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Zileli M, Oertel J, Sharif S, Zygourakis C. Lumbar disc herniation: Prevention and treatment of recurrence: WFNS spine committee recommendations. World Neurosurg X 2024; 22:100275. [PMID: 38385057 PMCID: PMC10878111 DOI: 10.1016/j.wnsx.2024.100275] [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: 02/23/2024] Open
Abstract
Objective This review aims to formulate the most current evidence-based recommendations on the epidemiology, prevention, and treatment of recurrent lumbar disc herniation (LDH). Methods We performed a systematic literature search in PubMed, Medline, and Google Scholar databases from 2012 to 2022 using the keywords "lumbar disc recurrence." Screening criteria resulted in 57 papers, which were summarized and presented at two international consensus meetings of the World Federation of Neurosurgical Societies (WFNS) Spine Committee. The 57 papers covered the following topics: (1) Definition and incidence of recurrence after lumbar disc surgery; (2) Prediction of recurrence before primary surgery; (3) Prevention of recurrence by surgical measures; (4) Prevention of recurrence by postoperative measures; (5) Treatment options for recurrent disc herniation; (6) The outcomes of recurrent disc herniation surgery. We utilized the Delphi method and voted on eight final consensus statements. Results and conclusion Recurrence after disc herniation surgery may be considered a surgical complication, its incidence is approximately 5% and is different from overall re-operation incidence. There are multiple risk factors predicting LDH recurrence, including smoking, younger age, male gender, obesity, diabetes, disc degeneration, and presence of lumbosacral transitional vertebrae. The level of lumbar discectomy surgery and the amount of disc material removed do not correlate with recurrence rate. Minimally invasive discectomies may have higher recurrence rates, especially during the surgeon's learning period. However, the experience of the surgeon is not related to recurrence. High-quality studies are needed to determine if activity restriction, weight loss, smoking cessation, and muscle-strengthening exercises after primary surgery can help prevent recurrence of LDH.The best treatment option for recurrent disc herniation is still being discussed. While complications of minimally invasive techniques may be lower than open discectomy, outcomes are similar. Fusion should only be considered when spinal instability and/or spinal deformity are present. Clinical outcomes and patient satisfaction after recurrent disc herniation surgery are inferior to those after initial discectomy.
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Affiliation(s)
- Mehmet Zileli
- Department of Neurosurgery, Sanko University Faculty of Medicine, Gaziantep, Turkey
| | - Joachim Oertel
- Department of Neurosurgery, Saarland University Medical Centre, Homburg, Germany
| | - Salman Sharif
- Department of Neurosurgery, Liaqat Medical School, Karachi, Pakistan
| | - Corinna Zygourakis
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA
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Tragaris T, Benetos IS, Vlamis J, Pneumaticos S. Machine Learning Applications in Spine Surgery. Cureus 2023; 15:e48078. [PMID: 38046496 PMCID: PMC10689893 DOI: 10.7759/cureus.48078] [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] [Accepted: 10/31/2023] [Indexed: 12/05/2023] Open
Abstract
This literature review sought to identify and evaluate the current applications of artificial intelligence (AI)/machine learning (ML) in spine surgery that can effectively guide clinical decision-making and surgical planning. By using specific keywords to maximize search sensitivity, a thorough literature research was conducted in several online databases: Scopus, PubMed, and Google Scholar, and the findings were filtered according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 46 studies met the requirements and were included in this review. According to this study, AI/ML models were sufficiently accurate with a mean overall value of 74.9%, and performed best at preoperative patient selection, cost prediction, and length of stay. Performance was also good at predicting functional outcomes and postoperative mortality. Regression analysis was the most frequently utilized application whereas deep learning/artificial neural networks had the highest sensitivity score (81.5%). Despite the relatively brief history of engagement with AI/ML, as evidenced by the fact that 77.5% of studies were published after 2018, the outcomes have been promising. In light of the Big Data era, the increasing prevalence of National Registries, and the wide-ranging applications of AI, such as exemplified by ChatGPT (OpenAI, San Francisco, California), it is highly likely that the field of spine surgery will gradually adopt and integrate AI/ML into its clinical practices. Consequently, it is of great significance for spine surgeons to acquaint themselves with the fundamental principles of AI/ML, as these technologies hold the potential for substantial improvements in overall patient care.
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Affiliation(s)
- Themistoklis Tragaris
- 1st Department of Orthopaedic Surgery, National and Kapodistrian University of Athens School of Medicine, KAT Hospital, Athens, GRC
| | - Ioannis S Benetos
- 3rd Department of Orthopaedic Surgery, National and Kapodistrian University of Athens School of Medicine, KAT Hospital, Athens, GRC
| | - John Vlamis
- 3rd Department of Orthopaedic Surgery, National and Kapodistrian University of Athens School of Medicine, KAT Hospital, Athens, GRC
| | - Spyridon Pneumaticos
- 3rd Department of Orthopaedic Surgery, National and Kapodistrian University of Athens School of Medicine, KAT Hospital, Athens, GRC
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Tangsrivimol JA, Schonfeld E, Zhang M, Veeravagu A, Smith TR, Härtl R, Lawton MT, El-Sherbini AH, Prevedello DM, Glicksberg BS, Krittanawong C. Artificial Intelligence in Neurosurgery: A State-of-the-Art Review from Past to Future. Diagnostics (Basel) 2023; 13:2429. [PMID: 37510174 PMCID: PMC10378231 DOI: 10.3390/diagnostics13142429] [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: 05/31/2023] [Revised: 07/06/2023] [Accepted: 07/10/2023] [Indexed: 07/30/2023] Open
Abstract
In recent years, there has been a significant surge in discussions surrounding artificial intelligence (AI), along with a corresponding increase in its practical applications in various facets of everyday life, including the medical industry. Notably, even in the highly specialized realm of neurosurgery, AI has been utilized for differential diagnosis, pre-operative evaluation, and improving surgical precision. Many of these applications have begun to mitigate risks of intraoperative and postoperative complications and post-operative care. This article aims to present an overview of the principal published papers on the significant themes of tumor, spine, epilepsy, and vascular issues, wherein AI has been applied to assess its potential applications within neurosurgery. The method involved identifying high-cited seminal papers using PubMed and Google Scholar, conducting a comprehensive review of various study types, and summarizing machine learning applications to enhance understanding among clinicians for future utilization. Recent studies demonstrate that machine learning (ML) holds significant potential in neuro-oncological care, spine surgery, epilepsy management, and other neurosurgical applications. ML techniques have proven effective in tumor identification, surgical outcomes prediction, seizure outcome prediction, aneurysm prediction, and more, highlighting its broad impact and potential in improving patient management and outcomes in neurosurgery. This review will encompass the current state of research, as well as predictions for the future of AI within neurosurgery.
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Affiliation(s)
- Jonathan A Tangsrivimol
- Division of Neurosurgery, Department of Surgery, Chulabhorn Hospital, Chulabhorn Royal Academy, Bangkok 10210, Thailand
- Department of Neurological Surgery, The Ohio State University Wexner Medical Center and Jame Cancer Institute, Columbus, OH 43210, USA
| | - Ethan Schonfeld
- Department Biomedical Informatics, Stanford University School of Medicine, Palo Alto, CA 94305, USA
| | - Michael Zhang
- Department of Neurosurgery, Stanford University School of Medicine, Palo Alto, CA 94305, USA
| | - Anand Veeravagu
- Stanford Neurosurgical Artificial Intelligence and Machine Learning Laboratory, Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Timothy R Smith
- Department of Neurosurgery, Computational Neuroscience Outcomes Center (CNOC), Mass General Brigham, Harvard Medical School, Boston, MA 02115, USA
| | - Roger Härtl
- Weill Cornell Medicine Brain and Spine Center, New York, NY 10022, USA
| | - Michael T Lawton
- Department of Neurosurgery, Barrow Neurological Institute (BNI), Phoenix, AZ 85013, USA
| | - Adham H El-Sherbini
- Faculty of Health Sciences, Queen's University, Kingston, ON K7L 3N6, Canada
| | - Daniel M Prevedello
- Department of Neurological Surgery, The Ohio State University Wexner Medical Center and Jame Cancer Institute, Columbus, OH 43210, USA
| | - Benjamin S Glicksberg
- Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Chayakrit Krittanawong
- Cardiology Division, New York University Langone Health, New York University School of Medicine, New York, NY 10016, USA
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Development and validation of a point-of-care clinical risk score to predict surgical site infection following open spinal fusion. NORTH AMERICAN SPINE SOCIETY JOURNAL 2022; 13:100196. [PMID: 36691580 PMCID: PMC9860512 DOI: 10.1016/j.xnsj.2022.100196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 11/25/2022] [Accepted: 12/19/2022] [Indexed: 12/25/2022]
Abstract
Background Surgical site infection (SSI) after open spine surgery increases healthcare costs and patient morbidity. Predictive analytics using large databases can be used to develop prediction tools to aid surgeons in identifying high-risk patients and strategies for optimization. The purpose of this study was to develop and validate an SSI risk-assessment score for patients undergoing open spine surgery. Methods The Premier Healthcare Database of adult open spine surgery patients (n = 157,664; 2,650 SSIs) was used to create an SSI risk scoring system using mixed effects logistic regression modeling. Full and reduced multilevel logistic regression models were developed using patient, surgery or facility predictors. The full model used 38 predictors and the reduced used 16 predictors. The resulting risk score was the sum of points assigned to 16 predictors. Results The reduced model showed good discriminatory capability (C-statistic = 0.75) and good fit of the model ([Pearson Chi-square/DF] = 0.90, CAIC=25,517) compared to the full model (C-statistic = 0.75, [Pearson Chi-square/DF] =0.90, CAIC=25,578). The risk scoring system, based on the reduced model, included the following: female (5 points), hypertension (4), blood disorder (8), peripheral vascular disease (9), chronic pulmonary disease (6), rheumatic disease (16), obesity (12), nicotine dependence (5), Charlson Comorbidity Index (2 per point), revision surgery (14), number of ICD-10 procedures (1 per procedure), operative time (1 per hour), and emergency/urgent surgery (12). A final risk score as the sum of the points for each surgery was validated using a 1,000-surgery random hold-out (independent from the study cohort) sample (C-statistic = 0.77). Conclusions The resulting SSI risk score composed of readily obtainable clinical information could serve as a strong prediction tool for SSI in preoperative settings when open spine surgery is considered.
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Say I, Chen YE, Sun MZ, Li JJ, Lu DC. Machine learning predicts improvement of functional outcomes in traumatic brain injury patients after inpatient rehabilitation. FRONTIERS IN REHABILITATION SCIENCES 2022; 3:1005168. [PMID: 36211830 PMCID: PMC9535093 DOI: 10.3389/fresc.2022.1005168] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 09/07/2022] [Indexed: 06/16/2023]
Abstract
Survivors of traumatic brain injury (TBI) have an unpredictable clinical course. This unpredictability makes clinical resource allocation for clinicians and anticipatory guidance for patients difficult. Historically, experienced clinicians and traditional statistical models have insufficiently considered all available clinical information to predict functional outcomes for a TBI patient. Here, we harness artificial intelligence and apply machine learning and statistical models to predict the Functional Independence Measure (FIM) scores after rehabilitation for traumatic brain injury (TBI) patients. Tree-based algorithmic analysis of 629 TBI patients admitted to a large acute rehabilitation facility showed statistically significant improvement in motor and cognitive FIM scores at discharge.
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Affiliation(s)
- Irene Say
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, CA, United States
| | - Yiling Elaine Chen
- Department of Statistics, University of California, Los Angeles, CA, United States
| | - Matthew Z. Sun
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, CA, United States
| | - Jingyi Jessica Li
- Department of Statistics, University of California, Los Angeles, CA, United States
| | - Daniel C. Lu
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, CA, United States
- Neuromotor Recovery and Rehabilitation Center, David Geffen School of Medicine, University of California, Los Angeles, CA, United States
- Brain Research Institute, University of California, Los Angeles, CA, United States
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Lopez CD, Boddapati V, Lombardi JM, Lee NJ, Mathew J, Danford NC, Iyer RR, Dyrszka MD, Sardar ZM, Lenke LG, Lehman RA. Artificial Learning and Machine Learning Applications in Spine Surgery: A Systematic Review. Global Spine J 2022; 12:1561-1572. [PMID: 35227128 PMCID: PMC9393994 DOI: 10.1177/21925682211049164] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
OBJECTIVES This current systematic review sought to identify and evaluate all current research-based spine surgery applications of AI/ML in optimizing preoperative patient selection, as well as predicting and managing postoperative outcomes and complications. METHODS A comprehensive search of publications was conducted through the EMBASE, Medline, and PubMed databases using relevant keywords to maximize the sensitivity of the search. No limits were placed on level of evidence or timing of the study. Findings were reported according to the PRISMA guidelines. RESULTS After application of inclusion and exclusion criteria, 41 studies were included in this review. Bayesian networks had the highest average AUC (.80), and neural networks had the best accuracy (83.0%), sensitivity (81.5%), and specificity (71.8%). Preoperative planning/cost prediction models (.89,82.2%) and discharge/length of stay models (.80,78.0%) each reported significantly higher average AUC and accuracy compared to readmissions/reoperation prediction models (.67,70.2%) (P < .001, P = .005, respectively). Model performance also significantly varied across postoperative management applications for average AUC and accuracy values (P < .001, P < .027, respectively). CONCLUSIONS Generally, authors of the reviewed studies concluded that AI/ML offers a potentially beneficial tool for providers to optimize patient care and improve cost-efficiency. More specifically, AI/ML models performed best, on average, when optimizing preoperative patient selection and planning and predicting costs, hospital discharge, and length of stay. However, models were not as accurate in predicting postoperative complications, adverse events, and readmissions and reoperations. An understanding of AI/ML-based applications is becoming increasingly important, particularly in spine surgery, as the volume of reported literature, technology accessibility, and clinical applications continue to rapidly expand.
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Affiliation(s)
- Cesar D. Lopez
- Department of Orthopaedic Surgery, The Spine Hospital, New York-Presbyterian/Columbia University Irving Medical Center, New York, NY, USA
| | - Venkat Boddapati
- Department of Orthopaedic Surgery, The Spine Hospital, New York-Presbyterian/Columbia University Irving Medical Center, New York, NY, USA,Venkat Boddapati, MD, Columbia University Irving Medical Center, 622 W. 168th St., PH-11, New York, NY 10032, USA.
| | - Joseph M. Lombardi
- Department of Orthopaedic Surgery, The Spine Hospital, New York-Presbyterian/Columbia University Irving Medical Center, New York, NY, USA
| | - Nathan J. Lee
- Department of Orthopaedic Surgery, The Spine Hospital, New York-Presbyterian/Columbia University Irving Medical Center, New York, NY, USA
| | - Justin Mathew
- Department of Orthopaedic Surgery, The Spine Hospital, New York-Presbyterian/Columbia University Irving Medical Center, New York, NY, USA
| | - Nicholas C. Danford
- Department of Orthopaedic Surgery, The Spine Hospital, New York-Presbyterian/Columbia University Irving Medical Center, New York, NY, USA
| | - Rajiv R. Iyer
- Department of Orthopaedic Surgery, The Spine Hospital, New York-Presbyterian/Columbia University Irving Medical Center, New York, NY, USA
| | - Marc D. Dyrszka
- Department of Orthopaedic Surgery, The Spine Hospital, New York-Presbyterian/Columbia University Irving Medical Center, New York, NY, USA
| | - Zeeshan M. Sardar
- Department of Orthopaedic Surgery, The Spine Hospital, New York-Presbyterian/Columbia University Irving Medical Center, New York, NY, USA
| | - Lawrence G. Lenke
- Department of Orthopaedic Surgery, The Spine Hospital, New York-Presbyterian/Columbia University Irving Medical Center, New York, NY, USA
| | - Ronald A. Lehman
- Department of Orthopaedic Surgery, The Spine Hospital, New York-Presbyterian/Columbia University Irving Medical Center, New York, NY, USA
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Xie N, Wilson PJ, Reddy R. Use of machine learning to model surgical decision-making in lumbar spine surgery. 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 2022; 31:2000-2006. [PMID: 35088119 DOI: 10.1007/s00586-021-07104-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 11/22/2021] [Accepted: 12/21/2021] [Indexed: 01/20/2023]
Abstract
PURPOSE The majority of lumbar spine surgery referrals do not proceed to surgery. Early identification of surgical candidates in the referral process could expedite their care, whilst allowing timelier implementation of non-operative strategies for those who are unlikely to require surgery. By identifying clinical and imaging features associated with progression to surgery in the literature, we aimed to develop a machine learning model able to mirror surgical decision-making and calculate the chance of surgery based on the identified features. MATERIAL AND METHODS In total, 55 factors were identified to predict surgical progression. All patients presenting with a lumbar spine complaint between 2013 and 2019 at a single Australian Tertiary Hospital (n = 483) had their medical records reviewed and relevant data collected. An Artificial Neural Network (ANN) was constructed to predict surgical candidacy. The model was evaluated on its accuracy, discrimination, and calibration. RESULTS Eight clinical and imaging predictive variables were included in the final model. The ANN was able to predict surgical progression with 92.1% accuracy. It also exhibited excellent discriminative ability (AUC = 0.90), with good fit of data (Calibration slope 0.938, Calibration intercept - 0.379, HLT > 0.05). CONCLUSION Through use of machine learning techniques, we were able to model surgical decision-making with a high degree of accuracy. By demonstrating that the operating patterns of single centres can be modelled successfully, the potential for more targeted and tailored referrals becomes possible, reducing outpatient wait-list duration and increasing surgical conversion rates.
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Affiliation(s)
- Nathan Xie
- School of Medical Sciences, University of New South Wales, Prince of Wales Private Hospital, High Street, Kensington, Sydney, 2052, Australia.
- Department of Neurosurgery, Prince of Wales Hospital, Sydney, Australia.
| | - Peter J Wilson
- School of Medical Sciences, University of New South Wales, Prince of Wales Private Hospital, High Street, Kensington, Sydney, 2052, Australia
- Department of Neurosurgery, Prince of Wales Hospital, Sydney, Australia
| | - Rajesh Reddy
- School of Medical Sciences, University of New South Wales, Prince of Wales Private Hospital, High Street, Kensington, Sydney, 2052, Australia
- Department of Neurosurgery, Prince of Wales Hospital, Sydney, Australia
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Chen CM, Chen PC, Chen YC, Wang GC. Use artificial neural network to recommend the lumbar spinal endoscopic surgical corridor. Tzu Chi Med J 2022; 34:434-440. [PMID: 36578635 PMCID: PMC9791850 DOI: 10.4103/tcmj.tcmj_281_21] [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: 10/11/2021] [Revised: 12/31/2021] [Accepted: 03/21/2022] [Indexed: 12/31/2022] Open
Abstract
Objectives The transforaminal and interlaminar approaches are the two main surgical corridors of full endoscopic lumbar surgery. However, there are no quantifying methods for assessing the best surgical approach for each patient. This study aimed to establish an artificial intelligence (AI) model using an artificial neural network (ANN). Materials and Methods Patients who underwent full endoscopic lumbar spinal surgery were enrolled in this research. Fourteen pre-operative factors were fed into the ANN. A three-layer deep neural network was constructed. Patient data were divided into the training, validation, and testing datasets. Results There were 899 patients enrolled. The accuracy of the training, validation, and test datasets were 87.3%, 85.5%, and 85.0%, respectively. The positive predictive values for the transforaminal and interlaminar approaches were 85.1% and 89.1%, respectively. The area under the curve of the receiver operating characteristic was 0.91. The SHapley Additive exPlanations algorithm was utilized to explain the relative importance of each factor. The surgical lumbar level was the most important factor, followed by herniated disc localization and migrating disc zone level. Conclusion ANN can effectively learn from the choice of an experienced spinal endoscopic surgeon and can accurately predict the appropriate surgical approach.
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Affiliation(s)
- Chien-Min Chen
- Division of Neurosurgery, Department of Surgery, Changhua Christian Hospital, Changhua, Taiwan,School of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan,College of Nursing and Health Sciences, Dayeh University, Changhua, Taiwan
| | - Pei-Chen Chen
- Department of Obstetrics and Gynecology, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation and Tzu Chi University, Hualien, Taiwan
| | - Ying-Chieh Chen
- Division of Neurosurgery, Department of Surgery, Changhua Christian Hospital, Changhua, Taiwan
| | - Guan-Chyuan Wang
- Division of Neurosurgery, Department of Surgery, Mennonite Christian Hospital, Hualien, Taiwan,Address for correspondence: Dr. Guan-Chyuan Wang, Division of Neurosurgery, Department of Surgery, Mennonite Christian Hospital, 44, Min-Chuan Road, Hualien, Taiwan. E-mail:
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André A, Peyrou B, Carpentier A, Vignaux JJ. Feasibility and Assessment of a Machine Learning-Based Predictive Model of Outcome After Lumbar Decompression Surgery. Global Spine J 2022; 12:894-908. [PMID: 33207969 PMCID: PMC9344503 DOI: 10.1177/2192568220969373] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
STUDY DESIGN Retrospective study at a unique center. OBJECTIVE The aim of this study is twofold, to develop a virtual patients model for lumbar decompression surgery and to evaluate the precision of an artificial neural network (ANN) model designed to accurately predict the clinical outcomes of lumbar decompression surgery. METHODS We performed a retrospective study of complete Electronic Health Records (EHR) to identify potential unfavorable criteria for spine surgery (predictors). A cohort of synthetics EHR was created to classify patients by surgical success (green zone) or partial failure (orange zone) using an Artificial Neural Network which screens all the available predictors. RESULTS In the actual cohort, we included 60 patients, with complete EHR allowing efficient analysis, 26 patients were in the orange zone (43.4%) and 34 were in the green zone (56.6%). The average positive criteria amount for actual patients was 8.62 for the green zone (SD+/- 3.09) and 10.92 for the orange zone (SD 3.38). The classifier (a neural network) was trained using 10,000 virtual patients and 2000 virtual patients were used for test purposes. The 12,000 virtual patients were generated from the 60 EHR, of which half were in the green zone and half in the orange zone. The model showed an accuracy of 72% and a ROC score of 0.78. The sensitivity was 0.885 and the specificity 0.59. CONCLUSION Our method can be used to predict a favorable patient to have lumbar decompression surgery. However, there is still a need to further develop its ability to analyze patients in the "failure of treatment" zone to offer precise management of patient health before spinal surgery.
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Affiliation(s)
- Arthur André
- Ramsay santé, Clinique Geoffroy
Saint-Hilaire, Paris, France,Neurosurgery Department,
Pitié-Salpêtrière University Hospital, Paris, France,Cortexx Medical Intelligence, Paris,
France,Arthur André, Cortexx Medical Intelligence,
156 Boulevard, Haussmann 75008, Paris.
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Chen X, Deng Q, Wang Q, Liu X, Chen L, Liu J, Li S, Wang M, Cao G. Image Quality Control in Lumbar Spine Radiography Using Enhanced U-Net Neural Networks. Front Public Health 2022; 10:891766. [PMID: 35558524 PMCID: PMC9087032 DOI: 10.3389/fpubh.2022.891766] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 04/01/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose To standardize the radiography imaging procedure, an image quality control framework using the deep learning technique was developed to segment and evaluate lumbar spine x-ray images according to a defined quality control standard. Materials and Methods A dataset comprising anteroposterior, lateral, and oblique position lumbar spine x-ray images from 1,389 patients was analyzed in this study. The training set consisted of digital radiography images of 1,070 patients (800, 798, and 623 images of the anteroposterior, lateral, and oblique position, respectively) and the validation set included 319 patients (200, 205, and 156 images of the anteroposterior, lateral, and oblique position, respectively). The quality control standard for lumbar spine x-ray radiography in this study was defined using textbook guidelines of as a reference. An enhanced encoder-decoder fully convolutional network with U-net as the backbone was implemented to segment the anatomical structures in the x-ray images. The segmentations were used to build an automatic assessment method to detect unqualified images. The dice similarity coefficient was used to evaluate segmentation performance. Results The dice similarity coefficient of the anteroposterior position images ranged from 0.82 to 0.96 (mean 0.91 ± 0.06); the dice similarity coefficient of the lateral position images ranged from 0.71 to 0.95 (mean 0.87 ± 0.10); the dice similarity coefficient of the oblique position images ranged from 0.66 to 0.93 (mean 0.80 ± 0.14). The accuracy, sensitivity, and specificity of the assessment method on the validation set were 0.971-0.990 (mean 0.98 ± 0.10), 0.714-0.933 (mean 0.86 ± 0.13), and 0.995-1.000 (mean 0.99 ± 0.12) for the three positions, respectively. Conclusion This deep learning-based algorithm achieves accurate segmentation of lumbar spine x-ray images. It provides a reliable and efficient method to identify the shape of the lumbar spine while automatically determining the radiographic image quality.
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Affiliation(s)
- Xiao Chen
- Department of Radiology, Key Laboratory of Intelligent Medical Imaging of Wenzhou, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Qingshan Deng
- Department of Radiology, Key Laboratory of Intelligent Medical Imaging of Wenzhou, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Qiang Wang
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Xinmiao Liu
- School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, China
| | - Lei Chen
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Jinjin Liu
- Department of Radiology, Key Laboratory of Intelligent Medical Imaging of Wenzhou, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Shuangquan Li
- Department of Radiology, Key Laboratory of Intelligent Medical Imaging of Wenzhou, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Meihao Wang
- Department of Radiology, Key Laboratory of Intelligent Medical Imaging of Wenzhou, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Guoquan Cao
- Department of Radiology, Key Laboratory of Intelligent Medical Imaging of Wenzhou, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
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DelSole EM, Keck WL, Patel AA. The State of Machine Learning in Spine Surgery: A Systematic Review. Clin Spine Surg 2022; 35:80-89. [PMID: 34121074 DOI: 10.1097/bsd.0000000000001208] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Accepted: 04/14/2021] [Indexed: 11/27/2022]
Abstract
STUDY DESIGN This was a systematic review of existing literature. OBJECTIVE The objective of this study was to evaluate the current state-of-the-art trends and utilization of machine learning in the field of spine surgery. SUMMARY OF BACKGROUND DATA The past decade has seen a rise in the clinical use of machine learning in many fields including diagnostic radiology and oncology. While studies have been performed that specifically pertain to spinal surgery, there have been relatively few aggregate reviews of the existing scientific literature as applied to clinical spine surgery. METHODS This study utilized Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to review the scientific literature from 2009 to 2019 with syntax specific for machine learning and spine surgery applications. Specific data was extracted from the available literature including algorithm application, algorithms tested, database type and size, algorithm training method, and outcome of interest. RESULTS A total of 44 studies met inclusion criteria, of which the majority were level III evidence. Studies were grouped into 4 general types: diagnostic tools, clinical outcome prediction, surgical assessment tools, and decision support tools. Across studies, a wide swath of algorithms were used, which were trained across multiple disparate databases. There were no studies identified that assessed the ethical implementation or patient perceptions of machine learning in clinical care. CONCLUSIONS The results reveal the broad range of clinical applications and methods used to create machine learning algorithms for use in the field of spine surgery. Notable disparities exist in algorithm choice, database characteristics, and training methods. Ongoing research is needed to make machine learning operational on a large scale.
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Affiliation(s)
- Edward M DelSole
- Department of Orthopaedic Surgery, Division of Spine Surgery, Geisinger Musculoskeletal Institute
| | - Wyatt L Keck
- Geisinger Commonwealth School of Medicine, Scranton
| | - Aalpen A Patel
- Department of Radiology (Geisinger), Steele Institute for Health Innovation and Geisinger, Danville, PA
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12
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Katsuura Y, Colón LF, Perez AA, Albert TJ, Qureshi SA. A Primer on the Use of Artificial Intelligence in Spine Surgery. Clin Spine Surg 2021; 34:316-321. [PMID: 34050043 DOI: 10.1097/bsd.0000000000001211] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Accepted: 04/14/2021] [Indexed: 11/26/2022]
Abstract
DESIGN This was a narrative review. PURPOSE Summarize artificial intelligence (AI) fundamentals as well as current and potential future uses in spine surgery. SUMMARY OF BACKGROUND DATA Although considered futuristic, the field of AI has already had a profound impact on many industries, including health care. Its ability to recognize patterns and self-correct to improve over time mimics human cognitive function, but on a much larger scale. METHODS Review of literature on AI fundamentals and uses in spine pathology. RESULTS Machine learning (ML), a subset of AI, increases in hierarchy of complexity from classic ML to unsupervised ML to deep leaning, where Language Processing and Computer Vision are possible. AI-based tools have been developed to segment spinal structures, acquire basic spinal measurements, and even identify pathology such as tumor or degeneration. AI algorithms could have use in guiding clinical management through treatment selection, patient-specific prognostication, and even has the potential to power neuroprosthetic devices after spinal cord injury. CONCLUSION While the use of AI has pitfalls and should be adopted with caution, future use is promising in the field of spine surgery and medicine as a whole. LEVEL OF EVIDENCE Level IV.
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Affiliation(s)
| | - Luis F Colón
- Department of Orthopedic Surgery, University of Tennessee College of Medicine in Chattanooga, Chattanooga, TN
| | - Alberto A Perez
- School of Medicine and Public Health, University of Wisconsin, Madison, WI
| | - Todd J Albert
- Hospital for Special Surgery
- Weill Cornell Medical College, New York, NY
| | - Sheeraz A Qureshi
- Hospital for Special Surgery
- Weill Cornell Medical College, New York, NY
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13
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McDonnell JM, Evans SR, McCarthy L, Temperley H, Waters C, Ahern D, Cunniffe G, Morris S, Synnott K, Birch N, Butler JS. The diagnostic and prognostic value of artificial intelligence and artificial neural networks in spinal surgery : a narrative review. Bone Joint J 2021; 103-B:1442-1448. [PMID: 34465148 DOI: 10.1302/0301-620x.103b9.bjj-2021-0192.r1] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
In recent years, machine learning (ML) and artificial neural networks (ANNs), a particular subset of ML, have been adopted by various areas of healthcare. A number of diagnostic and prognostic algorithms have been designed and implemented across a range of orthopaedic sub-specialties to date, with many positive results. However, the methodology of many of these studies is flawed, and few compare the use of ML with the current approach in clinical practice. Spinal surgery has advanced rapidly over the past three decades, particularly in the areas of implant technology, advanced surgical techniques, biologics, and enhanced recovery protocols. It is therefore regarded an innovative field. Inevitably, spinal surgeons will wish to incorporate ML into their practice should models prove effective in diagnostic or prognostic terms. The purpose of this article is to review published studies that describe the application of neural networks to spinal surgery and which actively compare ANN models to contemporary clinical standards allowing evaluation of their efficacy, accuracy, and relatability. It also explores some of the limitations of the technology, which act to constrain the widespread adoption of neural networks for diagnostic and prognostic use in spinal care. Finally, it describes the necessary considerations should institutions wish to incorporate ANNs into their practices. In doing so, the aim of this review is to provide a practical approach for spinal surgeons to understand the relevant aspects of neural networks. Cite this article: Bone Joint J 2021;103-B(9):1442-1448.
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Affiliation(s)
- Jake M McDonnell
- School of Medicine, Royal College of Surgeons Ireland, Dublin, Ireland.,National Spinal Injuries Unit, Mater Misericordiae University Hospital, Dublin, Ireland
| | | | | | | | | | - Daniel Ahern
- National Spinal Injuries Unit, Mater Misericordiae University Hospital, Dublin, Ireland.,Centre for Biomedical Engineering, Trinity College, Dublin, Ireland
| | - Gráinne Cunniffe
- National Spinal Injuries Unit, Mater Misericordiae University Hospital, Dublin, Ireland
| | - Seamus Morris
- National Spinal Injuries Unit, Mater Misericordiae University Hospital, Dublin, Ireland
| | - Keith Synnott
- National Spinal Injuries Unit, Mater Misericordiae University Hospital, Dublin, Ireland
| | - Nick Birch
- Bragborough Hall Health and Wellness Centre, Daventry, UK
| | - Joseph S Butler
- National Spinal Injuries Unit, Mater Misericordiae University Hospital, Dublin, Ireland.,School of Medicine, University College, Dublin, Ireland
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14
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Jiang S, Li Q, Wang H. Comparison of the clinical efficacy of percutaneous transforaminal endoscopic discectomy and traditional laminectomy in the treatment of recurrent lumbar disc herniation. Medicine (Baltimore) 2021; 100:e25806. [PMID: 34397681 PMCID: PMC8322506 DOI: 10.1097/md.0000000000025806] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Accepted: 04/15/2021] [Indexed: 01/04/2023] Open
Abstract
A few years ago, percutaneous transforaminal endoscopic discectomy (PTED) began to prevail in clinical treatment of recurrent lumbar disc herniation (RLDH), whereas traditional laminectomy (TL) was treated earlier in RLDH than PTED. This study aimed to compare the clinical efficacy of PTED and TL in the treatment of RLDH.Between November 2012 and October 2017, retrospective analysis of 48 patients with RLDH who were treated at the Cancer Hospital, Chinese Academy of Sciences, Hefei and Department of Orthopaedics, Second Affiliated Hospital of Anhui Medical University. Perioperative evaluation indicators included operation time, the intraoperative blood loss, length of incision and hospitalization time. Clinical outcomes were measured preoperatively, and at 1 days, 3 months, and 12 months postoperatively. The patients' lower limb pain was evaluated using Oswestry disability index (ODI) and visual analog scale (VAS) scores. The ODI is the most widely-used assessment method internationally for lumbar or leg pain at present. Every category comprises 6 options, with the highest score for each question being 5 points. higher scores represent more serious dysfunction. The VAS is the most commonly-used quantitative method for assessing the degree of pain in clinical practice. The measurement method is to draw a 10 cm horizontal line on a piece of paper, 1 end of which is 0, indicating no pain, which the other end is 10, which means severe pain, and the middle part indicates different degree of pain.Compared with the TL group, the operation time, postoperative bed-rest time, and hospitalization time of the PTED group were significantly shorter, and the intraoperative blood loss was also reduced. These differences were statistically significant (P < .01). There were no significant differences in VAS or ODI scores between the two groups before or after surgery (P > .05).PTED and TL have similar clinical efficacy in the treatment of RLDH, but PTED can shorten the operation time, postoperative bed-rest time and hospitalization time, and reduce intraoperative blood loss, so the PTED is a safe and effective surgical method for the treatment of RLDH than TL, but more randomized controlled trials are still required to further verify these conclusions.
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Affiliation(s)
- Shifeng Jiang
- Department of Orthopaedics, Cancer Hospital, Chinese Academy of Sciences, Hefei, Shushan lake road No.350, shushan district, hefei city, Anhui Province, china
| | - Qingning Li
- Department of Orthopaedics, The Second Affiliated Hospital of Anhui Medical University, 678 Furong Road, Hefei, Anhui, China
| | - Hongzhi Wang
- Department of Orthopaedics, Cancer Hospital, Chinese Academy of Sciences, Hefei, Shushan lake road No.350, shushan district, hefei city, Anhui Province, china
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15
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DeVries Z, Locke E, Hoda M, Moravek D, Phan K, Stratton A, Kingwell S, Wai EK, Phan P. Using a national surgical database to predict complications following posterior lumbar surgery and comparing the area under the curve and F1-score for the assessment of prognostic capability. Spine J 2021; 21:1135-1142. [PMID: 33601012 DOI: 10.1016/j.spinee.2021.02.007] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 01/11/2021] [Accepted: 02/11/2021] [Indexed: 02/03/2023]
Abstract
BACKGROUND With spinal surgery rates increasing in North America, models that are able to accurately predict which patients are at greater risk of developing complications are highly warranted. However, the previously published methods which have used large, multi-centre databases to develop their prediction models have relied on the receiver operator characteristics curve with the associated area under the curve (AUC) to assess their model's performance. Recently, it has been found that a precision-recall curve with the associated F1-score could provide a more realistic analysis for these models. PURPOSE To develop a logistic regression (LR) model for the prediction of complications following posterior lumbar spine surgery and to then assess for any difference in performance of the model when using the AUC versus the F1-score. STUDY DESIGN Retrospective review of a prospective cohort. PATIENT SAMPLE The American College of Surgeons National Surgical Quality Improvement Program (NSQIP) registry was used. All patients that underwent posterior lumbar spine surgery between 2005 to 2016 with appropriate data were included. OUTCOME MEASURES Both the AUC and F1-score were utilized to assess the prognostic performance of the prediction model. METHODS In order to develop the LR model used to predict a complication during or following spine surgery, 19 variables were selected by three orthopedic spine surgeons from the NSQIP registry. Two datasets were developed for this analysis: (1) an imbalanced dataset, which was taken directly from the NSQIP registry, and (2) a down-sampled set. The purpose of the down-sampled set was to balance the data in order to evaluate whether balancing the data had an effect on model performance. The AUC and F1-score were applied to both of these datasets. RESULTS Within the NSQIP database, 52,787 spine surgery cases were identified of which only 10% of these cases had complications during surgery. Applying the LR model showed a large difference between the AUC (0.69) and the F1 score (0.075) on the imbalanced dataset. However, no major differences existed between the AUC and F1-score when the data was balanced and the LR model was reapplied (0.69 and 0.62, AUC and F1-score, respectively). CONCLUSIONS The F1-score detected a drastically lower performance for the prediction of complications when using the imbalanced data, but detected a performance similar to the AUC level when balancing techniques were utilized for the dataset. This difference is due to a low precision score when many false positive classifications are present, which is not identified when using the AUC value. This lowers the utility of the AUC score, as many of the datasets used in medicine are imbalanced. Therefore, we recommend using the F1-score on large, prospective databases when the data is imbalanced with a large amount of true negative classifications.
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Affiliation(s)
- Zachary DeVries
- Ottawa Spine Collaborative Analytics Network, The Ottawa Hospital, Ottawa, ON, Canada K1Y 4E9
| | - Eric Locke
- Ottawa Spine Collaborative Analytics Network, The Ottawa Hospital, Ottawa, ON, Canada K1Y 4E9
| | - Mohamad Hoda
- Ottawa Spine Collaborative Analytics Network, The Ottawa Hospital, Ottawa, ON, Canada K1Y 4E9
| | - Dita Moravek
- Ottawa Spine Collaborative Analytics Network, The Ottawa Hospital, Ottawa, ON, Canada K1Y 4E9; Ottawa Hospital Research Institute, Ottawa, ON, Canada K1Y 4E9
| | - Kim Phan
- Ottawa Spine Collaborative Analytics Network, The Ottawa Hospital, Ottawa, ON, Canada K1Y 4E9
| | - Alexandra Stratton
- Ottawa Spine Collaborative Analytics Network, The Ottawa Hospital, Ottawa, ON, Canada K1Y 4E9; Division of Orthopaedic Surgery, Department of Surgery, Faculty of Medicine, University of Ottawa, The Ottawa Hospital, 1053 Carling Avenue, Ottawa, ON, Canada K1Y 4E9
| | - Stephen Kingwell
- Ottawa Spine Collaborative Analytics Network, The Ottawa Hospital, Ottawa, ON, Canada K1Y 4E9; Division of Orthopaedic Surgery, Department of Surgery, Faculty of Medicine, University of Ottawa, The Ottawa Hospital, 1053 Carling Avenue, Ottawa, ON, Canada K1Y 4E9
| | - Eugene K Wai
- Ottawa Spine Collaborative Analytics Network, The Ottawa Hospital, Ottawa, ON, Canada K1Y 4E9; Division of Orthopaedic Surgery, Department of Surgery, Faculty of Medicine, University of Ottawa, The Ottawa Hospital, 1053 Carling Avenue, Ottawa, ON, Canada K1Y 4E9; Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada K1Y 4E9
| | - Philippe Phan
- Ottawa Spine Collaborative Analytics Network, The Ottawa Hospital, Ottawa, ON, Canada K1Y 4E9; Ottawa Hospital Research Institute, Ottawa, ON, Canada K1Y 4E9; Division of Orthopaedic Surgery, Department of Surgery, Faculty of Medicine, University of Ottawa, The Ottawa Hospital, 1053 Carling Avenue, Ottawa, ON, Canada K1Y 4E9.
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16
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Shahjouei S, Ghodsi SM, Zangeneh Soroush M, Ansari S, Kamali-Ardakani S. Artificial Neural Network for Predicting the Safe Temporary Artery Occlusion Time in Intracranial Aneurysmal Surgery. J Clin Med 2021; 10:1464. [PMID: 33918168 PMCID: PMC8037800 DOI: 10.3390/jcm10071464] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2021] [Revised: 03/16/2021] [Accepted: 03/31/2021] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Temporary artery clipping facilitates safe cerebral aneurysm management, besides a risk for cerebral ischemia. We developed an artificial neural network (ANN) to predict the safe clipping time of temporary artery occlusion (TAO) during intracranial aneurysm surgery. METHOD We devised a three-layer model to predict the safe clipping time for TAO. We considered age, the diameter of the right and left middle cerebral arteries (MCAs), the diameter of the right and left A1 segment of anterior cerebral arteries (ACAs), the diameter of the anterior communicating artery, mean velocity of flow at the right and left MCAs, and the mean velocity of flow at the right and left ACAs, as well as the Fisher grading scale of brain CT scans as the input values for the model. RESULTS This study included 125 patients: 105 patients from a retrospective cohort for training the model and 20 patients from a prospective cohort for validating the model. The output of the neural network yielded up to 960 s overall safe clipping time for TAO. The input values with the greatest impact on safe TAO were mean velocity of blood at left MCA and left ACA, and Fisher grading scale of brain CT scan. CONCLUSION This study presents an axillary framework to improve the accuracy of the estimated safe clipping time interval of temporary artery occlusion in intracranial aneurysm surgery.
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Affiliation(s)
- Shima Shahjouei
- Neurology Department, Neuroscience Institute, Geisinger Health System, Danville, PA 17822, USA
- Department of Neurosurgery, Tehran University of Medical Sciences, Tehran 14155-6559, Iran; (S.M.G.); (S.K.-A.)
| | - Seyed Mohammad Ghodsi
- Department of Neurosurgery, Tehran University of Medical Sciences, Tehran 14155-6559, Iran; (S.M.G.); (S.K.-A.)
| | - Morteza Zangeneh Soroush
- Bio-Intelligence Research Unit, Electrical Engeneering Department, Sharif University of Technology, Tehran 14588-89694, Iran;
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran 14778-93855, Iran
| | - Saeed Ansari
- National Institute of Neurological Disorders and Stroke, National Institute of Health, Bethesda, MD 20892, USA;
| | - Shahab Kamali-Ardakani
- Department of Neurosurgery, Tehran University of Medical Sciences, Tehran 14155-6559, Iran; (S.M.G.); (S.K.-A.)
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17
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Lehner K, Ehresman J, Pennington Z, Ahmed AK, Lubelski D, Sciubba DM. Narrative Review of Predictive Analytics of Patient-Reported Outcomes in Adult Spinal Deformity Surgery. Global Spine J 2021; 11:89S-95S. [PMID: 33034220 PMCID: PMC8076815 DOI: 10.1177/2192568220963060] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
STUDY DESIGN Narrative review. OBJECTIVE Decision making in surgery for adult spinal deformity (ASD) is complex due to the multifactorial etiology, numerous surgical options, and influence of multiple medical and psychosocial factors on patient outcomes. Predictive analytics provide computational tools to analyze large data sets and generate hypotheses regarding new data. In this review, we examine the use of predictive analytics to predict patient-reported outcomes (PROs) in ASD surgery. METHODS A search of PubMed, Web of Science, and Embase databases was performed to identify all potentially relevant studies up to February 1, 2020. Studies were included based on the use of predictive analytics to predict PROs in ASD. RESULTS Of 57 studies identified and reviewed, 7 studies were included. Multiple algorithms including supervised and unsupervised methods were used. Significant heterogeneity was observed with choice of PROs modeled including ODI, SRS22, and SF36, assessment of model accuracy, and with the model accuracy and area under the receiver operating curve values (ranging from 30% to 86% and 0.57 to 0.96, respectively). Models were built with data sets of patients ranging from 89 to 570 patients with a range of 22 to 267 variables. CONCLUSIONS Predictive analytics makes accurate predictions regarding PROs regarding pain, disability, and work and social function; PROs regarding satisfaction, self-image, and psychologic aspects of ASD were predicted with the lowest accuracy. Our review demonstrates a relative paucity of studies on ASD with limited databases. Future studies should include larger and more diverse databases and provide external validation of preexisting models.
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Affiliation(s)
- Kurt Lehner
- Johns Hopkins University, Baltimore, MD, USA
| | | | | | | | | | - Daniel M. Sciubba
- Johns Hopkins University, Baltimore, MD, USA,Daniel M. Sciubba, Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA.
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Abstract
BACKGROUND Artificial intelligence (AI) in neurosurgery is becoming increasingly more important as the technology advances. This development can be measured by the increase of publications on AI in neurosurgery over the last years. OBJECTIVE This article provides insights into the current possibilities of using AI in neurosurgery. MATERIAL AND METHODS A review of the literature was carried out with a focus on exemplary work on the use of AI in neurosurgery. RESULTS The current neurosurgical publications on the use of AI show the diversity of the topic in this field. The main areas of application are diagnostics, outcome and treatment models. CONCLUSION The various areas of application of AI in the field of neurosurgery with a refined preoperative diagnostics and outcome predictions will significantly influence the future of neurosurgery. Neurosurgeons will continue to make the decisions on the indications for surgery but an optimized statement on diagnosis, treatment options and on the risk of surgery will be made by neurosurgeons with the help of AI in the future.
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Affiliation(s)
- M M Bonsanto
- Klinik für Neurochirurgie, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, 23538, Lübeck, Deutschland.
| | - V M Tronnier
- Klinik für Neurochirurgie, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, 23538, Lübeck, Deutschland
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19
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Tagliaferri SD, Angelova M, Zhao X, Owen PJ, Miller CT, Wilkin T, Belavy DL. Artificial intelligence to improve back pain outcomes and lessons learnt from clinical classification approaches: three systematic reviews. NPJ Digit Med 2020; 3:93. [PMID: 32665978 PMCID: PMC7347608 DOI: 10.1038/s41746-020-0303-x] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Accepted: 06/05/2020] [Indexed: 12/17/2022] Open
Abstract
Artificial intelligence and machine learning (AI/ML) could enhance the ability to detect patterns of clinical characteristics in low-back pain (LBP) and guide treatment. We conducted three systematic reviews to address the following aims: (a) review the status of AI/ML research in LBP, (b) compare its status to that of two established LBP classification systems (STarT Back, McKenzie). AI/ML in LBP is in its infancy: 45 of 48 studies assessed sample sizes <1000 people, 19 of 48 studies used ≤5 parameters in models, 13 of 48 studies applied multiple models and attained high accuracy, 25 of 48 studies assessed the binary classification of LBP versus no-LBP only. Beyond the 48 studies using AI/ML for LBP classification, no studies examined use of AI/ML in prognosis prediction of specific sub-groups, and AI/ML techniques are yet to be implemented in guiding LBP treatment. In contrast, the STarT Back tool has been assessed for internal consistency, test-retest reliability, validity, pain and disability prognosis, and influence on pain and disability treatment outcomes. McKenzie has been assessed for inter- and intra-tester reliability, prognosis, and impact on pain and disability outcomes relative to other treatments. For AI/ML methods to contribute to the refinement of LBP (sub-)classification and guide treatment allocation, large data sets containing known and exploratory clinical features should be examined. There is also a need to establish reliability, validity, and prognostic capacity of AI/ML techniques in LBP as well as its ability to inform treatment allocation for improved patient outcomes and/or reduced healthcare costs.
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Affiliation(s)
- Scott D. Tagliaferri
- Institute for Physical Activity and Nutrition (IPAN), School of Exercise and Nutrition Sciences, Deakin University, Geelong, VIC Australia
| | - Maia Angelova
- School of Information Technology, Deakin University, Geelong, VIC Australia
| | - Xiaohui Zhao
- Xi’an University of Architecture & Technology, Beilin, Xi’an China
| | - Patrick J. Owen
- Institute for Physical Activity and Nutrition (IPAN), School of Exercise and Nutrition Sciences, Deakin University, Geelong, VIC Australia
| | - Clint T. Miller
- Institute for Physical Activity and Nutrition (IPAN), School of Exercise and Nutrition Sciences, Deakin University, Geelong, VIC Australia
| | - Tim Wilkin
- School of Information Technology, Deakin University, Geelong, VIC Australia
| | - Daniel L. Belavy
- Institute for Physical Activity and Nutrition (IPAN), School of Exercise and Nutrition Sciences, Deakin University, Geelong, VIC Australia
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20
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Azimi P, Yazdanian T, Benzel EC, Aghaei HN, Azhari S, Sadeghi S, Montazeri A. A Review on the Use of Artificial Intelligence in Spinal Diseases. Asian Spine J 2020; 14:543-571. [PMID: 32326672 PMCID: PMC7435304 DOI: 10.31616/asj.2020.0147] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Accepted: 04/12/2020] [Indexed: 12/13/2022] Open
Abstract
Artificial neural networks (ANNs) have been used in a wide variety of real-world applications and it emerges as a promising field across various branches of medicine. This review aims to identify the role of ANNs in spinal diseases. Literature were searched from electronic databases of Scopus and Medline from 1993 to 2020 with English publications reported on the application of ANNs in spinal diseases. The search strategy was set as the combinations of the following keywords: “artificial neural networks,” “spine,” “back pain,” “prognosis,” “grading,” “classification,” “prediction,” “segmentation,” “biomechanics,” “deep learning,” and “imaging.” The main findings of the included studies were summarized, with an emphasis on the recent advances in spinal diseases and its application in the diagnostic and prognostic procedures. According to the search strategy, a set of 3,653 articles were retrieved from Medline and Scopus databases. After careful evaluation of the abstracts, the full texts of 89 eligible papers were further examined, of which 79 articles satisfied the inclusion criteria of this review. Our review indicates several applications of ANNs in the management of spinal diseases including (1) diagnosis and assessment of spinal disease progression in the patients with low back pain, perioperative complications, and readmission rate following spine surgery; (2) enhancement of the clinically relevant information extracted from radiographic images to predict Pfirrmann grades, Modic changes, and spinal stenosis grades on magnetic resonance images automatically; (3) prediction of outcomes in lumbar spinal stenosis, lumbar disc herniation and patient-reported outcomes in lumbar fusion surgery, and preoperative planning and intraoperative assistance; and (4) its application in the biomechanical assessment of spinal diseases. The evidence suggests that ANNs can be successfully used for optimizing the diagnosis, prognosis and outcome prediction in spinal diseases. Therefore, incorporation of ANNs into spine clinical practice may improve clinical decision making.
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Affiliation(s)
- Parisa Azimi
- Department of Neurosurgery, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | | | - Edward C Benzel
- Department of Neurosurgery, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Hossein Nayeb Aghaei
- Department of Neurosurgery, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Shirzad Azhari
- Department of Neurosurgery, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Sohrab Sadeghi
- Department of Neurosurgery, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ali Montazeri
- Mental Health Research Group, Health Metrics Research Centre, Iranian Institute for Health Sciences Research, ACECR, Tehran, Iran
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Bini SA, Shah RF, Bendich I, Patterson JT, Hwang KM, Zaid MB. Machine Learning Algorithms Can Use Wearable Sensor Data to Accurately Predict Six-Week Patient-Reported Outcome Scores Following Joint Replacement in a Prospective Trial. J Arthroplasty 2019; 34:2242-2247. [PMID: 31439405 DOI: 10.1016/j.arth.2019.07.024] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Revised: 07/16/2019] [Accepted: 07/18/2019] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND Tracking patient-generated health data (PGHD) following total joint arthroplasty (TJA) may enable data-driven early intervention to improve clinical results. We aim to demonstrate the feasibility of combining machine learning (ML) with PGHD in TJA to predict patient-reported outcome measures (PROMs). METHODS Twenty-two TJA patients were recruited for this pilot study. Three activity trackers collected 35 features from 4 weeks before to 6 weeks following surgery. PROMs were collected at both endpoints (Hip and Knee Disability and Osteoarthritis Outcome Score, Knee Osteoarthritis Outcome Score, and Veterans RAND 12-Item Health Survey Physical Component Score). We used ML to identify features with the highest correlation with PROMs. The algorithm trained on a subset of patients and used 3 feature sets (A, B, and C) to group the rest into one of the 3 PROM clusters. RESULTS Fifteen patients completed the study and collected 3 million data points. Three sets of features with the highest R2 values relative to PROMs were selected (A, B and C). Data collected through the 11th day had the highest predictive value. The ML algorithm grouped patients into 3 clusters predictive of 6-week PROM results, yielding total sum of squares values ranging from 3.86 (A) to 1.86 (C). CONCLUSION This small but critical proof-of-concept study demonstrates that ML can be used in combination with PGHD to predict 6-week PROM data as early as 11 days following TJA surgery. Further study is needed to confirm these findings and their clinical value.
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Affiliation(s)
- Stefano A Bini
- Department of Orthopaedic Surgery, University of California, San Francisco, San Francisco, CA
| | - Romil F Shah
- Department of Orthopaedic Surgery, University of California, San Francisco, San Francisco, CA
| | - Ilya Bendich
- Department of Orthopaedic Surgery, University of California, San Francisco, San Francisco, CA
| | - Joseph T Patterson
- Department of Orthopaedic Surgery, University of California, San Francisco, San Francisco, CA
| | - Kevin M Hwang
- Department of Orthopaedic Surgery, University of California, San Francisco, San Francisco, CA
| | - Musa B Zaid
- Department of Orthopaedic Surgery, University of California, San Francisco, San Francisco, CA
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Shah RF, Zaid MB, Bendich I, Hwang KM, Patterson JT, Bini SA. Optimal Sampling Frequency for Wearable Sensor Data in Arthroplasty Outcomes Research. A Prospective Observational Cohort Trial. J Arthroplasty 2019; 34:2248-2252. [PMID: 31445866 DOI: 10.1016/j.arth.2019.08.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/25/2019] [Revised: 07/31/2019] [Accepted: 08/01/2019] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND Wearable sensors can track patient activity after surgery. The optimal data sampling frequency to identify an association between patient-reported outcome measures (PROMs) and sensor data is unknown. Most commercial grade sensors report 24-hour average data. We hypothesize that increasing the frequency of data collection may improve the correlation with PROM data. METHODS Twenty-two total joint arthroplasty (TJA) patients were prospectively recruited and provided wearable sensors. Second-by-second (Raw) and 24-hour average data (24Hr) were collected on 7 gait metrics on the 1st, 7th, 14th, 21st, and 42nd days postoperatively. The average for each metric as well as the slope of a linear regression for 24Hr data (24HrLR) was calculated. The R2 associations were calculated using machine learning algorithms against individual PROM results at 6 weeks. The resulting R2 values were defined having a mild, moderate, or strong fit (R2 ≥ 0.2, ≥0.3, and ≥0.6, respectively) with PROM results. The difference in frequency of fit was analyzed with the McNemar's test. RESULTS The frequency of at least a mild fit (R2 ≥ 0.2) for any data point at any time frame relative to either of the PROMs measured was higher for Raw data (42%) than 24Hr data (32%; P = .041). There was no difference in frequency of fit for 24hrLR data (32%) and 24Hr data values (32%; P > .05). Longer data collection improved frequency of fit. CONCLUSION In this prospective trial, increasing sampling frequency above the standard 24Hr average provided by consumer grade activity sensors improves the ability of machine learning algorithms to predict 6-week PROMs in our total joint arthroplasty cohort.
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Affiliation(s)
- Romil F Shah
- Department of Orthopedic Surgery, University of California, San Francisco, San Francisco, CA
| | - Musa B Zaid
- Department of Orthopedic Surgery, University of California, San Francisco, San Francisco, CA
| | - Ilya Bendich
- Department of Orthopedic Surgery, University of California, San Francisco, San Francisco, CA
| | - Kevin M Hwang
- Department of Orthopedic Surgery, University of California, San Francisco, San Francisco, CA
| | - Joseph T Patterson
- Department of Orthopedic Surgery, University of California, San Francisco, San Francisco, CA
| | - Stefano A Bini
- Department of Orthopedic Surgery, University of California, San Francisco, San Francisco, CA
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Goyal A, Ngufor C, Kerezoudis P, McCutcheon B, Storlie C, Bydon M. Can machine learning algorithms accurately predict discharge to nonhome facility and early unplanned readmissions following spinal fusion? Analysis of a national surgical registry. J Neurosurg Spine 2019; 31:568-578. [PMID: 31174185 DOI: 10.3171/2019.3.spine181367] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2018] [Accepted: 03/12/2019] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Nonhome discharge and unplanned readmissions represent important cost drivers following spinal fusion. The authors sought to utilize different machine learning algorithms to predict discharge to rehabilitation and unplanned readmissions in patients receiving spinal fusion. METHODS The authors queried the 2012-2013 American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) for patients undergoing cervical or lumbar spinal fusion. Outcomes assessed included discharge to nonhome facility and unplanned readmissions within 30 days after surgery. A total of 7 machine learning algorithms were evaluated. Predictive hierarchical clustering of procedure codes was used to increase model performance. Model performance was evaluated using overall accuracy and area under the receiver operating characteristic curve (AUC), as well as sensitivity, specificity, and positive and negative predictive values. These performance metrics were computed for both the imputed and unimputed (missing values dropped) datasets. RESULTS A total of 59,145 spinal fusion cases were analyzed. The incidence rates of discharge to nonhome facility and 30-day unplanned readmission were 12.6% and 4.5%, respectively. All classification algorithms showed excellent discrimination (AUC > 0.80, range 0.85-0.87) for predicting nonhome discharge. The generalized linear model showed comparable performance to other machine learning algorithms. By comparison, all models showed poorer predictive performance for unplanned readmission, with AUC ranging between 0.63 and 0.66. Better predictive performance was noted with models using imputed data. CONCLUSIONS In an analysis of patients undergoing spinal fusion, multiple machine learning algorithms were found to reliably predict nonhome discharge with modest performance noted for unplanned readmissions. These results provide early evidence regarding the feasibility of modern machine learning classifiers in predicting these outcomes and serve as possible clinical decision support tools to facilitate shared decision making.
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Affiliation(s)
- Anshit Goyal
- 1Mayo Clinic Neuro-Informatics Laboratory
- 2Department of Neurosurgery, and
| | - Che Ngufor
- 3Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota
| | | | | | - Curtis Storlie
- 3Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota
| | - Mohamad Bydon
- 1Mayo Clinic Neuro-Informatics Laboratory
- 2Department of Neurosurgery, and
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Machine Learning and Neurosurgical Outcome Prediction: A Systematic Review. World Neurosurg 2018; 109:476-486.e1. [DOI: 10.1016/j.wneu.2017.09.149] [Citation(s) in RCA: 217] [Impact Index Per Article: 36.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2017] [Revised: 09/20/2017] [Accepted: 09/21/2017] [Indexed: 11/18/2022]
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