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Fan G, Li Y, Wang D, Zhang J, Du X, Liu H, Liao X. Automatic segmentation of dura for quantitative analysis of lumbar stenosis: A deep learning study with 518 CT myelograms. J Appl Clin Med Phys 2024; 25:e14378. [PMID: 38729652 PMCID: PMC11244674 DOI: 10.1002/acm2.14378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 04/01/2024] [Accepted: 04/18/2024] [Indexed: 05/12/2024] Open
Abstract
BACKGROUND The diagnosis of lumbar spinal stenosis (LSS) can be challenging because radicular pain is not often present in the culprit-level localization. Accurate segmentation and quantitative analysis of the lumbar dura on radiographic images are key to the accurate differential diagnosis of LSS. The aim of this study is to develop an automatic dura-contouring tool for radiographic quantification on computed tomography myelogram (CTM) for patients with LSS. METHODS A total of 518 CTM cases with or without lumbar stenosis were included in this study. A deep learning (DL) segmentation algorithm 3-dimensional (3D) U-Net was deployed. A total of 210 labeled cases were used to develop the dura-contouring tool, with the ratio of the training, independent testing, and external validation datasets being 150:30:30. The Dice score (DCS) was the primary measure to evaluate the segmentation performance of the 3D U-Net, which was subsequently developed as the dura-contouring tool to segment another unlabeled 308 CTM cases with LSS. Automatic masks of 446 slices on the stenotic levels were then meticulously reviewed and revised by human experts, and the cross-sectional area (CSA) of the dura was compared. RESULTS The mean DCS of the 3D U-Net were 0.905 ± 0.080, 0.933 ± 0.018, and 0.928 ± 0.034 in the five-fold cross-validation, the independent testing, and the external validation datasets, respectively. The segmentation performance of the dura-contouring tool was also comparable to that of the second observer (the human expert). With the dura-contouring tool, only 59.0% (263/446) of the automatic masks of the stenotic slices needed to be revised. In the revised cases, there were no significant differences in the dura CSA between automatic masks and corresponding revised masks (p = 0.652). Additionally, a strong correlation of dura CSA was found between the automatic masks and corresponding revised masks (r = 0.805). CONCLUSIONS A dura-contouring tool was developed that could automatically segment the dural sac on CTM, and it demonstrated high accuracy and generalization ability. Additionally, the dura-contouring tool has the potential to be applied in patients with LSS because it facilitates the quantification of the dural CSA on stenotic slices.
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Affiliation(s)
- Guoxin Fan
- Department of Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
- Department of Spine Surgery, Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Yufeng Li
- Department of Sports Medicine, Eighth Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China
| | - Dongdong Wang
- Department of Orthopaedics, Putuo People's Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Jianjin Zhang
- Department of Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
| | - Xiaokang Du
- Department of Orthopedics, The People's Hospital of Wenshang County, Wenshang, Shandong, China
| | - Huaqing Liu
- Artificial Intelligence Innovation Center, Research Institute of Tsinghua PearlRiverDelta, Guangzhou, China
| | - Xiang Liao
- Department of Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
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De Barros A, Abel F, Kolisnyk S, Geraci GC, Hill F, Engrav M, Samavedi S, Suldina O, Kim J, Rusakov A, Lebl DR, Mourad R. Determining Prior Authorization Approval for Lumbar Stenosis Surgery With Machine Learning. Global Spine J 2024; 14:1753-1759. [PMID: 36752058 PMCID: PMC11268295 DOI: 10.1177/21925682231155844] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/09/2023] Open
Abstract
STUDY DESIGN Medical vignettes. OBJECTIVES Lumbar spinal stenosis (LSS) is a degenerative condition with a high prevalence in the elderly population, that is associated with a significant economic burden and often requires spinal surgery. Prior authorization of surgical candidates is required before patients can be covered by a health plan and must be approved by medical directors (MDs), which is often subjective and clinician specific. In this study, we hypothesized that the prediction accuracy of machine learning (ML) methods regarding surgical candidates is comparable to that of a panel of MDs. METHODS Based on patient demographic factors, previous therapeutic history, symptoms and physical examinations and imaging findings, we propose an ML which computes the probability of spinal surgical recommendations for LSS. The model implements a random forest model trained from medical vignette data reviewed by MDs. Sets of 400 and 100 medical vignettes reviewed by MDs were used for training and testing. RESULTS The predictive accuracy of the machine learning model was with a root mean square error (RMSE) between model predictions and ground truth of .1123, while the average RMSE between individual MD's recommendations and ground truth was .2661. For binary classification, the AUROC and Cohen's kappa were .959 and .801, while the corresponding average metrics based on individual MD's recommendations were .844 and .564, respectively. CONCLUSIONS Our results suggest that ML can be used to automate prior authorization approval of surgery for LSS with performance comparable to a panel of MDs.
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Affiliation(s)
- Amaury De Barros
- Toulouse NeuroImaging Center (ToNIC), University of Toulouse Paul Sabatier-INSERM, Toulouse, France
- Neuroscience (Neurosurgery) Center, Toulouse University Hospital, Toulouse, France
| | | | | | | | | | | | | | | | | | | | | | - Raphael Mourad
- Remedy Logic, New York, NY, USA
- University of Toulouse, Toulouse, France
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Lee J, Ruiz-Cardozo MA, Patel RP, Javeed S, Lavadi RS, Newsom-Stewart C, Alyakin A, Molina CA, Agarwal N, Ray WZ, Santacatterina M, Pennicooke BH. Clinical prediction for surgical versus nonsurgical interventions in patients with vertebral osteomyelitis and discitis. JOURNAL OF SPINE SURGERY (HONG KONG) 2024; 10:204-213. [PMID: 38974494 PMCID: PMC11224782 DOI: 10.21037/jss-23-111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 01/15/2024] [Indexed: 07/09/2024]
Abstract
Background Vertebral osteomyelitis and discitis (VOD), an infection of intervertebral discs, often requires spine surgical intervention and timely management to prevent adverse outcomes. Our study aims to develop a machine learning (ML) model to predict the indication for surgical intervention (during the same hospital stay) versus nonsurgical management in patients with VOD. Methods This retrospective study included adult patients (≥18 years) with VOD (ICD-10 diagnosis codes M46.2,3,4,5) treated at a single institution between 01/01/2015 and 12/31/2019. The primary outcome studied was surgery. Candidate predictors were age, sex, race, Elixhauser comorbidity index, first-recorded lab values, first-recorded vital signs, and admit diagnosis. After splitting the dataset, XGBoost, logistic regression, and K-neighbor classifier algorithms were trained and tested for model development. Results A total of 1,111 patients were included in this study, among which 30% (n=339) of patients underwent surgical intervention. Age and sex did not significantly differ between the two groups; however, race did significantly differ (P<0.0001), with the surgical group having a higher percentage of white patients. The top ten model features for the best-performing model (XGBoost) were as follows (in descending order of importance): admit diagnosis of fever, negative culture, Staphylococcus aureus culture, partial pressure of arterial oxygen to fractional inspired oxygen ratio (PaO2:FiO2), admit diagnosis of intraspinal abscess and granuloma, admit diagnosis of sepsis, race, troponin I, acid-fast bacillus culture, and alveolar-arterial gradient (A-a gradient). XGBoost model metrics were as follows: accuracy =0.7534, sensitivity =0.7436, specificity =0.7586, and area under the curve (AUC) =0.8210. Conclusions The XGBoost model reliably predicts the indication for surgical intervention based on several readily available patient demographic information and clinical features. The interpretability of a supervised ML model provides robust insight into patient outcomes. Furthermore, it paves the way for the development of an efficient hospital resource allocation instrument, designed to guide clinical suggestions.
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Affiliation(s)
- Jennifer Lee
- Department of Neurological Surgery, Washington University School of Medicine, Saint Louis, MO, USA
| | - Miguel A. Ruiz-Cardozo
- Department of Neurological Surgery, Washington University School of Medicine, Saint Louis, MO, USA
| | - Rujvee P. Patel
- Department of Neurological Surgery, Washington University School of Medicine, Saint Louis, MO, USA
| | - Saad Javeed
- Department of Neurological Surgery, Washington University School of Medicine, Saint Louis, MO, USA
| | - Raj Swaroop Lavadi
- Department of Neurological Surgery, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Catherine Newsom-Stewart
- Department of Developmental Regenerative and Stem Cell Biology, Washington University in St. Louis, Saint Louis, MO, USA
| | - Anton Alyakin
- Department of Neurological Surgery, Washington University School of Medicine, Saint Louis, MO, USA
| | - Camilo A. Molina
- Department of Neurological Surgery, Washington University School of Medicine, Saint Louis, MO, USA
| | - Nitin Agarwal
- Department of Neurological Surgery, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Wilson Z. Ray
- Department of Neurological Surgery, Washington University School of Medicine, Saint Louis, MO, USA
| | - Michele Santacatterina
- Department of Population Health, New York University School of Medicine, New York City, NY, USA
| | - Brenton H. Pennicooke
- Department of Neurological Surgery, Washington University School of Medicine, Saint Louis, MO, USA
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Ping Y. Experience in psychological counseling supported by artificial intelligence technology. Technol Health Care 2024:THC230809. [PMID: 38968060 DOI: 10.3233/thc-230809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/07/2024]
Abstract
BACKGROUND In recent years, artificial intelligence (AI) technology has been continuously advancing and finding extensive applications, with one of its core technologies, machine learning, being increasingly utilized in the field of healthcare. OBJECTIVE This research aims to explore the role of Artificial Intelligence (AI) technology in psychological counseling and utilize machine learning algorithms to predict counseling outcomes. METHODS Firstly, by employing natural language processing techniques to analyze user conversations with AI chatbots, researchers can gain insights into the psychological states and needs of users during the counseling process. This involves detailed analysis using text analysis, sentiment analysis, and other relevant techniques. Subsequently, machine learning algorithms are used to establish predictive models that forecast counseling outcomes and user satisfaction based on data such as user language, emotions, and behavior. These predictive results can assist counselors or AI chatbots in adjusting counseling strategies, thereby enhancing counseling effectiveness and user experience. Additionally, this study explores the potential and prospects of AI technology in the field of psychological counseling. RESULTS The research findings indicate that the designed machine learning models achieve an accuracy rate of approximately 89% in analyzing psychological conditions. This demonstrates significant innovation and breakthroughs in AI technology. Consequently, AI technology will gradually become a highly important tool and method in the field of psychological counseling. CONCLUSION In the future, AI chatbots will become more intelligent and personalized, providing users with precise, efficient, and convenient psychological counseling services. The results of this research provide valuable technical insights for further improving AI-supported psychological counseling, contributing positively to the application and development of AI technology.
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Bcharah G, Gupta N, Panico N, Winspear S, Bagley A, Turnow M, D'Amico R, Ukachukwu AEK. Innovations in Spine Surgery: A Narrative Review of Current Integrative Technologies. World Neurosurg 2024; 184:127-136. [PMID: 38159609 DOI: 10.1016/j.wneu.2023.12.124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 12/22/2023] [Indexed: 01/03/2024]
Abstract
Neurosurgical technologies have become increasingly more adaptive, featuring real-time and patient-specific guidance in preoperative, intraoperative, and postoperative settings. This review offers insight into how these integrative innovations compare with conventional approaches in spine surgery, focusing on machine learning (ML), artificial intelligence, augmented reality and virtual reality, and spinal navigation systems. Data on technology applications, diagnostic and procedural accuracy, intraoperative times, radiation exposures, postoperative outcomes, and costs were extracted and compared with conventional methods to assess their advantages and limitations. Preoperatively, augmented reality and virtual reality have applications in surgical training and planning that are more immersive, case specific, and risk-free and have been shown to enhance accuracy and reduce complications. ML algorithms have demonstrated high accuracy in predicting surgical candidacy (up to 92.1%) and tailoring personalized treatments based on patient-specific variables. Intraoperatively, advantages include more accurate pedicle screw insertion (96%-99% with ML), enhanced visualization, reduced radiation exposure (49 μSv with O-arm navigation vs. 556 μSv with fluoroscopy), increased efficiency, and potential for fewer intraoperative complications compared with conventional approaches. Postoperatively, certain ML and artificial intelligence models have outperformed conventional methods in predicting all postoperative complications of >6000 patients as well as predicting variables contributing to in-hospital and 90-day mortality. However, applying these technologies comes with limitations, such as longer operative times (up to 35.6% longer) with navigation, dependency on datasets, costs, accessibility, steep learning curve, and inherent software malfunctions. As these technologies advance, continuing to assess their efficacy and limitations will be crucial to their successful integration within spine surgery.
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Affiliation(s)
- George Bcharah
- Mayo Clinic Alix School of Medicine, Scottsdale, Arizona, USA
| | - Nithin Gupta
- Campbell University School of Osteopathic Medicine, Lillington, North Carolina, USA
| | - Nicholas Panico
- Lake Erie College of Osteopathic Medicine, Erie, Pennsylvania, USA
| | - Spencer Winspear
- Campbell University School of Osteopathic Medicine, Lillington, North Carolina, USA
| | - Austin Bagley
- Campbell University School of Osteopathic Medicine, Lillington, North Carolina, USA
| | - Morgan Turnow
- Kentucky College of Osteopathic Medicine, Pikeville, Kentucky, USA
| | - Randy D'Amico
- Department of Neurosurgery, Lenox Hill Hospital, New York, New York, USA
| | - Alvan-Emeka K Ukachukwu
- Department of Neurosurgery, Duke University, Durham, North Carolina, USA; Duke Global Neurosurgery and Neurology, Durham, North Carolina, USA.
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Abel F, Garcia E, Andreeva V, Nikolaev NS, Kolisnyk S, Sarbaev R, Novikov I, Kozinchenko E, Kim J, Rusakov A, Mourad R, Lebl DR. An Artificial Intelligence-Based Support Tool for Lumbar Spinal Stenosis Diagnosis from Self-Reported History Questionnaire. World Neurosurg 2024; 181:e953-e962. [PMID: 37952887 DOI: 10.1016/j.wneu.2023.11.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 11/04/2023] [Accepted: 11/06/2023] [Indexed: 11/14/2023]
Abstract
OBJECTIVES Symptomatic lumbar spinal stenosis (LSS) leads to functional impairment and pain. While radiologic characterization of the morphological stenosis grade can aid in the diagnosis, it may not always correlate with patient symptoms. Artificial intelligence (AI) may diagnose symptomatic LSS in patients solely based on self-reported history questionnaires. METHODS We evaluated multiple machine learning (ML) models to determine the likelihood of LSS using a self-reported questionnaire in patients experiencing low back pain and/or numbness in the legs. The questionnaire was built from peer-reviewed literature and a multidisciplinary panel of experts. Random forest, lasso logistic regression, support vector machine, gradient boosting trees, deep neural networks, and automated machine learning models were trained and performance metrics were compared. RESULTS Data from 4827 patients (4690 patients without LSS: mean age 62.44, range 27-84 years, 62.8% females, and 137 patients with LSS: mean age 50.59, range 30-71 years, 59.9% females) were retrospectively collected. Among the evaluated models, the random forest model demonstrated the highest predictive accuracy with an area under the receiver operating characteristic curve (AUROC) between model prediction and LSS diagnosis of 0.96, a sensitivity of 0.94, a specificity of 0.88, a balanced accuracy of 0.91, and a Cohen's kappa of 0.85. CONCLUSIONS Our results indicate that ML can automate the diagnosis of LSS based on self-reported questionnaires with high accuracy. Implementation of standardized and intelligence-automated workflow may serve as a supportive diagnostic tool to streamline patient management and potentially lower health care costs.
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Affiliation(s)
- Frederik Abel
- Department of Spine Surgery, Hospital for Special Surgery, New York, New York, USA
| | | | - Vera Andreeva
- Federal State Budgetary Institution, Federal Center for Traumatology, Orthopedics and Arthroplasty, Ministry of Health of the Russian Federation, Cheboksary, Russia
| | - Nikolai S Nikolaev
- Federal State Budgetary Institution, Federal Center for Traumatology, Orthopedics and Arthroplasty, Ministry of Health of the Russian Federation, Cheboksary, Russia; Federal State Budgetary Educational Institution of Higher Education, Chuvash State University named after I.N. Ulyanov, Cheboksary, Russia
| | - Serhii Kolisnyk
- Department of Physical and Rehabilitation Medicine, Vinnitsa National Medical University, Vinnytsia, Ukraine
| | | | | | | | - Jack Kim
- Remedy Logic, New York, New York, USA
| | | | - Raphael Mourad
- University of Toulouse, CNRS, UPS, Toulouse, France; Remedy Logic, New York, New York, USA.
| | - Darren R Lebl
- Department of Spine Surgery, Hospital for Special Surgery, New York, New York, USA
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Fan G, Wang D, Li Y, Xu Z, Wang H, Liu H, Liao X. Machine Learning Predicts Decompression Levels for Lumbar Spinal Stenosis Using Canal Radiomic Features from Computed Tomography Myelography. Diagnostics (Basel) 2023; 14:53. [PMID: 38201362 PMCID: PMC10795799 DOI: 10.3390/diagnostics14010053] [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: 09/06/2023] [Revised: 11/17/2023] [Accepted: 11/29/2023] [Indexed: 01/12/2024] Open
Abstract
BACKGROUND The accurate preoperative identification of decompression levels is crucial for the success of surgery in patients with multi-level lumbar spinal stenosis (LSS). The objective of this study was to develop machine learning (ML) classifiers that can predict decompression levels using computed tomography myelography (CTM) data from LSS patients. METHODS A total of 1095 lumbar levels from 219 patients were included in this study. The bony spinal canal in CTM images was manually delineated, and radiomic features were extracted. The extracted data were randomly divided into training and testing datasets (8:2). Six feature selection methods combined with 12 ML algorithms were employed, resulting in a total of 72 ML classifiers. The main evaluation indicator for all classifiers was the area under the curve of the receiver operating characteristic (ROC-AUC), with the precision-recall AUC (PR-AUC) serving as the secondary indicator. The prediction outcome of ML classifiers was decompression level or not. RESULTS The embedding linear support vector (embeddingLSVC) was the optimal feature selection method. The feature importance analysis revealed the top 5 important features of the 15 radiomic predictors, which included 2 texture features, 2 first-order intensity features, and 1 shape feature. Except for shape features, these features might be eye-discernible but hardly quantified. The top two ML classifiers were embeddingLSVC combined with support vector machine (EmbeddingLSVC_SVM) and embeddingLSVC combined with gradient boosting (EmbeddingLSVC_GradientBoost). These classifiers achieved ROC-AUCs over 0.90 and PR-AUCs over 0.80 in independent testing among the 72 classifiers. Further comparisons indicated that EmbeddingLSVC_SVM appeared to be the optimal classifier, demonstrating superior discrimination ability, slight advantages in the Brier scores on the calibration curve, and Net benefits on the Decision Curve Analysis. CONCLUSIONS ML successfully extracted valuable and interpretable radiomic features from the spinal canal using CTM images, and accurately predicted decompression levels for LSS patients. The EmbeddingLSVC_SVM classifier has the potential to assist surgical decision making in clinical practice, as it showed high discrimination, advantageous calibration, and competitive utility in selecting decompression levels in LSS patients using canal radiomic features from CTM.
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Affiliation(s)
- Guoxin Fan
- Department of Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen 518056, China; (G.F.); (Z.X.); (H.W.)
- Department of Spine Surgery, Third Affiliated Hospital, Sun Yat-sen University, Guangzhou 510630, China
| | - Dongdong Wang
- Department of Orthopaedics, Putuo People’s Hospital, Tongji University, Shanghai 200060, China;
| | - Yufeng Li
- Department of Sports Medicine, Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen 518033, China;
| | - Zhipeng Xu
- Department of Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen 518056, China; (G.F.); (Z.X.); (H.W.)
| | - Hong Wang
- Department of Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen 518056, China; (G.F.); (Z.X.); (H.W.)
| | - Huaqing Liu
- Artificial Intelligence Innovation Center, Research Institute of Tsinghua, Guangzhou 510700, China
| | - Xiang Liao
- Department of Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen 518056, China; (G.F.); (Z.X.); (H.W.)
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Wellington IJ, Karsmarski OP, Murphy KV, Shuman ME, Ng MK, Antonacci CL. The use of machine learning for predicting candidates for outpatient spine surgery: a review. JOURNAL OF SPINE SURGERY (HONG KONG) 2023; 9:323-330. [PMID: 37841781 PMCID: PMC10570640 DOI: 10.21037/jss-22-121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Accepted: 06/14/2023] [Indexed: 10/17/2023]
Abstract
While spine surgery has historically been performed in the inpatient setting, in recent years there has been growing interest in performing certain cervical and lumbar spine procedures on an outpatient basis. While conducting these procedures in the outpatient setting may be preferable for both the surgeon and the patient, appropriate patient selection is crucial. The employment of machine learning techniques for data analysis and outcome prediction has grown in recent years within spine surgery literature. Machine learning is a form of statistics often applied to large datasets that creates predictive models, with minimal to no human intervention, that can be applied to previously unseen data. Machine learning techniques may outperform traditional logistic regression with regards to predictive accuracy when analyzing complex datasets. Researchers have applied machine learning to develop algorithms to aid in patient selection for spinal surgery and to predict postoperative outcomes. Furthermore, there has been increasing interest in using machine learning to assist in the selection of patients who may be appropriate candidates for outpatient cervical and lumbar spine surgery. The goal of this review is to discuss the current literature utilizing machine learning to predict appropriate patients for cervical and lumbar spine surgery, candidates for outpatient spine surgery, and outcomes following these procedures.
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Affiliation(s)
- Ian J. Wellington
- Department of Orthopaedic Surgery, University of Connecticut, Farmington, CT, USA
| | - Owen P. Karsmarski
- Department of Orthopaedic Surgery, University of Connecticut, Farmington, CT, USA
| | - Kyle V. Murphy
- Department of Orthopaedic Surgery, University of Connecticut, Farmington, CT, USA
| | - Matthew E. Shuman
- Department of Orthopaedic Surgery, University of Connecticut, Farmington, CT, USA
| | - Mitchell K. Ng
- Department of Orthopaedic Surgery, Maimonides Medical Center, Brooklyn, NY, USA
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Krebs B, Nataraj A, McCabe E, Clark S, Sufiyan Z, Yamamoto SS, Zaïane O, Gross DP. Developing a triage predictive model for access to a spinal surgeon using clinical variables and natural language processing of radiology reports. EUROPEAN SPINE JOURNAL : OFFICIAL PUBLICATION OF THE EUROPEAN SPINE SOCIETY, THE EUROPEAN SPINAL DEFORMITY SOCIETY, AND THE EUROPEAN SECTION OF THE CERVICAL SPINE RESEARCH SOCIETY 2023:10.1007/s00586-023-07552-4. [PMID: 36740609 DOI: 10.1007/s00586-023-07552-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 01/17/2023] [Accepted: 01/22/2023] [Indexed: 02/07/2023]
Abstract
PURPOSE To utilize natural language processing (NLP) of MRI reports and various clinical variables to develop a preliminary model predictive of the need for surgery in patients with low back and neck pain. Such a model would be beneficial for informing clinical practice decisions and help reduce the number of unnecessary surgical referrals, streamlining the surgical process. METHODS A historical cohort study was conducted using de-identified data from patients referred to a spine assessment clinic. Various demographic, clinical, and radiological variables were included as potential predictors. Full-text radiology reports of patients' MRI findings were vectorized using NLP before applying machine learning algorithms to develop models predicting who underwent surgery. Outputs from these models were then entered into a logistic regression model with clinical variables to develop a preliminary model predictive of surgical recommendations. RESULTS Of the 398 patients assessed, 71 underwent spine surgery. NLP variables were significant predictors in univariate analysis but did not remain in the final logistic regression model. An outcome of receiving surgery was predicted by a primary symptom of low back and leg pain (adjusted odds ratio 2.81), distal pain indicated by a pain diagram (adjusted odds ratio 2.49) and self-reported difficulties walking (adjusted odds ratio 2.73). CONCLUSION A logistic regression model was created to predict which patients may require spine surgery. Simple clinical variables appeared more predictive than variables created using NLP. However, additional research with more data samples is needed to validate this model and fully evaluate the usefulness of NLP for this task.
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Affiliation(s)
- Brandon Krebs
- Faculty of Rehabilitation Medicine, University of Alberta, Edmonton, Canada
| | - Andrew Nataraj
- Department of Surgery, University of Alberta, Edmonton, Canada
| | - Erin McCabe
- Faculty of Rehabilitation Medicine, University of Alberta, Edmonton, Canada
| | - Shannon Clark
- Department of Computing Science, University of Alberta, Edmonton, Canada
| | - Zahin Sufiyan
- Department of Computing Science, University of Alberta, Edmonton, Canada
| | | | - Osmar Zaïane
- Department of Computing Science, University of Alberta, Edmonton, Canada
| | - Douglas P Gross
- Department of Physical Therapy, University of Alberta, 2-50 Corbett Hall, Alberta, Edmonton, T6G 2G4, Canada.
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Jujjavarapu C, Suri P, Pejaver V, Friedly J, Gold LS, Meier E, Cohen T, Mooney SD, Heagerty PJ, Jarvik JG. Predicting decompression surgery by applying multimodal deep learning to patients' structured and unstructured health data. BMC Med Inform Decis Mak 2023; 23:2. [PMID: 36609379 PMCID: PMC9824905 DOI: 10.1186/s12911-022-02096-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 12/29/2022] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Low back pain (LBP) is a common condition made up of a variety of anatomic and clinical subtypes. Lumbar disc herniation (LDH) and lumbar spinal stenosis (LSS) are two subtypes highly associated with LBP. Patients with LDH/LSS are often started with non-surgical treatments and if those are not effective then go on to have decompression surgery. However, recommendation of surgery is complicated as the outcome may depend on the patient's health characteristics. We developed a deep learning (DL) model to predict decompression surgery for patients with LDH/LSS. MATERIALS AND METHOD We used datasets of 8387 and 8620 patients from a prospective study that collected data from four healthcare systems to predict early (within 2 months) and late surgery (within 12 months after a 2 month gap), respectively. We developed a DL model to use patients' demographics, diagnosis and procedure codes, drug names, and diagnostic imaging reports to predict surgery. For each prediction task, we evaluated the model's performance using classical and generalizability evaluation. For classical evaluation, we split the data into training (80%) and testing (20%). For generalizability evaluation, we split the data based on the healthcare system. We used the area under the curve (AUC) to assess performance for each evaluation. We compared results to a benchmark model (i.e. LASSO logistic regression). RESULTS For classical performance, the DL model outperformed the benchmark model for early surgery with an AUC of 0.725 compared to 0.597. For late surgery, the DL model outperformed the benchmark model with an AUC of 0.655 compared to 0.635. For generalizability performance, the DL model outperformed the benchmark model for early surgery. For late surgery, the benchmark model outperformed the DL model. CONCLUSIONS For early surgery, the DL model was preferred for classical and generalizability evaluation. However, for late surgery, the benchmark and DL model had comparable performance. Depending on the prediction task, the balance of performance may shift between DL and a conventional ML method. As a result, thorough assessment is needed to quantify the value of DL, a relatively computationally expensive, time-consuming and less interpretable method.
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Affiliation(s)
- Chethan Jujjavarapu
- Department of Biomedical Informatics and Medical Education, School of Medicine, University of Washington, Box 358047, Seattle, WA, 98195, USA
| | - Pradeep Suri
- Clinical Learning, Evidence and Research Center, University of Washington, 4333 Brooklyn Ave NE, Seattle, WA, 98105, USA
- Department of Rehabilitation Medicine, University of Washington, 1959 NE Pacific St, Seattle, WA, 98195, USA
| | - Vikas Pejaver
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Janna Friedly
- Clinical Learning, Evidence and Research Center, University of Washington, 4333 Brooklyn Ave NE, Seattle, WA, 98105, USA
- Department of Rehabilitation Medicine, University of Washington, 1959 NE Pacific St, Seattle, WA, 98195, USA
| | - Laura S Gold
- Clinical Learning, Evidence and Research Center, University of Washington, 4333 Brooklyn Ave NE, Seattle, WA, 98105, USA
- Department of Radiology, University of Washington, 1959 NE Pacific Street, Seattle, WA, 98195, USA
| | - Eric Meier
- Clinical Learning, Evidence and Research Center, University of Washington, 4333 Brooklyn Ave NE, Seattle, WA, 98105, USA
- Department of Biostatistics, University of Washington, Box 357232, Seattle, WA, 98195-7232, USA
- Center for Biomedical Statistics, University of Washington, Seattle, WA, USA
| | - Trevor Cohen
- Department of Biomedical Informatics and Medical Education, School of Medicine, University of Washington, Box 358047, Seattle, WA, 98195, USA
| | - Sean D Mooney
- Department of Biomedical Informatics and Medical Education, School of Medicine, University of Washington, Box 358047, Seattle, WA, 98195, USA
| | - Patrick J Heagerty
- Department of Biostatistics, University of Washington, Box 357232, Seattle, WA, 98195-7232, USA
- Center for Biomedical Statistics, University of Washington, Seattle, WA, USA
| | - Jeffrey G Jarvik
- Clinical Learning, Evidence and Research Center, University of Washington, 4333 Brooklyn Ave NE, Seattle, WA, 98105, USA.
- Department of Radiology, University of Washington, 1959 NE Pacific Street, Seattle, WA, 98195, USA.
- Department of Neurological Surgery, University of Washington, 1959 NE Pacific Street, Seattle, WA, 98195, USA.
- Department of Health Services, University of Washington, Box 357660, Seattle, WA, 98195-7660, USA.
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Ren G, Yu K, Xie Z, Wang P, Zhang W, Huang Y, Wang Y, Wu X. Current Applications of Machine Learning in Spine: From Clinical View. Global Spine J 2022; 12:1827-1840. [PMID: 34628966 PMCID: PMC9609532 DOI: 10.1177/21925682211035363] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
STUDY DESIGN Narrative review. OBJECTIVES This review aims to present current applications of machine learning (ML) in spine domain to clinicians. METHODS We conducted a comprehensive PubMed search of peer-reviewed articles that were published between 2006 and 2020 using terms (spine, spinal, lumbar, cervical, thoracic, machine learning) to examine ML in spine. Then exclude research of other domain, case report, review or meta-analysis, and which without available abstract or full text. RESULTS Total 1738 articles were retrieved from database, and 292 studies were finally included. Key findings of current applications were compiled and summarized in this review. Main clinical applications of those techniques including image processing, diagnosis, decision supporting, operative assistance, rehabilitation, surgery outcomes, complications, hospitalization and cost. CONCLUSIONS ML had achieved excellent performance and hold immense potential in spine. ML could help clinical staff to improve medical level, enhance work efficiency, and reduce adverse events. However more randomized controlled trials and improvement of interpretability are essential to clinicians accepting models' assistance in real work.
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Affiliation(s)
- GuanRui Ren
- Southeast University Medical College,
Nanjing, Jiangsu, China
| | - Kun Yu
- Nanjing Jiangbei Hospital, Nanjing,
Jiangsu, China
| | - ZhiYang Xie
- Department of Spine Surgery, Zhongda
Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China
| | - PeiYang Wang
- Southeast University Medical College,
Nanjing, Jiangsu, China
| | - Wei Zhang
- Southeast University Medical College,
Nanjing, Jiangsu, China
| | - Yong Huang
- Southeast University Medical College,
Nanjing, Jiangsu, China
| | - YunTao Wang
- Department of Spine Surgery, Zhongda
Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China,YunTao Wang, Department of Spine Surgery,
Zhongda Hospital, School of Medicine, Southeast University, No. 87, Dingjiaqiao
Road, Nanjing, Jiangsu 210009, China.
| | - XiaoTao Wu
- Department of Spine Surgery, Zhongda
Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China,XiaoTao Wu, Department of Spine Surgery,
Zhongda Hospital, School of Medicine, Southeast University, No. 87, Dingjiaqiao
Road, Nanjing, Jiangsu 210009, China.
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12
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Performance of hybrid artificial intelligence in determining candidacy for lumbar stenosis surgery. EUROPEAN SPINE JOURNAL : OFFICIAL PUBLICATION OF THE EUROPEAN SPINE SOCIETY, THE EUROPEAN SPINAL DEFORMITY SOCIETY, AND THE EUROPEAN SECTION OF THE CERVICAL SPINE RESEARCH SOCIETY 2022; 31:2149-2155. [PMID: 35802195 DOI: 10.1007/s00586-022-07307-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 05/16/2022] [Accepted: 06/24/2022] [Indexed: 01/20/2023]
Abstract
PURPOSE Lumbar spinal stenosis (LSS) is a condition affecting several hundreds of thousands of adults in the United States each year and is associated with significant economic burden. The current decision-making practice to determine surgical candidacy for LSS is often subjective and clinician specific. In this study, we hypothesize that the performance of artificial intelligence (AI) methods could prove comparable in terms of prediction accuracy to that of a panel of spine experts. METHODS We propose a novel hybrid AI model which computes the probability of spinal surgical recommendations for LSS, based on patient demographic factors, clinical symptom manifestations, and MRI findings. The hybrid model combines a random forest model trained from medical vignette data reviewed by surgeons, with an expert Bayesian network model built from peer-reviewed literature and the expert opinions of a multidisciplinary team in spinal surgery, rehabilitation medicine, interventional and diagnostic radiology. Sets of 400 and 100 medical vignettes reviewed by surgeons were used for training and testing. RESULTS The model demonstrated high predictive accuracy, with a root mean square error (RMSE) between model predictions and ground truth of 0.0964, while the average RMSE between individual doctor's recommendations and ground truth was 0.1940. For dichotomous classification, the AUROC and Cohen's kappa were 0.9266 and 0.6298, while the corresponding average metrics based on individual doctor's recommendations were 0.8412 and 0.5659, respectively. CONCLUSIONS Our results suggest that AI can be used to automate the evaluation of surgical candidacy for LSS with performance comparable to a multidisciplinary panel of physicians.
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