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Wilson SB, Ward J, Munjal V, Lam CSA, Patel M, Zhang P, Xu DS, Chakravarthy VB. Machine Learning in Spine Oncology: A Narrative Review. Global Spine J 2024:21925682241261342. [PMID: 38860699 DOI: 10.1177/21925682241261342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/12/2024] Open
Abstract
STUDY DESIGN Narrative Review. OBJECTIVE Machine learning (ML) is one of the latest advancements in artificial intelligence used in medicine and surgery with the potential to significantly impact the way physicians diagnose, prognose, and treat spine tumors. In the realm of spine oncology, ML is utilized to analyze and interpret medical imaging and classify tumors with incredible accuracy. The authors present a narrative review that specifically addresses the use of machine learning in spine oncology. METHODS This study was conducted in accordance with the Preferred Reporting Items of Systematic Reviews and Meta-Analysis (PRISMA) methodology. A systematic review of the literature in the PubMed, EMBASE, Web of Science, Scopus, and Cochrane Library databases since inception was performed to present all clinical studies with the search terms '[[Machine Learning] OR [Artificial Intelligence]] AND [[Spine Oncology] OR [Spine Cancer]]'. Data included studies that were extracted and included algorithms, training and test size, outcomes reported. Studies were separated based on the type of tumor investigated using the machine learning algorithms into primary, metastatic, both, and intradural. A minimum of 2 independent reviewers conducted the study appraisal, data abstraction, and quality assessments of the studies. RESULTS Forty-five studies met inclusion criteria out of 480 references screened from the initial search results. Studies were grouped by metastatic, primary, and intradural tumors. The majority of ML studies relevant to spine oncology focused on utilizing a mixture of clinical and imaging features to risk stratify mortality and frailty. Overall, these studies showed that ML is a helpful tool in tumor detection, differentiation, segmentation, predicting survival, predicting readmission rates of patients with either primary, metastatic, or intradural spine tumors. CONCLUSION Specialized neural networks and deep learning algorithms have shown to be highly effective at predicting malignant probability and aid in diagnosis. ML algorithms can predict the risk of tumor recurrence or progression based on imaging and clinical features. Additionally, ML can optimize treatment planning, such as predicting radiotherapy dose distribution to the tumor and surrounding normal tissue or in surgical resection planning. It has the potential to significantly enhance the accuracy and efficiency of health care delivery, leading to improved patient outcomes.
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Affiliation(s)
- Seth B Wilson
- Department of Neurosurgery, The Ohio State University, Columbus, OH, USA
| | - Jacob Ward
- Department of Neurosurgery, The Ohio State University, Columbus, OH, USA
| | - Vikas Munjal
- Department of Neurosurgery, The Ohio State University, Columbus, OH, USA
| | | | - Mayur Patel
- Department of Neurosurgery, The Ohio State University, Columbus, OH, USA
| | - Ping Zhang
- Department of Computer Science and Engineering, The Ohio State University College of Engineering, Columbus, OH, USA
- Department of Biomedical Informatics, The Ohio State University College of Medicine, Columbus, OH, USA
| | - David S Xu
- Department of Neurosurgery, The Ohio State University, Columbus, OH, USA
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Adida S, Legarreta AD, Hudson JS, McCarthy D, Andrews E, Shanahan R, Taori S, Lavadi RS, Buell TJ, Hamilton DK, Agarwal N, Gerszten PC. Machine Learning in Spine Surgery: A Narrative Review. Neurosurgery 2024; 94:53-64. [PMID: 37930259 DOI: 10.1227/neu.0000000000002660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 07/06/2023] [Indexed: 11/07/2023] Open
Abstract
Artificial intelligence and machine learning (ML) can offer revolutionary advances in their application to the field of spine surgery. Within the past 5 years, novel applications of ML have assisted in surgical decision-making, intraoperative imaging and navigation, and optimization of clinical outcomes. ML has the capacity to address many different clinical needs and improve diagnostic and surgical techniques. This review will discuss current applications of ML in the context of spine surgery by breaking down its implementation preoperatively, intraoperatively, and postoperatively. Ethical considerations to ML and challenges in ML implementation must be addressed to maximally benefit patients, spine surgeons, and the healthcare system. Areas for future research in augmented reality and mixed reality, along with limitations in generalizability and bias, will also be highlighted.
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Affiliation(s)
- Samuel Adida
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - Andrew D Legarreta
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - Joseph S Hudson
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - David McCarthy
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - Edward Andrews
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - Regan Shanahan
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - Suchet Taori
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - Raj Swaroop Lavadi
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - Thomas J Buell
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - D Kojo Hamilton
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - Nitin Agarwal
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
- Department of Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh , Pennsylvania , USA
| | - Peter C Gerszten
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
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Ghanem M, Ghaith AK, El-Hajj VG, Bhandarkar A, de Giorgio A, Elmi-Terander A, Bydon M. Limitations in Evaluating Machine Learning Models for Imbalanced Binary Outcome Classification in Spine Surgery: A Systematic Review. Brain Sci 2023; 13:1723. [PMID: 38137171 PMCID: PMC10741524 DOI: 10.3390/brainsci13121723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Revised: 12/12/2023] [Accepted: 12/15/2023] [Indexed: 12/24/2023] Open
Abstract
Clinical prediction models for spine surgery applications are on the rise, with an increasing reliance on machine learning (ML) and deep learning (DL). Many of the predicted outcomes are uncommon; therefore, to ensure the models' effectiveness in clinical practice it is crucial to properly evaluate them. This systematic review aims to identify and evaluate current research-based ML and DL models applied for spine surgery, specifically those predicting binary outcomes with a focus on their evaluation metrics. Overall, 60 papers were included, and the findings were reported according to the PRISMA guidelines. A total of 13 papers focused on lengths of stay (LOS), 12 on readmissions, 12 on non-home discharge, 6 on mortality, and 5 on reoperations. The target outcomes exhibited data imbalances ranging from 0.44% to 42.4%. A total of 59 papers reported the model's area under the receiver operating characteristic (AUROC), 28 mentioned accuracies, 33 provided sensitivity, 29 discussed specificity, 28 addressed positive predictive value (PPV), 24 included the negative predictive value (NPV), 25 indicated the Brier score with 10 providing a null model Brier, and 8 detailed the F1 score. Additionally, data visualization varied among the included papers. This review discusses the use of appropriate evaluation schemes in ML and identifies several common errors and potential bias sources in the literature. Embracing these recommendations as the field advances may facilitate the integration of reliable and effective ML models in clinical settings.
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Affiliation(s)
- Marc Ghanem
- Mayo Clinic Neuro-Informatics Laboratory, Mayo Clinic, Rochester, MN 55902, USA; (M.G.); (A.K.G.); (V.G.E.-H.); (A.B.); (M.B.)
- Department of Neurological Surgery, Mayo Clinic, Rochester, MN 55902, USA
- School of Medicine, Lebanese American University, Byblos 4504, Lebanon
| | - Abdul Karim Ghaith
- Mayo Clinic Neuro-Informatics Laboratory, Mayo Clinic, Rochester, MN 55902, USA; (M.G.); (A.K.G.); (V.G.E.-H.); (A.B.); (M.B.)
- Department of Neurological Surgery, Mayo Clinic, Rochester, MN 55902, USA
| | - Victor Gabriel El-Hajj
- Mayo Clinic Neuro-Informatics Laboratory, Mayo Clinic, Rochester, MN 55902, USA; (M.G.); (A.K.G.); (V.G.E.-H.); (A.B.); (M.B.)
- Department of Neurological Surgery, Mayo Clinic, Rochester, MN 55902, USA
- Department of Clinical Neuroscience, Karolinska Institutet, 17177 Stockholm, Sweden
| | - Archis Bhandarkar
- Mayo Clinic Neuro-Informatics Laboratory, Mayo Clinic, Rochester, MN 55902, USA; (M.G.); (A.K.G.); (V.G.E.-H.); (A.B.); (M.B.)
- Department of Neurological Surgery, Mayo Clinic, Rochester, MN 55902, USA
| | - Andrea de Giorgio
- Artificial Engineering, Via del Rione Sirignano, 80121 Naples, Italy;
| | - Adrian Elmi-Terander
- Department of Clinical Neuroscience, Karolinska Institutet, 17177 Stockholm, Sweden
- Department of Surgical Sciences, Uppsala University, 75236 Uppsala, Sweden
| | - Mohamad Bydon
- Mayo Clinic Neuro-Informatics Laboratory, Mayo Clinic, Rochester, MN 55902, USA; (M.G.); (A.K.G.); (V.G.E.-H.); (A.B.); (M.B.)
- Department of Neurological Surgery, Mayo Clinic, Rochester, MN 55902, USA
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Ren G, Yu K, Xie Z, Wang P, Zhang W, Huang Y, Wang Y, Wu X. Current Applications of Machine Learning in Spine: From Clinical View. Global Spine J 2022; 12:1827-1840. [PMID: 34628966 PMCID: PMC9609532 DOI: 10.1177/21925682211035363] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
STUDY DESIGN Narrative review. OBJECTIVES This review aims to present current applications of machine learning (ML) in spine domain to clinicians. METHODS We conducted a comprehensive PubMed search of peer-reviewed articles that were published between 2006 and 2020 using terms (spine, spinal, lumbar, cervical, thoracic, machine learning) to examine ML in spine. Then exclude research of other domain, case report, review or meta-analysis, and which without available abstract or full text. RESULTS Total 1738 articles were retrieved from database, and 292 studies were finally included. Key findings of current applications were compiled and summarized in this review. Main clinical applications of those techniques including image processing, diagnosis, decision supporting, operative assistance, rehabilitation, surgery outcomes, complications, hospitalization and cost. CONCLUSIONS ML had achieved excellent performance and hold immense potential in spine. ML could help clinical staff to improve medical level, enhance work efficiency, and reduce adverse events. However more randomized controlled trials and improvement of interpretability are essential to clinicians accepting models' assistance in real work.
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Affiliation(s)
- GuanRui Ren
- Southeast University Medical College,
Nanjing, Jiangsu, China
| | - Kun Yu
- Nanjing Jiangbei Hospital, Nanjing,
Jiangsu, China
| | - ZhiYang Xie
- Department of Spine Surgery, Zhongda
Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China
| | - PeiYang Wang
- Southeast University Medical College,
Nanjing, Jiangsu, China
| | - Wei Zhang
- Southeast University Medical College,
Nanjing, Jiangsu, China
| | - Yong Huang
- Southeast University Medical College,
Nanjing, Jiangsu, China
| | - YunTao Wang
- Department of Spine Surgery, Zhongda
Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China,YunTao Wang, Department of Spine Surgery,
Zhongda Hospital, School of Medicine, Southeast University, No. 87, Dingjiaqiao
Road, Nanjing, Jiangsu 210009, China.
| | - XiaoTao Wu
- Department of Spine Surgery, Zhongda
Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China,XiaoTao Wu, Department of Spine Surgery,
Zhongda Hospital, School of Medicine, Southeast University, No. 87, Dingjiaqiao
Road, Nanjing, Jiangsu 210009, China.
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Zhou S, Zhou F, Sun Y, Chen X, Diao Y, Zhao Y, Huang H, Fan X, Zhang G, Li X. The application of artificial intelligence in spine surgery. Front Surg 2022; 9:885599. [PMID: 36034349 PMCID: PMC9403075 DOI: 10.3389/fsurg.2022.885599] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 07/25/2022] [Indexed: 11/13/2022] Open
Abstract
Due to its obvious advantages in processing big data and image information, the combination of artificial intelligence and medical care may profoundly change medical practice and promote the gradual transition from traditional clinical care to precision medicine mode. In this artical, we reviewed the relevant literatures and found that artificial intelligence was widely used in spine surgery. The application scenarios included etiology, diagnosis, treatment, postoperative prognosis and decision support systems of spinal diseases. The shift to artificial intelligence model in medicine constantly improved the level of doctors' diagnosis and treatment and the development of orthopedics.
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Affiliation(s)
- Shuai Zhou
- Department of Orthopaedics, Peking University Third Hospital, Beijing, China
- Engineering Research Center of Bone and Joint Precision Medicine, Peking University Third Hospital, Beijing, China
- Beijing Key Laboratory of Spinal Disease Research, Beijing, China
| | - Feifei Zhou
- Department of Orthopaedics, Peking University Third Hospital, Beijing, China
- Engineering Research Center of Bone and Joint Precision Medicine, Peking University Third Hospital, Beijing, China
- Beijing Key Laboratory of Spinal Disease Research, Beijing, China
- Correspondence: Feifei Zhou
| | - Yu Sun
- Department of Orthopaedics, Peking University Third Hospital, Beijing, China
- Engineering Research Center of Bone and Joint Precision Medicine, Peking University Third Hospital, Beijing, China
- Beijing Key Laboratory of Spinal Disease Research, Beijing, China
| | - Xin Chen
- Department of Orthopaedics, Peking University Third Hospital, Beijing, China
- Engineering Research Center of Bone and Joint Precision Medicine, Peking University Third Hospital, Beijing, China
- Beijing Key Laboratory of Spinal Disease Research, Beijing, China
| | - Yinze Diao
- Department of Orthopaedics, Peking University Third Hospital, Beijing, China
- Engineering Research Center of Bone and Joint Precision Medicine, Peking University Third Hospital, Beijing, China
- Beijing Key Laboratory of Spinal Disease Research, Beijing, China
| | - Yanbin Zhao
- Department of Orthopaedics, Peking University Third Hospital, Beijing, China
- Engineering Research Center of Bone and Joint Precision Medicine, Peking University Third Hospital, Beijing, China
- Beijing Key Laboratory of Spinal Disease Research, Beijing, China
| | - Haoge Huang
- Department of Orthopaedics, Peking University Third Hospital, Beijing, China
- Engineering Research Center of Bone and Joint Precision Medicine, Peking University Third Hospital, Beijing, China
- Beijing Key Laboratory of Spinal Disease Research, Beijing, China
| | - Xiao Fan
- Department of Orthopaedics, Peking University Third Hospital, Beijing, China
- Engineering Research Center of Bone and Joint Precision Medicine, Peking University Third Hospital, Beijing, China
- Beijing Key Laboratory of Spinal Disease Research, Beijing, China
| | - Gangqiang Zhang
- Department of Orthopaedics, Peking University Third Hospital, Beijing, China
- Engineering Research Center of Bone and Joint Precision Medicine, Peking University Third Hospital, Beijing, China
- Beijing Key Laboratory of Spinal Disease Research, Beijing, China
| | - Xinhang Li
- Department of Orthopaedics, Peking University Third Hospital, Beijing, China
- Engineering Research Center of Bone and Joint Precision Medicine, Peking University Third Hospital, Beijing, China
- Beijing Key Laboratory of Spinal Disease Research, Beijing, China
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Ouyang H, Meng F, Liu J, Song X, Li Y, Yuan Y, Wang C, Lang N, Tian S, Yao M, Liu X, Yuan H, Jiang S, Jiang L. Evaluation of Deep Learning-Based Automated Detection of Primary Spine Tumors on MRI Using the Turing Test. Front Oncol 2022; 12:814667. [PMID: 35359400 PMCID: PMC8962659 DOI: 10.3389/fonc.2022.814667] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Accepted: 02/16/2022] [Indexed: 01/04/2023] Open
Abstract
BackgroundRecently, the Turing test has been used to investigate whether machines have intelligence similar to humans. Our study aimed to assess the ability of an artificial intelligence (AI) system for spine tumor detection using the Turing test.MethodsOur retrospective study data included 12179 images from 321 patients for developing AI detection systems and 6635 images from 187 patients for the Turing test. We utilized a deep learning-based tumor detection system with Faster R-CNN architecture, which generates region proposals by Region Proposal Network in the first stage and corrects the position and the size of the bounding box of the lesion area in the second stage. Each choice question featured four bounding boxes enclosing an identical tumor. Three were detected by the proposed deep learning model, whereas the other was annotated by a doctor; the results were shown to six doctors as respondents. If the respondent did not correctly identify the image annotated by a human, his answer was considered a misclassification. If all misclassification rates were >30%, the respondents were considered unable to distinguish the AI-detected tumor from the human-annotated one, which indicated that the AI system passed the Turing test.ResultsThe average misclassification rates in the Turing test were 51.2% (95% CI: 45.7%–57.5%) in the axial view (maximum of 62%, minimum of 44%) and 44.5% (95% CI: 38.2%–51.8%) in the sagittal view (maximum of 59%, minimum of 36%). The misclassification rates of all six respondents were >30%; therefore, our AI system passed the Turing test.ConclusionOur proposed intelligent spine tumor detection system has a similar detection ability to annotation doctors and may be an efficient tool to assist radiologists or orthopedists in primary spine tumor detection.
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Affiliation(s)
- Hanqiang Ouyang
- Department of Orthopaedics, Peking University Third Hospital, Beijing, China
- Engineering Research Center of Bone and Joint Precision Medicine, Beijing, China
- Beijing Key Laboratory of Spinal Disease Research, Beijing, China
| | - Fanyu Meng
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Jianfang Liu
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Xinhang Song
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
| | - Yuan Li
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Yuan Yuan
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Chunjie Wang
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Ning Lang
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Shuai Tian
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Meiyi Yao
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Xiaoguang Liu
- Department of Orthopaedics, Peking University Third Hospital, Beijing, China
- Engineering Research Center of Bone and Joint Precision Medicine, Beijing, China
- Beijing Key Laboratory of Spinal Disease Research, Beijing, China
| | - Huishu Yuan
- Department of Radiology, Peking University Third Hospital, Beijing, China
- *Correspondence: Huishu Yuan, ; Shuqiang Jiang, ; Liang Jiang,
| | - Shuqiang Jiang
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
- *Correspondence: Huishu Yuan, ; Shuqiang Jiang, ; Liang Jiang,
| | - Liang Jiang
- Department of Orthopaedics, Peking University Third Hospital, Beijing, China
- Engineering Research Center of Bone and Joint Precision Medicine, Beijing, China
- Beijing Key Laboratory of Spinal Disease Research, Beijing, China
- *Correspondence: Huishu Yuan, ; Shuqiang Jiang, ; Liang Jiang,
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Supervised Machine Learning for Predicting Length of Stay After Lumbar Arthrodesis: A Comprehensive Artificial Intelligence Approach. J Am Acad Orthop Surg 2022; 30:125-132. [PMID: 34928886 DOI: 10.5435/jaaos-d-21-00241] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Accepted: 10/14/2021] [Indexed: 02/01/2023] Open
Abstract
INTRODUCTION Few studies have evaluated the utility of machine learning techniques to predict and classify outcomes, such as length of stay (LOS), for lumbar fusion patients. Six supervised machine learning algorithms may be able to predict and classify whether a patient will experience a short or long hospital LOS after lumbar fusion surgery with a high degree of accuracy. METHODS Data were obtained from the National Surgical Quality Improvement Program between 2009 and 2018. Demographic and comorbidity information was collected for patients who underwent anterior, anterolateral, or lateral transverse process technique arthrodesis procedure; anterior lumbar interbody fusion (ALIF); posterior, posterolateral, or lateral transverse process technique arthrodesis procedure; posterior lumbar interbody fusion/transforaminal lumbar interbody fusion (PLIF/TLIF); and posterior fusion procedure posterior spine fusion (PSF). Machine learning algorithmic analyses were done with the scikit-learn package in Python on a high-performance computing cluster. In the total sample, 85% of patients were used for training the models, whereas the remaining patients were used for testing the models. C-statistic area under the curve and prediction accuracy (PA) were calculated for each of the models to determine their accuracy in correctly classifying the test cases. RESULTS In total, 12,915 ALIF patients, 27,212 PLIF/TLIF patients, and 23,406 PSF patients were included in the algorithmic analyses. The patient factors most strongly associated with LOS were sex, ethnicity, dialysis, and disseminated cancer. The machine learning algorithms yielded area under the curve values of between 0.673 and 0.752 (PA: 69.6% to 80.1%) for ALIF, 0.673 and 0.729 (PA: 66.0% to 81.3%) for PLIF/TLIF, and 0.698 and 0.749 (PA: 69.9% to 80.4%) for PSF. CONCLUSION Machine learning classification algorithms were able to accurately predict long LOS for ALIF, PLIF/TLIF, and PSF patients. Supervised machine learning algorithms may be useful in clinical and administrative settings. These data may additionally help inform predictive analytic models and assist in setting patient expectations. LEVEL III Diagnostic study, retrospective cohort study.
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