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Zhao R, Wang G, Li F, Wang J, Zhang Y, Li D, Liu S, Li J, Song J, Wei F, Wang C. Developing Machine Learning-Based Predictive Models for Hallux Valgus Recurrence Based on Measurements From Radiographs. Foot Ankle Int 2024:10711007241256648. [PMID: 38872342 DOI: 10.1177/10711007241256648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/15/2024]
Abstract
BACKGROUND Machine learning (ML) is increasingly used to predict the prognosis of numerous diseases. This retrospective analysis aimed to develop a prediction model using ML algorithms and to identify predictors associated with the recurrence of hallux valgus (HV) following surgery. METHODS A total of 198 symptomatic feet that underwent chevron osteotomy combined with a distal soft tissue procedure were enrolled and analyzed from 2 independent medical centers. The feet were grouped according to nonrecurrence or recurrence based on 1-year follow-up outcomes. Preoperative weightbearing radiographs and immediate postoperative nonweightbearing radiographs were obtained for each HV foot. Radiographic measurements (eg, HV angle and intermetatarsal angle) were acquired and used for ML model training. A total of 9 commonly used ML models were trained on the data obtained from one institute (108 feet), and tested on the other data set from another independent institute (90 feet) for external validation. Optimal feature sets for each model were identified based on a 2000-resample bootstrap-based internal validation via an exhaustive search. The performance of each model was then tested on the external validation set. The area under the curve (AUC), classification accuracy, sensitivity, and specificity of each model were calculated to evaluate the performance of each model. RESULTS The support vector machine (SVM) model showed the highest predictive accuracy compared to other methods, with an AUC of 0.88 and an accuracy of 75.6%. Preoperative hallux valgus angle, tibial sesamoid position, postoperative intermetatarsal angle, and postoperative tibial sesamoid position were identified as the most selected features by several ML models. CONCLUSION ML classifiers such as SVM could predict the recurrence of HV (an HVA >20 degrees) at a 1-year follow-up while identifying associated predictors in a multivariate manner. This study holds the potential for foot and ankle surgeons to effectively identify individuals at higher risk of HV recurrence postsurgery.
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Affiliation(s)
- Rui Zhao
- Department of Orthopedic Surgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Guobin Wang
- Department of Orthopedic Surgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Fengtan Li
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Jinchan Wang
- Department of Dermatology, Tianjin Medical University General Hospital, Tianjin, China
| | - Yuan Zhang
- Department of Orthopedic Surgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Dong Li
- Department of Orthopedic Surgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Shen Liu
- Department of Orthopedic Surgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Jie Li
- Graduate School, Tianjin Medical University, Tianjin, China
| | - Jiajun Song
- Department of Orthopedic Surgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Fangyuan Wei
- Department of Hand and Foot Surgery, Beijing University of Chinese Medicine Third Affiliated Hospital, Beijing, China
- Engineering Research Center of Chinese Orthopaedic and Sports Rehabilitation Artificial Intelligent, Ministry of Education, Beijing, China
| | - Chenguang Wang
- Department of Orthopedic Surgery, Tianjin Medical University General Hospital, Tianjin, China
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2
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Jang SJ, Rosenstadt J, Lee E, Kunze KN. Artificial Intelligence for Clinically Meaningful Outcome Prediction in Orthopedic Research: Current Applications and Limitations. Curr Rev Musculoskelet Med 2024; 17:185-206. [PMID: 38589721 DOI: 10.1007/s12178-024-09893-z] [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] [Accepted: 03/27/2024] [Indexed: 04/10/2024]
Abstract
PURPOSE OF REVIEW Patient-reported outcome measures (PROM) play a critical role in evaluating the success of treatment interventions for musculoskeletal conditions. However, predicting which patients will benefit from treatment interventions is complex and influenced by a multitude of factors. Artificial intelligence (AI) may better anticipate the propensity to achieve clinically meaningful outcomes through leveraging complex predictive analytics that allow for personalized medicine. This article provides a contemporary review of current applications of AI developed to predict clinically significant outcome (CSO) achievement after musculoskeletal treatment interventions. RECENT FINDINGS The highest volume of literature exists in the subspecialties of total joint arthroplasty, spine, and sports medicine, with only three studies identified in the remaining orthopedic subspecialties combined. Performance is widely variable across models, with most studies only reporting discrimination as a performance metric. Given the complexity inherent in predictive modeling for this task, including data availability, data handling, model architecture, and outcome selection, studies vary widely in their methodology and results. Importantly, the majority of studies have not been externally validated or demonstrate important methodological limitations, precluding their implementation into clinical settings. A substantial body of literature has accumulated demonstrating variable internal validity, limited scope, and low potential for clinical deployment. The majority of studies attempt to predict the MCID-the lowest bar of clinical achievement. Though a small proportion of models demonstrate promise and highlight the utility of AI, important methodological limitations need to be addressed moving forward to leverage AI-based applications for clinical deployment.
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Affiliation(s)
- Seong Jun Jang
- Department of Orthopedic Surgery, Hospital for Special Surgery, 535 East 70Th Street, New York, NY, 10021, USA
| | - Jake Rosenstadt
- Georgetown University School of Medicine, Washington, DC, USA
| | - Eugenia Lee
- Weill Cornell College of Medicine, New York, NY, USA
| | - Kyle N Kunze
- Department of Orthopedic Surgery, Hospital for Special Surgery, 535 East 70Th Street, New York, NY, 10021, USA.
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Lee S, Jung JY, Mahatthanatrakul A, Kim JS. Artificial Intelligence in Spinal Imaging and Patient Care: A Review of Recent Advances. Neurospine 2024; 21:474-486. [PMID: 38955525 PMCID: PMC11224760 DOI: 10.14245/ns.2448388.194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Revised: 05/14/2024] [Accepted: 05/23/2024] [Indexed: 07/04/2024] Open
Abstract
Artificial intelligence (AI) is transforming spinal imaging and patient care through automated analysis and enhanced decision-making. This review presents a clinical task-based evaluation, highlighting the specific impact of AI techniques on different aspects of spinal imaging and patient care. We first discuss how AI can potentially improve image quality through techniques like denoising or artifact reduction. We then explore how AI enables efficient quantification of anatomical measurements, spinal curvature parameters, vertebral segmentation, and disc grading. This facilitates objective, accurate interpretation and diagnosis. AI models now reliably detect key spinal pathologies, achieving expert-level performance in tasks like identifying fractures, stenosis, infections, and tumors. Beyond diagnosis, AI also assists surgical planning via synthetic computed tomography generation, augmented reality systems, and robotic guidance. Furthermore, AI image analysis combined with clinical data enables personalized predictions to guide treatment decisions, such as forecasting spine surgery outcomes. However, challenges still need to be addressed in implementing AI clinically, including model interpretability, generalizability, and data limitations. Multicenter collaboration using large, diverse datasets is critical to advance the field further. While adoption barriers persist, AI presents a transformative opportunity to revolutionize spinal imaging workflows, empowering clinicians to translate data into actionable insights for improved patient care.
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Affiliation(s)
- Sungwon Lee
- Department of Radiology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
- Visual Analysis and Learning for Improved Diagnostics (VALID) Lab, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Joon-Yong Jung
- Department of Radiology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
- Visual Analysis and Learning for Improved Diagnostics (VALID) Lab, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Akaworn Mahatthanatrakul
- Department of Orthopaedics, Faculty of Medicine, Naresuan University Hospital, Phitsanulok, Thailand
| | - Jin-Sung Kim
- Spine Center, Department of Neurosurgery, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
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Cai S, Liu W, Cai X, Xu C, Hu Z, Quan X, Deng Y, Yao H, Chen B, Li W, Yin C, Xu Q. Predicting osteoporotic fractures post-vertebroplasty: a machine learning approach with a web-based calculator. BMC Surg 2024; 24:142. [PMID: 38724895 PMCID: PMC11080251 DOI: 10.1186/s12893-024-02427-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Accepted: 04/25/2024] [Indexed: 05/13/2024] Open
Abstract
PURPOSE The aim of this study was to develop and validate a machine learning (ML) model for predicting the risk of new osteoporotic vertebral compression fracture (OVCF) in patients who underwent percutaneous vertebroplasty (PVP) and to create a user-friendly web-based calculator for clinical use. METHODS A retrospective analysis of patients undergoing percutaneous vertebroplasty: A retrospective analysis of patients treated with PVP between June 2016 and June 2018 at Liuzhou People's Hospital was performed. The independent variables of the model were screened using Boruta and modelled using 9 algorithms. Model performance was assessed using the area under the receiver operating characteristic curve (ROC_AUC), and clinical utility was assessed by clinical decision curve analysis (DCA). The best models were analysed for interpretability using SHapley Additive exPlanations (SHAP) and the models were deployed visually using a web calculator. RESULTS Training and test groups were split using time. The SVM model performed best in both the training group tenfold cross-validation (CV) and validation group AUC, with an AUC of 0.77. DCA showed that the model was beneficial to patients in both the training and test sets. A network calculator developed based on the SHAP-based SVM model can be used for clinical risk assessment ( https://nicolazhang.shinyapps.io/refracture_shap/ ). CONCLUSIONS The SVM-based ML model was effective in predicting the risk of new-onset OVCF after PVP, and the network calculator provides a practical tool for clinical decision-making. This study contributes to personalised care in spinal surgery.
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Affiliation(s)
- Sanying Cai
- Department of Anesthesiology, Mindong Hospital Affiliated to Fujian Medical University, Fuan, China
| | - Wencai Liu
- Department of Orthopaedic Surgery, the First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Xintian Cai
- Department of Graduate School, Xinjiang Medical University, Urumqi, China
| | - Chan Xu
- The State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics & Center for Molecular Imaging and Translational Medicine, School of Public Health, Xiamen University, Xiamen, China
| | - Zhaohui Hu
- Department of Spine Surgery, Liuzhou People's Hospital, Liuzhou, China
| | - Xubin Quan
- Department of Spine Surgery, Liuzhou People's Hospital, Liuzhou, China
- Graduate School of Guangxi Medical University, Nanning, Guangxi, China
| | - Yizhuo Deng
- Department of Spine Surgery, Liuzhou People's Hospital, Liuzhou, China
- Guilin Medical University, Guilin, Guangxi, China
| | - Hongjie Yao
- Department of Spine Surgery, Liuzhou People's Hospital, Liuzhou, China
- Graduate School of Guangxi Medical University, Nanning, Guangxi, China
| | - Binghao Chen
- Department of Spine Surgery, Liuzhou People's Hospital, Liuzhou, China
- Guilin Medical University, Guilin, Guangxi, China
| | - Wenle Li
- The State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics & Center for Molecular Imaging and Translational Medicine, School of Public Health, Xiamen University, Xiamen, China.
| | - Chengliang Yin
- Faculty of Medicine, Macau University of Science and Technology, Macau, 999078, P. R. China.
| | - Qingshan Xu
- Department of Orthopaedics, Mindong Hospital Affiliated to Fujian Medical University, Fuan, China.
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Khosravi S, Farahbakhsh F, Hesari M, Shahmohammadi A, Aliakbargolkar A, Baigi V, Eskandari Z, Ghodsi Z, Harrop J, Rahimi-Movaghar V, Ghodsi SM. Predictors of Outcome After Surgical Decompression for mild degenerative Cervical Myelopathy -A Systematic Review. Global Spine J 2024; 14:697-706. [PMID: 36912895 PMCID: PMC10802523 DOI: 10.1177/21925682231164346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/14/2023] Open
Abstract
STUDY DESIGN Systematic Reviews. OBJECTIVES To investigate predictors of surgical outcomes for mild Degenerative Cervical Myelopathy (DCM) by reviewing all related studies conducted at this point. METHODS An electronic search was carried out in PubMed, EMBASE, Scopus, and Web of Science until June 23, 2021. Full-text articles reporting surgical outcome predictors of mild DCM cases were eligible. We included studies with mild DCM which was defined as a modified Japanese Orthopaedic Association score of 15 to 17 or a Japanese Orthopaedic Association score of 13 to 16. Independent reviewers screened all the records, and discrepancies between the reviewers were solved in a session with the senior author. For risk of bias assessment, RoB 2 tool was used for randomized clinical trials and ROBINS-I for non-randomized studies. RESULTS After screening 6 087 manuscripts, only 8 studies met the inclusion criteria. Lower pre-operative mJOA scores and quality-of-life measurement scores were reported by multiple studies to predict better surgical outcomes compared to other groups. High-intensity pre-operative T2 magnetic resonance imaging (MRI) was also reported to predict poor outcomes. Neck pain before intervention resulted in improved patient-reported outcomes. Two studies also reported motor symptoms prior to surgery as outcome predictors. CONCLUSION Lower quality of life before surgery, neck pain, lower pre-operative mJOA scores, motor symptoms before surgery, female gender, gastrointestinal comorbidities, surgical procedure and surgeon's experience with specific techniques, and high signal intensity of cord in T2 MRI were the surgical outcome predictors reported in the literature. Lower Quality of Life (QoL) score and neck prior to surgery were reported as predictors of the more improved outcome, but high cord signal intensity in T2 MRI was reported as an unfavorable outcome predictor.
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Affiliation(s)
- Sepehr Khosravi
- Sina Trauma and Surgery Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Farzin Farahbakhsh
- Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran
- Department of Neurosurgery, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | | | | | - Alireza Aliakbargolkar
- Sina Trauma and Surgery Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Vali Baigi
- Sina Trauma and Surgery Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Zahra Eskandari
- Department of Management, Faculty of Social Sciences and Economics, Alzahra University, Tehran, Iran
| | - Zahra Ghodsi
- Sina Trauma and Surgery Research Center, Tehran University of Medical Sciences, Tehran, Iran
- Brain and Spinal Cord Injury Research Center, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - James Harrop
- Department of Neurological and Orthopedic Surgery Chief, Division of Spine and Peripheral Nerve Surgery Director, Enterprise Neuroscience Quality and Safety Neurosurgery Director of Delaware Valley SCI Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Vafa Rahimi-Movaghar
- Sina Trauma and Surgery Research Center, Tehran University of Medical Sciences, Tehran, Iran
- Department of Neurosurgery, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
- Brain and Spinal Cord Injury Research Center, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran
- Universal Scientific Education and Research Network (USERN), Tehran, Iran
- Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
- Visiting Professor, Spine Program, University of Toronto, Toronto, Canada
| | - Seyed Mohammad Ghodsi
- Sina Trauma and Surgery Research Center, Tehran University of Medical Sciences, Tehran, Iran
- Brain and Spinal Cord Injury Research Center, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran
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Huffman N, Pasqualini I, Khan ST, Klika AK, Deren ME, Jin Y, Kunze KN, Piuzzi NS. Enabling Personalized Medicine in Orthopaedic Surgery Through Artificial Intelligence: A Critical Analysis Review. JBJS Rev 2024; 12:01874474-202403000-00006. [PMID: 38466797 DOI: 10.2106/jbjs.rvw.23.00232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
» The application of artificial intelligence (AI) in the field of orthopaedic surgery holds potential for revolutionizing health care delivery across 3 crucial domains: (I) personalized prediction of clinical outcomes and adverse events, which may optimize patient selection, surgical planning, and enhance patient safety and outcomes; (II) diagnostic automated and semiautomated imaging analyses, which may reduce time burden and facilitate precise and timely diagnoses; and (III) forecasting of resource utilization, which may reduce health care costs and increase value for patients and institutions.» Computer vision is one of the most highly studied areas of AI within orthopaedics, with applications pertaining to fracture classification, identification of the manufacturer and model of prosthetic implants, and surveillance of prosthesis loosening and failure.» Prognostic applications of AI within orthopaedics include identifying patients who will likely benefit from a specified treatment, predicting prosthetic implant size, postoperative length of stay, discharge disposition, and surgical complications. Not only may these applications be beneficial to patients but also to institutions and payors because they may inform potential cost expenditure, improve overall hospital efficiency, and help anticipate resource utilization.» AI infrastructure development requires institutional financial commitment and a team of clinicians and data scientists with expertise in AI that can complement skill sets and knowledge. Once a team is established and a goal is determined, teams (1) obtain, curate, and label data; (2) establish a reference standard; (3) develop an AI model; (4) evaluate the performance of the AI model; (5) externally validate the model, and (6) reinforce, improve, and evaluate the model's performance until clinical implementation is possible.» Understanding the implications of AI in orthopaedics may eventually lead to wide-ranging improvements in patient care. However, AI, while holding tremendous promise, is not without methodological and ethical limitations that are essential to address. First, it is important to ensure external validity of programs before their use in a clinical setting. Investigators should maintain high quality data records and registry surveillance, exercise caution when evaluating others' reported AI applications, and increase transparency of the methodological conduct of current models to improve external validity and avoid propagating bias. By addressing these challenges and responsibly embracing the potential of AI, the medical field may eventually be able to harness its power to improve patient care and outcomes.
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Affiliation(s)
- Nickelas Huffman
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
| | | | - Shujaa T Khan
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
| | - Alison K Klika
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
| | - Matthew E Deren
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
| | - Yuxuan Jin
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
| | - Kyle N Kunze
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, New York
| | - Nicolas S Piuzzi
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
- Department of Biomedical Engineering, Cleveland Clinic Foundation, Cleveland, Ohio
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Touzet AY, Rujeedawa T, Munro C, Margetis K, Davies BM. Machine Learning and Symptom Patterns in Degenerative Cervical Myelopathy: Web-Based Survey Study. JMIR Form Res 2024; 8:e54747. [PMID: 38271070 PMCID: PMC10853854 DOI: 10.2196/54747] [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: 11/20/2023] [Revised: 12/18/2023] [Accepted: 12/19/2023] [Indexed: 01/27/2024] Open
Abstract
BACKGROUND Degenerative cervical myelopathy (DCM), a progressive spinal cord injury caused by spinal cord compression from degenerative pathology, often presents with neck pain, sensorimotor dysfunction in the upper or lower limbs, gait disturbance, and bladder or bowel dysfunction. Its symptomatology is very heterogeneous, making early detection as well as the measurement or understanding of the underlying factors and their consequences challenging. Increasingly, evidence suggests that DCM may consist of subgroups of the disease, which are yet to be defined. OBJECTIVE This study aimed to explore whether machine learning can identify clinically meaningful groups of patients based solely on clinical features. METHODS A survey was conducted wherein participants were asked to specify the clinical features they had experienced, their principal presenting complaint, and time to diagnosis as well as demographic information, including disease severity, age, and sex. K-means clustering was used to divide respondents into clusters according to their clinical features using the Euclidean distance measure and the Hartigan-Wong algorithm. The clinical significance of groups was subsequently explored by comparing their time to presentation, time with disease severity, and other demographics. RESULTS After a review of both ancillary and cluster data, it was determined by consensus that the optimal number of DCM response groups was 3. In Cluster 1, there were 40 respondents, and the ratio of male to female participants was 13:21. In Cluster 2, there were 92 respondents, with a male to female participant ratio of 27:65. Cluster 3 had 57 respondents, with a male to female participant ratio of 9:48. A total of 6 people did not report biological sex in Cluster 1. The mean age in this Cluster was 56.2 (SD 10.5) years; in Cluster 2, it was 54.7 (SD 9.63) years; and in Cluster 3, it was 51.8 (SD 8.4) years. Patients across clusters significantly differed in the total number of clinical features reported, with more clinical features in Cluster 3 and the least clinical features in Cluster 1 (Kruskal-Wallis rank sum test: χ22=159.46; P<.001). There was no relationship between the pattern of clinical features and severity. There were also no differences between clusters regarding time since diagnosis and time with DCM. CONCLUSIONS Using machine learning and patient-reported experience, 3 groups of patients with DCM were defined, which were different in the number of clinical features but not in the severity of DCM or time with DCM. Although a clearer biological basis for the clusters may have been missed, the findings are consistent with the emerging observation that DCM is a heterogeneous disease, difficult to diagnose or stratify. There is a place for machine learning methods to efficiently assist with pattern recognition. However, the challenge lies in creating quality data sets necessary to derive benefit from such approaches.
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Affiliation(s)
| | | | - Colin Munro
- University of Cambridge, Cambridge, United Kingdom
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Adida S, Legarreta AD, Hudson JS, McCarthy D, Andrews E, Shanahan R, Taori S, Lavadi RS, Buell TJ, Hamilton DK, Agarwal N, Gerszten PC. Machine Learning in Spine Surgery: A Narrative Review. Neurosurgery 2024; 94:53-64. [PMID: 37930259 DOI: 10.1227/neu.0000000000002660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 07/06/2023] [Indexed: 11/07/2023] Open
Abstract
Artificial intelligence and machine learning (ML) can offer revolutionary advances in their application to the field of spine surgery. Within the past 5 years, novel applications of ML have assisted in surgical decision-making, intraoperative imaging and navigation, and optimization of clinical outcomes. ML has the capacity to address many different clinical needs and improve diagnostic and surgical techniques. This review will discuss current applications of ML in the context of spine surgery by breaking down its implementation preoperatively, intraoperatively, and postoperatively. Ethical considerations to ML and challenges in ML implementation must be addressed to maximally benefit patients, spine surgeons, and the healthcare system. Areas for future research in augmented reality and mixed reality, along with limitations in generalizability and bias, will also be highlighted.
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Affiliation(s)
- Samuel Adida
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - Andrew D Legarreta
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - Joseph S Hudson
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - David McCarthy
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - Edward Andrews
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - Regan Shanahan
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - Suchet Taori
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - Raj Swaroop Lavadi
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - Thomas J Buell
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - D Kojo Hamilton
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - Nitin Agarwal
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
- Department of Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh , Pennsylvania , USA
| | - Peter C Gerszten
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
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Malhotra AK, Shakil H, Harrington EM, Fehlings MG, Wilson JR, Witiw CD. Early surgery compared to nonoperative management for mild degenerative cervical myelopathy: a cost-utility analysis. Spine J 2024; 24:21-31. [PMID: 37302415 DOI: 10.1016/j.spinee.2023.06.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/22/2023] [Revised: 05/10/2023] [Accepted: 06/03/2023] [Indexed: 06/13/2023]
Abstract
BACKGROUND CONTEXT Degenerative cervical myelopathy (DCM) is a form of acquired spinal cord compression and contributes to reduced quality of life secondary to neurological dysfunction and pain. There remains uncertainty regarding optimal management for individuals with mild myelopathy. Specifically, owing to lacking long-term natural history studies in this population, we do not know whether these individuals should be treated with initial surgery or observation. PURPOSE We sought to perform a cost-utility analysis to examine early surgery for mild degenerative cervical myelopathy from the healthcare payer perspective. STUDY DESIGN/SETTING We utilized data from the prospective observational cohorts included in the Cervical Spondylotic Myelopathy AO Spine International and North America studies to determine health related quality of life estimates and clinical myelopathy outcomes. PATIENT SAMPLE We recruited all patients that underwent surgery for DCM enrolled in the Cervical Spondylotic Myelopathy AO Spine International and North America studies between December 2005 and January 2011. OUTCOME MEASURES Clinical assessment measures were obtained using the Modified Japanese Orthopedic Association scale and health-related quality of life measures were obtained using the Short Form-6D utility score at baseline (preoperative), 6 months, 12 months and 24 months postsurgery. Cost measures inflated to January 2015 values were obtained using pooled estimates from the hospital payer perspective for surgical patients. METHODS We employed a Markov state transition model with Monte Carlo microsimulation using a lifetime horizon to obtain an incremental cost utility ratio associated with early surgery for mild myelopathy. Parameter uncertainty was assessed through deterministic means using one-way and two-way sensitivity analyses and probabilistically using parameter estimate distributions with microsimulation (10,000 trials). Costs and utilities were discounted at 3% per annum. RESULTS Initial surgery for mild degenerative cervical myelopathy was associated with an incremental lifetime increase of 1.26 quality-adjusted life years (QALY) compared to observation. The associated cost incurred to the healthcare payer over a lifetime horizon was $12,894.56, resulting in a lifetime incremental cost-utility ratio of $10,250.71/QALY. Utilizing a willingness to pay threshold in keeping with the World Health Organization definition of "very cost-effective" ($54,000 CDN), the probabilistic sensitivity analysis demonstrated that 100% of cases were cost-effective. CONCLUSIONS Surgery compared to initial observation for mild degenerative cervical myelopathy was cost-effective from the Canadian healthcare payer perspective and was associated with lifetime gains in health-related quality of life.
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Affiliation(s)
- Armaan K Malhotra
- Division of Neurosurgery, Department of Surgery, University of Toronto, 149 College St, Toronto, Ontario, M5T 1P5, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, 30 Bond St, Toronto, Ontario, M5B 1W8, Canada; Institute of Health Policy Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, 155 College St, Toronto, Ontario, M5T 3M6, Canada
| | - Husain Shakil
- Division of Neurosurgery, Department of Surgery, University of Toronto, 149 College St, Toronto, Ontario, M5T 1P5, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, 30 Bond St, Toronto, Ontario, M5B 1W8, Canada; Institute of Health Policy Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, 155 College St, Toronto, Ontario, M5T 3M6, Canada
| | - Erin M Harrington
- Li Ka Shing Knowledge Institute, St. Michael's Hospital, 30 Bond St, Toronto, Ontario, M5B 1W8, Canada
| | - Michael G Fehlings
- Division of Neurosurgery, Department of Surgery, University of Toronto, 149 College St, Toronto, Ontario, M5T 1P5, Canada; Krembil Research Institute, Toronto Western Hospital, 60 Leonard Ave, Toronto, Ontario, M5T 0S8, Canada
| | - Jefferson R Wilson
- Division of Neurosurgery, Department of Surgery, University of Toronto, 149 College St, Toronto, Ontario, M5T 1P5, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, 30 Bond St, Toronto, Ontario, M5B 1W8, Canada; Institute of Health Policy Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, 155 College St, Toronto, Ontario, M5T 3M6, Canada
| | - Christopher D Witiw
- Division of Neurosurgery, Department of Surgery, University of Toronto, 149 College St, Toronto, Ontario, M5T 1P5, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, 30 Bond St, Toronto, Ontario, M5B 1W8, Canada; Institute of Health Policy Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, 155 College St, Toronto, Ontario, M5T 3M6, Canada.
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Song J, Li J, Zhao R, Chu X. Developing predictive models for surgical outcomes in patients with degenerative cervical myelopathy: a comparison of statistical and machine learning approaches. Spine J 2024; 24:57-67. [PMID: 37531977 DOI: 10.1016/j.spinee.2023.07.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 07/16/2023] [Accepted: 07/26/2023] [Indexed: 08/04/2023]
Abstract
BACKGROUND CONTEXT Machine learning (ML) is widely used to predict the prognosis of numerous diseases. PURPOSE This retrospective analysis aimed to develop a prognostic prediction model using ML algorithms and identify predictors associated with poor surgical outcomes in patients with degenerative cervical myelopathy (DCM). STUDY DESIGN A retrospective study. PATIENT SAMPLE A total of 406 symptomatic DCM patients who underwent surgical decompression were enrolled and analyzed from three independent medical centers. OUTCOME MEASURES We calculated the area under the curve (AUC), classification accuracy, sensitivity, and specificity of each model. METHODS The Japanese Orthopedic Association (JOA) score was obtained before and 1 year following decompression surgery, and patients were grouped into good and poor outcome groups based on a cut-off value of 60% based on a previous study. Two datasets were fused for training, 1 dataset was held out as an external validation set. Optimal feature-subset and hyperparameters for each model were adjusted based on a 2,000-resample bootstrap-based internal validation via exhaustive search and grid search. The performance of each model was then tested on the external validation set. RESULTS The Support Vector Machine (SVM) model showed the highest predictive accuracy compared to other methods, with an AUC of 0.82 and an accuracy of 75.7%. Age, sex, disease duration, and preoperative JOA score were identified as the most commonly selected features by both the ML and statistical models. Grid search optimization for hyperparameters successfully enhanced the predictive performance of each ML model, and the SVM model still had the best performance with an AUC of 0.93 and an accuracy of 86.4%. CONCLUSIONS Overall, the study demonstrated that ML classifiers such as SVM can effectively predict surgical outcomes for patients with DCM while identifying associated predictors in a multivariate manner.
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Affiliation(s)
- Jiajun Song
- Department of Orthopedic Surgery, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Jie Li
- Department of Minimally Invasive Spine Surgery, Tianjin Hospital, Tianjin 300211, China
| | - Rui Zhao
- Department of Orthopedic Surgery, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Xu Chu
- Department of Orthopedic Surgery, Honghui Hospital, Xi'an Jiaotong University, Xi'an 710054, China.
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Lang FF, Liu LY, Wang SW. Predictive modeling of perioperative blood transfusion in lumbar posterior interbody fusion using machine learning. Front Physiol 2023; 14:1306453. [PMID: 38187137 PMCID: PMC10767743 DOI: 10.3389/fphys.2023.1306453] [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: 10/04/2023] [Accepted: 11/06/2023] [Indexed: 01/09/2024] Open
Abstract
Background: Accurate estimation of perioperative blood transfusion risk in lumbar posterior interbody fusion is essential to reduce the number, cost, and complications associated with blood transfusions. Machine learning algorithms have the potential to outperform traditional prediction methods in predicting perioperative blood transfusion. This study aimed to construct a machine learning-based perioperative transfusion risk prediction model for lumbar posterior interbody fusion in order to improve the efficacy of surgical decision-making. Methods: We retrospectively collected clinical data on 1905 patients who underwent lumbar posterior interbody fusion surgery at the Second Hospital of Shanxi Medical University between January 2021 and March 2023. All the data was randomly divided into a training set and a validation set, and the "feature_importances" method provided by eXtreme Gradient Boosting (XGBoost) algorithm was applied to select statistically significant features on the training set to establish five machine learning prediction models. The optimal model was identified by utilizing the area under the curve (AUC) and the probability calibration curve on the validation set. Shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME) were employed for interpretable analysis of the optimal model. Results: In the postoperative outcomes of patients, the number of hospital days in the transfusion group was longer than that in the non-transfusion group. Additionally, the transfusion group experienced higher total hospital costs, 90-day readmission rates, and complication rates within 90 days after surgery than the non-transfusion group. A total of 9 features were selected for the models. The XGBoost model performed best with an AUC value of 0.958. The SHAP values showed that intraoperative blood loss, intraoperative fluid infusion, and number of fused segments were the top 3 most important features affecting perioperative blood transfusion in lumbar posterior interbody fusion. The LIME algorithm was used to interpret the individualized prediction. Conclusion: Surgery, ASA class, levels fused, total intraoperative blood loss, operative time, and preoperative Hb are viable predictors of perioperative blood transfusion in lumbar posterior interbody fusion. The XGBoost model has demonstrated superior predictive efficacy compared to the traditional logistic regression model, making it a more effective decision-making tool for perioperative blood transfusion.
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Affiliation(s)
- Fang-Fang Lang
- School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Li-Ying Liu
- School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Shao-Wei Wang
- Department of Orthopedics, The Second Hospital of Shanxi Medical University, Taiyuan, China
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Flanders AE, Geis JR. NextGen Neuroradiology AI. Radiology 2023; 309:e231426. [PMID: 37987667 DOI: 10.1148/radiol.231426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Affiliation(s)
- Adam E Flanders
- From the Department of Radiology, Thomas Jefferson University, 132 S 10th St, Suite 1080B Main Building, Philadelphia, PA 19107 (A.E.F.); and Department of Radiology, National Jewish Health, Denver, Colo (J.R.G.)
| | - J Raymond Geis
- From the Department of Radiology, Thomas Jefferson University, 132 S 10th St, Suite 1080B Main Building, Philadelphia, PA 19107 (A.E.F.); and Department of Radiology, National Jewish Health, Denver, Colo (J.R.G.)
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Cho ST, Shin DE, Kim JW, Yoon S, Ii Lee H, Lee S. Prediction of Progressive Collapse in Osteoporotic Vertebral Fractures Using Conventional Statistics and Machine Learning. Spine (Phila Pa 1976) 2023; 48:1535-1543. [PMID: 37235792 DOI: 10.1097/brs.0000000000004598] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 12/29/2022] [Indexed: 05/28/2023]
Abstract
STUDY DESIGN Retrospective cohort study. OBJECTIVE The objective of this study was to determine prognostic factors for the progression of osteoporotic vertebral fracture (OVF) following conservative treatment. SUMMARY OF BACKGROUND DATA Few studies have evaluated factors associated with progressive collapse (PC) of OVFs. Furthermore, machine learning has not been applied in this context. MATERIALS AND METHODS The study involved the PC and non-PC groups based on a compression rate of 15%. Clinical data, fracture site, OVF shape, Cobb angle, and anterior wedge angle of the fractured vertebra were evaluated. The presence of intravertebral cleft and the type of bone marrow signal change were analyzed using magnetic resonance imaging. Multivariate logistic regression analysis was performed to identify prognostic factors. In machine learning methods, decision tree and random forest models were used. RESULTS There were no significant differences in clinical data between the groups. The proportion of fracture shape ( P <0.001) and bone marrow signal change ( P =0.01) were significantly different between the groups. Moderate wedge shape was frequently observed in the non-PC group (31.7%), whereas the normative shape was most common in the PC group (54.7%). The Cobb angle and anterior wedge angle at diagnosis of OVFs were higher in the non-PC group (13.2±10.9, P =0.001; 14.3±6.6, P <0.001) than in the PC group (10.3±11.8, 10.4±5.5). The bone marrow signal change at the superior aspect of the vertebra was more frequently found in the PC group (42.5%) than in the non-PC group (34.9%). Machine learning revealed that vertebral shape at initial diagnosis was a main predictor of progressive vertebral collapse. CONCLUSION The initial shape of the vertebra and bone edema pattern on magnetic resonance imaging appear to be useful prognostic factors for progressive collapse in osteoporotic vertebral fractures.
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Affiliation(s)
- Sung Tan Cho
- Department of Orthopedic Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Dong-Eun Shin
- Department of Orthopaedic Surgery, CHA University School of Medicine, Seongnam-si, Gyeonggi-do Province, Republic of Korea
| | - Jin-Woo Kim
- Department of Orthopaedic Surgery, Nowon Eulji Medical Center, Eulji University, Seoul, Korea
| | - Siyeoung Yoon
- Department of Orthopaedic Surgery, CHA University School of Medicine, Seongnam-si, Gyeonggi-do Province, Republic of Korea
| | - Hyun Ii Lee
- Department of Orthopedic Surgery, Ilsan Paik Hospital, Inje University, Goyang-si, Gyeonggi-do Province, Republic of Korea
| | - Soonchul Lee
- Department of Orthopaedic Surgery, CHA University School of Medicine, Seongnam-si, Gyeonggi-do Province, Republic of Korea
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Lee Y, Issa TZ, Vaccaro AR. State-of-the-art Applications of Patient-Reported Outcome Measures in Spinal Care. J Am Acad Orthop Surg 2023; 31:e890-e897. [PMID: 36727887 DOI: 10.5435/jaaos-d-22-01009] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 12/13/2022] [Indexed: 02/03/2023] Open
Abstract
Patient-reported outcome measures (PROMs) assign objective measures to patient's subjective experiences of health, pain, disability, function, and quality of life. PROMs can be useful for providers in shared decision making, outcome assessment, and indicating patients for surgery. In this article, we provide an overview of the legacy PROMs used in spinal care, recent advancements in patient-reported outcomes, and future directions in PROMs. Recent advances in patient-reported outcome assessments have included standardization of measurement tools, integration of data collection into workflow, and applications of outcome measures in predictive models and decision-making tools. Continual appraisal of instruments and incorporation into artificial intelligence and machine learning analytics will continue to augment the delivery of high-value spinal care.
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Affiliation(s)
- Yunsoo Lee
- From the Rothman Orthopaedic Institute, Thomas Jefferson University, Philadelphia, PA
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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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Shan ZM, Ren XS, Shi H, Zheng SJ, Zhang C, Zhuang SY, Wu XT, Xie XH. Machine Learning Prediction Model and Risk Factor Analysis of Reoperation in Recurrent Lumbar Disc Herniation Patients After Percutaneous Endoscopic Lumbar Discectomy. Global Spine J 2023:21925682231173353. [PMID: 37161730 DOI: 10.1177/21925682231173353] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/11/2023] Open
Abstract
OBJECTIVE To investigate the risk factors of reoperation after percutaneous endoscopic lumbar discectomy (PELD) due to recurrent lumbar disc herniation (rLDH) and to establish a set of individualized prediction models. METHODS Patients who underwent PELD successfully from January 2016 to February 2022 in a single institution were enrolled in this study. Six methods of machine learning (ML) were used to establish an individualized prediction model for reoperation in rLDH patients after PELD, and these models were compared with logistics regression model to select optimal model. RESULTS A total of 2603 patients were enrolled in this study. 57 patients had repeated operation due to rLDH and 114 patients were selected from the remaining 2546 nonrecurrent patients as matched controls. Multivariate logistic regression analysis showed that disc herniation type (P < .001), Modic changes (type II) (P = .003), sagittal range of motion (sROM) (P = .022), facet orientation (FO) (P = .028) and fat infiltration (FI) (P = .001) were independent risk factors for reoperation in rLDH patients after PELD. The XGBoost AUC was of 90.71%, accuracy was approximately 88.87%, sensitivity was 70.81%, specificity was 97.19%. The traditional logistic regression AUC was 77.4%, accuracy was about 77.73%, sensitivity was 47.15%, specificity was 92.12%. CONCLUSION This study showed that disc herniation type (extrusion, sequestration), Modic changes (type II), a large sROM, a large FO and high FI were independent risk factors for reoperation in LDH patients after PELD. The prediction efficiency of XGBoost model was higher than traditional Logistic regression analysis model.
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Affiliation(s)
- Zheng-Ming Shan
- Department of Spine Surgery, ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Xue-Song Ren
- School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Hang Shi
- Department of Spine Surgery, ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Shi-Jie Zheng
- Department of Spine Surgery, ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Cong Zhang
- Department of Spine Surgery, ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Su-Yang Zhuang
- Department of Spine Surgery, ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Xiao-Tao Wu
- Department of Spine Surgery, ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Xin-Hui Xie
- Department of Spine Surgery, ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
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17
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Hejrati N, Pedro K, Alvi MA, Quddusi A, Fehlings MG. Degenerative cervical myelopathy: Where have we been? Where are we now? Where are we going? Acta Neurochir (Wien) 2023; 165:1105-1119. [PMID: 37004568 DOI: 10.1007/s00701-023-05558-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 03/06/2023] [Indexed: 04/04/2023]
Abstract
Degenerative cervical myelopathy (DCM), a recently coined term, encompasses a group of age-related and genetically associated pathologies that affect the cervical spine, including cervical spondylotic myelopathy and ossification of the posterior longitudinal ligament (OPLL). Given the significant contribution of DCM to global disease and disability, there are worldwide efforts to promote research and innovation in this area. An AO Spine effort termed 'RECODE-DCM' was initiated to create an international multistakeholder consensus group, involving patients, caregivers, physicians and researchers, to focus on launching actionable discourse on DCM. In order to improve the management, treatment and results for DCM, the RECODE-DCM consensus group recently identified ten priority areas for translational research. The current article summarizes recent advancements in the field of DCM. We first discuss the comprehensive definition recently refined by the RECODE-DCM group, including steps taken to arrive at this definition and the supporting rationale. We then provide an overview of the recent advancements in our understanding of the pathophysiology of DCM and modalities to clinically assess and diagnose DCM. A focus will be set on advanced imaging techniques that may offer the opportunity to improve characterization and diagnosis of DCM. A summary of treatment modalities, including surgical and nonoperative options, is then provided along with future neuroprotective and neuroregenerative strategies. This review concludes with final remarks pertaining to the genetics involved in DCM and the opportunity to leverage this knowledge toward a personalized medicine approach.
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Affiliation(s)
- Nader Hejrati
- Division of Genetics and Development, Krembil Research Institute, Toronto Western Hospital, University Health Network, 399 Bathurst Street, Suite 4WW-449, Toronto, ON, M5T 2S8, Canada
- Division of Neurosurgery and Spine Program, Department of Surgery, University of Toronto, Toronto, ON, Canada
| | - Karlo Pedro
- Division of Neurosurgery and Spine Program, Department of Surgery, University of Toronto, Toronto, ON, Canada
| | - Mohammed Ali Alvi
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Ayesha Quddusi
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Michael G Fehlings
- Division of Genetics and Development, Krembil Research Institute, Toronto Western Hospital, University Health Network, 399 Bathurst Street, Suite 4WW-449, Toronto, ON, M5T 2S8, Canada.
- Division of Neurosurgery and Spine Program, Department of Surgery, University of Toronto, Toronto, ON, Canada.
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada.
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Katsos K, Johnson SE, Ibrahim S, Bydon M. Current Applications of Machine Learning for Spinal Cord Tumors. Life (Basel) 2023; 13:life13020520. [PMID: 36836877 PMCID: PMC9962966 DOI: 10.3390/life13020520] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 02/01/2023] [Accepted: 02/06/2023] [Indexed: 02/16/2023] Open
Abstract
Spinal cord tumors constitute a diverse group of rare neoplasms associated with significant mortality and morbidity that pose unique clinical and surgical challenges. Diagnostic accuracy and outcome prediction are critical for informed decision making and can promote personalized medicine and facilitate optimal patient management. Machine learning has the ability to analyze and combine vast amounts of data, allowing the identification of patterns and the establishment of clinical associations, which can ultimately enhance patient care. Although artificial intelligence techniques have been explored in other areas of spine surgery, such as spinal deformity surgery, precise machine learning models for spinal tumors are lagging behind. Current applications of machine learning in spinal cord tumors include algorithms that improve diagnostic precision by predicting genetic, molecular, and histopathological profiles. Furthermore, artificial intelligence-based systems can assist surgeons with preoperative planning and surgical resection, potentially reducing the risk of recurrence and consequently improving clinical outcomes. Machine learning algorithms promote personalized medicine by enabling prognostication and risk stratification based on accurate predictions of treatment response, survival, and postoperative complications. Despite their promising potential, machine learning models require extensive validation processes and quality assessments to ensure safe and effective translation to clinical practice.
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Affiliation(s)
- Konstantinos Katsos
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN 55902, USA
- Mayo Clinic Neuro-Informatics Laboratory, Department of Neurologic Surgery, Mayo Clinic, Rochester, MN 55902, USA
| | - Sarah E. Johnson
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN 55902, USA
- Mayo Clinic Neuro-Informatics Laboratory, Department of Neurologic Surgery, Mayo Clinic, Rochester, MN 55902, USA
| | - Sufyan Ibrahim
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN 55902, USA
- Mayo Clinic Neuro-Informatics Laboratory, Department of Neurologic Surgery, Mayo Clinic, Rochester, MN 55902, USA
| | - Mohamad Bydon
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN 55902, USA
- Mayo Clinic Neuro-Informatics Laboratory, Department of Neurologic Surgery, Mayo Clinic, Rochester, MN 55902, USA
- Correspondence:
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Lei M, Han Z, Wang S, Han T, Fang S, Lin F, Huang T. A machine learning-based prediction model for in-hospital mortality among critically ill patients with hip fracture: An internal and external validated study. Injury 2023; 54:636-644. [PMID: 36414503 DOI: 10.1016/j.injury.2022.11.031] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 10/17/2022] [Accepted: 11/08/2022] [Indexed: 11/13/2022]
Abstract
INTRODUCTION Few studies have investigated the in-hospital mortality among critically ill patients with hip fracture. This study aimed to develop and validate a model to estimate the risk of in-hospital mortality among critically ill patients with hip fracture. METHODS For this study, data from the Medical Information Mart for Intensive Care III (MIMIC-III) Database and electronic Intensive Care Unit (eICU) Collaborative Research Database were evaluated. Enrolled patients (n=391) in the MIMIC-III database were divided into a training (2/3, n=260) and a validation (1/3, n=131) group at random. Using machine learning algorithms such as random forest, gradient boosting machine, decision tree, and eXGBoosting machine approach, the training group was utilized to train and optimize models. The validation group was used to internally validate models and the optimal model could be obtained in terms of discrimination (area under the receiver operating characteristic curve, AUROC) and calibration (calibration curve). External validation was done in the eICU Collaborative Research Database (n=165). To encourage practical use of the model, a web-based calculator was developed according to the eXGBoosting machine approach. RESULTS The in-hospital death rate was 13.81% (54/391) in the MIMIC-III database and 10.91% (18/165) in the eICU Collaborative Research Database. Age, gender, anemia, mechanical ventilation, cardiac arrest, and chronic airway obstruction were the six model parameters which were identified using the Least Absolute Shrinkage and Selection Operator (LASSO) method combined with 10-fold cross-validation. The model established using the eXGBoosting machine approach showed the highest area under curve (AUC) value (0.797, 95% CI: 0.696-0.898) and the best calibrating ability, with a calibration slope of 0.999 and intercept of -0.019. External validation also revealed favorable discrimination (AUC: 0.715, 95% CI: 0.566-0.864; accuracy: 0.788) and calibration (calibration slope: 0.805) in the eICU Collaborative Research Database. The web-based calculator could be available at https://doctorwangsj-webcalculator-main-yw69yd.streamlitapp.com/. CONCLUSION The model has the potential to be a pragmatic risk prediction tool that is able to identify hip fracture patients who are at a high risk of in-hospital mortality in ICU settings, guide patient risk counseling, and simplify prognosis bench-marking by controlling for baseline risk.
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Affiliation(s)
- Mingxing Lei
- Department of Orthopedic Surgery, Hainan Hospital of Chinese PLA General Hospital, 80 Jianglin Road, Sanya 572022, China; Chinese PLA Medical School, 28 Fuxing Road, Beijing 100853, China; Department of Orthopedic Surgery, National Clinical Research Center for Orthopedics, Sports Medicine & Rehabilitation, Chinese PLA General Hospital, 28 Fuxing Road, Beijing 100853, China
| | - Zhencan Han
- Xiangya School of Medicine, Central South University, 172 Tongzipo Road, Changsha 410013, China
| | - Shengjie Wang
- Department of Orthopaedic Surgery, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University, 600 Yishan Road, Shanghai, 200233, China
| | - Tao Han
- Department of Orthopedic Surgery, Hainan Hospital of Chinese PLA General Hospital, 80 Jianglin Road, Sanya 572022, China
| | - Shenyun Fang
- Department of Orthopedic Surgery, the First Affiliated Hospital of Huzhou University, 158 Guangchang Back Road, Huzhou 313000, China; Department of Orthopedics Surgery, the First People Hospital of Huzhou, 158 Guangchang Back Road, Huzhou 313000, China.
| | - Feng Lin
- Department of Orthopedic Surgery, Hainan Hospital of Chinese PLA General Hospital, 80 Jianglin Road, Sanya 572022, China; Department of Orthopedic Surgery, National Clinical Research Center for Orthopedics, Sports Medicine & Rehabilitation, Chinese PLA General Hospital, 28 Fuxing Road, Beijing 100853, China.
| | - Tianlong Huang
- Department of Orthopedic Surgery, the Second Xiangya Hospital of Central South University, 139 Renmin Middle Road, Changsha 410011, China.
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Shah AA, Karhade AV, Groot OQ, Olson TE, Schoenfeld AJ, Bono CM, Harris MB, Ferrone ML, Nelson SB, Park DY, Schwab JH. External validation of a predictive algorithm for in-hospital and ninety-day mortality after spinal epidural abscess. Spine J 2023; 23:760-765. [PMID: 36736740 DOI: 10.1016/j.spinee.2023.01.013] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 01/05/2023] [Accepted: 01/21/2023] [Indexed: 02/05/2023]
Abstract
BACKGROUND CONTEXT Mortality in patients with spinal epidural abscess (SEA) remains high. Accurate prediction of patient-specific prognosis in SEA can improve patient counseling as well as guide management decisions. There are no externally validated studies predicting short-term mortality in patients with SEA. PURPOSE The purpose of this study was to externally validate the Skeletal Oncology Research Group (SORG) stochastic gradient boosting algorithm for prediction of in-hospital and 90-day postdischarge mortality in SEA. STUDY DESIGN/SETTING Retrospective, case-control study at a tertiary care academic medical center from 2003 to 2021. PATIENT SAMPLE Adult patients admitted for radiologically confirmed diagnosis of SEA who did not initiate treatment at an outside institution. OUTCOME MEASURES In-hospital and 90-day postdischarge mortality. METHODS We tested the SORG stochastic gradient boosting algorithm on an independent validation cohort. We assessed its performance with discrimination, calibration, decision curve analysis, and overall performance. RESULTS A total of 212 patients met inclusion criteria, with a short-term mortality rate of 10.4%. The area under the receiver operating characteristic curve (AUROC) of the SORG algorithm when tested on the full validation cohort was 0.82, the calibration intercept was -0.08, the calibration slope was 0.96, and the Brier score was 0.09. CONCLUSIONS With a contemporaneous and geographically distinct independent cohort, we report successful external validation of a machine learning algorithm for prediction of in-hospital and 90-day postdischarge mortality in SEA.
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Affiliation(s)
- Akash A Shah
- Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, 10833 Le Conte Avenue, Los Angeles, CA 90095, USA.
| | - Aditya V Karhade
- Department of Orthopaedic Surgery, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
| | - Olivier Q Groot
- Department of Orthopaedic Surgery, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
| | - Thomas E Olson
- Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, 10833 Le Conte Avenue, Los Angeles, CA 90095, USA
| | - Andrew J Schoenfeld
- Department of Orthopaedic Surgery, Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02115, USA
| | - Christopher M Bono
- Department of Orthopaedic Surgery, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
| | - Mitchel B Harris
- Department of Orthopaedic Surgery, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
| | - Marco L Ferrone
- Department of Orthopaedic Surgery, Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02115, USA
| | - Sandra B Nelson
- Division of Infectious Diseases, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
| | - Don Y Park
- Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, 10833 Le Conte Avenue, Los Angeles, CA 90095, USA
| | - Joseph H Schwab
- Department of Orthopaedic Surgery, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
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Detection of cervical spondylotic myelopathy based on gait analysis and deterministic learning. Artif Intell Rev 2023. [DOI: 10.1007/s10462-023-10404-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
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22
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de Jonge M, Wubben N, van Kaam CR, Frenzel T, Hoedemaekers CWE, Ambrogioni L, van der Hoeven JG, van den Boogaard M, Zegers M. Optimizing an existing prediction model for quality of life one-year post-intensive care unit: An exploratory analysis. Acta Anaesthesiol Scand 2022; 66:1228-1236. [PMID: 36054515 DOI: 10.1111/aas.14138] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 07/12/2022] [Accepted: 07/31/2022] [Indexed: 01/07/2023]
Abstract
BACKGROUND This study aimed to improve the PREPARE model, an existing linear regression prediction model for long-term quality of life (QoL) of intensive care unit (ICU) survivors by incorporating additional ICU data from patients' electronic health record (EHR) and bedside monitors. METHODS The 1308 adult ICU patients, aged ≥16, admitted between July 2016 and January 2019 were included. Several regression-based machine learning models were fitted on a combination of patient-reported data and expert-selected EHR variables and bedside monitor data to predict change in QoL 1 year after ICU admission. Predictive performance was compared to a five-feature linear regression prediction model using only 24-hour data (R2 = 0.54, mean square error (MSE) = 0.031, mean absolute error (MAE) = 0.128). RESULTS The 67.9% of the included ICU survivors was male and the median age was 65.0 [IQR: 57.0-71.0]. Median length of stay (LOS) was 1 day [IQR 1.0-2.0]. The incorporation of the additional data pertaining to the entire ICU stay did not improve the predictive performance of the original linear regression model. The best performing machine learning model used seven features (R2 = 0.52, MSE = 0.032, MAE = 0.125). Pre-ICU QoL, the presence of a cerebro vascular accident (CVA) upon admission and the highest temperature measured during the ICU stay were the most important contributors to predictive performance. Pre-ICU QoL's contribution to predictive performance far exceeded that of the other predictors. CONCLUSION Pre-ICU QoL was by far the most important predictor for change in QoL 1 year after ICU admission. The incorporation of the numerous additional features pertaining to the entire ICU stay did not improve predictive performance although the patients' LOS was relatively short.
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Affiliation(s)
- Manon de Jonge
- Department Intensive Care Medicine, Radboud University Medical Center, Radboud Institute for Health Sciences, Nijmegen, Netherlands
| | - Nina Wubben
- Department Intensive Care Medicine, Radboud University Medical Center, Radboud Institute for Health Sciences, Nijmegen, Netherlands
| | - Christiaan R van Kaam
- Department Intensive Care Medicine, Radboud University Medical Center, Radboud Institute for Health Sciences, Nijmegen, Netherlands
| | - Tim Frenzel
- Department Intensive Care Medicine, Radboud University Medical Center, Radboud Institute for Health Sciences, Nijmegen, Netherlands
| | - Cornelia W E Hoedemaekers
- Department Intensive Care Medicine, Radboud University Medical Center, Radboud Institute for Health Sciences, Nijmegen, Netherlands
| | - Luca Ambrogioni
- Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, Netherlands
| | - Johannes G van der Hoeven
- Department Intensive Care Medicine, Radboud University Medical Center, Radboud Institute for Health Sciences, Nijmegen, Netherlands
| | - Mark van den Boogaard
- Department Intensive Care Medicine, Radboud University Medical Center, Radboud Institute for Health Sciences, Nijmegen, Netherlands
| | - Marieke Zegers
- Department Intensive Care Medicine, Radboud University Medical Center, Radboud Institute for Health Sciences, Nijmegen, Netherlands
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Jiang D, Sun G, Jia R, Zhang Y, Wang X, Xu Z. Comparing Bone Graft Techniques for Interbody Fusion through a Posterior Approach for Treating Mid-Thoracic Spinal Tuberculosis: A Retrospective Analysis. Orthop Surg 2022; 15:53-61. [PMID: 36222206 PMCID: PMC9837254 DOI: 10.1111/os.13565] [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: 07/21/2022] [Revised: 09/07/2022] [Accepted: 09/19/2022] [Indexed: 01/21/2023] Open
Abstract
OBJECTIVE Mid-thoracic spinal tuberculosis is prone to kyphotic deformities and neurologic impairment. Posterior approach can effectively restore the spinal stability by reconstructing the anterior and middle spinal columns. Titanium mesh cages (TMC), allogeneic bone (ALB), and autogenous bone (AUB) are three main bone graft struts. We aimed to compare the therapeutic efficacy of three bone graft struts, for anterior and middle column reconstruction through a posterior approach in cases of mid-thoracic spinal tuberculosis. METHODS Hundred and thirty seven patients with thoracic spinal tuberculosis who had undergone a posterior approach from June 2010 to December 2018 were enrolled. Of them, 46 patients were treated using a titanium mesh cage (TMC group), 44 with allogenic bone grafts (ALB group), and 47 using autogenous bone grafts (AUB group). The following were analyzed to evaluate clinical efficacy: visual analogue scale (VAS) values, erythrocyte sedimentation rate (ESR), C-reactive protein (CRP) levels, kyphotic Cobb's angle, operation duration, intraoperative blood loss, improvement in American Spinal Injury Association (ASIA) grade and in the mental component summary (MCS) and physical component summary (PCS) of Short Form-36 (SF-36), duration of bone graft fusion. The data of the three groups were compared by way of variance analysis, followed by the LSD⁃t test to compare each group. A repeated measures ANOVA was used to analyze the dates of pre-, postoperative and final follow-up. RESULTS The follow-up duration was at least 3 years. All patients achieved a complete cure for spinal TB. Neurological performance and quality of life were remarkably improved at the final follow-up. The intraoperative blood loss, operation time and VAS values 1 day postoperatively for TMC group and ALB group were significantly lower than those in AUB group (P < 0.05). The duration of bone graft fusion in ALB group (18.1 ± 3.7 months) was longer than that in TMC group and AUB group (9.5 ± 2.8 and 9.2 ± 1.9 months) (P < 0.05). No significant intergroup differences were observed in terms of age or preoperative, 3-months postoperative, and final follow-up indices of ESR and CRP among the three groups (P > 0.05). At the final follow-up, the correction loss was mild (2.1 ± 0.9, 2.2 ± 1.0, 2.1 ± 0.8) and Cobb's angles of the three groups were 20.1 ± 2.9, 20.5 ± 3.2, 20.9 ± 3.4, respectively, which were remarkably rectified in comparison with the preoperative measurements (P < 0.05). CONCLUSIONS In terms of postoperative recovery and successful fusion rate of bone graft, it seems that posterior instrumentation, debridement, and interbody fusion with titanium mesh cages are more effective and appropriate surgical methods for mid-thoracic spinal tuberculosis.
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Affiliation(s)
- Dingyu Jiang
- Department of Spine Surgery and OrthopaedicsXiangya Hospital of Central South UniversityChangshaChina,Hunan Engineering Laboratory of Advanced Artificial Osteo‐Materials, Xiangya Hospital Central South UniversityChangshaChina
| | - Guannan Sun
- Department of Spine Surgery and OrthopaedicsXiangya Hospital of Central South UniversityChangshaChina,Hunan Engineering Laboratory of Advanced Artificial Osteo‐Materials, Xiangya Hospital Central South UniversityChangshaChina
| | - Runze Jia
- Department of Spine Surgery and OrthopaedicsXiangya Hospital of Central South UniversityChangshaChina,Hunan Engineering Laboratory of Advanced Artificial Osteo‐Materials, Xiangya Hospital Central South UniversityChangshaChina
| | - Yilu Zhang
- Department of Spine Surgery and OrthopaedicsXiangya Hospital of Central South UniversityChangshaChina,Hunan Engineering Laboratory of Advanced Artificial Osteo‐Materials, Xiangya Hospital Central South UniversityChangshaChina
| | - Xiyang Wang
- Department of Spine Surgery and OrthopaedicsXiangya Hospital of Central South UniversityChangshaChina,Hunan Engineering Laboratory of Advanced Artificial Osteo‐Materials, Xiangya Hospital Central South UniversityChangshaChina
| | - Zhenchao Xu
- Department of Spine Surgery and OrthopaedicsXiangya Hospital of Central South UniversityChangshaChina,Hunan Engineering Laboratory of Advanced Artificial Osteo‐Materials, Xiangya Hospital Central South UniversityChangshaChina
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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: 6] [Impact Index Per Article: 3.0] [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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Kheram N, Pfender N, Boraschi A, Farshad M, Kurtcuoglu V, Curt A, Schubert M, Zipser CM. Cerebrospinal fluid pressure dynamics reveal signs of effective spinal canal narrowing in ambiguous spine conditions. Front Neurol 2022; 13:951018. [PMID: 36016547 PMCID: PMC9397118 DOI: 10.3389/fneur.2022.951018] [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: 05/23/2022] [Accepted: 07/15/2022] [Indexed: 12/03/2022] Open
Abstract
Spinal canal narrowing with consecutive spinal cord compression is considered a key mechanism in degenerative cervical myelopathy (DCM). DCM is a common spine condition associated with progressive neurological disability, and timely decompressive surgery is recommended. However, the clinical and radiological diagnostic workup is often ambiguous, challenging confident proactive treatment recommendations. Cerebrospinal fluid pressure dynamics (CSFP) are altered by spinal canal narrowing. Therefore, we aim to explore the potential value of bedside CSFP assessments for qualitative and quantitative assessment of spinal canal narrowing in DCM. In this prospective case series, seven patients with DCM underwent bedside lumbar puncture with measurement of CSFP dynamics and routine CSF analysis (NCT02170155). The patients were enrolled when standard diagnostic algorithms did not permit a clear treatment decision. Measurements include baseline CSFP, cardiac-driven CSFP peak-to-trough amplitude (CSFPp), and the Queckenstedt's test (firm pressure on jugular veins) in neutral and reclined head position. From the Queckenstedt's test, proxies for craniospinal elastance (i.e., relative pulse pressure coefficient; RPPC-Q) were calculated analogously to infusion testing. CSFP metrics were deemed suspicious of canal narrowing when numbers were lower than the minimum value from a previously tested elderly spine-healthy cohort (N = 14). Mean age was 56 ± 13 years (range, 38–75; 2F); symptom severity was mostly mild to moderate (mean mJOA, 13.5 ± 2.6; range, 9–17). All the patients showed some extent of cervical stenosis in the MRI of unclear significance (5/7 following decompressive cervical spine surgery with an adjacent level or residual stenosis). Baseline CSFP was normal except for one patient (range, 4.7–17.4 mmHg). Normal values were found for CSFPp (0.4–1.3 mmHg) and the Queckenstedt's test in normal head positioning (9.-25.3 mmHg). During reclination, the Queckenstedt's test significantly decreased in one, and CSFPp in another case (>50% compared to normal position). RPPC-Q (0.07–0.19) aligned with lower values from spine-healthy (0.10–0.44). Routine CSF examinations showed mild total protein elevation (mean, 522 ± 108 mg/ml) without further evidence for the disturbed blood brain barrier. Intrathecal CSFP measurements allow discerning disturbed from normal CSFP dynamics in this population. Prospective longitudinal studies should further evaluate the diagnostic utility of CSFP assessments in DCM.
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Affiliation(s)
- Najmeh Kheram
- Spinal Cord Injury Center, Balgrist University Hospital, Zurich, Switzerland
- University Spine Center, Balgrist University Hospital, Zurich, Switzerland
- Institute of Physiology, University of Zurich, Zurich, Switzerland
| | - Nikolai Pfender
- Spinal Cord Injury Center, Balgrist University Hospital, Zurich, Switzerland
- University Spine Center, Balgrist University Hospital, Zurich, Switzerland
| | - Andrea Boraschi
- Institute of Physiology, University of Zurich, Zurich, Switzerland
| | - Mazda Farshad
- University Spine Center, Balgrist University Hospital, Zurich, Switzerland
| | | | - Armin Curt
- Spinal Cord Injury Center, Balgrist University Hospital, Zurich, Switzerland
- University Spine Center, Balgrist University Hospital, Zurich, Switzerland
| | - Martin Schubert
- Spinal Cord Injury Center, Balgrist University Hospital, Zurich, Switzerland
- University Spine Center, Balgrist University Hospital, Zurich, Switzerland
| | - Carl M. Zipser
- Spinal Cord Injury Center, Balgrist University Hospital, Zurich, Switzerland
- University Spine Center, Balgrist University Hospital, Zurich, Switzerland
- *Correspondence: Carl M. Zipser
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Saravi B, Hassel F, Ülkümen S, Zink A, Shavlokhova V, Couillard-Despres S, Boeker M, Obid P, Lang GM. Artificial Intelligence-Driven Prediction Modeling and Decision Making in Spine Surgery Using Hybrid Machine Learning Models. J Pers Med 2022; 12:jpm12040509. [PMID: 35455625 PMCID: PMC9029065 DOI: 10.3390/jpm12040509] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Revised: 03/18/2022] [Accepted: 03/19/2022] [Indexed: 12/22/2022] Open
Abstract
Healthcare systems worldwide generate vast amounts of data from many different sources. Although of high complexity for a human being, it is essential to determine the patterns and minor variations in the genomic, radiological, laboratory, or clinical data that reliably differentiate phenotypes or allow high predictive accuracy in health-related tasks. Convolutional neural networks (CNN) are increasingly applied to image data for various tasks. Its use for non-imaging data becomes feasible through different modern machine learning techniques, converting non-imaging data into images before inputting them into the CNN model. Considering also that healthcare providers do not solely use one data modality for their decisions, this approach opens the door for multi-input/mixed data models which use a combination of patient information, such as genomic, radiological, and clinical data, to train a hybrid deep learning model. Thus, this reflects the main characteristic of artificial intelligence: simulating natural human behavior. The present review focuses on key advances in machine and deep learning, allowing for multi-perspective pattern recognition across the entire information set of patients in spine surgery. This is the first review of artificial intelligence focusing on hybrid models for deep learning applications in spine surgery, to the best of our knowledge. This is especially interesting as future tools are unlikely to use solely one data modality. The techniques discussed could become important in establishing a new approach to decision-making in spine surgery based on three fundamental pillars: (1) patient-specific, (2) artificial intelligence-driven, (3) integrating multimodal data. The findings reveal promising research that already took place to develop multi-input mixed-data hybrid decision-supporting models. Their implementation in spine surgery may hence be only a matter of time.
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Affiliation(s)
- Babak Saravi
- Department of Orthopedics and Trauma Surgery, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79108 Freiburg, Germany; (S.Ü.); (P.O.); (G.M.L.)
- Department of Spine Surgery, Loretto Hospital, 79100 Freiburg, Germany; (F.H.); (A.Z.)
- Institute of Experimental Neuroregeneration, Spinal Cord Injury and Tissue Regeneration Center Salzburg (SCI-TReCS), Paracelsus Medical University, 5020 Salzburg, Austria;
- Correspondence:
| | - Frank Hassel
- Department of Spine Surgery, Loretto Hospital, 79100 Freiburg, Germany; (F.H.); (A.Z.)
| | - Sara Ülkümen
- Department of Orthopedics and Trauma Surgery, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79108 Freiburg, Germany; (S.Ü.); (P.O.); (G.M.L.)
- Department of Spine Surgery, Loretto Hospital, 79100 Freiburg, Germany; (F.H.); (A.Z.)
| | - Alisia Zink
- Department of Spine Surgery, Loretto Hospital, 79100 Freiburg, Germany; (F.H.); (A.Z.)
| | - Veronika Shavlokhova
- Department of Oral and Maxillofacial Surgery, University Hospital Heidelberg, 69120 Heidelberg, Germany;
| | - Sebastien Couillard-Despres
- Institute of Experimental Neuroregeneration, Spinal Cord Injury and Tissue Regeneration Center Salzburg (SCI-TReCS), Paracelsus Medical University, 5020 Salzburg, Austria;
- Austrian Cluster for Tissue Regeneration, 1200 Vienna, Austria
| | - Martin Boeker
- Intelligence and Informatics in Medicine, Medical Center Rechts der Isar, School of Medicine, Technical University of Munich, 81675 Munich, Germany;
| | - Peter Obid
- Department of Orthopedics and Trauma Surgery, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79108 Freiburg, Germany; (S.Ü.); (P.O.); (G.M.L.)
| | - Gernot Michael Lang
- Department of Orthopedics and Trauma Surgery, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79108 Freiburg, Germany; (S.Ü.); (P.O.); (G.M.L.)
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The Influence of Baseline Clinical Status and Surgical Strategy on Early Good to Excellent Result in Spinal Lumbar Arthrodesis: A Machine Learning Approach. J Pers Med 2021; 11:jpm11121377. [PMID: 34945849 PMCID: PMC8705358 DOI: 10.3390/jpm11121377] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Revised: 12/10/2021] [Accepted: 12/13/2021] [Indexed: 12/23/2022] Open
Abstract
The study aims to create a preoperative model from baseline demographic and health-related quality of life scores (HRQOL) to predict a good to excellent early clinical outcome using a machine learning (ML) approach. A single spine surgery center retrospective review of prospectively collected data from January 2016 to December 2020 from the institutional registry (SpineREG) was performed. The inclusion criteria were age ≥ 18 years, both sexes, lumbar arthrodesis procedure, a complete follow up assessment (Oswestry Disability Index-ODI, SF-36 and COMI back) and the capability to read and understand the Italian language. A delta of improvement of the ODI higher than 12.7/100 was considered a "good early outcome". A combined target model of ODI (Δ ≥ 12.7/100), SF-36 PCS (Δ ≥ 6/100) and COMI back (Δ ≥ 2.2/10) was considered an "excellent early outcome". The performance of the ML models was evaluated in terms of sensitivity, i.e., True Positive Rate (TPR), specificity, i.e., True Negative Rate (TNR), accuracy and area under the receiver operating characteristic curve (AUC ROC). A total of 1243 patients were included in this study. The model for predicting ODI at 6 months' follow up showed a good balance between sensitivity (74.3%) and specificity (79.4%), while providing a good accuracy (75.8%) with ROC AUC = 0.842. The combined target model showed a sensitivity of 74.2% and specificity of 71.8%, with an accuracy of 72.8%, and an ROC AUC = 0.808. The results of our study suggest that a machine learning approach showed high performance in predicting early good to excellent clinical results.
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Machine Learning Approach in Predicting Clinically Significant Improvements After Surgery in Patients with Cervical Ossification of the Posterior Longitudinal Ligament. Spine (Phila Pa 1976) 2021; 46:1683-1689. [PMID: 34027925 DOI: 10.1097/brs.0000000000004125] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
STUDY DESIGN A retrospective analysis of prospectively collected data. OBJECTIVE This study aimed to create a prognostic model for surgical outcomes in patients with cervical ossification of the posterior longitudinal ligament (OPLL) using machine learning (ML). SUMMARY OF BACKGROUND DATA Determining surgical outcomes helps surgeons provide prognostic information to patients and manage their expectations. ML is a mathematical model that finds patterns from a large sample of data and makes predictions outperforming traditional statistical methods. METHODS Of 478 patients, 397 and 370 patients had complete follow-up information at 1 and 2 years, respectively, and were included in the analysis. A minimal clinically important difference (MCID) was defined as an acquired Japanese Orthopedic Association (JOA) score of ≥2.5 points, after which a ML model that predicts whether MCID can be achieved 1 and 2 years after surgery was created. Patient background, clinical symptoms, and imaging findings were used as variables for analysis. The ML model was created using LightGBM, XGBoost, random forest, and logistic regression, after which the accuracy and area under the receiver-operating characteristic curve (AUC) were calculated. RESULTS The mean JOA score was 10.3 preoperatively, 13.4 at 1 year after surgery, and 13.5 at 2 years after surgery. XGBoost showed the highest AUC (0.72) and high accuracy (67.8) for predicting MCID at 1 year, whereas random forest had the highest AUC (0.75) and accuracy (69.6) for predicting MCID at 2 years. Among the included features, total preoperative JOA score, duration of symptoms, body weight, sensory function of the lower extremity sub-score of the JOA, and age were identified as having the most significance in most of ML models. CONCLUSION Constructing a prognostic ML model for surgical outcomes in patients with OPLL is feasible, suggesting the potential application of ML for predictive models of spinal surgery.Level of Evidence: 4.
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Lee M, Noh Y, Youm C, Kim S, Park H, Noh B, Kim B, Choi H, Yoon H. Estimating Health-Related Quality of Life Based on Demographic Characteristics, Questionnaires, Gait Ability, and Physical Fitness in Korean Elderly Adults. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph182211816. [PMID: 34831575 PMCID: PMC8624167 DOI: 10.3390/ijerph182211816] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/04/2021] [Revised: 10/31/2021] [Accepted: 11/10/2021] [Indexed: 01/14/2023]
Abstract
The elderly population in South Korea accounted for 15.5% of the total population in 2019. Thus, it is important to study the various elements governing the process of healthy aging. Therefore, this study investigated multiple prediction models to determine the health-related quality of life (HRQoL) in elderly adults based on the demographics, questionnaires, gait ability, and physical fitness. We performed eight physical fitness tests on 775 participants wearing shoe-type inertial measurement units and completing walking tasks at slower, preferred, and faster speeds. The HRQoL for physical and mental components was evaluated using a 36-item, short-form health survey. The prediction models based on multiple linear regression with feature importance were analyzed considering the best physical and mental components. We used 11 variables and 5 variables to form the best subset of features underlying the physical and mental components, respectively. We laid particular emphasis on evaluating the functional endurance, muscle strength, stress level, and falling risk. Furthermore, stress, insomnia severity, number of diseases, lower body strength, and fear of falling were taken into consideration in addition to mental-health-related variables. Thus, the study findings provide reliable and objective results to improve the understanding of HRQoL in elderly adults.
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Affiliation(s)
- Myeounggon Lee
- Center for Neuromotor and Biomechanics Research, Department of Health and Human Performance, University of Houston, Houston, TX 77004, USA;
| | - Yoonjae Noh
- Department of Management Information Systems, Dong-A University, Busan 49236, Korea; (Y.N.); (H.Y.)
| | - Changhong Youm
- Department of Health Sciences, The Graduate School of Dong-A University, Busan 49315, Korea; (H.P.); (B.K.); (H.C.)
- Correspondence: (C.Y.); (S.K.); Tel.: +82-51-200-7830 (C.Y.); +82-05-200-7484 (S.K.); Fax: +82-51-200-7505 (C.Y.)
| | - Sangjin Kim
- Department of Management Information Systems, Dong-A University, Busan 49236, Korea; (Y.N.); (H.Y.)
- Correspondence: (C.Y.); (S.K.); Tel.: +82-51-200-7830 (C.Y.); +82-05-200-7484 (S.K.); Fax: +82-51-200-7505 (C.Y.)
| | - Hwayoung Park
- Department of Health Sciences, The Graduate School of Dong-A University, Busan 49315, Korea; (H.P.); (B.K.); (H.C.)
| | - Byungjoo Noh
- Department of Kinesiology, Jeju National University, Jeju 63243, Korea;
| | - Bohyun Kim
- Department of Health Sciences, The Graduate School of Dong-A University, Busan 49315, Korea; (H.P.); (B.K.); (H.C.)
| | - Hyejin Choi
- Department of Health Sciences, The Graduate School of Dong-A University, Busan 49315, Korea; (H.P.); (B.K.); (H.C.)
| | - Hyemin Yoon
- Department of Management Information Systems, Dong-A University, Busan 49236, Korea; (Y.N.); (H.Y.)
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Affiliation(s)
- Brook I Martin
- Departments of Orthopaedics and Population Health Sciences, University of Utah, Salt Lake City, UT, USA.
| | - Christopher M Bono
- Department of Orthopedic Surgery, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, Boston, MA, USA; The Spine Journal, North American Spine Society, 7075 Veterans Boulevard, Burr Ridge, IL, USA
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Khan O, Badhiwala JH, Grasso G, Fehlings MG. Use of Machine Learning and Artificial Intelligence to Drive Personalized Medicine Approaches for Spine Care. World Neurosurg 2020; 140:512-518. [PMID: 32797983 DOI: 10.1016/j.wneu.2020.04.022] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Accepted: 04/04/2020] [Indexed: 01/14/2023]
Abstract
Personalized medicine is a new paradigm of healthcare in which interventions are based on individual patient characteristics rather than on "one-size-fits-all" guidelines. As epidemiological datasets continue to burgeon in size and complexity, powerful methods such as statistical machine learning and artificial intelligence (AI) become necessary to interpret and develop prognostic models from underlying data. Through such analysis, machine learning can be used to facilitate personalized medicine via its precise predictions. Additionally, other AI tools, such as natural language processing and computer vision, can play an instrumental part in personalizing the care provided to patients with spine disease. In the present report, we discuss the current strides made in incorporating AI into research on spine disease, especially traumatic spinal cord injury and degenerative spine disease. We describe studies using AI to build accurate prognostic models, extract important information from medical reports via natural language processing, and evaluate functional status in a granular manner using computer vision. Through a case illustration, we have demonstrated how these breakthroughs can facilitate an increased role for more personalized medicine and, thus, change the landscape of spine care.
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Affiliation(s)
- Omar Khan
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, Ontario, Canada
| | - Jetan H Badhiwala
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, Ontario, Canada; Division of Neurosurgery, Toronto Western Hospital, University Health Network, Toronto, Ontario, Canada
| | - Giovanni Grasso
- Neurosurgical Unit, Department of Biomedicine, Neurosciences and Advanced Diagnostics, University of Palermo, Palermo, Italy
| | - Michael G Fehlings
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, Ontario, Canada; Division of Neurosurgery, Toronto Western Hospital, University Health Network, Toronto, Ontario, Canada.
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Kunze KN, Polce EM, Sadauskas AJ, Levine BR. Development of Machine Learning Algorithms to Predict Patient Dissatisfaction After Primary Total Knee Arthroplasty. J Arthroplasty 2020; 35:3117-3122. [PMID: 32564970 DOI: 10.1016/j.arth.2020.05.061] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Revised: 05/21/2020] [Accepted: 05/26/2020] [Indexed: 02/02/2023] Open
Abstract
BACKGROUND Postoperative dissatisfaction after primary total knee arthroplasty (TKA) that requires additional care or readmission may impose a significant financial burden to healthcare systems. The purpose of the current study is to develop machine learning algorithms to predict dissatisfaction after TKA. METHODS A retrospective review of consecutive TKA patients between 2014 and 2016 from 1 large academic and 2 community hospitals was performed. Preoperative variables considered for prediction included demographics, medical history, flexion contracture, knee flexion, and outcome scores (patient-reported health state, Knee Society Score [KSS], and KSS-Function [KSS-F]). Recursive feature elimination was used to select features that optimized algorithm performance. Five supervised machine learning algorithms were developed by training with 10-fold cross-validation 3 times. These algorithms were subsequently applied to an independent testing set of patients and assessed by discrimination, calibration, Brier score, and decision curve analysis. RESULTS Of 430 patients, a total of 40 (9.0%) were dissatisfied with their outcome after primary TKA at a minimum of 2 years postoperatively. The random forest algorithm achieved the best performance in the independent testing set not used for algorithm development (c-statistic: 0.77, calibration intercept: 0.087, calibration slope: 0.74, Brier score: 0.082). The most important factors for predicting dissatisfaction were age, number of medical comorbidities, presence of one or more drug allergies, preoperative patient-reported health state score, and preoperative KSS. CONCLUSION The current study developed machine learning algorithms based on partially modifiable risk factors for predicting dissatisfaction after TKA. This model demonstrates good discriminative capacity for identifying those at greatest risk for dissatisfaction and may allow for preoperative health optimization.
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Affiliation(s)
- Kyle N Kunze
- Division of Adult Reconstruction, Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL
| | - Evan M Polce
- Division of Adult Reconstruction, Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL
| | - Alexander J Sadauskas
- Division of Adult Reconstruction, Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL
| | - Brett R Levine
- Division of Adult Reconstruction, Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL
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Chang M, Canseco JA, Nicholson KJ, Patel N, Vaccaro AR. The Role of Machine Learning in Spine Surgery: The Future Is Now. Front Surg 2020; 7:54. [PMID: 32974382 PMCID: PMC7472375 DOI: 10.3389/fsurg.2020.00054] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Accepted: 07/13/2020] [Indexed: 12/12/2022] Open
Abstract
The recent influx of machine learning centered investigations in the spine surgery literature has led to increased enthusiasm as to the prospect of using artificial intelligence to create clinical decision support tools, optimize postoperative outcomes, and improve technologies used in the operating room. However, the methodology underlying machine learning in spine research is often overlooked as the subject matter is quite novel and may be foreign to practicing spine surgeons. Improper application of machine learning is a significant bioethics challenge, given the potential consequences of over- or underestimating the results of such studies for clinical decision-making processes. Proper peer review of these publications requires a baseline familiarity of the language associated with machine learning, and how it differs from classical statistical analyses. This narrative review first introduces the overall field of machine learning and its role in artificial intelligence, and defines basic terminology. In addition, common modalities for applying machine learning, including classification and regression decision trees, support vector machines, and artificial neural networks are examined in the context of examples gathered from the spine literature. Lastly, the ethical challenges associated with adapting machine learning for research related to patient care, as well as future perspectives on the potential use of machine learning in spine surgery, are discussed specifically.
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Affiliation(s)
- Michael Chang
- Department of Orthopaedic Surgery, Thomas Jefferson University, Philadelphia, PA, United States
- Rothman Orthopaedic Institute, Philadelphia, PA, United States
| | - Jose A. Canseco
- Department of Orthopaedic Surgery, Thomas Jefferson University, Philadelphia, PA, United States
- Rothman Orthopaedic Institute, Philadelphia, PA, United States
| | | | - Neil Patel
- Department of Orthopaedic Surgery, Thomas Jefferson University, Philadelphia, PA, United States
- Rothman Orthopaedic Institute, Philadelphia, PA, United States
| | - Alexander R. Vaccaro
- Department of Orthopaedic Surgery, Thomas Jefferson University, Philadelphia, PA, United States
- Rothman Orthopaedic Institute, Philadelphia, PA, United States
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