the threshold for inclusion was set to 25 patients per diagnosis). METHODS Preoperative MRI, clinical data, and pathological results were processed by a deep learning model built on the Fast.ai framework on top of the PyTorch environment. RESULTS A total of 231 patients diagnosed with carcinoma (80), infection (57), meningioma (52), or schwannoma (42), were included in our model. The mean overall accuracy was 0.78±0.06 for the validation, and 0.93±0.03 for the test dataset. CONCLUSION Deep learning algorithm for differentiation between the aforementioned spinal pathologies, based solely on clinical MRI, proves as a feasible primary diagnostic modality. Larger studies should be performed to validate and improve this algorithm for clinical use. CLINICAL SIGNIFICANCE This study provides a proof-of-concept for predicting spinal pathologies solely by MRI based DL technology, allowing for a rapid, targeted, and cost-effective work-up and subsequent treatment.
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Affiliation(s)
- Oz Haim
- Department of Neurosurgery, Tel Aviv Medical Center, Tel-Aviv, Israel; Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA
| | - Ariel Agur
- Department of Neurosurgery, Tel Aviv Medical Center, Tel-Aviv, Israel
| | - Segev Gabay
- Department of Neurosurgery, Tel Aviv Medical Center, Tel-Aviv, Israel
| | - Lee Azolai
- Department of Neurosurgery, Tel Aviv Medical Center, Tel-Aviv, Israel
| | - Itay Shutan
- Department of Neurosurgery, Tel Aviv Medical Center, Tel-Aviv, Israel
| | - May Chitayat
- The Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv University, Tel-Aviv 6997801, Israel; Sagol Brain Institute, Tel Aviv Medical Center, Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, 6 Weizman St, Tel-Aviv, Israel
| | - Michal Katirai
- The Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv University, Tel-Aviv 6997801, Israel; Sagol Brain Institute, Tel Aviv Medical Center, Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, 6 Weizman St, Tel-Aviv, Israel
| | - Sapir Sadon
- Department of Cardiology, Tel Aviv Medical Center, Tel-Aviv, Israel
| | - Moran Artzi
- Sagol Brain Institute, Tel Aviv Medical Center, Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, 6 Weizman St, Tel-Aviv, Israel.
| | - Zvi Lidar
- Department of Neurosurgery, Tel Aviv Medical Center, Tel-Aviv, Israel
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9
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Kita K, Fujimori T, Suzuki Y, Kanie Y, Takenaka S, Kaito T, Taki T, Ukon Y, Furuya M, Saiwai H, Nakajima N, Sugiura T, Ishiguro H, Kamatani T, Tsukazaki H, Sakai Y, Takami H, Tateiwa D, Hashimoto K, Wataya T, Nishigaki D, Sato J, Hoshiyama M, Tomiyama N, Okada S, Kido S. Bimodal artificial intelligence using TabNet for differentiating spinal cord tumors-Integration of patient background information and images. iScience 2023; 26:107900. [PMID: 37766987 PMCID: PMC10520519 DOI: 10.1016/j.isci.2023.107900] [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: 12/20/2022] [Revised: 02/18/2023] [Accepted: 09/08/2023] [Indexed: 09/29/2023] Open
Abstract
We proposed a bimodal artificial intelligence that integrates patient information with images to diagnose spinal cord tumors. Our model combines TabNet, a state-of-the-art deep learning model for tabular data for patient information, and a convolutional neural network for images. As training data, we collected 259 spinal tumor patients (158 for schwannoma and 101 for meningioma). We compared the performance of the image-only unimodal model, table-only unimodal model, bimodal model using a gradient-boosting decision tree, and bimodal model using TabNet. Our proposed bimodal model using TabNet performed best (area under the receiver-operating characteristic curve [AUROC]: 0.91) in the training data and significantly outperformed the physicians' performance. In the external validation using 62 cases from the other two facilities, our bimodal model showed an AUROC of 0.92, proving the robustness of the model. The bimodal analysis using TabNet was effective for differentiating spinal tumors.
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Affiliation(s)
- Kosuke Kita
- Osaka University School of Medicine Graduate School of Medicine Diagnostic and Interventional Radiology, Suita, Osaka, Japan
| | - Takahito Fujimori
- Osaka University Graduate School of Medicine Department of Orthopaedic Surgery, Suita, Osaka, Japan
| | - Yuki Suzuki
- Osaka University School of Medicine Graduate School of Medicine Diagnostic and Interventional Radiology, Suita, Osaka, Japan
| | - Yuya Kanie
- Osaka University Graduate School of Medicine Department of Orthopaedic Surgery, Suita, Osaka, Japan
| | - Shota Takenaka
- Osaka University Graduate School of Medicine Department of Orthopaedic Surgery, Suita, Osaka, Japan
| | - Takashi Kaito
- Osaka University Graduate School of Medicine Department of Orthopaedic Surgery, Suita, Osaka, Japan
| | - Takuyu Taki
- Department of Neurosurgery, Iseikai Hospital, Osaka, Osaka, Japan
| | - Yuichiro Ukon
- Osaka University Graduate School of Medicine Department of Orthopaedic Surgery, Suita, Osaka, Japan
| | | | - Hirokazu Saiwai
- Department of Orthopedic Surgery, Graduate School of Medical Sciences, Kyusyu University, Higashi, Fukuoka, Japan
| | - Nozomu Nakajima
- Japanese Red Cross Society Himeji Hospital, Himeji, Hyogo, Japan
| | - Tsuyoshi Sugiura
- General Incorporated Foundation Sumitomo Hospital, Osaka, Osaka, Japan
| | - Hiroyuki Ishiguro
- National Hospital Organization Osaka National Hospital, Osaka, Osaka, Japan
| | | | | | | | - Haruna Takami
- Osaka International Cancer Institute, Osaka, Osaka, Japan
| | | | | | - Tomohiro Wataya
- Osaka University School of Medicine Graduate School of Medicine Diagnostic and Interventional Radiology, Suita, Osaka, Japan
| | - Daiki Nishigaki
- Osaka University School of Medicine Graduate School of Medicine Diagnostic and Interventional Radiology, Suita, Osaka, Japan
| | - Junya Sato
- Osaka University School of Medicine Graduate School of Medicine Diagnostic and Interventional Radiology, Suita, Osaka, Japan
| | | | - Noriyuki Tomiyama
- Osaka University School of Medicine Graduate School of Medicine Diagnostic and Interventional Radiology, Suita, Osaka, Japan
| | - Seiji Okada
- Osaka University Graduate School of Medicine Department of Orthopaedic Surgery, Suita, Osaka, Japan
| | - Shoji Kido
- Osaka University School of Medicine Graduate School of Medicine Diagnostic and Interventional Radiology, Suita, Osaka, Japan
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10
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Zhong B, Yi J, Jin Z. AC-Faster R-CNN: an improved detection architecture with high precision and sensitivity for abnormality in spine x-ray images. Phys Med Biol 2023; 68:195021. [PMID: 37678268 DOI: 10.1088/1361-6560/acf7a8] [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: 05/16/2023] [Accepted: 09/07/2023] [Indexed: 09/09/2023]
Abstract
Objective.In clinical medicine, localization and identification of disease on spinal radiographs are difficult and require a high level of expertise in the radiological discipline and extensive clinical experience. The model based on deep learning acquires certain disease recognition abilities through continuous training, thereby assisting clinical physicians in disease diagnosis. This study aims to develop an object detection network that accurately locates and classifies the abnormal parts in spinal x-ray photographs.Approach.This study proposes a deep learning-based automated multi-disease detection architecture called Abnormality Capture-Faster Region-based Convolutional Neural Network (AC-Faster R-CNN), which develops the feature fusion structure Deformable Convolution Feature Pyramid Network and the abnormality capture structure Abnormality Capture Head. Through the combination of dilated and deformable convolutions, the model better captures the multi-scale information of lesions. To further improve the detection performance, the contrast enhancement algorithm Contrast Limited Adaptive Histogram Equalization is used for image preprocessing.Main results.The proposed model is extensively evaluated on a testing set containing 1007 spine x-ray images and the experimental results show that the AC-Faster R-CNN architecture outperforms the baseline model and other advanced detection architectures. The mean Average Precision at Intersection over Union of 50% are 39.8%, the Precision and Sensitivity at the optimal cutoff point of Precision-Recall curve are 48.6% and 46.3%, respectively, reaching the current state-of-the-art detection level.Significance.AC-Faster R-CNN exhibits high precision and sensitivity in abnormality detection tasks of spinal x-ray images, and effectively locates and identifies abnormal areas. Additionally, this study would provide reference and comparison for the further development of medical automatic detection.
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Affiliation(s)
- Bolin Zhong
- College of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, People's Republic of China
| | - Jizheng Yi
- College of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, People's Republic of China
- Yuelushan Laboratory Carbon Sinks Forests Variety Innovation Center, Changsha 410000, People's Republic of China
| | - Ze Jin
- Suzuki lab, Information and Artificial Intelligence Research International Hub Group, Tokyo 226-8503, Japan
- Laboratory for Future Interdisciplinary Research of Science and Technology, Institute of Innovative Research, Tokyo Institute of Technology, Tokyo 226-8503, Japan
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11
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Ito S, Nakashima H, Segi N, Ouchida J, Oda M, Yamauchi I, Oishi R, Miyairi Y, Mori K, Imagama S. Automated Detection and Diagnosis of Spinal Schwannomas and Meningiomas Using Deep Learning and Magnetic Resonance Imaging. J Clin Med 2023; 12:5075. [PMID: 37568477 PMCID: PMC10419638 DOI: 10.3390/jcm12155075] [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: 06/24/2023] [Revised: 07/24/2023] [Accepted: 07/31/2023] [Indexed: 08/13/2023] Open
Abstract
Spinal cord tumors are infrequently identified spinal diseases that are often difficult to diagnose even with magnetic resonance imaging (MRI) findings. To minimize the probability of overlooking these tumors and improve diagnostic accuracy, an automatic diagnostic system is needed. We aimed to develop an automated system for detecting and diagnosing spinal schwannomas and meningiomas based on deep learning using You Only Look Once (YOLO) version 4 and MRI. In this retrospective diagnostic accuracy study, the data of 50 patients with spinal schwannomas, 45 patients with meningiomas, and 100 control cases were reviewed, respectively. Sagittal T1-weighted (T1W) and T2-weighted (T2W) images were used for object detection, classification, training, and validation. The object detection and diagnosis system was developed using YOLO version 4. The accuracies of the proposed object detections based on T1W, T2W, and T1W + T2W images were 84.8%, 90.3%, and 93.8%, respectively. The accuracies of the object detection for two spine surgeons were 88.9% and 90.1%, respectively. The accuracies of the proposed diagnoses based on T1W, T2W, and T1W + T2W images were 76.4%, 83.3%, and 84.1%, respectively. The accuracies of the diagnosis for two spine surgeons were 77.4% and 76.1%, respectively. We demonstrated an accurate, automated detection and diagnosis of spinal schwannomas and meningiomas using the developed deep learning-based method based on MRI. This system could be valuable in supporting radiological diagnosis of spinal schwannomas and meningioma, with a potential of reducing the radiologist's overall workload.
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Affiliation(s)
- Sadayuki Ito
- Department of Orthopedic Surgery, Nagoya University Graduate School of Medicine, Nagoya 466-8560, Japan (Y.M.)
| | - Hiroaki Nakashima
- Department of Orthopedic Surgery, Nagoya University Graduate School of Medicine, Nagoya 466-8560, Japan (Y.M.)
| | - Naoki Segi
- Department of Orthopedic Surgery, Nagoya University Graduate School of Medicine, Nagoya 466-8560, Japan (Y.M.)
| | - Jun Ouchida
- Department of Orthopedic Surgery, Nagoya University Graduate School of Medicine, Nagoya 466-8560, Japan (Y.M.)
| | - Masahiro Oda
- Information Strategy Office, Information and Communications, Nagoya University, Nagoya 464-8601, Japan
| | - Ippei Yamauchi
- Department of Orthopedic Surgery, Nagoya University Graduate School of Medicine, Nagoya 466-8560, Japan (Y.M.)
| | - Ryotaro Oishi
- Department of Orthopedic Surgery, Nagoya University Graduate School of Medicine, Nagoya 466-8560, Japan (Y.M.)
| | - Yuichi Miyairi
- Department of Orthopedic Surgery, Nagoya University Graduate School of Medicine, Nagoya 466-8560, Japan (Y.M.)
| | - Kensaku Mori
- Information Strategy Office, Information and Communications, Nagoya University, Nagoya 464-8601, Japan
- Department of Intelligent Systems, Nagoya University Graduate School of Informatics, Nagoya 464-8601, Japan
- Research Center for Medical Bigdata, National Institute of Informatics, Tokyo 101-8430, Japan
| | - Shiro Imagama
- Department of Orthopedic Surgery, Nagoya University Graduate School of Medicine, Nagoya 466-8560, Japan (Y.M.)
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12
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Koechli C, Zwahlen DR, Schucht P, Windisch P. Radiomics and machine learning for predicting the consistency of benign tumors of the central nervous system: A systematic review. Eur J Radiol 2023; 164:110866. [PMID: 37207398 DOI: 10.1016/j.ejrad.2023.110866] [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: 03/14/2023] [Revised: 04/28/2023] [Accepted: 05/03/2023] [Indexed: 05/21/2023]
Abstract
PURPOSE Predicting the consistency of benign central nervous system (CNS) tumors prior to surgery helps to improve surgical outcomes. This review summarizes and analyzes the literature on using radiomics and/or machine learning (ML) for consistency prediction. METHOD The Medical Literature Analysis and Retrieval System Online (MEDLINE) database was screened for studies published in English from January 1st 2000. Data was extracted according to the PRISMA guidelines and quality of the studies was assessed in compliance with the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). RESULTS Eight publications were included focusing on pituitary macroadenomas (n = 5), pituitary adenomas (n = 1), and meningiomas (n = 2) using a retrospective (n = 6), prospective (n = 1), and unknown (n = 1) study design with a total of 763 patients for the consistency prediction. The studies reported an area under the curve (AUC) of 0.71-0.99 for their respective best performing model regarding the consistency prediction. Of all studies, four articles validated their models internally whereas none validated their models externally. Two articles stated making data available on request with the remaining publications lacking information with regard to data availability. CONCLUSIONS The research on consistency prediction of CNS tumors is still at an early stage regarding the use of radiomics and different ML techniques. Best-practice procedures regarding radiomics and ML need to be followed more rigorously to facilitate the comparison between publications and, accordingly, the possible implementation into clinical practice in the future.
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Affiliation(s)
- Carole Koechli
- Department of Radiation Oncology, Kantonsspital Winterthur, 8401 Winterthur, Switzerland; Universitätsklinik für Neurochirurgie, Bern University Hospital, 3010 Bern, Switzerland.
| | - Daniel R Zwahlen
- Department of Radiation Oncology, Kantonsspital Winterthur, 8401 Winterthur, Switzerland
| | - Philippe Schucht
- Universitätsklinik für Neurochirurgie, Bern University Hospital, 3010 Bern, Switzerland
| | - Paul Windisch
- Department of Radiation Oncology, Kantonsspital Winterthur, 8401 Winterthur, Switzerland
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13
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Differentiating Magnetic Resonance Images of Pyogenic Spondylitis and Spinal Modic Change Using a Convolutional Neural Network. Spine (Phila Pa 1976) 2023; 48:288-294. [PMID: 36692159 DOI: 10.1097/brs.0000000000004532] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Accepted: 10/23/2022] [Indexed: 01/25/2023]
Abstract
STUDY DESIGN A retrospective analysis of magnetic resonance imaging (MRI). OBJECTIVE The study aimed to evaluate the performance of a convolutional neural network (CNN) to differentiate spinal pyogenic spondylitis from Modic change on MRI. We compared the performance of CNN to that of four clinicians. SUMMARY OF BACKGROUND DATA Discrimination between pyogenic spondylitis and spinal Modic change is crucial in clinical practice. CNN deep-learning approaches for medical imaging are being increasingly utilized. METHODS We retrospectively reviewed MRIs from pyogenic spondylitis and spinal Modic change patients. There were 50 patients per group. Sagittal T1-weighted (T1WI), sagittal T2-weighted (T2WI), and short TI inversion recovery (STIR) MRIs were used for CNN training and validation. The deep learning framework Tensorflow was used to construct the CNN architecture. To evaluate CNN performance, we plotted the receiver operating characteristic curve and calculated the area under the curve. We compared the accuracy, sensitivity, and specificity of CNN diagnosis to that of a radiologist, spine surgeon, and two orthopedic surgeons. RESULTS The CNN-based area under the curves of the receiver operating characteristic curve from the T1WI, T2WI, and STIR were 0.95, 0.94, and 0.95, respectively. The accuracy of the CNN was significantly greater than that of the four clinicians on T1WI and STIR (P<0.05), and better than a radiologist and one orthopedic surgeon on the T2WI (P<0.05). The sensitivity was significantly better than that of the four clincians on T1WI and STIR (P<0.05), and better than a radiologist and one orthopedic surgeon on the T2WI (P<0.05). The specificity was significantly better than one orthopedic surgeon on T1WI and T2WI (P<0.05) and better than both orthopedic surgeons on STIR (P<0.05). CONCLUSION We differentiated between Modic changes and pyogenic spondylitis using a CNN that interprets MRI. The performance of the CNN was comparable to, or better than, that of the four clinicians.
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14
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Katsos K, Johnson SE, Ibrahim S, Bydon M. Current Applications of Machine Learning for Spinal Cord Tumors. Life (Basel) 2023; 13:life13020520. [PMID: 36836877 PMCID: PMC9962966 DOI: 10.3390/life13020520] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 02/01/2023] [Accepted: 02/06/2023] [Indexed: 02/16/2023] Open
Abstract
Spinal cord tumors constitute a diverse group of rare neoplasms associated with significant mortality and morbidity that pose unique clinical and surgical challenges. Diagnostic accuracy and outcome prediction are critical for informed decision making and can promote personalized medicine and facilitate optimal patient management. Machine learning has the ability to analyze and combine vast amounts of data, allowing the identification of patterns and the establishment of clinical associations, which can ultimately enhance patient care. Although artificial intelligence techniques have been explored in other areas of spine surgery, such as spinal deformity surgery, precise machine learning models for spinal tumors are lagging behind. Current applications of machine learning in spinal cord tumors include algorithms that improve diagnostic precision by predicting genetic, molecular, and histopathological profiles. Furthermore, artificial intelligence-based systems can assist surgeons with preoperative planning and surgical resection, potentially reducing the risk of recurrence and consequently improving clinical outcomes. Machine learning algorithms promote personalized medicine by enabling prognostication and risk stratification based on accurate predictions of treatment response, survival, and postoperative complications. Despite their promising potential, machine learning models require extensive validation processes and quality assessments to ensure safe and effective translation to clinical practice.
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Affiliation(s)
- Konstantinos Katsos
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN 55902, USA
- Mayo Clinic Neuro-Informatics Laboratory, Department of Neurologic Surgery, Mayo Clinic, Rochester, MN 55902, USA
| | - Sarah E. Johnson
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN 55902, USA
- Mayo Clinic Neuro-Informatics Laboratory, Department of Neurologic Surgery, Mayo Clinic, Rochester, MN 55902, USA
| | - Sufyan Ibrahim
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN 55902, USA
- Mayo Clinic Neuro-Informatics Laboratory, Department of Neurologic Surgery, Mayo Clinic, Rochester, MN 55902, USA
| | - Mohamad Bydon
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN 55902, USA
- Mayo Clinic Neuro-Informatics Laboratory, Department of Neurologic Surgery, Mayo Clinic, Rochester, MN 55902, USA
- Correspondence:
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15
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Automated fracture screening using an object detection algorithm on whole-body trauma computed tomography. Sci Rep 2022; 12:16549. [PMID: 36192521 PMCID: PMC9529907 DOI: 10.1038/s41598-022-20996-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 09/21/2022] [Indexed: 11/28/2022] Open
Abstract
The emergency department is an environment with a potential risk for diagnostic errors during trauma care, particularly for fractures. Convolutional neural network (CNN) deep learning methods are now widely used in medicine because they improve diagnostic accuracy, decrease misinterpretation, and improve efficiency. In this study, we investigated whether automatic localization and classification using CNN could be applied to pelvic, rib, and spine fractures. We also examined whether this fracture detection algorithm could help physicians in fracture diagnosis. A total of 7664 whole-body CT axial slices (chest, abdomen, pelvis) from 200 patients were used. Sensitivity, precision, and F1-score were calculated to evaluate the performance of the CNN model. For the grouped mean values for pelvic, spine, or rib fractures, the sensitivity was 0.786, precision was 0.648, and F1-score was 0.711. Moreover, with CNN model assistance, surgeons showed improved sensitivity for detecting fractures and the time of reading and interpreting CT scans was reduced, especially for less experienced orthopedic surgeons. Application of the CNN model may lead to reductions in missed fractures from whole-body CT images and to faster workflows and improved patient care through efficient diagnosis in polytrauma patients.
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16
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Ren G, Yu K, Xie Z, Wang P, Zhang W, Huang Y, Wang Y, Wu X. Current Applications of Machine Learning in Spine: From Clinical View. Global Spine J 2022; 12:1827-1840. [PMID: 34628966 PMCID: PMC9609532 DOI: 10.1177/21925682211035363] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
STUDY DESIGN Narrative review. OBJECTIVES This review aims to present current applications of machine learning (ML) in spine domain to clinicians. METHODS We conducted a comprehensive PubMed search of peer-reviewed articles that were published between 2006 and 2020 using terms (spine, spinal, lumbar, cervical, thoracic, machine learning) to examine ML in spine. Then exclude research of other domain, case report, review or meta-analysis, and which without available abstract or full text. RESULTS Total 1738 articles were retrieved from database, and 292 studies were finally included. Key findings of current applications were compiled and summarized in this review. Main clinical applications of those techniques including image processing, diagnosis, decision supporting, operative assistance, rehabilitation, surgery outcomes, complications, hospitalization and cost. CONCLUSIONS ML had achieved excellent performance and hold immense potential in spine. ML could help clinical staff to improve medical level, enhance work efficiency, and reduce adverse events. However more randomized controlled trials and improvement of interpretability are essential to clinicians accepting models' assistance in real work.
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Affiliation(s)
- GuanRui Ren
- Southeast University Medical College,
Nanjing, Jiangsu, China
| | - Kun Yu
- Nanjing Jiangbei Hospital, Nanjing,
Jiangsu, China
| | - ZhiYang Xie
- Department of Spine Surgery, Zhongda
Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China
| | - PeiYang Wang
- Southeast University Medical College,
Nanjing, Jiangsu, China
| | - Wei Zhang
- Southeast University Medical College,
Nanjing, Jiangsu, China
| | - Yong Huang
- Southeast University Medical College,
Nanjing, Jiangsu, China
| | - YunTao Wang
- Department of Spine Surgery, Zhongda
Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China,YunTao Wang, Department of Spine Surgery,
Zhongda Hospital, School of Medicine, Southeast University, No. 87, Dingjiaqiao
Road, Nanjing, Jiangsu 210009, China.
| | - XiaoTao Wu
- Department of Spine Surgery, Zhongda
Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China,XiaoTao Wu, Department of Spine Surgery,
Zhongda Hospital, School of Medicine, Southeast University, No. 87, Dingjiaqiao
Road, Nanjing, Jiangsu 210009, China.
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17
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Artificial Intelligence Accurately Detects Traumatic Thoracolumbar Fractures on Sagittal Radiographs. MEDICINA (KAUNAS, LITHUANIA) 2022; 58:medicina58080998. [PMID: 35893113 PMCID: PMC9330443 DOI: 10.3390/medicina58080998] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 07/20/2022] [Accepted: 07/22/2022] [Indexed: 11/18/2022]
Abstract
Background and Objectives: Commonly being the first step in trauma routine imaging, up to 67% fractures are missed on plain radiographs of the thoracolumbar (TL) spine. The aim of this study was to develop a deep learning model that detects traumatic fractures on sagittal radiographs of the TL spine. Identifying vertebral fractures in simple radiographic projections would have a significant clinical and financial impact, especially for low- and middle-income countries where computed tomography (CT) and magnetic resonance imaging (MRI) are not readily available and could help select patients that need second level imaging, thus improving the cost-effectiveness. Materials and Methods: Imaging studies (radiographs, CT, and/or MRI) of 151 patients were used. An expert group of three spinal surgeons reviewed all available images to confirm presence and type of fractures. In total, 630 single vertebra images were extracted from the sagittal radiographs of the 151 patients—302 exhibiting a vertebral body fracture, and 328 exhibiting no fracture. Following augmentation, these single vertebra images were used to train, validate, and comparatively test two deep learning convolutional neural network models, namely ResNet18 and VGG16. A heatmap analysis was then conducted to better understand the predictions of each model. Results: ResNet18 demonstrated a better performance, achieving higher sensitivity (91%), specificity (89%), and accuracy (88%) compared to VGG16 (90%, 83%, 86%). In 81% of the cases, the “warm zone” in the heatmaps correlated with the findings, suggestive of fracture within the vertebral body seen in the imaging studies. Vertebras T12 to L2 were the most frequently involved, accounting for 48% of the fractures. A4, A3, and A1 were the most frequent fracture types according to the AO Spine Classification. Conclusions: ResNet18 could accurately identify the traumatic vertebral fractures on the TL sagittal radiographs. In most cases, the model based its prediction on the same areas that human expert classifiers used to determine the presence of a fracture.
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18
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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.
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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
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Machine Learning for the Detection and Segmentation of Benign Tumors of the Central Nervous System: A Systematic Review. Cancers (Basel) 2022; 14:cancers14112676. [PMID: 35681655 PMCID: PMC9179850 DOI: 10.3390/cancers14112676] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 05/18/2022] [Accepted: 05/26/2022] [Indexed: 11/20/2022] Open
Abstract
Simple Summary Machine learning in radiology of the central nervous system has seen many interesting publications in the past few years. Since the focus has largely been on malignant tumors such as brain metastases and high-grade gliomas, we conducted a systematic review on benign tumors to summarize what has been published and where there might be gaps in the research. We found several studies that report good results, but the descriptions of methodologies could be improved to enable better comparisons and assessment of biases. Abstract Objectives: To summarize the available literature on using machine learning (ML) for the detection and segmentation of benign tumors of the central nervous system (CNS) and to assess the adherence of published ML/diagnostic accuracy studies to best practice. Methods: The MEDLINE database was searched for the use of ML in patients with any benign tumor of the CNS, and the records were screened according to PRISMA guidelines. Results: Eleven retrospective studies focusing on meningioma (n = 4), vestibular schwannoma (n = 4), pituitary adenoma (n = 2) and spinal schwannoma (n = 1) were included. The majority of studies attempted segmentation. Links to repositories containing code were provided in two manuscripts, and no manuscripts shared imaging data. Only one study used an external test set, which raises the question as to whether some of the good performances that have been reported were caused by overfitting and may not generalize to data from other institutions. Conclusions: Using ML for detecting and segmenting benign brain tumors is still in its infancy. Stronger adherence to ML best practices could facilitate easier comparisons between studies and contribute to the development of models that are more likely to one day be used in clinical practice.
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Brunasso L, Ferini G, Bonosi L, Costanzo R, Musso S, Benigno UE, Gerardi RM, Giammalva GR, Paolini F, Umana GE, Graziano F, Scalia G, Sturiale CL, Di Bonaventura R, Iacopino DG, Maugeri R. A Spotlight on the Role of Radiomics and Machine-Learning Applications in the Management of Intracranial Meningiomas: A New Perspective in Neuro-Oncology: A Review. Life (Basel) 2022; 12:life12040586. [PMID: 35455077 PMCID: PMC9026541 DOI: 10.3390/life12040586] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 04/05/2022] [Accepted: 04/06/2022] [Indexed: 12/12/2022] Open
Abstract
Background: In recent decades, the application of machine learning technologies to medical imaging has opened up new perspectives in neuro-oncology, in the so-called radiomics field. Radiomics offer new insight into glioma, aiding in clinical decision-making and patients’ prognosis evaluation. Although meningiomas represent the most common primary CNS tumor and the majority of them are benign and slow-growing tumors, a minor part of them show a more aggressive behavior with an increased proliferation rate and a tendency to recur. Therefore, their treatment may represent a challenge. Methods: According to PRISMA guidelines, a systematic literature review was performed. We included selected articles (meta-analysis, review, retrospective study, and case–control study) concerning the application of radiomics method in the preoperative diagnostic and prognostic algorithm, and planning for intracranial meningiomas. We also analyzed the contribution of radiomics in differentiating meningiomas from other CNS tumors with similar radiological features. Results: In the first research stage, 273 papers were identified. After a careful screening according to inclusion/exclusion criteria, 39 articles were included in this systematic review. Conclusions: Several preoperative features have been identified to increase preoperative intracranial meningioma assessment for guiding decision-making processes. The development of valid and reliable non-invasive diagnostic and prognostic modalities could have a significant clinical impact on meningioma treatment.
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Affiliation(s)
- Lara Brunasso
- Neurosurgical Clinic AOUP “Paolo Giaccone”, Post Graduate Residency Program in Neurologic Surgery, Department of Biomedicine Neurosciences and Advanced Diagnostics, School of Medicine, University of Palermo, 90127 Palermo, Italy; (L.B.); (R.C.); (S.M.); (U.E.B.); (R.M.G.); (G.R.G.); (F.P.); (D.G.I.); (R.M.)
- Correspondence:
| | - Gianluca Ferini
- Department of Radiation Oncology, REM Radioterapia SRL, 95125 Catania, Italy;
| | - Lapo Bonosi
- Neurosurgical Clinic AOUP “Paolo Giaccone”, Post Graduate Residency Program in Neurologic Surgery, Department of Biomedicine Neurosciences and Advanced Diagnostics, School of Medicine, University of Palermo, 90127 Palermo, Italy; (L.B.); (R.C.); (S.M.); (U.E.B.); (R.M.G.); (G.R.G.); (F.P.); (D.G.I.); (R.M.)
| | - Roberta Costanzo
- Neurosurgical Clinic AOUP “Paolo Giaccone”, Post Graduate Residency Program in Neurologic Surgery, Department of Biomedicine Neurosciences and Advanced Diagnostics, School of Medicine, University of Palermo, 90127 Palermo, Italy; (L.B.); (R.C.); (S.M.); (U.E.B.); (R.M.G.); (G.R.G.); (F.P.); (D.G.I.); (R.M.)
| | - Sofia Musso
- Neurosurgical Clinic AOUP “Paolo Giaccone”, Post Graduate Residency Program in Neurologic Surgery, Department of Biomedicine Neurosciences and Advanced Diagnostics, School of Medicine, University of Palermo, 90127 Palermo, Italy; (L.B.); (R.C.); (S.M.); (U.E.B.); (R.M.G.); (G.R.G.); (F.P.); (D.G.I.); (R.M.)
| | - Umberto E. Benigno
- Neurosurgical Clinic AOUP “Paolo Giaccone”, Post Graduate Residency Program in Neurologic Surgery, Department of Biomedicine Neurosciences and Advanced Diagnostics, School of Medicine, University of Palermo, 90127 Palermo, Italy; (L.B.); (R.C.); (S.M.); (U.E.B.); (R.M.G.); (G.R.G.); (F.P.); (D.G.I.); (R.M.)
| | - Rosa M. Gerardi
- Neurosurgical Clinic AOUP “Paolo Giaccone”, Post Graduate Residency Program in Neurologic Surgery, Department of Biomedicine Neurosciences and Advanced Diagnostics, School of Medicine, University of Palermo, 90127 Palermo, Italy; (L.B.); (R.C.); (S.M.); (U.E.B.); (R.M.G.); (G.R.G.); (F.P.); (D.G.I.); (R.M.)
| | - Giuseppe R. Giammalva
- Neurosurgical Clinic AOUP “Paolo Giaccone”, Post Graduate Residency Program in Neurologic Surgery, Department of Biomedicine Neurosciences and Advanced Diagnostics, School of Medicine, University of Palermo, 90127 Palermo, Italy; (L.B.); (R.C.); (S.M.); (U.E.B.); (R.M.G.); (G.R.G.); (F.P.); (D.G.I.); (R.M.)
| | - Federica Paolini
- Neurosurgical Clinic AOUP “Paolo Giaccone”, Post Graduate Residency Program in Neurologic Surgery, Department of Biomedicine Neurosciences and Advanced Diagnostics, School of Medicine, University of Palermo, 90127 Palermo, Italy; (L.B.); (R.C.); (S.M.); (U.E.B.); (R.M.G.); (G.R.G.); (F.P.); (D.G.I.); (R.M.)
| | - Giuseppe E. Umana
- Gamma Knife Center, Trauma Center, Department of Neurosurgery, Cannizzaro Hospital, 95100 Catania, Italy;
| | - Francesca Graziano
- Unit of Neurosurgery, Garibaldi Hospital, 95124 Catania, Italy; (F.G.); (G.S.)
| | - Gianluca Scalia
- Unit of Neurosurgery, Garibaldi Hospital, 95124 Catania, Italy; (F.G.); (G.S.)
| | - Carmelo L. Sturiale
- Division of Neurosurgery, Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, 00100 Rome, Italy; (C.L.S.); (R.D.B.)
| | - Rina Di Bonaventura
- Division of Neurosurgery, Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, 00100 Rome, Italy; (C.L.S.); (R.D.B.)
| | - Domenico G. Iacopino
- Neurosurgical Clinic AOUP “Paolo Giaccone”, Post Graduate Residency Program in Neurologic Surgery, Department of Biomedicine Neurosciences and Advanced Diagnostics, School of Medicine, University of Palermo, 90127 Palermo, Italy; (L.B.); (R.C.); (S.M.); (U.E.B.); (R.M.G.); (G.R.G.); (F.P.); (D.G.I.); (R.M.)
| | - Rosario Maugeri
- Neurosurgical Clinic AOUP “Paolo Giaccone”, Post Graduate Residency Program in Neurologic Surgery, Department of Biomedicine Neurosciences and Advanced Diagnostics, School of Medicine, University of Palermo, 90127 Palermo, Italy; (L.B.); (R.C.); (S.M.); (U.E.B.); (R.M.G.); (G.R.G.); (F.P.); (D.G.I.); (R.M.)
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A deep learning algorithm to identify cervical ossification of posterior longitudinal ligaments on radiography. Sci Rep 2022; 12:2113. [PMID: 35136170 PMCID: PMC8826389 DOI: 10.1038/s41598-022-06140-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 01/25/2022] [Indexed: 12/16/2022] Open
Abstract
The cervical ossification of the posterior longitudinal ligament (cOPLL) is sometimes misdiagnosed or overlooked on radiography. Thus, this study aimed to validate the diagnostic yield of our deep learning algorithm which diagnose the presence/absence of cOPLL on cervical radiography and highlighted areas of ossification in positive cases and compare its diagnostic accuracy with that of experienced spine physicians. Firstly, the radiographic data of 486 patients (243 patients with cOPLL and 243 age and sex matched controls) who received cervical radiography and a computer tomography were used to create the deep learning algorithm. The diagnostic accuracy of our algorithm was 0.88 (area under curve, 0.94). Secondly, the numbers of correct diagnoses were compared between the algorithm and consensus of four spine physicians using 50 independent samples. The algorithm had significantly more correct diagnoses than spine physicians (47/50 versus 39/50, respectively; p = 0.041). In conclusion, the accuracy of our deep learning algorithm for cOPLL diagnosis was significantly higher than that of experienced spine physicians. We believe our algorithm, which uses different diagnostic criteria than humans, can significantly improve the diagnostic accuracy of cOPLL when radiography is used.
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22
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Yabu A, Hoshino M, Tabuchi H, Takahashi S, Masumoto H, Akada M, Morita S, Maeno T, Iwamae M, Inose H, Kato T, Yoshii T, Tsujio T, Terai H, Toyoda H, Suzuki A, Tamai K, Ohyama S, Hori Y, Okawa A, Nakamura H. Using artificial intelligence to diagnose fresh osteoporotic vertebral fractures on magnetic resonance images. Spine J 2021; 21:1652-1658. [PMID: 33722728 DOI: 10.1016/j.spinee.2021.03.006] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Revised: 02/21/2021] [Accepted: 03/08/2021] [Indexed: 02/03/2023]
Abstract
BACKGROUND CONTEXT Accurate diagnosis of osteoporotic vertebral fracture (OVF) is important for improving treatment outcomes; however, the gold standard has not been established yet. A deep-learning approach based on convolutional neural network (CNN) has attracted attention in the medical imaging field. PURPOSE To construct a CNN to detect fresh OVF on magnetic resonance (MR) images. STUDY DESIGN/SETTING Retrospective analysis of MR images PATIENT SAMPLE: This retrospective study included 814 patients with fresh OVF. For CNN training and validation, 1624 slices of T1-weighted MR image were obtained and used. OUTCOME MEASURE We plotted the receiver operating characteristic (ROC) curve and calculated the area under the curve (AUC) in order to evaluate the performance of the CNN. Consequently, the sensitivity, specificity, and accuracy of the diagnosis by CNN and that of the two spine surgeons were compared. METHODS We constructed an optimal model using ensemble method by combining nine types of CNNs to detect fresh OVFs. Furthermore, two spine surgeons independently evaluated 100 vertebrae, which were randomly extracted from test data. RESULTS The ensemble method using VGG16, VGG19, DenseNet201, and ResNet50 was the combination with the highest AUC of ROC curves. The AUC was 0.949. The evaluation metrics of the diagnosis (CNN/surgeon 1/surgeon 2) for 100 vertebrae were as follows: sensitivity: 88.1%/88.1%/100%; specificity: 87.9%/86.2%/65.5%; accuracy: 88.0%/87.0%/80.0%. CONCLUSIONS In detecting fresh OVF using MR images, the performance of the CNN was comparable to that of two spine surgeons.
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Affiliation(s)
- Akito Yabu
- Department of Orthopaedic Surgery, Osaka City University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka 545-8585, Japan
| | - Masatoshi Hoshino
- Department of Orthopaedic Surgery, Osaka City University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka 545-8585, Japan.
| | - Hitoshi Tabuchi
- Department of Ophthalmology, Tsukazaki Hospital, 68-1 Waku, Aboshi-ku, Himeji, Hyogo 671-1227, Japan; Department of Technology and Design Thinking for Medicine, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima City, Hiroshima 734-8551, Japan
| | - Shinji Takahashi
- Department of Orthopaedic Surgery, Osaka City University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka 545-8585, Japan
| | - Hiroki Masumoto
- Department of Ophthalmology, Tsukazaki Hospital, 68-1 Waku, Aboshi-ku, Himeji, Hyogo 671-1227, Japan
| | - Masahiro Akada
- Department of Ophthalmology, Tsukazaki Hospital, 68-1 Waku, Aboshi-ku, Himeji, Hyogo 671-1227, Japan
| | - Shoji Morita
- Graduate School of Engineering, University of Hyogo, 2167, Shosha, Himeji, Hyogo 671-2280, Japan
| | - Takafumi Maeno
- Department of Orthopaedic Surgery, Ishikiriseiki Hospital, 18-28, Yayoi-machi, Higashiosaka, Osaka 579-8026, Japan
| | - Masayoshi Iwamae
- Department of Orthopaedic Surgery, Ishikiriseiki Hospital, 18-28, Yayoi-machi, Higashiosaka, Osaka 579-8026, Japan
| | - Hiroyuki Inose
- Department of Orthopaedic Surgery, Tokyo Medical and Dental University, Graduate School, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Tsuyoshi Kato
- Department of Orthopaedic Surgery, Ome municipal general Hospital, 4-16-5, Higashiome, Ome, Tokyo 198-0042, Japan
| | - Toshitaka Yoshii
- Department of Orthopaedic Surgery, Tokyo Medical and Dental University, Graduate School, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Tadao Tsujio
- Department of Orthopaedic Surgery, Shiraniwa Hospital, 6-10-1, Shiraniwadai, Ikoma, Nara 630-0136, Japan
| | - Hidetomi Terai
- Department of Orthopaedic Surgery, Osaka City University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka 545-8585, Japan
| | - Hiromitsu Toyoda
- Department of Orthopaedic Surgery, Osaka City University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka 545-8585, Japan
| | - Akinobu Suzuki
- Department of Orthopaedic Surgery, Osaka City University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka 545-8585, Japan
| | - Koji Tamai
- Department of Orthopaedic Surgery, Osaka City University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka 545-8585, Japan
| | - Shoichiro Ohyama
- Department of Orthopaedic Surgery, Osaka City University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka 545-8585, Japan
| | - Yusuke Hori
- Department of Orthopaedic Surgery, Osaka City University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka 545-8585, Japan
| | - Atsushi Okawa
- Department of Orthopaedic Surgery, Tokyo Medical and Dental University, Graduate School, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Hiroaki Nakamura
- Department of Orthopaedic Surgery, Osaka City University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka 545-8585, Japan
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Murata K, Endo K, Aihara T, Suzuki H, Sawaji Y, Matsuoka Y, Takamatsu T, Konishi T, Yamauchi H, Endo H, Yamamoto K. Use of residual neural network for the detection of ossification of the posterior longitudinal ligament on plain cervical radiography. EUROPEAN SPINE JOURNAL : OFFICIAL PUBLICATION OF THE EUROPEAN SPINE SOCIETY, THE EUROPEAN SPINAL DEFORMITY SOCIETY, AND THE EUROPEAN SECTION OF THE CERVICAL SPINE RESEARCH SOCIETY 2021; 30:2185-2190. [PMID: 34196802 DOI: 10.1007/s00586-021-06914-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2020] [Revised: 05/23/2021] [Accepted: 06/26/2021] [Indexed: 11/25/2022]
Abstract
Ossification of the posterior longitudinal ligament (OPLL) causes serious problems, such as myelopathy and acute spinal cord injury. The early and accurate diagnosis of OPLL would hence prevent the miserable prognoses. Plain lateral radiography is an essential method for the evaluation of OPLL. Therefore, minimizing the diagnostic errors of OPLL on radiography is crucial. Image identification based on a residual neural network (RNN) has been recognized to be potentially effective as a diagnostic strategy for orthopedic diseases; however, the accuracy of detecting OPLL using RNN has remained unclear. An RNN was trained with plain lateral cervical radiography images of 2,318 images from 672 patients (535 images from 304 patients with OPLL and 1,773 images from 368 patients of Negative). The accuracy, sensitivity, specificity, false positive rate, and false negative rate of diagnosis of the RNN were calculated. The mean accuracy, sensitivity, specificity, false positive rate, and false negative rate of the model were 98.9%, 97.0%, 99.4%, 2.2%, and 1.0%, respectively. The model achieved an overall area under the curve of 0.99 (95% confidence interval, 0.97-1.00) in which AUC in each fold estimated was 0.99, 0.99, 0.98, 0.98, and 0.99, respectively. An algorithm trained by an RNN could make binary classification of OPLL on cervical lateral X-ray images. RNN may hence be useful as a screening tool to assist physicians in identifying patients with OPLL in future setting. To achieve accurate identification of OPLL patients clinically, RNN has to be trained with other cause of myelopathy.
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Kokabu T, Kanai S, Kawakami N, Uno K, Kotani T, Suzuki T, Tachi H, Abe Y, Iwasaki N, Sudo H. An algorithm for using deep learning convolutional neural networks with three dimensional depth sensor imaging in scoliosis detection. Spine J 2021; 21:980-987. [PMID: 33540125 DOI: 10.1016/j.spinee.2021.01.022] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Revised: 01/19/2021] [Accepted: 01/26/2021] [Indexed: 02/03/2023]
Abstract
BACKGROUND CONTEXT Timely intervention in growing individuals, such as brace treatment, relies on early detection of adolescent idiopathic scoliosis (AIS). To this end, several screening methods have been implemented. However, these methods have limitations in predicting the Cobb angle. PURPOSE This study aimed to evaluate the performance of a three-dimensional depth sensor imaging system with a deep learning algorithm, in predicting the Cobb angle in AIS. STUDY DESIGN Retrospective analysis of prospectively collected, consecutive, nonrandomized series of patients at five scoliosis centers in Japan. PATIENT SAMPLE One hundred and-sixty human subjects suspected to have AIS were included. OUTCOME MEASURES Patient demographics, radiographic measurements, and predicted Cobb angle derived from the deep learning algorithm were the outcome measures for this study. METHODS One hundred and sixty data files were shuffled into five datasets with 32 data files at random (dataset 1, 2, 3, 4, and 5) and five-fold cross validation was performed. The relationships between the actual and predicted Cobb angles were calculated using Pearson's correlation coefficient analyses. The prediction performances of the network models were evaluated using mean absolute error and root mean square error between the actual and predicted Cobb angles. The shuffling into five datasets and five-fold cross validation was conducted ten times. There were no study-specific biases related to conflicts of interest. RESULTS The correlation between the actual and the mean predicted Cobb angles was 0.91. The mean absolute error and root mean square error were 4.0° and 5.4°, respectively. The accuracy of the mean predicted Cobb angle was 94% for identifying a Cobb angle of ≥10° and 89% for that of ≥20°. CONCLUSIONS The three-dimensional depth sensor imaging system with its newly innovated convolutional neural network for regression is objective and has significant ability to predict the Cobb angle in children and adolescents. This system is expected to be used for screening scoliosis in clinics or physical examination at schools.
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Affiliation(s)
- Terufumi Kokabu
- Department of Orthopedic Surgery, Hokkaido University Hospital, Nishi 5 Chome Kita 14 Jo, Kita Ward, Sapporo, Hokkaido 060-8648, Japan; Department of Orthopedic Surgery, Eniwa Hospital, Koganechuo 2-1-1, Eniwa, Hokkaido 061-1449, Japan
| | - Satoshi Kanai
- Division of Systems Science and Informatics, Hokkaido University Graduate School of Information Science and Technology, Nishi 9 Chome Kita 13 Jo, Kita Ward, Sapporo, Hokkaido 060-0813, Japan
| | - Noriaki Kawakami
- Department of Orthopedic Surgery, Ichinomiyanishi Hospital, Ichinomiya, Kaimei, Aza Hira 1, 494-0001 Aichi, Japan
| | - Koki Uno
- Department of Orthopedic Surgery, National Hospital Organization, Kobe Medical Center, 3 Chome-1-1 Nishiochiai, Suma Ward, Kobe, Hyogo 654-0155, Japan
| | - Toshiaki Kotani
- Department of Orthopedic Surgery, Seirei Sakura Citizen Hospital, 2 Chome-36-2 Ebaradai, Sakura, Chiba 285-8765, Japan
| | - Teppei Suzuki
- Department of Orthopedic Surgery, National Hospital Organization, Kobe Medical Center, 3 Chome-1-1 Nishiochiai, Suma Ward, Kobe, Hyogo 654-0155, Japan
| | - Hiroyuki Tachi
- Department of Orthopedic Surgery, Hokkaido University Hospital, Nishi 5 Chome Kita 14 Jo, Kita Ward, Sapporo, Hokkaido 060-8648, Japan; Department of Orthopedic Surgery, Eniwa Hospital, Koganechuo 2-1-1, Eniwa, Hokkaido 061-1449, Japan
| | - Yuichiro Abe
- Department of Orthopedic Surgery, Eniwa Hospital, Koganechuo 2-1-1, Eniwa, Hokkaido 061-1449, Japan
| | - Norimasa Iwasaki
- Department of Orthopedic Surgery, Hokkaido University Hospital, Nishi 5 Chome Kita 14 Jo, Kita Ward, Sapporo, Hokkaido 060-8648, Japan
| | - Hideki Sudo
- Department of Orthopedic Surgery, Hokkaido University Hospital, Nishi 5 Chome Kita 14 Jo, Kita Ward, Sapporo, Hokkaido 060-8648, Japan; Department of Advanced Medicine for Spine and Spinal Cord Disorders, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, N15W7, Sapporo, Hokkaido 060-8638, Japan.
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Zegers C, Posch J, Traverso A, Eekers D, Postma A, Backes W, Dekker A, van Elmpt W. Current applications of deep-learning in neuro-oncological MRI. Phys Med 2021; 83:161-173. [DOI: 10.1016/j.ejmp.2021.03.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 02/01/2021] [Accepted: 03/02/2021] [Indexed: 12/18/2022] Open
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Cepeda S, García-García S, Arrese I, Fernández-Pérez G, Velasco-Casares M, Fajardo-Puentes M, Zamora T, Sarabia R. Comparison of Intraoperative Ultrasound B-Mode and Strain Elastography for the Differentiation of Glioblastomas From Solitary Brain Metastases. An Automated Deep Learning Approach for Image Analysis. Front Oncol 2021; 10:590756. [PMID: 33604286 PMCID: PMC7884775 DOI: 10.3389/fonc.2020.590756] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2020] [Accepted: 12/17/2020] [Indexed: 12/29/2022] Open
Abstract
Background The differential diagnosis of glioblastomas (GBM) from solitary brain metastases (SBM) is essential because the surgical strategy varies according to the histopathological diagnosis. Intraoperative ultrasound elastography (IOUS-E) is a relatively novel technique implemented in the surgical management of brain tumors that provides additional information about the elasticity of tissues. This study compares the discriminative capacity of intraoperative ultrasound B-mode and strain elastography to differentiate GBM from SBM. Methods We performed a retrospective analysis of patients who underwent craniotomy between March 2018 to June 2020 with glioblastoma (GBM) and solitary brain metastases (SBM) diagnoses. Cases with an intraoperative ultrasound study were included. Images were acquired before dural opening, first in B-mode, and then using the strain elastography module. After image pre-processing, an analysis based on deep learning was conducted using the open-source software Orange. We have trained an existing neural network to classify tumors into GBM and SBM via the transfer learning method using Inception V3. Then, logistic regression (LR) with LASSO (least absolute shrinkage and selection operator) regularization, support vector machine (SVM), random forest (RF), neural network (NN), and k-nearest neighbor (kNN) were used as classification algorithms. After the models’ training, ten-fold stratified cross-validation was performed. The models were evaluated using the area under the curve (AUC), classification accuracy, and precision. Results A total of 36 patients were included in the analysis, 26 GBM and 10 SBM. Models were built using a total of 812 ultrasound images, 435 of B-mode, 265 (60.92%) corresponded to GBM and 170 (39.8%) to metastases. In addition, 377 elastograms, 232 (61.54%) GBM and 145 (38.46%) metastases were analyzed. For B-mode, AUC and accuracy values of the classification algorithms ranged from 0.790 to 0.943 and from 72 to 89%, respectively. For elastography, AUC and accuracy values ranged from 0.847 to 0.985 and from 79% to 95%, respectively. Conclusion Automated processing of ultrasound images through deep learning can generate high-precision classification algorithms that differentiate glioblastomas from metastases using intraoperative ultrasound. The best performance regarding AUC was achieved by the elastography-based model supporting the additional diagnostic value that this technique provides.
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Affiliation(s)
- Santiago Cepeda
- Neurosurgery Department, University Hospital Río Hortega, Valladolid, Spain
| | | | - Ignacio Arrese
- Neurosurgery Department, University Hospital Río Hortega, Valladolid, Spain
| | | | | | | | - Tomás Zamora
- Pathology Department, University Hospital Río Hortega, Valladolid, Spain
| | - Rosario Sarabia
- Neurosurgery Department, University Hospital Río Hortega, Valladolid, Spain
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Ito S, Ando K, Kobayashi K, Nakashima H, Oda M, Machino M, Kanbara S, Inoue T, Yamaguchi H, Koshimizu H, Mori K, Ishiguro N, Imagama S. Automated Detection of Spinal Schwannomas Utilizing Deep Learning Based on Object Detection From Magnetic Resonance Imaging. Spine (Phila Pa 1976) 2021; 46:95-100. [PMID: 33079909 DOI: 10.1097/brs.0000000000003749] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
STUDY DESIGN A retrospective analysis of magnetic resonance imaging (MRI) was conducted. OBJECTIVE This study aims to develop an automated system for the detection of spinal schwannoma, by employing deep learning based on object detection from MRI. The performance of the proposed system was verified to compare the performances of spine surgeons. SUMMARY OF BACKGROUND DATA Several MRI scans were conducted for the diagnoses of patients suspected to suffer from spinal diseases. Typically, spinal diseases do not involve tumors on the spinal cord, although a few tumors may exist at the unexpectable level or without symptom by chance. It is difficult to recognize these tumors; in some cases, these tumors may be overlooked. Hence, a deep learning approach based on object detection can minimize the probability of overlooking these tumors. METHODS Data from 50 patients with spinal schwannoma who had undergone MRI were retrospectively reviewed. Sagittal T1- and T2-weighted magnetic resonance imaging (T1WI and T2WI) were used in the object detection training and for validation. You Only Look Once version3 was used to develop the object detection system, and its accuracy was calculated. The performance of the proposed system was compared to that of two doctors. RESULTS The accuracies of the proposed object detection based on T1W1, T2W1, and both T1W1 and T2W1 were 80.3%, 91.0%, and 93.5%, respectively. The accuracies of the doctors were 90.2% and 89.3%. CONCLUSION Automated object detection of spinal schwannoma was achieved. The proposed system yielded a high accuracy that was comparable to that of the doctors.Level of Evidence: 4.
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Affiliation(s)
- Sadayuki Ito
- Department of Orthopedic Surgery, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Kei Ando
- Department of Orthopedic Surgery, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Kazuyoshi Kobayashi
- Department of Orthopedic Surgery, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Hiroaki Nakashima
- Department of Orthopedic Surgery, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Masahiro Oda
- Department of Intelligent Systems, Nagoya University Graduate School of Informatics, Nagoya, Japan
| | - Masaaki Machino
- Department of Orthopedic Surgery, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Shunsuke Kanbara
- Department of Orthopedic Surgery, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Taro Inoue
- Department of Orthopedic Surgery, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Hidetoshi Yamaguchi
- Department of Orthopedic Surgery, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Hiroyuki Koshimizu
- Department of Orthopedic Surgery, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Kensaku Mori
- Department of Intelligent Systems, Nagoya University Graduate School of Informatics, Nagoya, Japan
- Reseach Center for Medical Bigdata, National Institute of Informatics, Tokyo, Japan
| | - Naoki Ishiguro
- Department of Orthopedic Surgery, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Shiro Imagama
- Department of Orthopedic Surgery, Nagoya University Graduate School of Medicine, Nagoya, Japan
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28
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Neromyliotis E, Kalamatianos T, Paschalis A, Komaitis S, Fountas KN, Kapsalaki EZ, Stranjalis G, Tsougos I. Machine Learning in Meningioma MRI: Past to Present. A Narrative Review. J Magn Reson Imaging 2020; 55:48-60. [PMID: 33006425 DOI: 10.1002/jmri.27378] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Revised: 09/10/2020] [Accepted: 09/10/2020] [Indexed: 12/28/2022] Open
Abstract
Meningioma is one of the most frequent primary central nervous system tumors. While magnetic resonance imaging (MRI), is the standard radiologic technique for provisional diagnosis and surveillance of meningioma, it nevertheless lacks the prima facie capacity in determining meningioma biological aggressiveness, growth, and recurrence potential. An increasing body of evidence highlights the potential of machine learning and radiomics in improving the consistency and productivity and in providing novel diagnostic, treatment, and prognostic modalities in neuroncology imaging. The aim of the present article is to review the evolution and progress of approaches utilizing machine learning in meningioma MRI-based sementation, diagnosis, grading, and prognosis. We provide a historical perspective on original research on meningioma spanning over two decades and highlight recent studies indicating the feasibility of pertinent approaches, including deep learning in addressing several clinically challenging aspects. We indicate the limitations of previous research designs and resources and propose future directions by highlighting areas of research that remain largely unexplored. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 2.
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Affiliation(s)
- Eleftherios Neromyliotis
- Departent of Neurosurgery, University of Athens Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Theodosis Kalamatianos
- Departent of Neurosurgery, University of Athens Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Athanasios Paschalis
- Department of Neurosurgery, School of Medicine, University of Thessaly, Larisa, Greece
| | - Spyridon Komaitis
- Departent of Neurosurgery, University of Athens Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Konstantinos N Fountas
- Department of Clinical and Laboratory Research, School of Medicine, University of Thessaly, Larisa, Greece
| | - Eftychia Z Kapsalaki
- Department of Clinical and Laboratory Research, School of Medicine, University of Thessaly, Larisa, Greece
| | - George Stranjalis
- Departent of Neurosurgery, University of Athens Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Ioannis Tsougos
- Department of Medical Physics, School of Medicine, University of Thessaly, Larisa, Greece
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29
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Abouzari M, Goshtasbi K, Sarna B, Khosravi P, Reutershan T, Mostaghni N, Lin HW, Djalilian HR. Prediction of vestibular schwannoma recurrence using artificial neural network. Laryngoscope Investig Otolaryngol 2020; 5:278-285. [PMID: 32337359 PMCID: PMC7178452 DOI: 10.1002/lio2.362] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Revised: 01/28/2020] [Accepted: 02/08/2020] [Indexed: 12/12/2022] Open
Abstract
OBJECTIVES To compare two statistical models, namely logistic regression and artificial neural network (ANN), in prediction of vestibular schwannoma (VS) recurrence. METHODS Seven hundred eighty-nine patients with VS diagnosis completed an online survey. Potential predictors for recurrence were derived from univariate analysis by reaching the cut off P value of .05. Those nine potential predictors were years since treatment, surgeon's specialty, resection amount, and having incomplete eye closure, dry eye, double vision, facial pain, seizure, and voice/swallowing problem as a complication following treatment. Multivariate binary logistic regression model was compared with a four-layer 9-5-10-1 feedforward backpropagation ANN for prediction of recurrence. RESULTS The overall recurrence rate was 14.5%. Significant predictors of recurrence in the regression model were years since treatment and resection amount (both P < .001). The regression model did not show an acceptable performance (area under the curve [AUC] = 0.64; P = .27). The regression model's sensitivity and specificity were 44% and 69%, respectively and correctly classified 56% of cases. The ANN showed a superior performance compared to the regression model (AUC = 0.79; P = .001) with higher sensitivity (61%) and specificity (81%), and correctly classified 70% of cases. CONCLUSION The constructed ANN model was superior to logistic regression in predicting patient-answered VS recurrence in an anonymous survey with higher sensitivity and specificity. Since artificial intelligence tools such as neural networks can have higher predictive abilities compared to logistic regression models, continuous investigation into their utility as complementary clinical tools in predicting certain surgical outcomes is warranted.
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Affiliation(s)
- Mehdi Abouzari
- Division of Neurotology and Skull Base Surgery, Department of Otolaryngology‐Head and Neck SurgeryUniversity of CaliforniaIrvineCalifornia
- Division of Pediatric OtolaryngologyChildren's Hospital of Orange CountyOrangeCalifornia
| | - Khodayar Goshtasbi
- Division of Neurotology and Skull Base Surgery, Department of Otolaryngology‐Head and Neck SurgeryUniversity of CaliforniaIrvineCalifornia
| | - Brooke Sarna
- Division of Neurotology and Skull Base Surgery, Department of Otolaryngology‐Head and Neck SurgeryUniversity of CaliforniaIrvineCalifornia
| | - Pooya Khosravi
- Division of Neurotology and Skull Base Surgery, Department of Otolaryngology‐Head and Neck SurgeryUniversity of CaliforniaIrvineCalifornia
- Department of Biomedical EngineeringUniversity of CaliforniaIrvineCalifornia
| | - Trevor Reutershan
- Division of Neurotology and Skull Base Surgery, Department of Otolaryngology‐Head and Neck SurgeryUniversity of CaliforniaIrvineCalifornia
- Department of Biomedical EngineeringUniversity of CaliforniaIrvineCalifornia
| | - Navid Mostaghni
- Division of Neurotology and Skull Base Surgery, Department of Otolaryngology‐Head and Neck SurgeryUniversity of CaliforniaIrvineCalifornia
- Department of Biomedical EngineeringUniversity of CaliforniaIrvineCalifornia
| | - Harrison W. Lin
- Division of Neurotology and Skull Base Surgery, Department of Otolaryngology‐Head and Neck SurgeryUniversity of CaliforniaIrvineCalifornia
| | - Hamid R. Djalilian
- Division of Neurotology and Skull Base Surgery, Department of Otolaryngology‐Head and Neck SurgeryUniversity of CaliforniaIrvineCalifornia
- Department of Biomedical EngineeringUniversity of CaliforniaIrvineCalifornia
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