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Ruitenbeek HC, Oei EHG, Visser JJ, Kijowski R. Artificial intelligence in musculoskeletal imaging: realistic clinical applications in the next decade. Skeletal Radiol 2024:10.1007/s00256-024-04684-6. [PMID: 38902420 DOI: 10.1007/s00256-024-04684-6] [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: 02/01/2024] [Revised: 04/06/2024] [Accepted: 04/15/2024] [Indexed: 06/22/2024]
Abstract
This article will provide a perspective review of the most extensively investigated deep learning (DL) applications for musculoskeletal disease detection that have the best potential to translate into routine clinical practice over the next decade. Deep learning methods for detecting fractures, estimating pediatric bone age, calculating bone measurements such as lower extremity alignment and Cobb angle, and grading osteoarthritis on radiographs have been shown to have high diagnostic performance with many of these applications now commercially available for use in clinical practice. Many studies have also documented the feasibility of using DL methods for detecting joint pathology and characterizing bone tumors on magnetic resonance imaging (MRI). However, musculoskeletal disease detection on MRI is difficult as it requires multi-task, multi-class detection of complex abnormalities on multiple image slices with different tissue contrasts. The generalizability of DL methods for musculoskeletal disease detection on MRI is also challenging due to fluctuations in image quality caused by the wide variety of scanners and pulse sequences used in routine MRI protocols. The diagnostic performance of current DL methods for musculoskeletal disease detection must be further evaluated in well-designed prospective studies using large image datasets acquired at different institutions with different imaging parameters and imaging hardware before they can be fully implemented in clinical practice. Future studies must also investigate the true clinical benefits of current DL methods and determine whether they could enhance quality, reduce error rates, improve workflow, and decrease radiologist fatigue and burnout with all of this weighed against the costs.
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Affiliation(s)
- Huibert C Ruitenbeek
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center, P.O. Box 2040, 3000 CA, Rotterdam, The Netherlands
| | - Edwin H G Oei
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center, P.O. Box 2040, 3000 CA, Rotterdam, The Netherlands
| | - Jacob J Visser
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center, P.O. Box 2040, 3000 CA, Rotterdam, The Netherlands
| | - Richard Kijowski
- Department of Radiology, New York University Grossman School of Medicine, 660 First Avenue, 3rd Floor, New York, NY, 10016, USA.
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Chen Y, Lin F, Wang K, Chen F, Wang R, Lai M, Chen C, Wang R. Development of a predictive model for 1-year postoperative recovery in patients with lumbar disk herniation based on deep learning and machine learning. Front Neurol 2024; 15:1255780. [PMID: 38919973 PMCID: PMC11197993 DOI: 10.3389/fneur.2024.1255780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Accepted: 05/23/2024] [Indexed: 06/27/2024] Open
Abstract
Background The aim of this study is to develop a predictive model utilizing deep learning and machine learning techniques that will inform clinical decision-making by predicting the 1-year postoperative recovery of patients with lumbar disk herniation. Methods The clinical data of 470 inpatients who underwent tubular microdiscectomy (TMD) between January 2018 and January 2021 were retrospectively analyzed as variables. The dataset was randomly divided into a training set (n = 329) and a test set (n = 141) using a 10-fold cross-validation technique. Various deep learning and machine learning algorithms including Random Forests, Extreme Gradient Boosting, Support Vector Machines, Extra Trees, K-Nearest Neighbors, Logistic Regression, Light Gradient Boosting Machine, and MLP (Artificial Neural Networks) were employed to develop predictive models for the recovery of patients with lumbar disk herniation 1 year after surgery. The cure rate score of lumbar JOA score 1 year after TMD was used as an outcome indicator. The primary evaluation metric was the area under the receiver operating characteristic curve (AUC), with additional measures including decision curve analysis (DCA), accuracy, sensitivity, specificity, and others. Results The heat map of the correlation matrix revealed low inter-feature correlation. The predictive model employing both machine learning and deep learning algorithms was constructed using 15 variables after feature engineering. Among the eight algorithms utilized, the MLP algorithm demonstrated the best performance. Conclusion Our study findings demonstrate that the MLP algorithm provides superior predictive performance for the recovery of patients with lumbar disk herniation 1 year after surgery.
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Affiliation(s)
- Yan Chen
- Pingtan Comprehensive Experimentation Area Hospital, Pingtan, China
- Fujian Medical University Union Hospital, Fuzhou, Fujian, China
| | - Fabin Lin
- Pingtan Comprehensive Experimentation Area Hospital, Pingtan, China
- Fujian Medical University Union Hospital, Fuzhou, Fujian, China
| | - Kaifeng Wang
- Fujian Medical University, Fuzhou, Fujian, China
| | - Feng Chen
- Fujian Medical University, Fuzhou, Fujian, China
| | - Ruxian Wang
- Fujian Medical University, Fuzhou, Fujian, China
| | - Minyun Lai
- Fujian Medical University, Fuzhou, Fujian, China
| | - Chunmei Chen
- Pingtan Comprehensive Experimentation Area Hospital, Pingtan, China
- Fujian Medical University Union Hospital, Fuzhou, Fujian, China
| | - Rui Wang
- Pingtan Comprehensive Experimentation Area Hospital, Pingtan, China
- Fujian Medical University Union Hospital, Fuzhou, Fujian, China
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Kalanjiyam GP, Chandramohan T, Raman M, Kalyanasundaram H. Artificial intelligence: a new cutting-edge tool in spine surgery. Asian Spine J 2024; 18:458-471. [PMID: 38917854 DOI: 10.31616/asj.2023.0382] [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: 12/07/2023] [Accepted: 01/11/2024] [Indexed: 06/27/2024] Open
Abstract
The purpose of this narrative review was to comprehensively elaborate the various components of artificial intelligence (AI), their applications in spine surgery, practical concerns, and future directions. Over the years, spine surgery has been continuously transformed in various aspects, including diagnostic strategies, surgical approaches, procedures, and instrumentation, to provide better-quality patient care. Surgeons have also augmented their surgical expertise with rapidly growing technological advancements. AI is an advancing field that has the potential to revolutionize many aspects of spine surgery. We performed a comprehensive narrative review of the various aspects of AI and machine learning in spine surgery. To elaborate on the current role of AI in spine surgery, a review of the literature was performed using PubMed and Google Scholar databases for articles published in English in the last 20 years. The initial search using the keywords "artificial intelligence" AND "spine," "machine learning" AND "spine," and "deep learning" AND "spine" extracted a total of 78, 60, and 37 articles and 11,500, 4,610, and 2,270 articles on PubMed and Google Scholar. After the initial screening and exclusion of unrelated articles, duplicates, and non-English articles, 405 articles were identified. After the second stage of screening, 93 articles were included in the review. Studies have shown that AI can be used to analyze patient data and provide personalized treatment recommendations in spine care. It also provides valuable insights for planning surgeries and assisting with precise surgical maneuvers and decisionmaking during the procedures. As more data become available and with further advancements, AI is likely to improve patient outcomes.
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Affiliation(s)
- Guna Pratheep Kalanjiyam
- Spine Surgery Unit, Department of Orthopaedics, Meenakshi Mission Hospital and Research Centre, Madurai, India
| | - Thiyagarajan Chandramohan
- Department of Orthopaedics, Government Stanley Medical College, Chennai, India
- Department of Emergency Medicine, Government Stanley Medical College, Chennai, India
| | - Muthu Raman
- Department of Orthopaedics, Tenkasi Government Hospital, Tenkasi, India
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Pojskic M, Bisson E, Oertel J, Takami T, Zygourakis C, Costa F. Lumbar disc herniation: Epidemiology, clinical and radiologic diagnosis WFNS spine committee recommendations. World Neurosurg X 2024; 22:100279. [PMID: 38440379 PMCID: PMC10911853 DOI: 10.1016/j.wnsx.2024.100279] [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: 07/28/2023] [Accepted: 02/01/2024] [Indexed: 03/06/2024] Open
Abstract
Objective To formulate the most current, evidence-based recommendations regarding the epidemiology, clinical diagnosis, and radiographic diagnosis of lumbar herniated disk (LDH). Methods A systematic literature search in PubMed, MEDLINE, and CENTRAL was performed from 2012 to 2022 using the search terms "herniated lumbar disc", "epidemiology", "prevention" "clinical diagnosis", and "radiological diagnosis". Screening criteria resulted in 17, 16, and 90 studies respectively that were analyzed regarding epidemiology, clinical diagnosis, and radiographic diagnosis of LDH. Using the Delphi method and two rounds of voting at two separate international meetings, ten members of the WFNS (World Federation of Neurosurgical Societies) Spine Committee generated eleven final consensus statements. Results The lifetime risk for symptomatic LDH is 1-3%; of these, 60-90% resolve spontaneously. Risk factors for LDH include genetic and environmental factors, strenuous activity, and smoking. LDH is more common in males and in 30-50 year olds. A set of clinical tests, including manual muscle testing, sensory testing, Lasegue sign, and crossed Lasegue sign are recommended to diagnose LDH. Magnetic resonance imaging (MRI) is the gold standard for confirming suspected LDH. Conclusions These eleven final consensus statements provide current, evidence-based guidelines on the epidemiology, clinical diagnosis, and radiographic diagnosis of LDH for practicing spine surgeons worldwide.
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Affiliation(s)
- Mirza Pojskic
- Department of Neurosurgery, University of Marburg, Germany
| | - Erica Bisson
- Department of Neurosurgery, University of Utah, Salt Lake City, UT, USA
| | - Joachim Oertel
- Department of Neurosurgery, Saarland University Medical Centre, Homburg, Saarland, Germany
| | - Toshihiro Takami
- Department of Neurosurgery, Osaka Medical and Pharmaceutical University, Japan
| | - Corinna Zygourakis
- Department of Neurosurgery, Stanford University Medical Center, Stanford, CA, USA
| | - Francesco Costa
- Spine Surgery Unit (NCH4) - Department of Neurosurgery - Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
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Zerunian M, Pucciarelli F, Caruso D, De Santis D, Polici M, Masci B, Nacci I, Del Gaudio A, Argento G, Redler A, Laghi A. Fast high-quality MRI protocol of the lumbar spine with deep learning-based algorithm: an image quality and scanning time comparison with standard protocol. Skeletal Radiol 2024; 53:151-159. [PMID: 37369725 PMCID: PMC10661795 DOI: 10.1007/s00256-023-04390-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 06/13/2023] [Accepted: 06/13/2023] [Indexed: 06/29/2023]
Abstract
OBJECTIVE The objective of this study is to prospectively compare quantitative and subjective image quality, scanning time, and diagnostic confidence between a new deep learning-based reconstruction(DLR) algorithm and standard MRI protocol of lumbar spine. MATERIALS AND METHODS Eighty healthy volunteers underwent 1.5T MRI examination of lumbar spine from September 2021 to May 2023. Protocol acquisition comprised sagittal T1- and T2-weighted fast spin echo and short-tau inversion recovery images and axial multislices T2-weighted fast spin echo images. All sequences were acquired with both DLR algorithm and standard protocols. Two radiologists, blinded to the reconstruction technique, performed quantitative and qualitative image quality analysis in consensus reading; diagnostic confidence was also assessed. Quantitative image quality analysis was assessed by calculating signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Qualitative image quality analysis and diagnostic confidence were assessed with a five-point Likert scale. Scanning times were also compared. RESULTS DLR SNR was higher in all sequences (all p<0.001). CNR of the DLR was superior to conventional dataset only for axial and sagittal T2-weighted fast spin echo images (p<0.001). Qualitative analysis showed DLR had higher overall quality in all sequences (all p<0.001), with an inter-rater agreement of 0.83 (0.78-0.86). DLR total protocol scanning time was lower compared to standard protocol (6:26 vs 12:59 min, p<0.001). Diagnostic confidence for DLR algorithm was not inferior to standard protocol. CONCLUSION DLR applied to 1.5T MRI is a feasible method for lumbar spine imaging providing morphologic sequences with higher image quality and similar diagnostic confidence compared with standard protocol, enabling a remarkable time saving (up to 50%).
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Affiliation(s)
- Marta Zerunian
- Department of Medical Surgical Sciences and Translational Medicine, University of Rome "Sapienza" Radiology Unit - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Francesco Pucciarelli
- Department of Medical Surgical Sciences and Translational Medicine, University of Rome "Sapienza" Radiology Unit - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Damiano Caruso
- Department of Medical Surgical Sciences and Translational Medicine, University of Rome "Sapienza" Radiology Unit - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Domenico De Santis
- Department of Medical Surgical Sciences and Translational Medicine, University of Rome "Sapienza" Radiology Unit - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Michela Polici
- Department of Medical Surgical Sciences and Translational Medicine, University of Rome "Sapienza" Radiology Unit - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Benedetta Masci
- Department of Medical Surgical Sciences and Translational Medicine, University of Rome "Sapienza" Radiology Unit - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Ilaria Nacci
- Department of Medical Surgical Sciences and Translational Medicine, University of Rome "Sapienza" Radiology Unit - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Antonella Del Gaudio
- Department of Medical Surgical Sciences and Translational Medicine, University of Rome "Sapienza" Radiology Unit - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Giuseppe Argento
- Department of Medical Surgical Sciences and Translational Medicine, University of Rome "Sapienza" Radiology Unit - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Andrea Redler
- Orthopaedic Unit and Kirk Kilgour Sports Injury Centre, University of Rome "Sapienza" - Sant'Andrea University Hospital, Rome, Italy
| | - Andrea Laghi
- Department of Medical Surgical Sciences and Translational Medicine, University of Rome "Sapienza" Radiology Unit - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy.
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Compte R, Granville Smith I, Isaac A, Danckert N, McSweeney T, Liantis P, Williams FMK. Are current machine learning applications comparable to radiologist classification of degenerate and herniated discs and Modic change? A systematic review and meta-analysis. 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 2023; 32:3764-3787. [PMID: 37150769 PMCID: PMC10164619 DOI: 10.1007/s00586-023-07718-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 02/08/2023] [Accepted: 04/09/2023] [Indexed: 05/09/2023]
Abstract
INTRODUCTION Low back pain is the leading contributor to disability burden globally. It is commonly due to degeneration of the lumbar intervertebral discs (LDD). Magnetic resonance imaging (MRI) is the current best tool to visualize and diagnose LDD, but places high time demands on clinical radiologists. Automated reading of spine MRIs could improve speed, accuracy, reliability and cost effectiveness in radiology departments. The aim of this review and meta-analysis was to determine if current machine learning algorithms perform well identifying disc degeneration, herniation, bulge and Modic change compared to radiologists. METHODS A PRISMA systematic review protocol was developed and four electronic databases and reference lists were searched. Strict inclusion and exclusion criteria were defined. A PROBAST risk of bias and applicability analysis was performed. RESULTS 1350 articles were extracted. Duplicates were removed and title and abstract searching identified original research articles that used machine learning (ML) algorithms to identify disc degeneration, herniation, bulge and Modic change from MRIs. 27 studies were included in the review; 25 and 14 studies were included multi-variate and bivariate meta-analysis, respectively. Studies used machine learning algorithms to assess LDD, disc herniation, bulge and Modic change. Models using deep learning, support vector machine, k-nearest neighbors, random forest and naïve Bayes algorithms were included. Meta-analyses found no differences in algorithm or classification performance. When algorithms were tested in replication or external validation studies, they did not perform as well as when assessed in developmental studies. Data augmentation improved algorithm performance when compared to models used with smaller datasets, there were no performance differences between augmented data and large datasets. DISCUSSION This review highlights several shortcomings of current approaches, including few validation attempts or use of large sample sizes. To the best of the authors' knowledge, this is the first systematic review to explore this topic. We suggest the utilization of deep learning coupled with semi- or unsupervised learning approaches. Use of all information contained in MRI data will improve accuracy. Clear and complete reporting of study design, statistics and results will improve the reliability and quality of published literature.
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Affiliation(s)
- Roger Compte
- Department of Twin Research, King's College London, St Thomas' Hospital Campus, 4th Floor South Wing, Block D, Westminster Bridge Road, London, SE1 7EH, UK.
| | - Isabelle Granville Smith
- Department of Twin Research, King's College London, St Thomas' Hospital Campus, 4th Floor South Wing, Block D, Westminster Bridge Road, London, SE1 7EH, UK.
| | - Amanda Isaac
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Nathan Danckert
- Department of Twin Research, King's College London, St Thomas' Hospital Campus, 4th Floor South Wing, Block D, Westminster Bridge Road, London, SE1 7EH, UK
| | - Terence McSweeney
- Research Unit of Health Sciences and Technology, University of Oulu, Oulu, Finland
| | - Panagiotis Liantis
- Guy's and St Thomas' National Health Services Foundation Trust, London, UK
| | - Frances M K Williams
- Department of Twin Research, King's College London, St Thomas' Hospital Campus, 4th Floor South Wing, Block D, Westminster Bridge Road, London, SE1 7EH, UK
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Prisilla AA, Guo YL, Jan YK, Lin CY, Lin FY, Liau BY, Tsai JY, Ardhianto P, Pusparani Y, Lung CW. An approach to the diagnosis of lumbar disc herniation using deep learning models. Front Bioeng Biotechnol 2023; 11:1247112. [PMID: 37731760 PMCID: PMC10507264 DOI: 10.3389/fbioe.2023.1247112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 08/09/2023] [Indexed: 09/22/2023] Open
Abstract
Background: In magnetic resonance imaging (MRI), lumbar disc herniation (LDH) detection is challenging due to the various shapes, sizes, angles, and regions associated with bulges, protrusions, extrusions, and sequestrations. Lumbar abnormalities in MRI can be detected automatically by using deep learning methods. As deep learning models gain recognition, they may assist in diagnosing LDH with MRI images and provide initial interpretation in clinical settings. YOU ONLY LOOK ONCE (YOLO) model series are often used to train deep learning algorithms for real-time biomedical image detection and prediction. This study aims to confirm which YOLO models (YOLOv5, YOLOv6, and YOLOv7) perform well in detecting LDH in different regions of the lumbar intervertebral disc. Materials and methods: The methodology involves several steps, including converting DICOM images to JPEG, reviewing and selecting MRI slices for labeling and augmentation using ROBOFLOW, and constructing YOLOv5x, YOLOv6, and YOLOv7 models based on the dataset. The training dataset was combined with the radiologist's labeling and annotation, and then the deep learning models were trained using the training/validation dataset. Results: Our result showed that the 550-dataset with augmentation (AUG) or without augmentation (non-AUG) in YOLOv5x generates satisfactory training performance in LDH detection. The AUG dataset overall performance provides slightly higher accuracy than the non-AUG. YOLOv5x showed the highest performance with 89.30% mAP compared to YOLOv6, and YOLOv7. Also, YOLOv5x in non-AUG dataset showed the balance LDH region detections in L2-L3, L3-L4, L4-L5, and L5-S1 with above 90%. And this illustrates the competitiveness of using non-AUG dataset to detect LDH. Conclusion: Using YOLOv5x and the 550 augmented dataset, LDH can be detected with promising both in non-AUG and AUG dataset. By utilizing the most appropriate YOLO model, clinicians have a greater chance of diagnosing LDH early and preventing adverse effects for their patients.
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Affiliation(s)
- Ardha Ardea Prisilla
- Department of Fashion Design, LaSalle College Jakarta, Jakarta, Indonesia
- Department of Digital Media Design, Asia University, Taichung, Taiwan
| | - Yue Leon Guo
- Environmental and Occupational Medicine, College of Medicine, National Taiwan University (NTU) and NTU Hospital, Taipei, Taiwan
- Graduate Institute of Environmental and Occupational Health Sciences, College of Public Health, National Taiwan University, Taipei, Taiwan
- National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli, Taiwan
| | - Yih-Kuen Jan
- Rehabilitation Engineering Lab, Department of Kinesiology and Community Health, University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - Chih-Yang Lin
- Department of Mechanical Engineering, National Central University, Taoyuan, Taiwan
| | - Fu-Yu Lin
- Department of Neurology, China Medical University Hospital, Taichung, Taiwan
| | - Ben-Yi Liau
- Department of Automatic Control Engineering, Feng Chia University, Taichung, Taiwan
| | - Jen-Yung Tsai
- Department of Digital Media Design, Asia University, Taichung, Taiwan
| | - Peter Ardhianto
- Department of Visual Communication Design, Soegijapranata Catholic University, Semarang, Indonesia
| | - Yori Pusparani
- Department of Digital Media Design, Asia University, Taichung, Taiwan
- Department of Visual Communication Design, Budi Luhur University, Jakarta, Indonesia
| | - Chi-Wen Lung
- Rehabilitation Engineering Lab, Department of Kinesiology and Community Health, University of Illinois at Urbana-Champaign, Urbana, IL, United States
- Department of Creative Product Design, Asia University, Taichung, Taiwan
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Zhang W, Chen Z, Su Z, Wang Z, Hai J, Huang C, Wang Y, Yan B, Lu H. Deep learning-based detection and classification of lumbar disc herniation on magnetic resonance images. JOR Spine 2023; 6:e1276. [PMID: 37780833 PMCID: PMC10540823 DOI: 10.1002/jsp2.1276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 07/03/2023] [Accepted: 08/03/2023] [Indexed: 10/03/2023] Open
Abstract
Background The severity assessment of lumbar disc herniation (LDH) on MR images is crucial for selecting suitable surgical candidates. However, the interpretation of MR images is time-consuming and requires repetitive work. This study aims to develop and evaluate a deep learning-based diagnostic model for automated LDH detection and classification on lumbar axial T2-weighted MR images. Methods A total of 1115 patients were analyzed in this retrospective study; both a development dataset (1015 patients, 15 249 images) and an external test dataset (100 patients, 1273 images) were utilized. According to the Michigan State University (MSU) classification criterion, experts labeled all images with consensus, and the final labeled results were regarded as the reference standard. The automated diagnostic model comprised Faster R-CNN and ResNeXt101 as the detection and classification network, respectively. The deep learning-based diagnostic performance was evaluated by calculating mean intersection over union (IoU), accuracy, precision, sensitivity, specificity, F1 score, the area under the receiver operating characteristics curve (AUC), and intraclass correlation coefficient (ICC) with 95% confidence intervals (CIs). Results High detection consistency was obtained in the internal test dataset (mean IoU = 0.82, precision = 98.4%, sensitivity = 99.4%) and external test dataset (mean IoU = 0.70, precision = 96.3%, sensitivity = 97.8%). Overall accuracy for LDH classification was 87.70% (95% CI: 86.59%-88.86%) and 74.23% (95% CI: 71.83%-76.75%) in the internal and external test datasets, respectively. For internal testing, the proposed model achieved a high agreement in classification (ICC = 0.87, 95% CI: 0.86-0.88, P < 0.001), which was higher than that of external testing (ICC = 0.79, 95% CI: 0.76-0.81, P < 0.001). The AUC for model classification was 0.965 (95% CI: 0.962-0.968) and 0.916 (95% CI: 0.908-0.925) in the internal and external test datasets, respectively. Conclusions The automated diagnostic model achieved high performance in detecting and classifying LDH and exhibited considerable consistency with experts' classification.
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Affiliation(s)
- Weicong Zhang
- Department of Spinal SurgeryThe Fifth Affiliated Hospital of Sun Yat‐sen UniversityZhuhaiGuangdongChina
| | - Ziyang Chen
- Department of Spinal SurgeryThe Fifth Affiliated Hospital of Sun Yat‐sen UniversityZhuhaiGuangdongChina
| | - Zhihai Su
- Department of Spinal SurgeryThe Fifth Affiliated Hospital of Sun Yat‐sen UniversityZhuhaiGuangdongChina
| | - Zhengyan Wang
- Henan Key Laboratory of Imaging and Intelligent ProcessingPLA Strategic Support Force Information Engineering UniversityZhengzhouChina
| | - Jinjin Hai
- Henan Key Laboratory of Imaging and Intelligent ProcessingPLA Strategic Support Force Information Engineering UniversityZhengzhouChina
| | - Chengjie Huang
- Department of Spinal SurgeryThe Fifth Affiliated Hospital of Sun Yat‐sen UniversityZhuhaiGuangdongChina
| | - Yuhan Wang
- Department of Spinal SurgeryThe Fifth Affiliated Hospital of Sun Yat‐sen UniversityZhuhaiGuangdongChina
| | - Bin Yan
- Henan Key Laboratory of Imaging and Intelligent ProcessingPLA Strategic Support Force Information Engineering UniversityZhengzhouChina
| | - Hai Lu
- Department of Spinal SurgeryThe Fifth Affiliated Hospital of Sun Yat‐sen UniversityZhuhaiGuangdongChina
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Liao F, Jan YK. Assessing skin blood flow function in people with spinal cord injury using the time domain, time–frequency domain and deep learning approaches. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
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10
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Chen K, Zhai X, Wang S, Li X, Lu Z, Xia D, Li M. Emerging trends and research foci of deep learning in spine: bibliometric and visualization study. Neurosurg Rev 2023; 46:81. [PMID: 37000304 DOI: 10.1007/s10143-023-01987-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 03/10/2023] [Accepted: 03/26/2023] [Indexed: 04/01/2023]
Abstract
As the cognition of spine develops, deep learning (DL) emerges as a powerful tool with tremendous potential for advancing research in this field. To provide a comprehensive overview of DL-spine research, our study utilized bibliometric and visual methods to retrieve relevant articles from the Web of Science database. VOSviewer and CiteSpace were primarily used for literature measurement and knowledge graph analysis. A total of 273 studies focusing on deep learning in the spine, with a combined total of 2302 citations, were retrieved. Additionally, the overall number of articles published on this topic demonstrated a continuous upward trend. China was the country with the highest number of publications, whereas the USA had the most citations. The two most prominent journals were "European Spine Journal" and "Medical Image Analysis," and the most involved research area was Radiology Nuclear Medicine Medical Imaging. VOSviewer identified three visually distinct clusters: "segmentation," "area," and "neural network." Meanwhile, CiteSpace highlighted "magnetic resonance image" and "lumbar" as the keywords with the longest usage, and "agreement" and "automated detection" as the most commonly used keywords. Although the application of DL in spine is still in its infancy, its future is promising. Intercontinental cooperation, extensive application, and more interpretable algorithms will invigorate DL in the field of spine.
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Affiliation(s)
- Kai Chen
- Department of Orthopedics, Shanghai Changhai Hospital, Shanghai, 200433, China
| | - Xiao Zhai
- Department of Orthopedics, Shanghai Changhai Hospital, Shanghai, 200433, China
| | - Sheng Wang
- Department of Emergency, Shanghai Changhai Hospital, Shanghai, China
| | - Xiaoyu Li
- Department of Orthopedics, Shanghai Changhai Hospital, Shanghai, 200433, China
| | - Zhikai Lu
- Department of Orthopedics, No. 906 Hospital of Joint Logistic Support Force of PLA, Ningbo, Zhejiang, China.
| | - Demeng Xia
- Luodian Clinical Drug Research Center, Shanghai Baoshan Luodian Hospital, Shanghai University, Shanghai, China.
- Emergency Department, Naval Hospital of Eastern Theater, Zhoushan, Zhejiang, China.
| | - Ming Li
- Department of Orthopedics, Shanghai Changhai Hospital, Shanghai, 200433, China.
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11
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Clinical Efficacy Analysis of Fast Rehabilitation Nursing on Pain Mitigation after Lumbar Discectomy and Bone Graft Fusion and Internal Fixation. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE 2022; 2022:3665919. [PMID: 35855830 PMCID: PMC9288289 DOI: 10.1155/2022/3665919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 05/17/2022] [Accepted: 06/06/2022] [Indexed: 11/17/2022]
Abstract
Objective To investigate clinical efficacy analysis of fast rehabilitation nursing on pain mitigation after lumbar discectomy and bone graft fusion and internal fixation. Methods A total of 60 patients with lumbar disc herniation who underwent lumbar discectomy and bone graft fusion and internal fixation in our hospital from January 2021 to December 2021 were randomized either into routine group (n = 30) or rehabilitation group (n = 30) via the random number table method. The patients in the routine group were intervened with the routine postoperative nursing mode, and the patients in the rehabilitation group were intervened with the fast rehabilitation nursing mode on the basis of the nursing of the patients in the routine group. The rehabilitation effect, self-rating anxiety scale (SAS) and self-rating depression scale (SDS) scores, postoperative pain improvement, and nursing satisfaction were compared between the two groups. Results The postoperative rehabilitation effect of the rehabilitation group was significantly better than that of the routine group (P < 0.05). The fast rehabilitation nursing resulted in a notably lower postoperative SAS and SDS scores versus the routine nursing (P < 0.001). The postoperative pain was significantly mitigated in the rehabilitation group when compared with the routine group (P < 0.001). The fast rehabilitation nursing implemented in the rehabilitation group led to a remarkably higher nursing satisfaction of the patients (P < 0.001). Conclusion The fast rehabilitation nursing mode intervention for lumbar disc herniation patients undergoing lumbar discectomy and bone graft fusion and internal fixation is a promising approach to improve the postoperative rehabilitation, mitigate postoperative pain, and relieve anxiety, depression, and other negative emotions, with higher clinical nursing satisfaction.
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12
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Gong G, Huang J, Wang H. Flaw Detection in White Porcelain Wine Bottles Based on Improved YOLOv4 Algorithm. Front Bioeng Biotechnol 2022; 10:928900. [PMID: 35898650 PMCID: PMC9309803 DOI: 10.3389/fbioe.2022.928900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 05/30/2022] [Indexed: 11/13/2022] Open
Abstract
Aiming at the problems of low detection accuracy and slow detection speed in white porcelain wine bottle flaw detection, an improved flaw detection algorithm based on YOLOv4 was proposed. By adding Coordinate Attention to the backbone feature extraction network, the extracting ability of white porcelain bottle flaw features was improved. Deformable convolution is added to locate flaws more accurately, so as to improve the detection accuracy of flaws by the model. Efficient Intersection over Union was used to replace Complete Intersection over Union in YOLOv4 to improve the loss function and improve the model detection speed and accuracy. Experimental results on the surface flaw data set of white porcelain wine bottles show that the proposed algorithm can effectively detect white porcelain wine bottle flaws, the mean Average Precision of the model can reach 92.56%, and the detection speed can reach 37.17 frames/s.
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Deep Learning Model for Grading Metastatic Epidural Spinal Cord Compression on Staging CT. Cancers (Basel) 2022; 14:cancers14133219. [PMID: 35804990 PMCID: PMC9264856 DOI: 10.3390/cancers14133219] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 06/21/2022] [Accepted: 06/24/2022] [Indexed: 02/02/2023] Open
Abstract
Background: Metastatic epidural spinal cord compression (MESCC) is a disastrous complication of advanced malignancy. Deep learning (DL) models for automatic MESCC classification on staging CT were developed to aid earlier diagnosis. Methods: This retrospective study included 444 CT staging studies from 185 patients with suspected MESCC who underwent MRI spine studies within 60 days of the CT studies. The DL model training/validation dataset consisted of 316/358 (88%) and the test set of 42/358 (12%) CT studies. Training/validation and test datasets were labeled in consensus by two subspecialized radiologists (6 and 11-years-experience) using the MRI studies as the reference standard. Test sets were labeled by the developed DL models and four radiologists (2−7 years of experience) for comparison. Results: DL models showed almost-perfect interobserver agreement for classification of CT spine images into normal, low, and high-grade MESCC, with kappas ranging from 0.873−0.911 (p < 0.001). The DL models (lowest κ = 0.873, 95% CI 0.858−0.887) also showed superior interobserver agreement compared to two of the four radiologists for three-class classification, including a specialist (κ = 0.820, 95% CI 0.803−0.837) and general radiologist (κ = 0.726, 95% CI 0.706−0.747), both p < 0.001. Conclusion: DL models for the MESCC classification on a CT showed comparable to superior interobserver agreement to radiologists and could be used to aid earlier diagnosis.
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D’Antoni F, Russo F, Ambrosio L, Bacco L, Vollero L, Vadalà G, Merone M, Papalia R, Denaro V. Artificial Intelligence and Computer Aided Diagnosis in Chronic Low Back Pain: A Systematic Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19105971. [PMID: 35627508 PMCID: PMC9141006 DOI: 10.3390/ijerph19105971] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 05/09/2022] [Accepted: 05/12/2022] [Indexed: 12/10/2022]
Abstract
Low Back Pain (LBP) is currently the first cause of disability in the world, with a significant socioeconomic burden. Diagnosis and treatment of LBP often involve a multidisciplinary, individualized approach consisting of several outcome measures and imaging data along with emerging technologies. The increased amount of data generated in this process has led to the development of methods related to artificial intelligence (AI), and to computer-aided diagnosis (CAD) in particular, which aim to assist and improve the diagnosis and treatment of LBP. In this manuscript, we have systematically reviewed the available literature on the use of CAD in the diagnosis and treatment of chronic LBP. A systematic research of PubMed, Scopus, and Web of Science electronic databases was performed. The search strategy was set as the combinations of the following keywords: “Artificial Intelligence”, “Machine Learning”, “Deep Learning”, “Neural Network”, “Computer Aided Diagnosis”, “Low Back Pain”, “Lumbar”, “Intervertebral Disc Degeneration”, “Spine Surgery”, etc. The search returned a total of 1536 articles. After duplication removal and evaluation of the abstracts, 1386 were excluded, whereas 93 papers were excluded after full-text examination, taking the number of eligible articles to 57. The main applications of CAD in LBP included classification and regression. Classification is used to identify or categorize a disease, whereas regression is used to produce a numerical output as a quantitative evaluation of some measure. The best performing systems were developed to diagnose degenerative changes of the spine from imaging data, with average accuracy rates >80%. However, notable outcomes were also reported for CAD tools executing different tasks including analysis of clinical, biomechanical, electrophysiological, and functional imaging data. Further studies are needed to better define the role of CAD in LBP care.
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Affiliation(s)
- Federico D’Antoni
- Unit of Computer Systems and Bioinformatics, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo, 21, 00128 Rome, Italy; (F.D.); (L.B.); (L.V.)
| | - Fabrizio Russo
- Department of Orthopaedic Surgery, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo, 200, 00128 Rome, Italy; (L.A.); (G.V.); (R.P.); (V.D.)
- Correspondence: (F.R.); (M.M.)
| | - Luca Ambrosio
- Department of Orthopaedic Surgery, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo, 200, 00128 Rome, Italy; (L.A.); (G.V.); (R.P.); (V.D.)
| | - Luca Bacco
- Unit of Computer Systems and Bioinformatics, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo, 21, 00128 Rome, Italy; (F.D.); (L.B.); (L.V.)
- ItaliaNLP Lab, Istituto di Linguistica Computazionale “Antonio Zampolli”, National Research Council, Via Giuseppe Moruzzi, 1, 56124 Pisa, Italy
- Webmonks S.r.l., Via del Triopio, 5, 00178 Rome, Italy
| | - Luca Vollero
- Unit of Computer Systems and Bioinformatics, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo, 21, 00128 Rome, Italy; (F.D.); (L.B.); (L.V.)
| | - Gianluca Vadalà
- Department of Orthopaedic Surgery, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo, 200, 00128 Rome, Italy; (L.A.); (G.V.); (R.P.); (V.D.)
| | - Mario Merone
- Unit of Computer Systems and Bioinformatics, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo, 21, 00128 Rome, Italy; (F.D.); (L.B.); (L.V.)
- Correspondence: (F.R.); (M.M.)
| | - Rocco Papalia
- Department of Orthopaedic Surgery, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo, 200, 00128 Rome, Italy; (L.A.); (G.V.); (R.P.); (V.D.)
| | - Vincenzo Denaro
- Department of Orthopaedic Surgery, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo, 200, 00128 Rome, Italy; (L.A.); (G.V.); (R.P.); (V.D.)
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Development and Validation of Deep Learning-Based Algorithms for Predicting Lumbar Herniated Nucleus Pulposus Using Lumbar X-rays. J Pers Med 2022; 12:jpm12050767. [PMID: 35629187 PMCID: PMC9145973 DOI: 10.3390/jpm12050767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Revised: 05/04/2022] [Accepted: 05/07/2022] [Indexed: 02/05/2023] Open
Abstract
Lumbar herniated nucleus pulposus (HNP) is difficult to diagnose using lumbar radiography. HNP is typically diagnosed using magnetic resonance imaging (MRI). This study developed and validated an artificial intelligence model that predicts lumbar HNP using lumbar radiography. A total of 180,271 lumbar radiographs were obtained from 34,661 patients in the form of lumbar X-ray and MRI images, which were matched together and labeled accordingly. The data were divided into a training set (31,149 patients and 162,257 images) and a test set (3512 patients and 18,014 images). Training data were used for learning using the EfficientNet-B5 model and four-fold cross-validation. The area under the curve (AUC) of the receiver operating characteristic (ROC) for the prediction of lumbar HNP was 0.73. The AUC of the ROC for predicting lumbar HNP in L (lumbar) 1-2, L2-3, L3-4, L4-5, and L5-S (sacrum)1 levels were 0.68, 0.68, 0.63, 0.67, and 0.72, respectively. Finally, an HNP prediction model was developed, although it requires further improvements.
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Ardhianto P, Subiakto RBR, Lin CY, Jan YK, Liau BY, Tsai JY, Akbari VBH, Lung CW. A Deep Learning Method for Foot Progression Angle Detection in Plantar Pressure Images. SENSORS 2022; 22:s22072786. [PMID: 35408399 PMCID: PMC9003219 DOI: 10.3390/s22072786] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 03/31/2022] [Accepted: 04/01/2022] [Indexed: 02/01/2023]
Abstract
Foot progression angle (FPA) analysis is one of the core methods to detect gait pathologies as basic information to prevent foot injury from excessive in-toeing and out-toeing. Deep learning-based object detection can assist in measuring the FPA through plantar pressure images. This study aims to establish a precision model for determining the FPA. The precision detection of FPA can provide information with in-toeing, out-toeing, and rearfoot kinematics to evaluate the effect of physical therapy programs on knee pain and knee osteoarthritis. We analyzed a total of 1424 plantar images with three different You Only Look Once (YOLO) networks: YOLO v3, v4, and v5x, to obtain a suitable model for FPA detection. YOLOv4 showed higher performance of the profile-box, with average precision in the left foot of 100.00% and the right foot of 99.78%, respectively. Besides, in detecting the foot angle-box, the ground-truth has similar results with YOLOv4 (5.58 ± 0.10° vs. 5.86 ± 0.09°, p = 0.013). In contrast, there was a significant difference in FPA between ground-truth vs. YOLOv3 (5.58 ± 0.10° vs. 6.07 ± 0.06°, p < 0.001), and ground-truth vs. YOLOv5x (5.58 ± 0.10° vs. 6.75 ± 0.06°, p < 0.001). This result implies that deep learning with YOLOv4 can enhance the detection of FPA.
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Affiliation(s)
- Peter Ardhianto
- Department of Visual Communication Design, Soegijapranata Catholic University, Semarang 50234, Indonesia;
- Department of Digital Media Design, Asia University, Taichung 413305, Taiwan;
| | | | - Chih-Yang Lin
- Department of Electrical Engineering, Yuan Ze University, Chung-Li 32003, Taiwan;
| | - Yih-Kuen Jan
- Rehabilitation Engineering Lab, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA;
- Kinesiology and Community Health, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA
- Computational Science and Engineering, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA
| | - Ben-Yi Liau
- Department of Biomedical Engineering, Hungkuang University, Taichung 433304, Taiwan;
| | - Jen-Yung Tsai
- Department of Digital Media Design, Asia University, Taichung 413305, Taiwan;
| | | | - Chi-Wen Lung
- Rehabilitation Engineering Lab, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA;
- Department of Creative Product Design, Asia University, Taichung 413305, Taiwan;
- Correspondence: or
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Matsuzaka Y, Uesawa Y. A Deep Learning-Based Quantitative Structure-Activity Relationship System Construct Prediction Model of Agonist and Antagonist with High Performance. Int J Mol Sci 2022; 23:ijms23042141. [PMID: 35216254 PMCID: PMC8877122 DOI: 10.3390/ijms23042141] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 02/12/2022] [Accepted: 02/14/2022] [Indexed: 01/27/2023] Open
Abstract
Molecular design and evaluation for drug development and chemical safety assessment have been advanced by quantitative structure–activity relationship (QSAR) using artificial intelligence techniques, such as deep learning (DL). Previously, we have reported the high performance of prediction models molecular initiation events (MIEs) on the adverse toxicological outcome using a DL-based QSAR method, called DeepSnap-DL. This method can extract feature values from images generated on a three-dimensional (3D)-chemical structure as a novel QSAR analytical system. However, there is room for improvement of this system’s time-consumption. Therefore, in this study, we constructed an improved DeepSnap-DL system by combining the processes of generating an image from a 3D-chemical structure, DL using the image as input data, and statistical calculation of prediction-performance. Consequently, we obtained that the three prediction models of agonists or antagonists of MIEs achieved high prediction-performance by optimizing the parameters of DeepSnap, such as the angle used in the depiction of the image of a 3D-chemical structure, data-split, and hyperparameters in DL. The improved DeepSnap-DL system will be a powerful tool for computer-aided molecular design as a novel QSAR system.
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Affiliation(s)
- Yasunari Matsuzaka
- Department of Medical Molecular Informatics, Meiji Pharmaceutical University, Kiyose 204-8588, Japan;
- Center for Gene and Cell Therapy, Division of Molecular and Medical Genetics, The Institute of Medical Science, University of Tokyo, Minato City 108-8639, Japan
| | - Yoshihiro Uesawa
- Department of Medical Molecular Informatics, Meiji Pharmaceutical University, Kiyose 204-8588, Japan;
- Correspondence: ; Tel.: +81-42-495-8983
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Wu Z, Ye X, Ye Z, Hong K, Chen Z, Wang Y, Li C, Li J, Huang J, Zhu Y, Lu Y, Liu W, Xu X. Asymmetric Biomechanical Properties of the Paravertebral Muscle in Elderly Patients With Unilateral Chronic Low Back Pain: A Preliminary Study. Front Bioeng Biotechnol 2022; 10:814099. [PMID: 35223786 PMCID: PMC8866935 DOI: 10.3389/fbioe.2022.814099] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 01/17/2022] [Indexed: 11/13/2022] Open
Abstract
Background: Clinical incidences of chronic low back pain among the elderly are increasing. However, studies have not fully elucidated on changes in biomechanical properties of paravertebral muscles in patients with unilateral chronic low back pain. We evaluated the changes in biomechanical properties of painful and non-painful paravertebral muscles in elderly patients with unilateral chronic low back pain.Methods: Biomechanical properties of paravertebral muscles, including muscle tone and stiffness, in elderly patients with unilateral chronic low back pain were measured using MyotonPRO. Lumbar Lordosis and Sacral Slope were measured by magnetic resonance imaging. Cross-sectional areas of paravertebral muscles were evaluated using ImageJ software version 1.53. Chronic low back pain severity was assessed by Visual Analogue Scale (VAS) and Oswestry Disability Index (ODI) scores. The correlations between VAS scores, ODI scores, Lumbar Lordosis, Sacral Slope, cross-sectional areas (painful side), disease duration, and biomechanical properties of paravertebral muscles in the painful side were analyzed.Results: A total of 60 elderly patients with unilateral chronic low back pain were enrolled in this study. The muscle tone and stiffness of paravertebral muscles on the painful side were significantly higher than those on the non-painful side (p < .05). Cross-sectional areas of paravertebral muscles on the painful side at the L3 level were smaller than those of the non-painful side (p < .05). The VAS scores and ODI scores were significantly positively correlated with muscle tone and stiffness of paravertebral muscles on the painful side (p < .05 and p < .01, respectively). There were no significant correlations between disease duration, cross-sectional areas (painful side), Lumbar Lordosis, or Sacral Slope and muscle tone and stiffness of paravertebral muscles on the painful side (p > .05).Conclusion: In elderly patients with unilateral chronic low back pain, muscle tone and stiffness of paravertebral muscles on the painful side are higher than for those on the non-painful side. The asymmetry of biomechanical properties of paravertebral muscles is associated with severity of chronic low back pain.
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Affiliation(s)
- Zugui Wu
- The Fifth Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Xiangling Ye
- The Fifth Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Zixuan Ye
- The Fifth Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Kunhao Hong
- The Fifth Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China
- Guangdong Second Traditional Chinese Medicine Hospital, Guangzhou, China
| | - Zehua Chen
- The Fifth Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yi Wang
- The Fifth Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Congcong Li
- The Fifth Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Junyi Li
- The Fifth Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Jinyou Huang
- Guangdong Second Traditional Chinese Medicine Hospital, Guangzhou, China
| | - Yue Zhu
- Baishui Health Center, Qujing, China
| | - Yanyan Lu
- Luoyang Orthopedic Hospital Of Henan Province (Orthopedic Hospital of Henan Province), Zhengzhou, China
- *Correspondence: Xuemeng Xu, ; Wengang Liu, ; Yanyan Lu,
| | - Wengang Liu
- The Fifth Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China
- Guangdong Second Traditional Chinese Medicine Hospital, Guangzhou, China
- *Correspondence: Xuemeng Xu, ; Wengang Liu, ; Yanyan Lu,
| | - Xuemeng Xu
- The Fifth Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China
- Guangdong Second Traditional Chinese Medicine Hospital, Guangzhou, China
- *Correspondence: Xuemeng Xu, ; Wengang Liu, ; Yanyan Lu,
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Su ZH, Liu J, Yang MS, Chen ZY, You K, Shen J, Huang CJ, Zhao QH, Liu EQ, Zhao L, Feng QJ, Pang SM, Li SL, Lu H. Automatic Grading of Disc Herniation, Central Canal Stenosis and Nerve Roots Compression in Lumbar Magnetic Resonance Image Diagnosis. Front Endocrinol (Lausanne) 2022; 13:890371. [PMID: 35733770 PMCID: PMC9207332 DOI: 10.3389/fendo.2022.890371] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Accepted: 04/27/2022] [Indexed: 11/30/2022] Open
Abstract
AIM Accurate severity grading of lumbar spine disease by magnetic resonance images (MRIs) plays an important role in selecting appropriate treatment for the disease. However, interpreting these complex MRIs is a repetitive and time-consuming workload for clinicians, especially radiologists. Here, we aim to develop a multi-task classification model based on artificial intelligence for automated grading of lumbar disc herniation (LDH), lumbar central canal stenosis (LCCS) and lumbar nerve roots compression (LNRC) at lumbar axial MRIs. METHODS Total 15254 lumbar axial T2W MRIs as the internal dataset obtained from the Fifth Affiliated Hospital of Sun Yat-sen University from January 2015 to May 2019 and 1273 axial T2W MRIs as the external test dataset obtained from the Third Affiliated Hospital of Southern Medical University from June 2016 to December 2017 were analyzed in this retrospective study. Two clinicians annotated and graded all MRIs using the three international classification systems. In agreement, these results served as the reference standard; In disagreement, outcomes were adjudicated by an expert surgeon to establish the reference standard. The internal dataset was randomly split into an internal training set (70%), validation set (15%) and test set (15%). The multi-task classification model based on ResNet-50 consists of a backbone network for feature extraction and three fully-connected (FC) networks for classification and performs the classification tasks of LDH, LCCS, and LNRC at lumbar MRIs. Precision, accuracy, sensitivity, specificity, F1 scores, confusion matrices, receiver-operating characteristics and interrater agreement (Gwet k) were utilized to assess the model's performance on the internal test dataset and external test datasets. RESULTS A total of 1115 patients, including 1015 patients from the internal dataset and 100 patients from the external test dataset [mean age, 49 years ± 15 (standard deviation); 543 women], were evaluated in this study. The overall accuracies of grading for LDH, LCCS and LNRC were 84.17% (74.16%), 86.99% (79.65%) and 81.21% (74.16%) respectively on the internal (external) test dataset. Internal and external testing of three spinal diseases showed substantial to the almost perfect agreement (k, 0.67 - 0.85) for the multi-task classification model. CONCLUSION The multi-task classification model has achieved promising performance in the automated grading of LDH, LCCS and LNRC at lumbar axial T2W MRIs.
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Affiliation(s)
- Zhi-Hai Su
- Department of Spinal Surgery, The Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, China
| | - Jin Liu
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, China
| | - Min-Sheng Yang
- Department of Spinal Surgery, The Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, China
| | - Zi-Yang Chen
- Department of Spinal Surgery, The Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, China
| | - Ke You
- Department of Spinal Surgery, The Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, China
| | - Jun Shen
- Department of Spinal Surgery, The Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, China
| | - Cheng-Jie Huang
- Department of Spinal Surgery, The Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, China
| | - Qing-Hao Zhao
- Department of Spinal Surgery, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China
| | - En-Qing Liu
- Department of Spinal Surgery, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China
| | - Lei Zhao
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China
| | - Qian-Jin Feng
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China
| | - Shu-Mao Pang
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China
| | - Shao-Lin Li
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, China
- *Correspondence: Hai Lu, ; Shao-Lin Li,
| | - Hai Lu
- Department of Spinal Surgery, The Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, China
- *Correspondence: Hai Lu, ; Shao-Lin Li,
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Colombo T, Mangone M, Agostini F, Bernetti A, Paoloni M, Santilli V, Palagi L. Supervised and unsupervised learning to classify scoliosis and healthy subjects based on non-invasive rasterstereography analysis. PLoS One 2021; 16:e0261511. [PMID: 34941924 PMCID: PMC8699618 DOI: 10.1371/journal.pone.0261511] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 12/05/2021] [Indexed: 11/18/2022] Open
Abstract
The aim of our study was to classify scoliosis compared to to healthy patients using non-invasive surface acquisition via Video-raster-stereography, without prior knowledge of radiographic data. Data acquisitions were made using Rasterstereography; unsupervised learning was adopted for clustering and supervised learning was used for prediction model Support Vector Machine and Deep Network architectures were compared. A M-fold cross validation procedure was performed to evaluate the results. The accuracy and balanced accuracy of the best supervised model were close to 85%. Classification rates by class were measured using the confusion matrix, giving a low percentage of unclassified patients. Rasterstereography has turned out to be a good tool to distinguish subject with scoliosis from healthy patients limiting the exposure to unnecessary radiations.
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Affiliation(s)
- Tommaso Colombo
- Department of Computer, Control and Management Engineering Antonio Ruberti, Sapienza University of Rome, Rome, Italy
- aHead Research ETS, Rome, Italy
| | - Massimiliano Mangone
- Department of Anatomical and Histological Sciences, Legal Medicine and Orthopedics, Sapienza University of Rome, Rome, Italy
| | - Francesco Agostini
- Department of Anatomical and Histological Sciences, Legal Medicine and Orthopedics, Sapienza University of Rome, Rome, Italy
| | - Andrea Bernetti
- Department of Anatomical and Histological Sciences, Legal Medicine and Orthopedics, Sapienza University of Rome, Rome, Italy
| | - Marco Paoloni
- Department of Anatomical and Histological Sciences, Legal Medicine and Orthopedics, Sapienza University of Rome, Rome, Italy
| | - Valter Santilli
- Department of Anatomical and Histological Sciences, Legal Medicine and Orthopedics, Sapienza University of Rome, Rome, Italy
| | - Laura Palagi
- Department of Computer, Control and Management Engineering Antonio Ruberti, Sapienza University of Rome, Rome, Italy
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