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Fields MW, Zaifman J, Malka MS, Lee NJ, Rymond CC, Simhon ME, Quan T, Roye BD, Vitale MG. Utilizing a comprehensive machine learning approach to identify patients at high risk for extended length of stay following spinal deformity surgery in pediatric patients with early onset scoliosis. Spine Deform 2024; 12:1477-1483. [PMID: 38702550 DOI: 10.1007/s43390-024-00889-w] [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: 10/28/2023] [Accepted: 04/23/2024] [Indexed: 05/06/2024]
Abstract
PURPOSE Early onset scoliosis (EOS) patient diversity makes outcome prediction challenging. Machine learning offers an innovative approach to analyze patient data and predict results, including LOS in pediatric spinal deformity surgery. METHODS Children under 10 with EOS were chosen from the American College of Surgeon's NSQIP database. Extended LOS, defined as over 5 days, was predicted using feature selection and machine learning in Python. The best model, determined by the area under the curve (AUC), was optimized and used to create a risk calculator for prolonged LOS. RESULTS The study included 1587 patients, mostly young (average age: 6.94 ± 2.58 years), with 33.1% experiencing prolonged LOS (n = 526). Most patients were female (59.2%, n = 940), with an average BMI of 17.0 ± 8.7. Factors influencing LOS were operative time, age, BMI, ASA class, levels operated on, etiology, nutritional support, pulmonary and neurologic comorbidities. The gradient boosting model performed best with a test accuracy of 0.723, AUC of 0.630, and a Brier score of 0.189, leading to a patient-specific risk calculator for prolonged LOS. CONCLUSIONS Machine learning algorithms accurately predict extended LOS across a national patient cohort and characterize key preoperative drivers of increased LOS after PSIF in pediatric patients with EOS.
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Affiliation(s)
- Michael W Fields
- Department of Orthopaedic Surgery, Columbia University, New York, NY, USA
| | - Jay Zaifman
- Department of Orthopaedic Surgery, New York University Langone Health, New York, NY, USA
| | - Matan S Malka
- Department of Orthopaedic Surgery, Columbia University, New York, NY, USA.
- Department of Orthopaedic Surgery, Morgan Stanley Children's Hospital of New York Presbyterian, Columbia University Medical Center, 3959 Broadway, CHONY 8-N, New York, NY, 10032-3784, USA.
| | - Nathan J Lee
- Department of Orthopaedic Surgery, Columbia University, New York, NY, USA
| | - Christina C Rymond
- Department of Orthopaedic Surgery, Columbia University, New York, NY, USA
| | - Matthew E Simhon
- Department of Orthopaedic Surgery, Columbia University, New York, NY, USA
| | - Theodore Quan
- Department of Orthopaedic Surgery, Columbia University, New York, NY, USA
| | - Benjamin D Roye
- Department of Orthopaedic Surgery, Columbia University, New York, NY, USA
- Department of Orthopaedic Surgery, Morgan Stanley Children's Hospital of New York Presbyterian, Columbia University Medical Center, 3959 Broadway, CHONY 8-N, New York, NY, 10032-3784, USA
| | - Michael G Vitale
- Department of Orthopaedic Surgery, Columbia University, New York, NY, USA
- Department of Orthopaedic Surgery, Morgan Stanley Children's Hospital of New York Presbyterian, Columbia University Medical Center, 3959 Broadway, CHONY 8-N, New York, NY, 10032-3784, 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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Scheer JK, Ames CP. Artificial Intelligence in Spine Surgery. Neurosurg Clin N Am 2024; 35:253-262. [PMID: 38423741 DOI: 10.1016/j.nec.2023.11.001] [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] [Indexed: 03/02/2024]
Abstract
The amount and quality of data being used in our everyday lives continue to advance in an unprecedented pace. This digital revolution has permeated healthcare, specifically spine surgery, allowing for very advanced and complex computational analytics, such as artificial intelligence (AI) and machine learning (ML). The integration of these methods into clinical practice has just begun, and the following review article will describe AI/ML, demonstrate how it has been applied in adult spinal deformity surgery, and show its potential to improve patient care touching on future directions.
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Affiliation(s)
- Justin K Scheer
- Department of Neurological Surgery, University of California, San Francisco, CA, USA.
| | - Christopher P Ames
- Department of Neurological Surgery, University of California, San Francisco, CA, USA
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Arjmandnia F, Alimohammadi E. The value of machine learning technology and artificial intelligence to enhance patient safety in spine surgery: a review. Patient Saf Surg 2024; 18:11. [PMID: 38528562 DOI: 10.1186/s13037-024-00393-0] [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: 02/25/2024] [Accepted: 03/15/2024] [Indexed: 03/27/2024] Open
Abstract
Machine learning algorithms have the potential to significantly improve patient safety in spine surgeries by providing healthcare professionals with valuable insights and predictive analytics. These algorithms can analyze preoperative data, such as patient demographics, medical history, and imaging studies, to identify potential risk factors and predict postoperative complications. By leveraging machine learning, surgeons can make more informed decisions, personalize treatment plans, and optimize surgical techniques to minimize risks and enhance patient outcomes. Moreover, by harnessing the power of machine learning, healthcare providers can make data-driven decisions, personalize treatment plans, and optimize surgical interventions, ultimately enhancing the quality of care in spine surgery. The findings highlight the potential of integrating artificial intelligence in healthcare settings to mitigate risks and enhance patient safety in surgical practices. The integration of machine learning holds immense potential for enhancing patient safety in spine surgeries. By leveraging advanced algorithms and predictive analytics, healthcare providers can optimize surgical decision-making, mitigate risks, and personalize treatment strategies to improve outcomes and ensure the highest standard of care for patients undergoing spine procedures. As technology continues to evolve, the future of spine surgery lies in harnessing the power of machine learning to transform patient safety and revolutionize surgical practices. The present review article was designed to discuss the available literature in the field of machine learning techniques to enhance patient safety in spine surgery.
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Affiliation(s)
- Fatemeh Arjmandnia
- Department of Aneasthesiology, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Ehsan Alimohammadi
- Department of Neurosurgery, Kermanshah University of Medical Sciences, Imam Reza Hospital, Kermanshah, Iran.
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Jiang F, Li X, Liu L, Xie Z, Wu X, Wang Y. Automated machine learning-based model for the prediction of pedicle screw loosening after degenerative lumbar fusion surgery. Biosci Trends 2024; 18:83-93. [PMID: 38417874 DOI: 10.5582/bst.2023.01327] [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] [Indexed: 03/01/2024]
Abstract
The adequacy of screw anchorage is a critical factor in achieving successful spinal fusion. This study aimed to use machine learning algorithms to identify critical variables and predict pedicle screw loosening after degenerative lumbar fusion surgery. A total of 552 patients who underwent primary transpedicular lumbar fixation for lumbar degenerative disease were included. The LASSO method identified key features associated with pedicle screw loosening. Patient clinical characteristics, intraoperative variables, and radiographic parameters were collected and used to construct eight machine learning models, including a training set (80% of participants) and a test set (20% of participants). The XGBoost model exhibited the best performance, with an AUC of 0.884 (95% CI: 0.825-0.944) in the test set, along with the lowest Brier score. Ten crucial variables, including age, disease diagnosis: degenerative scoliosis, number of fused levels, fixation to S1, HU value, preoperative PT, preoperative PI-LL, postoperative LL, postoperative PT, and postoperative PI-LL were selected. In the prospective cohort, the XGBoost model demonstrated substantial performance with an accuracy of 83.32%. This study identified crucial variables associated with pedicle screw loosening after degenerative lumbar fusion surgery and successfully developed a machine learning model to predict pedicle screw loosening. The findings of this study may provide valuable information for clinical decision-making.
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Affiliation(s)
- Feng Jiang
- Southeast University Medical College, Nanjing, Jiangsu, China
| | - Xinxin Li
- Southeast University Medical College, Nanjing, Jiangsu, China
| | - Lei Liu
- Department of Spine Surgery, Southeast University ZhongDa Hospital, Nanjing, Jiangsu, China
| | - Zhiyang Xie
- Department of Spine Surgery, Southeast University ZhongDa Hospital, Nanjing, Jiangsu, China
| | - Xiaotao Wu
- Southeast University Medical College, Nanjing, Jiangsu, China
- Department of Spine Surgery, Southeast University ZhongDa Hospital, Nanjing, Jiangsu, China
| | - Yuntao Wang
- Southeast University Medical College, Nanjing, Jiangsu, China
- Department of Spine Surgery, Southeast University ZhongDa Hospital, Nanjing, Jiangsu, China
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Lee CH, Jo DJ, Oh JK, Hyun SJ, Park JH, Kim KH, Bae JS, Moon BJ, Lee CK, Shin MH, Jang HJ, Han MS, Kim CH, Chung CK, Moon SM. Development and Validation of an Online Calculator to Predict Proximal Junctional Kyphosis After Adult Spinal Deformity Surgery Using Machine Learning. Neurospine 2023; 20:1272-1280. [PMID: 38171294 PMCID: PMC10762414 DOI: 10.14245/ns.2342434.217] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 08/16/2023] [Accepted: 08/29/2023] [Indexed: 01/05/2024] Open
Abstract
OBJECTIVE Although adult spinal deformity (ASD) surgery aims to restore and maintain alignment, proximal junctional kyphosis (PJK) may occur. While existing scoring systems predict PJK, they predominantly offer a generalized 3-tier risk classification, limiting their utility for nuanced treatment decisions. This study seeks to establish a personalized risk calculator for PJK, aiming to enhance treatment planning precision. METHODS Patient data for ASD were sourced from the Korean spinal deformity database. PJK was defined a proximal junctional angle (PJA) of ≥ 20° at the final follow-up, or an increase in PJA of ≥ 10° compared to the preoperative values. Multivariable analysis was performed to identify independent variables. Subsequently, 5 machine learning models were created to predict individualized PJK risk post-ASD surgery. The most efficacious model was deployed as an online and interactive calculator. RESULTS From a pool of 201 patients, 49 (24.4%) exhibited PJK during the follow-up period. Through multivariable analysis, postoperative PJA, body mass index, and deformity type emerged as independent predictors for PJK. When testing machine learning models using study results and previously reported variables as hyperparameters, the random forest model exhibited the highest accuracy, reaching 83%, with an area under the receiver operating characteristics curve of 0.76. This model has been launched as a freely accessible tool at: (https://snuspine.shinyapps.io/PJKafterASD/). CONCLUSION An online calculator, founded on the random forest model, has been developed to gauge the risk of PJK following ASD surgery. This may be a useful clinical tool for surgeons, allowing them to better predict PJK probabilities and refine subsequent therapeutic strategies.
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Affiliation(s)
- Chang-Hyun Lee
- Department of Neurosurgery, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea
| | - Dae-Jean Jo
- Department of Neurosurgery, Kyung Hee University Hospital at Gangdong, Seoul, Korea
| | - Jae Keun Oh
- Department of Neurosurgery, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang, Korea
| | - Seung-Jae Hyun
- Department of Neurosurgery, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
| | - Jin Hoon Park
- Department of Neurosurgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Kyung Hyun Kim
- Department of Neurosurgery, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | | | - Bong Ju Moon
- Department of Neurosurgery, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
- Department of Neurosurgery, Chonnam National University Research Institute of Medical Sciences, Chonnam National University Hospital & Medical School, Gwangju, Korea
| | - Chang-Kyu Lee
- Department of Neurosurgery, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Myoung Hoon Shin
- Department of Neurosurgery, Incheon St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Incheon, Korea
| | - Hyun Jun Jang
- Department of Neurosurgery, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Moon-Soo Han
- Department of Neurosurgery, Chonnam National University Research Institute of Medical Sciences, Chonnam National University Hospital & Medical School, Gwangju, Korea
| | - Chi Heon Kim
- Department of Neurosurgery, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea
| | - Chun Kee Chung
- Department of Neurosurgery, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea
| | - Seung-Myung Moon
- Department of Neurosurgery, Kangnam Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Korea
| | - for the Korean Spinal Deformity Society
- Department of Neurosurgery, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea
- Department of Neurosurgery, Kyung Hee University Hospital at Gangdong, Seoul, Korea
- Department of Neurosurgery, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang, Korea
- Department of Neurosurgery, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
- Department of Neurosurgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
- Department of Neurosurgery, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
- Wooridul Spine Hospital, Seoul, Korea
- Department of Neurosurgery, Chonnam National University Research Institute of Medical Sciences, Chonnam National University Hospital & Medical School, Gwangju, Korea
- Department of Neurosurgery, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
- Department of Neurosurgery, Incheon St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Incheon, Korea
- Department of Neurosurgery, Kangnam Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Korea
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Han IH. Commentary on "Development and Validation of an Online Calculator to Predict Proximal Junctional Kyphosis After Adult Spinal Deformity Surgery Using Machine Learning". Neurospine 2023; 20:1281-1283. [PMID: 38171295 PMCID: PMC10762415 DOI: 10.14245/ns.2347302.651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2024] Open
Affiliation(s)
- In Ho Han
- Department of Neurosurgery, Pusan National University Hospital, Pusan National University School of Medicine, Busan, Korea
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Johnson GW, Chanbour H, Ali MA, Chen J, Metcalf T, Doss D, Younus I, Jonzzon S, Roth SG, Abtahi AM, Stephens BF, Zuckerman SL. Artificial Intelligence to Preoperatively Predict Proximal Junction Kyphosis Following Adult Spinal Deformity Surgery: Soft Tissue Imaging May Be Necessary for Accurate Models. Spine (Phila Pa 1976) 2023; 48:1688-1695. [PMID: 37644737 PMCID: PMC11101214 DOI: 10.1097/brs.0000000000004816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 08/21/2023] [Indexed: 08/31/2023]
Abstract
STUDY DESIGN Retrospective cohort. OBJECTIVE In a cohort of patients undergoing adult spinal deformity (ASD) surgery, we used artificial intelligence to compare three models of preoperatively predicting radiographic proximal junction kyphosis (PJK) using: (1) traditional demographics and radiographic measurements, (2) raw preoperative scoliosis radiographs, and (3) raw preoperative thoracic magnetic resonance imaging (MRI). SUMMARY OF BACKGROUND DATA Despite many proposed risk factors, PJK following ASD surgery remains difficult to predict. MATERIALS AND METHODS A single-institution, retrospective cohort study was undertaken for patients undergoing ASD surgery from 2009 to 2021. PJK was defined as a sagittal Cobb angle of upper-instrumented vertebra (UIV) and UIV+2>10° and a postoperative change in UIV/UIV+2>10°. For model 1, a support vector machine was used to predict PJK within 2 years postoperatively using clinical and traditional sagittal/coronal radiographic variables and intended levels of instrumentation. Next, for model 2, a convolutional neural network (CNN) was trained on raw preoperative lateral and posterior-anterior scoliosis radiographs. Finally, for model 3, a CNN was trained on raw preoperative thoracic T1 MRIs. RESULTS A total of 191 patients underwent ASD surgery with at least 2-year follow-up and 89 (46.6%) developed radiographic PJK within 2 years. Model 1: Using clinical variables and traditional radiographic measurements, the model achieved a sensitivity: 57.2% and a specificity: 56.3%. Model 2: a CNN with raw scoliosis x-rays predicted PJK with a sensitivity: 68.2% and specificity: 58.3%. Model 3: a CNN with raw thoracic MRIs predicted PJK with average sensitivity: 73.1% and specificity: 79.5%. Finally, an attention map outlined the imaging features used by model 3 elucidated that soft tissue features predominated all true positive PJK predictions. CONCLUSIONS The use of raw MRIs in an artificial intelligence model improved the accuracy of PJK prediction compared with raw scoliosis radiographs and traditional clinical/radiographic measurements. The improved predictive accuracy using MRI may indicate that PJK is best predicted by soft tissue degeneration and muscle atrophy.
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Affiliation(s)
| | - Hani Chanbour
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN
| | - Mir Amaan Ali
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN
| | - Jeffrey Chen
- Vanderbilt University School of Medicine, Nashville, TN
| | - Tyler Metcalf
- Department of Orthopedic Surgery, Vanderbilt University Medical Center, Nashville, TN
| | - Derek Doss
- Vanderbilt University School of Medicine, Nashville, TN
| | - Iyan Younus
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN
| | - Soren Jonzzon
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN
| | - Steven G. Roth
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN
| | - Amir M. Abtahi
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN
- Department of Orthopedic Surgery, Vanderbilt University Medical Center, Nashville, TN
| | - Byron F. Stephens
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN
- Department of Orthopedic Surgery, Vanderbilt University Medical Center, Nashville, TN
| | - Scott L. Zuckerman
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN
- Department of Orthopedic Surgery, Vanderbilt University Medical Center, Nashville, TN
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Abdel Hady DA, Abd El-Hafeez T. Predicting female pelvic tilt and lumbar angle using machine learning in case of urinary incontinence and sexual dysfunction. Sci Rep 2023; 13:17940. [PMID: 37863988 PMCID: PMC10589228 DOI: 10.1038/s41598-023-44964-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Accepted: 10/13/2023] [Indexed: 10/22/2023] Open
Abstract
Urinary incontinence (UI) is defined as any uncontrolled urine leakage. Pelvic floor muscles (PFM) appear to be a crucial aspect of trunk and lumbo-pelvic stability, and UI is one indication of pelvic floor dysfunction. The evaluation of pelvic tilt and lumbar angle is critical in assessing the alignment and posture of the spine in the lower back region and pelvis, and both of these variables are directly related to female dysfunction in the pelvic floor. UI affects a significant number of women worldwide and can have a major impact on their quality of life. However, traditional methods of assessing these parameters involve manual measurements, which are time-consuming and prone to variability. The rehabilitation programs for pelvic floor dysfunction (FSD) in physical therapy often focus on pelvic floor muscles (PFMs), while other core muscles are overlooked. Therefore, this study aimed to predict the activity of various core muscles in multiparous women with FSD using multiple scales instead of relying on Ultrasound imaging. Decision tree, SVM, random forest, and AdaBoost models were applied to predict pelvic tilt and lumbar angle using the train set. Performance was evaluated on the test set using MSE, RMSE, MAE, and R2. Pelvic tilt prediction achieved R2 values > 0.9, with AdaBoost (R2 = 0.944) performing best. Lumbar angle prediction performed slightly lower with decision tree achieving the highest R2 of 0.976. Developing a machine learning model to predict pelvic tilt and lumbar angle has the potential to revolutionize the assessment and management of this condition, providing faster, more accurate, and more objective assessments than traditional methods.
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Affiliation(s)
- Doaa A Abdel Hady
- Department of Physical Therapy for Women's Health, Faculty of Physiotherapy, Deraya University, EL-Minia, Egypt.
| | - Tarek Abd El-Hafeez
- Department of Computer Science, Faculty of Science, Minia University, EL-Minia, Egypt.
- Computer Science Unit, Deraya University, EL-Minia, Egypt.
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Song SY, Seo MS, Kim CW, Kim YH, Yoo BC, Choi HJ, Seo SH, Kang SW, Song MG, Nam DC, Kim DH. AI-Driven Segmentation and Automated Analysis of the Whole Sagittal Spine from X-ray Images for Spinopelvic Parameter Evaluation. Bioengineering (Basel) 2023; 10:1229. [PMID: 37892959 PMCID: PMC10604000 DOI: 10.3390/bioengineering10101229] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 10/15/2023] [Accepted: 10/18/2023] [Indexed: 10/29/2023] Open
Abstract
Spinal-pelvic parameters are utilized in orthopedics for assessing patients' curvature and body alignment in diagnosing, treating, and planning surgeries for spinal and pelvic disorders. Segmenting and autodetecting the whole spine from lateral radiographs is challenging. Recent efforts have employed deep learning techniques to automate the segmentation and analysis of whole-spine lateral radiographs. This study aims to develop an artificial intelligence (AI)-based deep learning approach for the automated segmentation, alignment, and measurement of spinal-pelvic parameters through whole-spine lateral radiographs. We conducted the study on 932 annotated images from various spinal pathologies. Using a deep learning (DL) model, anatomical landmarks of the cervical, thoracic, lumbar vertebrae, sacrum, and femoral head were automatically distinguished. The algorithm was designed to measure 13 radiographic alignment and spinal-pelvic parameters from the whole-spine lateral radiographs. Training data comprised 748 digital radiographic (DR) X-ray images, while 90 X-ray images were used for validation. Another set of 90 X-ray images served as the test set. Inter-rater reliability between orthopedic spine specialists, orthopedic residents, and the DL model was evaluated using the intraclass correlation coefficient (ICC). The segmentation accuracy for anatomical landmarks was within an acceptable range (median error: 1.7-4.1 mm). The inter-rater reliability between the proposed DL model and individual experts was fair to good for measurements of spinal curvature characteristics (all ICC values > 0.62). The developed DL model in this study demonstrated good levels of inter-rater reliability for predicting anatomical landmark positions and measuring radiographic alignment and spinal-pelvic parameters. Automated segmentation and analysis of whole-spine lateral radiographs using deep learning offers a promising tool to enhance accuracy and efficiency in orthopedic diagnostics and treatments.
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Affiliation(s)
- Sang-Youn Song
- Department of Orthopaedic Surgery, Institute of Medical Science, Gyeongsang National University Hospital and Gyeongsang National University School of Medicine, Jinju 52727, Republic of Korea; (S.-Y.S.); (M.-S.S.); (C.-W.K.)
| | - Min-Seok Seo
- Department of Orthopaedic Surgery, Institute of Medical Science, Gyeongsang National University Hospital and Gyeongsang National University School of Medicine, Jinju 52727, Republic of Korea; (S.-Y.S.); (M.-S.S.); (C.-W.K.)
| | - Chang-Won Kim
- Department of Orthopaedic Surgery, Institute of Medical Science, Gyeongsang National University Hospital and Gyeongsang National University School of Medicine, Jinju 52727, Republic of Korea; (S.-Y.S.); (M.-S.S.); (C.-W.K.)
| | - Yun-Heung Kim
- Deepnoid. Inc., Seoul 08376, Republic of Korea; (Y.-H.K.); (B.-C.Y.); (H.-J.C.)
| | - Byeong-Cheol Yoo
- Deepnoid. Inc., Seoul 08376, Republic of Korea; (Y.-H.K.); (B.-C.Y.); (H.-J.C.)
| | - Hyun-Ju Choi
- Deepnoid. Inc., Seoul 08376, Republic of Korea; (Y.-H.K.); (B.-C.Y.); (H.-J.C.)
| | - Sung-Hyo Seo
- Department of Biomedical Research Institute, Gyeongsang National University Hospital, Jinju 52727, Republic of Korea;
| | - Sung-Wook Kang
- Precision Mechanical Process and Control R&D Group, Korea Institute of Industrial Technology, Seoul 06211, Republic of Korea;
| | - Myung-Geun Song
- Department of Orthopaedic Surgery, College of Medicine, Inha University Hospital, Incheon 22212, Republic of Korea;
| | - Dae-Cheol Nam
- Department of Orthopaedic Surgery, Institute of Medical Science, Gyeongsang National University Hospital and Gyeongsang National University School of Medicine, Jinju 52727, Republic of Korea; (S.-Y.S.); (M.-S.S.); (C.-W.K.)
| | - Dong-Hee Kim
- Department of Orthopaedic Surgery, Institute of Medical Science, Gyeongsang National University Hospital and Gyeongsang National University School of Medicine, Jinju 52727, Republic of Korea; (S.-Y.S.); (M.-S.S.); (C.-W.K.)
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Dalton J, Mohamed A, Akioyamen N, Schwab FJ, Lafage V. PreOperative Planning for Adult Spinal Deformity Goals: Level Selection and Alignment Goals. Neurosurg Clin N Am 2023; 34:527-536. [PMID: 37718099 DOI: 10.1016/j.nec.2023.06.016] [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] [Indexed: 09/19/2023]
Abstract
Adult Spinal Deformity (ASD) is a complex pathologic condition with significant impact on quality of life, including pain, loss of function, and fatigue. Achieving realignment goals is crucial for long-term results. Reliable preoperative planning strategies, including nomograms, measurement tools, and level selection, are key to maximizing the likelihood of achieving a good outcome following ASD corrective surgery. This review covers recent literature on such strategies, including review of the different targets for realignment and their association with outcomes (both patients-reported outcomes and complications), selection of upper and lower instrumented vertebrae, and the latest innovation in preoperative planning for deformity surgery.
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Affiliation(s)
- Jay Dalton
- Department of Orthopaedic Surgery, University of Pittsburgh Medical Center, 3471 Fifth Avenue, Pittsburgh, PA 15213, USA
| | - Ayman Mohamed
- Department of Orthopaedic Surgery, Lenox Hill Hospital, 130 East 77th Street, 11th Floor, New York, NY 10075, USA
| | - Noel Akioyamen
- Department of Orthopaedic Surgery, Monteriore Medical Center, 1250 Waters Place, Tower 1, 11th Floor, Bronx, NY 10461, USA
| | - Frank J Schwab
- Department of Orthopaedic Surgery, Lenox Hill Hospital, 130 East 77th Street, 11th Floor, New York, NY 10075, USA
| | - Virginie Lafage
- Department of Orthopaedic Surgery, Lenox Hill Hospital, 130 East 77th Street, 11th Floor, New York, NY 10075, USA.
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12
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Noh SH, Lee HS, Park GE, Ha Y, Park JY, Kuh SU, Chin DK, Kim KS, Cho YE, Kim SH, Kim KH. Predicting Mechanical Complications After Adult Spinal Deformity Operation Using a Machine Learning Based on Modified Global Alignment and Proportion Scoring With Body Mass Index and Bone Mineral Density. Neurospine 2023; 20:265-274. [PMID: 37016873 PMCID: PMC10080453 DOI: 10.14245/ns.2244854.427] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 12/28/2022] [Indexed: 04/03/2023] Open
Abstract
Objective: This study aimed to create an ideal machine learning model to predict mechanical complications in adult spinal deformity (ASD) surgery based on GAPB (modified global alignment and proportion scoring with body mass index and bone mineral density) factors.Methods: Between January 2009 and December 2018, 238 consecutive patients with ASD, who received at least 4-level fusions and were followed-up for ≥ 2 years, were included in the study. The data were stratified into training (n = 167, 70%) and test (n = 71, 30%) sets and input to machine learning algorithms, including logistic regression, random forest gradient boosting system, and deep neural network.Results: Body mass index, bone mineral density, the relative pelvic version score, the relative lumbar lordosis score, and the relative sagittal alignment score of the global alignment and proportion score were significantly different in the training and test sets (p < 0.05) between the complication and no complication groups. In the training set, the area under receiver operating characteristics (AUROCs) for logistic regression, gradient boosting, random forest, and deep neural network were 0.871 (0.817–0.925), 0.942 (0.911–0.974), 1.000 (1.000–1.000), and 0.947 (0.915–0.980), respectively, and the accuracies were 0.784 (0.722–0.847), 0.868 (0.817–0.920), 1.000 (1.000–1.000), and 0.856 (0.803–0.909), respectively. In the test set, the AUROCs were 0.785 (0.678–0.893), 0.808 (0.702–0.914), 0.810 (0.710–0.910), and 0.730 (0.610–0.850), respectively, and the accuracies were 0.732 (0.629–0.835), 0.718 (0.614–0.823), 0.732 (0.629–0.835), and 0.620 (0.507–0.733), respectively. The random forest achieved the best predictive performance on the training and test dataset.Conclusion: This study created a comprehensive model to predict mechanical complications after ASD surgery. The best prediction accuracy was 73.2% for predicting mechanical complications after ASD surgery.
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Affiliation(s)
- Sung Hyun Noh
- Department of Neurosurgery, Ajou University College of Medicine, Suwon, Korea
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Korea
| | - Hye Sun Lee
- Biostatistics Collaboration Unit, Yonsei University College of Medicine, Seoul, Korea
| | - Go Eun Park
- Biostatistics Collaboration Unit, Yonsei University College of Medicine, Seoul, Korea
| | - Yoon Ha
- Department of Neurosurgery, Spine and Spinal Cord Institute, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Jeong Yoon Park
- Department of Neurosurgery, Spine and Spinal Cord Institute, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Sung Uk Kuh
- Department of Neurosurgery, Spine and Spinal Cord Institute, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Dong Kyu Chin
- Department of Neurosurgery, Spine and Spinal Cord Institute, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Keun Su Kim
- Department of Neurosurgery, Spine and Spinal Cord Institute, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Yong Eun Cho
- Department of Neurosurgery, Spine and Spinal Cord Institute, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Sang Hyun Kim
- Department of Neurosurgery, Ajou University College of Medicine, Suwon, Korea
- Corresponding Author Kyung Hyun Kim Department of Neurosurgery, Spine and Spinal Cord Institute, Gangnam Severance Hospital, Yonsei University College of Medicine, 211 Eonju-ro, Gangnam-gu, Seoul 06273, Korea
| | - Kyung Hyun Kim
- Department of Neurosurgery, Spine and Spinal Cord Institute, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
- Co-corresponding Author Sang Hyun Kim Department of Neurosurgery, Ajou University Hospital, Ajou University School of Medicine, 164 World cup-ro, Yeongtong-gu, Suwon 16499, Korea
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13
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Charles YP, Lamas V, Ntilikina Y. Artificial intelligence and treatment algorithms in spine surgery. Orthop Traumatol Surg Res 2023; 109:103456. [PMID: 36302452 DOI: 10.1016/j.otsr.2022.103456] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Revised: 05/12/2022] [Accepted: 05/25/2022] [Indexed: 12/24/2022]
Abstract
Artificial intelligence (AI) is a set of theories and techniques in which machines are used to simulate human intelligence with complex computer programs. The various machine learning (ML) methods are a subtype of AI. They originate from computer science and use algorithms established from analyzing a database to accomplish certain tasks. Among these methods are decision trees or random forests, support vector machines along with artificial neural networks. Convolutive neural networks were inspired from the visual cortex; they process combinations of information used in image or voice recognition. Deep learning (DL) groups together a set of ML methods and is useful for modeling complex relationships with a high degree of abstraction by using multiple layers of artificial neurons. ML techniques have a growing role in spine surgery. The main applications are the segmentation of intraoperative images for surgical navigation or robotics used for pedicle screw placement, the interpretation of images of intervertebral discs or full spine radiographs, which can be automated using ML algorithms. ML techniques can also be used as aids for surgical decision-making in complex fields, such as preoperative evaluation of adult spinal deformity. ML algorithms "learn" from large clinical databases. They make it possible to establish the intraoperative risk level and make a prognosis on how the postoperative functional scores will change over time as a function of the patient profile. These applications open a new path relative to standard statistical analyses. They make it possible to explore more complex relationships with multiple indirect interactions. In the future, AI algorithms could have a greater role in clinical research, evaluating clinical and surgical practices, and conducting health economics analyses.
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Affiliation(s)
- Yann Philippe Charles
- Service de chirurgie du rachis, hôpitaux universitaires de Strasbourg, université de Strasbourg, 1, avenue Molière, 67200 Strasbourg, France.
| | - Vincent Lamas
- Service de chirurgie du rachis, hôpitaux universitaires de Strasbourg, université de Strasbourg, 1, avenue Molière, 67200 Strasbourg, France
| | - Yves Ntilikina
- Service de chirurgie du rachis, hôpitaux universitaires de Strasbourg, université de Strasbourg, 1, avenue Molière, 67200 Strasbourg, France
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14
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Lee JH, Woo H, Jang JS, Kim JI, Na YC, Kim KR, Cho E, Lee JH, Park TY. Comparison of Concordance between Chuna Manual Therapy Diagnostic Methods (Palpation, X-ray, Artificial Intelligence Program) in Lumbar Spine: An Exploratory, Cross-Sectional Clinical Study. Diagnostics (Basel) 2022; 12:2732. [PMID: 36359575 PMCID: PMC9689192 DOI: 10.3390/diagnostics12112732] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 11/04/2022] [Accepted: 11/07/2022] [Indexed: 10/15/2023] Open
Abstract
Before Chuna manual therapy (CMT), a manual therapy applied in Korean medicine, CMT spinal diagnosis using palpation or X-ray is performed. However, studies on the inter-rater concordance of CMT diagnostic methods, concordance among diagnostic methods, and standard CMT diagnostic methods are scarce. Moreover, no clinical studies have used artificial intelligence (AI) programs for X-ray image-based CMT diagnosis. Therefore, this study sought a feasible and standard CMT spinal diagnostic method and explored the clinical applicability of the CMT-AI program. One hundred participants were recruited, and the concordance within and among different diagnostic modalities was analyzed by dividing them into manual diagnosis (MD), X-ray image-based diagnosis (XRD) by experts and non-experts, and XRD using a CMT-AI program by non-experts. Regarding intra-group concordance, XRD by experts showed the highest concordance (used as a gold standard when comparing inter-group concordance), followed by XRD using the AI program, XRD by non-experts, and then MD. Comparing diagnostic results between the groups, concordance with the gold standard was the highest for XRD using the AI program, followed by XRD by non-experts, and MD. Therefore, XRD is a more reasonable CMT diagnostic method than MD. Furthermore, the clinical applicability of the CMT-AI program is high.
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Affiliation(s)
- Jin-Hyun Lee
- Institute for Integrative Medicine, Catholic Kwandong University International St. Mary’s Hospital, 25 Simgok-ro 100 Beon-gil, Seo-gu, Incheon 22711, Republic of Korea
| | - Hyeonjun Woo
- Department of Korean Medicine Rehabilitation, College of Korean Medicine, Wonkwang University, 460 Iksan-daero, Iksan-si 54538, Republic of Korea
| | - Jun-Su Jang
- Digital Health Research Division, Korea Institute of Oriental Medicine, 1672 Yuseong-daero, Yuseong-gu, Daejeon 34054, Republic of Korea
| | - Joong Il Kim
- Digital Health Research Division, Korea Institute of Oriental Medicine, 1672 Yuseong-daero, Yuseong-gu, Daejeon 34054, Republic of Korea
| | - Young Cheol Na
- Department of Neurosurgery, Catholic Kwandong University International St. Mary’s Hospital, Catholic Kwandong University College of Medicine, 25 Simgok-ro 100 Beon-gil, Seo-gu, Incheon 22711, Republic of Korea
| | - Kwang-Ryeol Kim
- Department of Neurosurgery, Catholic Kwandong University International St. Mary’s Hospital, Catholic Kwandong University College of Medicine, 25 Simgok-ro 100 Beon-gil, Seo-gu, Incheon 22711, Republic of Korea
| | - Eunbyul Cho
- Department of Acupuncture and Moxibustion Medicine, College of Korean Medicine, Wonkwang University, 460 Iksan-daero, Iksan-si 54538, Republic of Korea
| | - Jung-Han Lee
- Department of Korean Medicine Rehabilitation, College of Korean Medicine, Wonkwang University, 460 Iksan-daero, Iksan-si 54538, Republic of Korea
| | - Tae-Yong Park
- Institute for Integrative Medicine, Catholic Kwandong University International St. Mary’s Hospital, 25 Simgok-ro 100 Beon-gil, Seo-gu, Incheon 22711, Republic of Korea
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15
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Alsoof D, McDonald CL, Kuris EO, Daniels AH. Machine Learning for the Orthopaedic Surgeon: Uses and Limitations. J Bone Joint Surg Am 2022; 104:1586-1594. [PMID: 35383655 DOI: 10.2106/jbjs.21.01305] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
➤ Machine learning is a subset of artificial intelligence in which computer algorithms are trained to make classifications and predictions based on patterns in data. The utilization of these techniques is rapidly expanding in the field of orthopaedic research. ➤ There are several domains in which machine learning has application to orthopaedics, including radiographic diagnosis, gait analysis, implant identification, and patient outcome prediction. ➤ Several limitations prevent the widespread use of machine learning in the daily clinical environment. However, future work can overcome these issues and enable machine learning tools to be a useful adjunct for orthopaedic surgeons in their clinical decision-making.
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Affiliation(s)
- Daniel Alsoof
- Department of Orthopedic Surgery, Warren Alpert Medical School of Brown University, Providence, Rhode Island
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16
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Hornung AL, Hornung CM, Mallow GM, Barajas JN, Espinoza Orías AA, Galbusera F, Wilke HJ, Colman M, Phillips FM, An HS, Samartzis D. Artificial intelligence and spine imaging: limitations, regulatory issues and future direction. 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; 31:2007-2021. [PMID: 35084588 DOI: 10.1007/s00586-021-07108-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 11/29/2021] [Accepted: 12/30/2021] [Indexed: 01/20/2023]
Abstract
BACKGROUND As big data and artificial intelligence (AI) in spine care, and medicine as a whole, continue to be at the forefront of research, careful consideration to the quality and techniques utilized is necessary. Predictive modeling, data science, and deep analytics have taken center stage. Within that space, AI and machine learning (ML) approaches toward the use of spine imaging have gathered considerable attention in the past decade. Although several benefits of such applications exist, limitations are also present and need to be considered. PURPOSE The following narrative review presents the current status of AI, in particular, ML, with special regard to imaging studies, in the field of spinal research. METHODS A multi-database assessment of the literature was conducted up to September 1, 2021, that addressed AI as it related to imaging of the spine. Articles written in English were selected and critically assessed. RESULTS Overall, the review discussed the limitations, data quality and applications of ML models in the context of spine imaging. In particular, we addressed the data quality and ML algorithms in spine imaging research by describing preliminary results from a widely accessible imaging algorithm that is currently available for spine specialists to reference for information on severity of spine disease and degeneration which ultimately may alter clinical decision-making. In addition, awareness of the current, under-recognized regulation surrounding the execution of ML for spine imaging was raised. CONCLUSIONS Recommendations were provided for conducting high-quality, standardized AI applications for spine imaging.
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Affiliation(s)
- Alexander L Hornung
- Department of Orthopaedic Surgery, Rush University Medical Center, Orthopaedic Building, Suite 204-G, 1611 W. Harrison Street, Chicago, IL, 60612, USA
| | | | - G Michael Mallow
- Department of Orthopaedic Surgery, Rush University Medical Center, Orthopaedic Building, Suite 204-G, 1611 W. Harrison Street, Chicago, IL, 60612, USA
| | - J Nicolas Barajas
- Department of Orthopaedic Surgery, Rush University Medical Center, Orthopaedic Building, Suite 204-G, 1611 W. Harrison Street, Chicago, IL, 60612, USA
| | - Alejandro A Espinoza Orías
- Department of Orthopaedic Surgery, Rush University Medical Center, Orthopaedic Building, Suite 204-G, 1611 W. Harrison Street, Chicago, IL, 60612, USA
| | | | - Hans-Joachim Wilke
- Institute of Orthopaedic Research and Biomechanics, Trauma Research Center Ulm, Ulm University, Ulm, Germany
| | - Matthew Colman
- Department of Orthopaedic Surgery, Rush University Medical Center, Orthopaedic Building, Suite 204-G, 1611 W. Harrison Street, Chicago, IL, 60612, USA
| | - Frank M Phillips
- Department of Orthopaedic Surgery, Rush University Medical Center, Orthopaedic Building, Suite 204-G, 1611 W. Harrison Street, Chicago, IL, 60612, USA
| | - Howard S An
- Department of Orthopaedic Surgery, Rush University Medical Center, Orthopaedic Building, Suite 204-G, 1611 W. Harrison Street, Chicago, IL, 60612, USA
| | - Dino Samartzis
- Department of Orthopaedic Surgery, Rush University Medical Center, Orthopaedic Building, Suite 204-G, 1611 W. Harrison Street, Chicago, IL, 60612, USA.
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17
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Hornung AL, Hornung CM, Mallow GM, Barajas JN, Rush A, Sayari AJ, Galbusera F, Wilke HJ, Colman M, Phillips FM, An HS, Samartzis D. Artificial intelligence in spine care: current applications and future utility. 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; 31:2057-2081. [PMID: 35347425 DOI: 10.1007/s00586-022-07176-0] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 01/18/2022] [Accepted: 03/08/2022] [Indexed: 01/20/2023]
Abstract
PURPOSE The field of artificial intelligence is ever growing and the applications of machine learning in spine care are continuously advancing. Given the advent of the intelligence-based spine care model, understanding the evolution of computation as it applies to diagnosis, treatment, and adverse event prediction is of great importance. Therefore, the current review sought to synthesize findings from the literature at the interface of artificial intelligence and spine research. METHODS A narrative review was performed based on the literature of three databases (MEDLINE, CINAHL, and Scopus) from January 2015 to March 2021 that examined historical and recent advancements in the understanding of artificial intelligence and machine learning in spine research. Studies were appraised for their role in, or description of, advancements within image recognition and predictive modeling for spinal research. Only English articles that fulfilled inclusion criteria were ultimately incorporated in this review. RESULTS This review briefly summarizes the history and applications of artificial intelligence and machine learning in spine. Three basic machine learning training paradigms: supervised learning, unsupervised learning, and reinforced learning are also discussed. Artificial intelligence and machine learning have been utilized in almost every facet of spine ranging from localization and segmentation techniques in spinal imaging to pathology specific algorithms which include but not limited to; preoperative risk assessment of postoperative complications, screening algorithms for patients at risk of osteoporosis and clustering analysis to identify subgroups within adolescent idiopathic scoliosis. The future of artificial intelligence and machine learning in spine surgery is also discussed with focusing on novel algorithms, data collection techniques and increased utilization of automated systems. CONCLUSION Improvements to modern-day computing and accessibility to various imaging modalities allow for innovative discoveries that may arise, for example, from management. Given the imminent future of AI in spine surgery, it is of great importance that practitioners continue to inform themselves regarding AI, its goals, use, and progression. In the future, it will be critical for the spine specialist to be able to discern the utility of novel AI research, particularly as it continues to pervade facets of everyday spine surgery.
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Affiliation(s)
- Alexander L Hornung
- Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA
| | | | - G Michael Mallow
- Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA
| | - J Nicolás Barajas
- Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA
| | - Augustus Rush
- Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA
| | - Arash J Sayari
- Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA
| | | | - Hans-Joachim Wilke
- Institute of Orthopaedic Research and Biomechanics, Trauma Research Center Ulm, Ulm University, Ulm, Germany
| | - Matthew Colman
- Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA
| | - Frank M Phillips
- Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA
| | - Howard S An
- Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA
| | - Dino Samartzis
- Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA.
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18
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Rudisill SS, Hornung AL, Barajas JN, Bridge JJ, Mallow GM, Lopez W, Sayari AJ, Louie PK, Harada GK, Tao Y, Wilke HJ, Colman MW, Phillips FM, An HS, Samartzis D. Artificial intelligence in predicting early-onset adjacent segment degeneration following anterior cervical discectomy and fusion. 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; 31:2104-2114. [PMID: 35543762 DOI: 10.1007/s00586-022-07238-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 02/12/2022] [Accepted: 04/17/2022] [Indexed: 01/20/2023]
Abstract
PURPOSE Anterior cervical discectomy and fusion (ACDF) is a common surgical treatment for degenerative disease in the cervical spine. However, resultant biomechanical alterations may predispose to early-onset adjacent segment degeneration (EO-ASD), which may become symptomatic and require reoperation. This study aimed to develop and validate a machine learning (ML) model to predict EO-ASD following ACDF. METHODS Retrospective review of prospectively collected data of patients undergoing ACDF at a quaternary referral medical center was performed. Patients > 18 years of age with > 6 months of follow-up and complete pre- and postoperative X-ray and MRI imaging were included. An ML-based algorithm was developed to predict EO-ASD based on preoperative demographic, clinical, and radiographic parameters, and model performance was evaluated according to discrimination and overall performance. RESULTS In total, 366 ACDF patients were included (50.8% male, mean age 51.4 ± 11.1 years). Over 18.7 ± 20.9 months of follow-up, 97 (26.5%) patients developed EO-ASD. The model demonstrated good discrimination and overall performance according to precision (EO-ASD: 0.70, non-ASD: 0.88), recall (EO-ASD: 0.73, non-ASD: 0.87), accuracy (0.82), F1-score (0.79), Brier score (0.203), and AUC (0.794), with C4/C5 posterior disc bulge, C4/C5 anterior disc bulge, C6 posterior superior osteophyte, presence of osteophytes, and C6/C7 anterior disc bulge identified as the most important predictive features. CONCLUSIONS Through an ML approach, the model identified risk factors and predicted development of EO-ASD following ACDF with good discrimination and overall performance. By addressing the shortcomings of traditional statistics, ML techniques can support discovery, clinical decision-making, and precision-based spine care.
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Affiliation(s)
- Samuel S Rudisill
- Department of Orthopaedic Surgery, Rush University Medical Center, 1611 W. Harrison Street, Chicago, IL, USA.,International Spine Research and Innovation Initiative, Rush University Medical Center, Chicago, IL, USA
| | - Alexander L Hornung
- Department of Orthopaedic Surgery, Rush University Medical Center, 1611 W. Harrison Street, Chicago, IL, USA.,International Spine Research and Innovation Initiative, Rush University Medical Center, Chicago, IL, USA
| | - J Nicolás Barajas
- Department of Orthopaedic Surgery, Rush University Medical Center, 1611 W. Harrison Street, Chicago, IL, USA.,International Spine Research and Innovation Initiative, Rush University Medical Center, Chicago, IL, USA
| | - Jack J Bridge
- Department of Orthopaedic Surgery, Rush University Medical Center, 1611 W. Harrison Street, Chicago, IL, USA.,Department of Data Science and Analytics, University of Missouri, Colombia, MO, USA
| | - G Michael Mallow
- Department of Orthopaedic Surgery, Rush University Medical Center, 1611 W. Harrison Street, Chicago, IL, USA.,International Spine Research and Innovation Initiative, Rush University Medical Center, Chicago, IL, USA
| | - Wylie Lopez
- Department of Orthopaedic Surgery, Rush University Medical Center, 1611 W. Harrison Street, Chicago, IL, USA.,International Spine Research and Innovation Initiative, Rush University Medical Center, Chicago, IL, USA
| | - Arash J Sayari
- Department of Orthopaedic Surgery, Rush University Medical Center, 1611 W. Harrison Street, Chicago, IL, USA.,International Spine Research and Innovation Initiative, Rush University Medical Center, Chicago, IL, USA
| | - Philip K Louie
- Virginia Mason Medical Center, Neuroscience Institute, Seattle, WA, USA
| | - Garrett K Harada
- Department of Orthopaedic Surgery, Rush University Medical Center, 1611 W. Harrison Street, Chicago, IL, USA.,International Spine Research and Innovation Initiative, Rush University Medical Center, Chicago, IL, USA
| | - Youping Tao
- Institute of Orthopaedic Research and Biomechanics, Ulm University Medical Centre, Ulm, Germany
| | - Hans-Joachim Wilke
- Institute of Orthopaedic Research and Biomechanics, Ulm University Medical Centre, Ulm, Germany
| | - Matthew W Colman
- Department of Orthopaedic Surgery, Rush University Medical Center, 1611 W. Harrison Street, Chicago, IL, USA.,International Spine Research and Innovation Initiative, Rush University Medical Center, Chicago, IL, USA
| | - Frank M Phillips
- Department of Orthopaedic Surgery, Rush University Medical Center, 1611 W. Harrison Street, Chicago, IL, USA.,International Spine Research and Innovation Initiative, Rush University Medical Center, Chicago, IL, USA
| | - Howard S An
- Department of Orthopaedic Surgery, Rush University Medical Center, 1611 W. Harrison Street, Chicago, IL, USA.,International Spine Research and Innovation Initiative, Rush University Medical Center, Chicago, IL, USA
| | - Dino Samartzis
- Department of Orthopaedic Surgery, Rush University Medical Center, 1611 W. Harrison Street, Chicago, IL, USA. .,International Spine Research and Innovation Initiative, Rush University Medical Center, Chicago, IL, USA.
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19
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Kim T, Kim YG, Park S, Lee JK, Lee CH, Hyun SJ, Kim CH, Kim KJ, Chung CK. Diagnostic triage in patients with central lumbar spinal stenosis using a deep learning system of radiographs. J Neurosurg Spine 2022; 37:104-111. [PMID: 35061993 DOI: 10.3171/2021.11.spine211136] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 11/11/2021] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Magnetic resonance imaging (MRI) is the gold-standard tool for diagnosing lumbar spinal stenosis (LSS), but it is difficult to promptly examine all suspected cases with MRI considering the modality's high cost and limited accessibility. Although radiography is an efficient screening technique owing to its low cost, rapid operability, and wide availability, its diagnostic accuracy is relatively poor. In this study, the authors aimed to develop a deep learning model with a convolutional neural network (CNN) for diagnosing severe central LSS using radiography and to evaluate radiological diagnostic features using gradient-weighted class activation mapping (Grad-CAM). METHODS Patients who had undergone both spinal MRI and radiography in the period from May 1, 2005, to December 31, 2017, were screened. According to the formal MRI report, participants were consecutively included in the severe central LSS or healthy control group, and radiographs for both groups were collected. A CNN-based transfer learning algorithm was developed to classify radiographic findings as LSS or normal (binary classification). The proposed models were evaluated using six performance metrics: area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity, and positive and negative predictive values. RESULTS The VGG19 model achieved the highest accuracy with an AUROC of 90.0% (95% CI 89.8%-90.3%) by training 12,442 images. Accuracy was 82.8% (95% CI 82.5%-83.1%) by averaging 5-fold models. Feature points on Grad-CAM were reasonable, and the features could be categorized into reduced disc height, narrow foramina, short pedicle, and hyperdense facet joint. The AUROC in the extra validation was 89.3% (95% CI 88.7%-90.0%). Accuracy was 81.8% (95% CI 80.6%-83.0%) by averaging 5-fold models. Multivariate logistic regression analysis showed that a combination of demographic factors (age and sex) did not improve the model performance. CONCLUSIONS The algorithm trained by a CNN to identify central LSS on radiographs showed high diagnostic accuracy and is expected to be useful as a triage tool. The algorithm could accurately localize the stenotic lesion to assist physicians in the identification of LSS.
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Affiliation(s)
- Tackeun Kim
- 1Department of Neurosurgery, Seoul National University Bundang Hospital, Seongnam
| | - Young-Gon Kim
- 2Transdisciplinary Department of Medicine & Advanced Technology, Seoul National University Hospital, Seoul
- 3AI Institute, Seoul National University, Seoul
| | - Seyeon Park
- 2Transdisciplinary Department of Medicine & Advanced Technology, Seoul National University Hospital, Seoul
| | - Jae-Koo Lee
- 1Department of Neurosurgery, Seoul National University Bundang Hospital, Seongnam
| | - Chang-Hyun Lee
- 1Department of Neurosurgery, Seoul National University Bundang Hospital, Seongnam
- 4Department of Neurosurgery, Seoul National University Hospital, Seoul
| | - Seung-Jae Hyun
- 1Department of Neurosurgery, Seoul National University Bundang Hospital, Seongnam
- 5Seoul National University College of Medicine, Seoul; and
| | - Chi Heon Kim
- 4Department of Neurosurgery, Seoul National University Hospital, Seoul
- 5Seoul National University College of Medicine, Seoul; and
| | - Ki-Jeong Kim
- 1Department of Neurosurgery, Seoul National University Bundang Hospital, Seongnam
- 5Seoul National University College of Medicine, Seoul; and
| | - Chun Kee Chung
- 4Department of Neurosurgery, Seoul National University Hospital, Seoul
- 5Seoul National University College of Medicine, Seoul; and
- 6Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea
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20
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Lee NJ, Sardar ZM, Boddapati V, Mathew J, Cerpa M, Leung E, Lombardi J, Lenke LG, Lehman RA. Can Machine Learning Accurately Predict Postoperative Compensation for the Uninstrumented Thoracic Spine and Pelvis After Fusion From the Lower Thoracic Spine to the Sacrum? Global Spine J 2022; 12:559-566. [PMID: 33030054 PMCID: PMC9109562 DOI: 10.1177/2192568220956978] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
STUDY DESIGN Consecutively collected cases. OBJECTIVE To determine if a machine-learning (ML) program can accurately predict the postoperative thoracic kyphosis through the uninstrumented thoracic spine and pelvic compensation in patients who undergo fusion from the lower thoracic spine (T10 or T11) to the sacrum. METHODS From 2015 to 2019, a consecutive series of adult (≥18 years old) patients with adult spinal deformity underwent corrective spinal fusion from the lower thoracic spine (T10 or T11) to the sacrum. Deidentified data was processed by a ML system-based platform to predict the postoperative thoracic kyphosis (TK) and pelvic tilt (PT) for each patient. To validate the ML model, the postoperative TK (T4-T12, instrumented thoracic, and uninstrumented thoracic) and the pelvic tilt were compared against the predicted values. RESULTS A total of 20 adult patients with a minimum 6-month follow-up (mean: 22.4 ± 11.3 months) were included in this study. No significant differences were observed for TK (predicted 37.6° vs postoperative 38.3°, P = .847), uninstrumented TK (predicted 33.9° vs postoperative 29.8°, P = .188), and PT (predicted 23.4° vs postoperative 22.7°, P = .754). The predicted PT and the TK of the uninstrumented thoracic spine correlated well with postoperative values (uninstrumented TK: R2 = 0.764, P < .001; PT: R2 = 0.868, P < .001). The mean error with which kyphosis through the uninstrumented thoracic spine can be measured was 4.8° ± 4.0°. The mean error for predicting PT was 2.5° ± 1.7°. CONCLUSION ML algorithms can accurately predict the spinopelvic compensation after spinal fusion from the lower thoracic spine to the sacrum. These findings suggest that surgeons may be able to leverage this technology to reduce the risk of proximal junctional kyphosis in this population.
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Affiliation(s)
- Nathan J. Lee
- Columbia University Medical Center,
The Och Spine Hospital at New York–Presbyterian, New York, NY, USA,Nathan J. Lee, Columbia University Medical
Center, 161 Fort Washington Avenue, New York, NY 10032, USA.
| | - Zeeshan M. Sardar
- Columbia University Medical Center,
The Och Spine Hospital at New York–Presbyterian, New York, NY, USA
| | - Venkat Boddapati
- Columbia University Medical Center,
The Och Spine Hospital at New York–Presbyterian, New York, NY, USA
| | - Justin Mathew
- Columbia University Medical Center,
The Och Spine Hospital at New York–Presbyterian, New York, NY, USA
| | - Meghan Cerpa
- Columbia University Medical Center,
The Och Spine Hospital at New York–Presbyterian, New York, NY, USA
| | - Eric Leung
- Columbia University Medical Center,
The Och Spine Hospital at New York–Presbyterian, New York, NY, USA
| | - Joseph Lombardi
- Columbia University Medical Center,
The Och Spine Hospital at New York–Presbyterian, New York, NY, USA
| | - Lawrence G. Lenke
- Columbia University Medical Center,
The Och Spine Hospital at New York–Presbyterian, New York, NY, USA
| | - Ronald A. Lehman
- Columbia University Medical Center,
The Och Spine Hospital at New York–Presbyterian, New York, NY, USA
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21
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Laverdière C, Georgiopoulos M, Ames CP, Corban J, Ahangar P, Awadhi K, Weber MH. Adult Spinal Deformity Surgery and Frailty: A Systematic Review. Global Spine J 2022; 12:689-699. [PMID: 33769119 PMCID: PMC9109568 DOI: 10.1177/21925682211004250] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
STUDY DESIGN Systematic review. OBJECTIVES Adult spinal deformity (ASD) can be a debilitating condition with a profound impact on patients' health-related quality of life (HRQoL). Many reports have suggested that the frailty status of a patient can have a significant impact on the outcome of the surgery. The present review aims to identify all pre-operative patient-specific frailty markers that are associated with postoperative outcomes following corrective surgery for ASD of the lumbar and thoracic spine. METHODS A systematic review of the literature was performed to identify findings regarding pre-operative markers of frailty and their association with postoperative outcomes in patients undergoing ASD surgery of the lumbar and thoracic spine. The search was performed in the following databases: PubMed, Embase, Cochrane and CINAHL. RESULTS An association between poorer performance on frailty scales and worse postoperative outcomes. Comorbidity indices were even more frequently employed with similar patterns of association between increased comorbidity burden and postoperative outcomes. Regarding the assessment of HRQoL, worse pre-operative ODI, SF-36, SRS-22 and NRS were shown to be predictors of post-operative complications, while ODI, SF-36 and SRS-22 were found to improve post-operatively. CONCLUSIONS The findings of this review highlight the true breadth of the concept of "frailty" in ASD surgical correction. These parameters, which include frailty scales and various comorbidity and HRQoL indices, highlight the importance of identifying these factors preoperatively to ensure appropriate patient selection while helping to limit poor postoperative outcomes.
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Affiliation(s)
- Carl Laverdière
- McGill University Faculty of
Medicine, Scoliosis and Spinal Research Unit, Montreal, Quebec, Canada
| | - Miltiadis Georgiopoulos
- McGill University Faculty of
Medicine, Scoliosis and Spinal Research Unit, Montreal, Quebec, Canada
| | - Christopher P. Ames
- Department of Neurological Surgery,
University of California, San Francisco, CA, USA
| | - Jason Corban
- McGill University Faculty of
Medicine, Scoliosis and Spinal Research Unit, Montreal, Quebec, Canada
| | - Pouyan Ahangar
- McGill University Faculty of
Medicine, Scoliosis and Spinal Research Unit, Montreal, Quebec, Canada
| | - Khaled Awadhi
- McGill University Faculty of
Medicine, Scoliosis and Spinal Research Unit, Montreal, Quebec, Canada
| | - Michael H. Weber
- McGill University Faculty of
Medicine, Scoliosis and Spinal Research Unit, Montreal, Quebec, Canada,Michael Weber, Department of Orthopedic
Surgery, McGill University Health Centre, Montreal General Hospital, 1650 Cedar
Avenue, Room A5-169, Montréal, Quebec, Canada H3G 1A4.
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22
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Anderson DE, Schmid S, Scheys L, Bazrgari B. Editorial: Using Motion Analysis Techniques and Musculoskeletal Modeling of the Spine to Better Understand Spinal Disorders and Evaluate Treatment Effects. Front Bioeng Biotechnol 2022; 10:884123. [PMID: 35464729 PMCID: PMC9019724 DOI: 10.3389/fbioe.2022.884123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 03/16/2022] [Indexed: 11/30/2022] Open
Affiliation(s)
- Dennis E Anderson
- Beth Israel Deaconess Medical Center, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
| | - Stefan Schmid
- School of Health Professions, Spinal Movement Biomechanics Group, Bern University of Applied Sciences, Bern, Switzerland
- Faculty of Medicine, University of Basel, Basel, Switzerland
| | - Lennart Scheys
- Department of Development and Regeneration, Faculty of Medicine, Institute for Orthopaedic Research and Training (IORT), KU Leuven, Leuven, Belgium
| | - Babak Bazrgari
- F. Joseph Halcomb III, M.D. Department of Biomedical Engineering, University of Kentucky, Lexington, KY, United States
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23
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Liu Y, Lee MG, Kim JS. Spine Surgery Assisted by Augmented Reality: Where Have We Been? Yonsei Med J 2022; 63:305-316. [PMID: 35352881 PMCID: PMC8965436 DOI: 10.3349/ymj.2022.63.4.305] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 02/02/2022] [Accepted: 02/09/2022] [Indexed: 11/27/2022] Open
Abstract
This present systematic review examines spine surgery literature supporting augmented reality (AR) technology and summarizes its current status in spinal surgery technology. Database search strategies were retrieved from PubMed, Web of Science, Cochrane Library, Embase, from the earliest records to April 1, 2021. Our review briefly examines the history of AR, and enumerates different device application workflows in a variety of spinal surgeries. We also sort out the pros and cons of current mainstream AR devices and the latest updates. A total of 45 articles are included in our review. The most prevalent surgical applications included are the augmented reality surgical navigation system and head-mounted display. The most popular application of AR is pedicle screw instrumentation in spine surgery, and the primary responsible surgical levels are thoracic and lumbar. AR guidance systems show high potential value in practical clinical applications for the spine. The overall number of cases in AR-related studies is still rare compared to traditional surgical-assisted techniques. These lack long-term clinical efficacy and robust surgical-related statistical data. Changing healthcare laws as well as the increasing prevalence of spinal surgery are generating critical data that determines the value of AR technology.
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Affiliation(s)
- Yanting Liu
- Department of Neurosurgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Min-Gi Lee
- Department of Neurosurgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Jin-Sung Kim
- Department of Neurosurgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea.
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24
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Kim DH, Hyun SJ, Lee CH, Kim KJ. The Last Touched Vertebra on Supine Radiographs Can Be the Optimal Lower Instrumented Vertebra in Adolescent Idiopathic Scoliosis Patients. Neurospine 2022; 19:236-243. [PMID: 35378591 PMCID: PMC8987554 DOI: 10.14245/ns.2143224.612] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 12/24/2021] [Indexed: 11/19/2022] Open
Abstract
Objective To determine whether the last touched vertebra (LTV) on supine radiographs is suitable for the lower instrumented vertebra (LIV) in adolescent idiopathic scoliosis (AIS) correction surgery.
Methods In total, 57 patients were included in the study following posterior instrumentation and fusion. The average follow-up period was 2.2 years. Patients were classified into 4 groups according to the relationship of the location of LIV, LTV, and the last substantially touched vertebra (LSTV) on upright radiographs and the LTV on supine radiographs. In group 1, the upright LTV and supine LTV were the same. Group 1 was subdivided into group 1A and group 1B according to whether the LTV and LSTV were different or the same, respectively. In group 2, the upright LTV was selected as the LIV, whereas in group 3, the supine LTV was selected as the LIV. The baseline characteristics and the preoperative and postoperative radiographic/clinical outcomes of the groups were analyzed.
Results No differences were found in the preoperative clinical and radiographic baseline characteristics of the 4 groups except the LIV-central sacral vertical line distance. The immediate, 6-month, 1-year, and 2-year postoperative outcomes were not significantly different among the 4 groups. One patient (4.3%) in group 1A experienced radiographic adding-on without clinical symptoms. No patients underwent revision surgery.
Conclusion The group in whom the LIV was selected as the LTV on supine x-rays showed similar postoperative radiographic and clinical results to other groups. The LTV on preoperative supine radiographs is acceptable as the LIV in AIS surgery to maximize motion segments.
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Affiliation(s)
- Do-Hyoung Kim
- Department of Neurosurgery, Spine Center, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
| | - Seung-Jae Hyun
- Department of Neurosurgery, Spine Center, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
- Corresponding Author Seung-Jae Hyun https://orcid.org/0000-0003-2937-5300 Department of Neurosurgery, Spine Center, Seoul National University Bundang Hospital, Seoul National University College of Medicine, 82 Gumi-ro 173beon-gil, Bundang-gu, Seongnam 13620, Korea
| | - Chang-Hyun Lee
- Department of Neurosurgery, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea
| | - Ki-Jeong Kim
- Department of Neurosurgery, Spine Center, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
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25
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Banna HU, Zanabli A, McMillan B, Lehmann M, Gupta S, Gerbo M, Palko J. Evaluation of machine learning algorithms for trabeculectomy outcome prediction in patients with glaucoma. Sci Rep 2022; 12:2473. [PMID: 35169235 PMCID: PMC8847459 DOI: 10.1038/s41598-022-06438-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 01/18/2022] [Indexed: 02/04/2023] Open
Abstract
The purpose of this study was to evaluate the performance of machine learning algorithms to predict trabeculectomy surgical outcomes. Preoperative systemic, demographic and ocular data from consecutive trabeculectomy surgeries from a single academic institution between January 2014 and December 2018 were incorporated into models using random forest, support vector machine, artificial neural networks and multivariable logistic regression. Mean area under the receiver operating characteristic curve (AUC) and accuracy were used to evaluate the discrimination of each model to predict complete success of trabeculectomy surgery at 1 year. The top performing model was optimized using recursive feature selection and hyperparameter tuning. Calibration and net benefit of the final models were assessed. Among the 230 trabeculectomy surgeries performed on 184 patients, 104 (45.2%) were classified as complete success. Random forest was found to be the top performing model with an accuracy of 0.68 and AUC of 0.74 using 5-fold cross-validation to evaluate the final optimized model. These results provide evidence that machine learning models offer value in predicting trabeculectomy outcomes in patients with refractory glaucoma.
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Affiliation(s)
- Hasan Ul Banna
- Department of Ophthalmology and Visual Sciences, West Virginia University School of Medicine, Morgantown, WV, 26506, USA
| | - Ahmed Zanabli
- Department of Ophthalmology and Visual Sciences, West Virginia University School of Medicine, Morgantown, WV, 26506, USA
| | - Brian McMillan
- Department of Ophthalmology and Visual Sciences, West Virginia University School of Medicine, Morgantown, WV, 26506, USA
| | - Maria Lehmann
- Department of Ophthalmology and Visual Sciences, West Virginia University School of Medicine, Morgantown, WV, 26506, USA
| | - Sumeet Gupta
- Department of Ophthalmology and Visual Sciences, West Virginia University School of Medicine, Morgantown, WV, 26506, USA
| | - Michael Gerbo
- Department of Ophthalmology and Visual Sciences, West Virginia University School of Medicine, Morgantown, WV, 26506, USA
| | - Joel Palko
- Department of Ophthalmology and Visual Sciences, West Virginia University School of Medicine, Morgantown, WV, 26506, USA.
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26
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Hyun SJ. Commentary on "Emerging Technologies in the Treatment of Adult Spinal Deformity". Neurospine 2021; 18:428-429. [PMID: 34610670 PMCID: PMC8497250 DOI: 10.14245/ns.2142818.409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Affiliation(s)
- Seung-Jae Hyun
- Department of Neurosurgery, Spine Center, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
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27
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Kim KH. The Important Role of Paraspinal Muscle Quality for Maintaining Sagittal Balance While Walking: Commentary on "Correlation of Paraspinal Muscle Mass With Decompensation of Sagittal Adult Spinal Deformity After Setting of Fatigue Post 10-Minute Walk". Neurospine 2021; 18:504-505. [PMID: 34610682 PMCID: PMC8497254 DOI: 10.14245/ns.2142878.439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Affiliation(s)
- Kyung-Hyun Kim
- Department of Neurosurgery, Spine and Spinal Cord Institute, Yonsei University College of Medicine, Seoul, Korea
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28
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Bae J. Commentary on "Characteristics and Risk Factors of Rod Fracture Following Adult Spinal Deformity Surgery: A Systematic Review and Meta-Analysis". Neurospine 2021; 18:455-456. [PMID: 34610674 PMCID: PMC8497230 DOI: 10.14245/ns.2142816.408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Affiliation(s)
- Junseok Bae
- Department of Neurosurgery, Wooridul Spine Hospital, Seoul, Korea
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29
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Shin JJ, Kim B, Kang J, Choi J, Moon BJ, Ryu DS, Yoon SH, Chin DK, Lee JK, Kim KN, Ha Y. Clinical, Radiographic, and Genetic Analyses in a Population-Based Cohort of Adult Spinal Deformity in the Older Population. Neurospine 2021; 18:608-617. [PMID: 34610692 PMCID: PMC8497247 DOI: 10.14245/ns.2142544.272] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Accepted: 07/27/2021] [Indexed: 11/19/2022] Open
Abstract
OBJECTIVE This study aimed to identify the sagittal parameters associated with health-related quality of life and genetic variations that increase the risk of adult spinal deformity (ASD) onset in the older population. METHODS We recruited 120 participants who had a sagittal vertical axis > 50 mm in a sagittal imbalance study. Sagittal radiographic parameters, cross-sectional area, and intramuscular fatty infiltration using the Goutallier classification in the paraspinal lumbar muscles were evaluated. Functional scales included the self-reported Oswestry Disability Index (ODI), 36-item Short Form Health Survey (SF-36), and visual analogue scales (VAS) for back and leg pain. We performed whole-exome sequencing and an exome-wide association study using the 100 control subjects and 63 individuals with severe phenotypes of sagittal imbalance. RESULTS Pelvic incidence minus lumbar lordosis (PI-LL) mismatch was negatively associated with the SF-36 and positively correlated with ODI and VAS for back and leg pain. PI-LL was related to the quality and size of the paraspinal muscles, especially the multifidus muscle. We identified common individual variants that reached exome-wide significance using single-variant analysis. The most significant single-nucleotide polymorphism was rs78773460, situated in an exon of the SVIL gene (odds ratio, 9.61; p = 1.15 × 10-9). CONCLUSION Older age, higher body mass index, and a more significant PI-LL mismatch were associated with unfavorable results on functional scales. We found a genetic variation in the SVIL gene, which has been associated with the integrity of the cytoskeleton and the development of skeletal muscles, in severe ASD phenotypes. Our results help to elucidate the pathogenesis of ASD.
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Affiliation(s)
- Jun Jae Shin
- Department of Neurosurgery, Yongin Severance Hospital, Yonsei University School of Medicine, Yongin, Korea
| | - Byeongwoo Kim
- Department of Neurosurgery, Champodonamu Hospital, Seoul, Korea
| | - Juwon Kang
- College of Pharmacy, Yonsei Institute of Pharmaceutical Sciences, Yonsei University, Incheon, Korea
| | - Junjeong Choi
- College of Pharmacy, Yonsei Institute of Pharmaceutical Sciences, Yonsei University, Incheon, Korea
| | - Bong Ju Moon
- Department of Neurosurgery, Chonnam National University Medical School & Research Institute of Medical Sciences, Gwangju, Korea
| | - Dal Sung Ryu
- Department of Neurosurgery, Inha University Hospital, Inha University School of Medicine, Incheon, Korea
| | - Seung Hwan Yoon
- Department of Neurosurgery, Inha University Hospital, Inha University School of Medicine, Incheon, Korea
| | - Dong Kyu Chin
- Department of Neurosurgery, Spine and Spinal Cord Institute, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Jung-Kil Lee
- Department of Neurosurgery, Chonnam National University Medical School & Research Institute of Medical Sciences, Gwangju, Korea
| | - Keung Nyun Kim
- Department of Neurosurgery, Spine and Spinal Cord Institute, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Yoon Ha
- Department of Neurosurgery, Spine and Spinal Cord Institute, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.,POSTECH Biotech Center, Pohang University of Science and Technology, Pohang, Korea
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30
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Joshi RS, Lau D, Ames CP. Artificial intelligence for adult spinal deformity: current state and future directions. Spine J 2021; 21:1626-1634. [PMID: 33971322 DOI: 10.1016/j.spinee.2021.04.019] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 04/07/2021] [Accepted: 04/27/2021] [Indexed: 02/03/2023]
Abstract
As we experience a technological revolution unlike any other time in history, spinal surgery as a discipline is poised to undergo a dramatic transformation. As enormous amounts of data become digitized and more readily available, medical professionals approach a critical juncture with respect to how advanced computational techniques may be incorporated into clinical practices. Within neurosurgery, spinal disorders in particular, represent a complex and heterogeneous disease entity that can vary dramatically in its clinical presentation and how it may impact patients' lives. The spectrum of pathologies is extremely diverse, including many different etiologies such as trauma, oncology, spinal deformity, infection, inflammatory conditions, and degenerative disease among others. The decision to perform spine surgery, especially complex spine surgery, involves several nuances due to the interplay of biomechanical forces, bony composition, neurologic deficits, and the patient's desired goals. Adult spinal deformity as an example is one of the most complex, given its involvement of not only the spine, but rather the entirety of the skeleton in order to appreciate radiographic completeness. With the vast array of variables contributing to spinal disorders, treatment algorithms can vary significantly, and it is very difficult for surgeons to predict how patients will respond to surgery. As such, it will become imperative for spine surgeons to utilize the burgeoning availability of advanced computational tools to process unprecedented amounts of data and provide novel insights into spinal disease. These tools range from predictive models built using machine learning algorithms, to deep learning methods for imaging analysis, to natural language processing that can mine text from electronic medical records or transcribed patient visits - all to better treat the intricacies of spinal disorders. The adoption of such techniques will empower patients and propel spine surgeons into the era of personalized medicine, by allowing clinical plans to be tailored to address individual patients' needs. This paper, which exists in the context of a larger body of literatutre, provides a comprehensive review of the current state and future of artificial intelligence and machine learning with a particular emphasis on Adult spinal deformity surgery.
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Affiliation(s)
- Rushikesh S Joshi
- Department of Neurological Surgery, University of California San Diego, La Jolla, CA, USA.
| | - Darryl Lau
- Department of Neurosurgery, New York University, New York, NY, USA
| | - Christopher P Ames
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA
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31
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Lee SH, Son DW, Shin JJ, Ha Y, Song GS, Lee JS, Lee SW. Preoperative Radiological Parameters to Predict Clinical and Radiological Outcomes after Laminoplasty. J Korean Neurosurg Soc 2021; 64:677-692. [PMID: 34044492 PMCID: PMC8435653 DOI: 10.3340/jkns.2020.0294] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 12/19/2020] [Indexed: 12/23/2022] Open
Abstract
Many studies have focused on pre-operative sagittal alignment parameters which could predict poor clinical or radiological outcomes after laminoplasty. However, the influx of too many new factors causes confusion. This study reviewed sagittal alignment parameters, predictive of clinical or radiological outcomes, in the literature. Preoperative kyphotic alignment was initially proposed as a predictor of clinical outcomes. The clinical significance of the K-line and K-line variants also has been studied. Sagittal vertical axis, T1 slope (T1s), T1s-cervical lordosis (CL), anterolisthesis, local kyphosis, the longitudinal distance index, and range of motion were proposed to have relationships with clinical outcomes. The relationship between loss of cervical lordosis (LCL) and T1s has been widely studied, but controversy remains. Extension function, the ratio of CL to T1s (CL/T1s), and Sharma classification were recently proposed as LCL predictors. In predicting postoperative kyphosis, T1s cannot predict postoperative kyphosis, but a low CL/T1s ratio was associated with postoperative kyphosis.
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Affiliation(s)
- Su Hun Lee
- Department of Neurosurgery, Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan, Korea
| | - Dong Wuk Son
- Department of Neurosurgery, Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan, Korea.,Department of Neurosurgery, School of Medicine, Pusan National University, Busan, Korea
| | - Jun Jae Shin
- Department of Neurosurgery, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Korea
| | - Yoon Ha
- Department of Neurosurgery, Spine and Spinal Cord Institute, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Geun Sung Song
- Department of Neurosurgery, Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan, Korea.,Department of Neurosurgery, School of Medicine, Pusan National University, Busan, Korea
| | - Jun Seok Lee
- Department of Neurosurgery, Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan, Korea
| | - Sang Weon Lee
- Department of Neurosurgery, Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan, Korea.,Department of Neurosurgery, School of Medicine, Pusan National University, Busan, Korea
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32
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Kim DH, Hyun SJ, Kim KJ. Selection of Fusion Level for Adolescent Idiopathic Scoliosis Surgery : Selective Fusion versus Postoperative Decompensation. J Korean Neurosurg Soc 2021; 64:473-485. [PMID: 34044493 PMCID: PMC8273784 DOI: 10.3340/jkns.2020.0258] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Accepted: 10/09/2020] [Indexed: 11/27/2022] Open
Abstract
Adolescent idiopathic scoliosis (AIS), which is associated with an extensive range of clinical and radiological presentations, is the one of the most challenging spinal disorders. The goals of surgery are to correct the deformity in 3 dimensions and to preserve motion segments while avoiding complications. Despite the ongoing evolution of classification systems and algorithms for the surgical treatment of AIS, there has been considerable debate regarding the selection of an appropriate fusion level in AIS. In addition, there is no consensus regarding the exact description, relationship, and risk factors of coronal decompensation following selective fusion. In this review, we summarize the current concepts of selection of the fusion level for AIS and review the available information about postoperative coronal decompensation.
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Affiliation(s)
- Do-Hyoung Kim
- Department of Neurosurgery, Spine Center, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
| | - Seung-Jae Hyun
- Department of Neurosurgery, Spine Center, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
| | - Ki-Jeong Kim
- Department of Neurosurgery, Spine Center, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
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33
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Abstract
Advanced age is a well-known risk factor for spinal surgery-related complications. Decisions on spine surgery in the elderly are difficult due to higher morbidity and mortality than in younger age groups. In addition, spine surgery is a kind of ‘functional surgery’ which does not directly affect the survival of patients. In recent years, individualized risk stratification has gained ground over simple chronological age-based assessment. In the elderly, frailty is one of the strongest factors which affect surgical outcomes for both cervical and thoracolumbar spine surgery, regardless of the surgical technique used. Spine surgery in the elderly have worse surgical outcomes in terms of duration of hospital stay, degree of functional recovery, and complication, readmission, and mortality rates. However, the benefit of spine surgery even in the very-elderly is substantial. In conclusion, surgical decisions should be made based on both personalized risk assessment and benefits of surgery. Recent advanced surgical techniques such as minimally invasive surgical techniques and robotics assistance are likely to be helpful in minimizing surgical complications. Therefore, advanced age in itself should not be considered as a contraindication for spine surgery.
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Poelstra KA. CORR Insights®: Are Higher Global Alignment and Proportion Scores Associated With Increased Risks of Mechanical Complications After Adult Spinal Deformity Surgery? An External Validation. Clin Orthop Relat Res 2021; 479:321-323. [PMID: 33332886 PMCID: PMC7899402 DOI: 10.1097/corr.0000000000001612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2020] [Accepted: 11/25/2020] [Indexed: 01/31/2023]
Affiliation(s)
- Kornelis A Poelstra
- K. A. Poelstra, The Robotic Spine Institute of Las Vegas at Allegiant Pain & Spine, Las Vegas, NV, USA
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Chen K, Zhai X, Sun K, Wang H, Yang C, Li M. A narrative review of machine learning as promising revolution in clinical practice of scoliosis. ANNALS OF TRANSLATIONAL MEDICINE 2021; 9:67. [PMID: 33553360 PMCID: PMC7859734 DOI: 10.21037/atm-20-5495] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Accepted: 11/27/2020] [Indexed: 11/06/2022]
Abstract
Machine learning (ML), as an advanced domain of artificial intelligence (AI), is progressively changing our view of the world. By implementing its algorithms, our ability to detect previously undiscoverable patterns in data has the potential to revolutionize predictive analytics. Scoliosis, as a relatively specialized branch in the spine field, mainly covers the pediatric, adult and the elderly populations, and its diagnosis and treatment remain difficult. With recent efforts and interdisciplinary cooperation, ML has been widely applied to investigate issues related to scoliosis, and surprisingly augment a surgeon's ability in clinical practice related to scoliosis. Meanwhile, ML models penetrate in every stage of the clinical practice procedure of scoliosis. In this review, we first present a brief description of the application of ML in the clinical practice procedures regarding scoliosis, including screening, diagnosis and classification, surgical decision making, intraoperative manipulation, complication prediction, prognosis prediction and rehabilitation. Meanwhile, the ML models and specific applications adopted are presented. Additionally, current limitations and future directions are briefly discussed regarding its use in the field of scoliosis. We believe that the implementation of ML is a promising revolution to assist surgeons in all aspects of clinical practice related to scoliosis in the near future.
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Affiliation(s)
- Kai Chen
- Department of Orthopedics, Shanghai Changhai Hospital, Shanghai, China
| | - Xiao Zhai
- Department of Orthopedics, Shanghai Changhai Hospital, Shanghai, China
| | - Kaiqiang Sun
- Department of Orthopedics, Shanghai Changzheng Hospital, Shanghai, China
| | - Haojue Wang
- Basic medicine college, Navy Medical University, Shanghai, China
| | - Changwei Yang
- Department of Orthopedics, Shanghai Changhai Hospital, Shanghai, China
| | - Ming Li
- Department of Orthopedics, Shanghai Changhai Hospital, Shanghai, China
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Vaishya R, Javaid M, Haleem A, Khan I, Vaish A. Extending capabilities of artificial intelligence for decision-making and healthcare education. APOLLO MEDICINE 2020. [DOI: 10.4103/am.am_10_20] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
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Affiliation(s)
- Lawrence G. Lenke
- The Daniel and Jane Och Spine Hospital at New York-Presbyterian/Allen, New York, NY, USA
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