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Fares MY, Liu HH, da Silva Etges APB, Zhang B, Warner JJP, Olson JJ, Fedorka CJ, Khan AZ, Best MJ, Kirsch JM, Simon JE, Sanders B, Costouros JG, Zhang X, Jones P, Haas DA, Abboud JA. Utility of Machine Learning, Natural Language Processing, and Artificial Intelligence in Predicting Hospital Readmissions After Orthopaedic Surgery: A Systematic Review and Meta-Analysis. JBJS Rev 2024; 12:01874474-202408000-00011. [PMID: 39172864 DOI: 10.2106/jbjs.rvw.24.00075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/24/2024]
Abstract
BACKGROUND Numerous applications and strategies have been utilized to help assess the trends and patterns of readmissions after orthopaedic surgery in an attempt to extrapolate possible risk factors and causative agents. The aim of this work is to systematically summarize the available literature on the extent to which natural language processing, machine learning, and artificial intelligence (AI) can help improve the predictability of hospital readmissions after orthopaedic and spine surgeries. METHODS This is a systematic review and meta-analysis. PubMed, Embase and Google Scholar were searched, up until August 30, 2023, for studies that explore the use of AI, natural language processing, and machine learning tools for the prediction of readmission rates after orthopedic procedures. Data regarding surgery type, patient population, readmission outcomes, advanced models utilized, comparison methods, predictor sets, the inclusion of perioperative predictors, validation method, size of training and testing sample, accuracy, and receiver operating characteristics (C-statistic), among other factors, were extracted and assessed. RESULTS A total of 26 studies were included in our final dataset. The overall summary C-statistic showed a mean of 0.71 across all models, indicating a reasonable level of predictiveness. A total of 15 articles (57%) were attributed to the spine, making it the most commonly explored orthopaedic field in our study. When comparing accuracy of prediction models between different fields, models predicting readmissions after hip/knee arthroplasty procedures had a higher prediction accuracy (mean C-statistic = 0.79) than spine (mean C-statistic = 0.7) and shoulder (mean C-statistic = 0.67). In addition, models that used single institution data, and those that included intraoperative and/or postoperative outcomes, had a higher mean C-statistic than those utilizing other data sources, and that include only preoperative predictors. According to the Prediction model Risk of Bias Assessment Tool, the majority of the articles in our study had a high risk of bias. CONCLUSION AI tools perform reasonably well in predicting readmissions after orthopaedic procedures. Future work should focus on standardizing study methodologies and designs, and improving the data analysis process, in an attempt to produce more reliable and tangible results. LEVEL OF EVIDENCE Level III. See Instructions for Authors for a complete description of levels of evidence.
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Affiliation(s)
- Mohamad Y Fares
- Rothman Institute, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania
| | | | | | | | - Jon J P Warner
- Department of Orthopaedic Surgery, Harvard Medical School, Boston Shoulder Institute, Massachusetts General Hospital, Boston, Massachusetts
| | | | - Catherine J Fedorka
- Cooper Bone and Joint Institute, Cooper University Hospital, Camden, New Jersey
| | - Adam Z Khan
- Department of Orthopaedic Surgery, Southern California Permanente Medical Group, Panorama City, California
| | - Matthew J Best
- Department of Orthopaedic Surgery, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Jacob M Kirsch
- Department of Orthopaedic Surgery, New England Baptist Hospital, Tufts University School of Medicine, Boston, Massachusetts
| | - Jason E Simon
- Department of Orthopaedic Surgery, Massachusetts General Hospital/Newton-Wellesley Hospital, Boston, Massachusetts
| | - Brett Sanders
- Center for Sports Medicine and Orthopaedics, Chattanooga, Tennessee
| | - John G Costouros
- Institute for Joint Restoration and Research, California Shoulder Center, Menlo Park, California
| | | | | | | | - Joseph A Abboud
- Rothman Institute, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania
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Liawrungrueang W, Cho ST, Kotheeranurak V, Pun A, Jitpakdee K, Sarasombath P. Artificial neural networks for the detection of odontoid fractures using the Konstanz Information Miner Analytics Platform. Asian Spine J 2024; 18:407-414. [PMID: 38917858 PMCID: PMC11222894 DOI: 10.31616/asj.2023.0259] [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] [Received: 08/10/2023] [Revised: 09/30/2023] [Accepted: 10/23/2023] [Indexed: 06/27/2024] Open
Abstract
STUDY DESIGN An experimental study. PURPOSE This study aimed to investigate the potential use of artificial neural networks (ANNs) in the detection of odontoid fractures using the Konstanz Information Miner (KNIME) Analytics Platform that provides a technique for computer-assisted diagnosis using radiographic X-ray imaging. OVERVIEW OF LITERATURE In medical image processing, computer-assisted diagnosis with ANNs from radiographic X-ray imaging is becoming increasingly popular. Odontoid fractures are a common fracture of the axis and account for 10%-15% of all cervical fractures. However, a literature review of computer-assisted diagnosis with ANNs has not been made. METHODS This study analyzed 432 open-mouth (odontoid) radiographic views of cervical spine X-ray images obtained from dataset repositories, which were used in developing ANN models based on the convolutional neural network theory. All the images contained diagnostic information, including 216 radiographic images of individuals with normal odontoid processes and 216 images of patients with acute odontoid fractures. The model classified each image as either showing an odontoid fracture or not. Specifically, 70% of the images were training datasets used for model training, and 30% were used for testing. KNIME's graphic user interface-based programming enabled class label annotation, data preprocessing, model training, and performance evaluation. RESULTS The graphic user interface program by KNIME was used to report all radiographic X-ray imaging features. The ANN model performed 50 epochs of training. The performance indices in detecting odontoid fractures included sensitivity, specificity, F-measure, and prediction error of 100%, 95.4%, 97.77%, and 2.3%, respectively. The model's accuracy accounted for 97% of the area under the receiver operating characteristic curve for the diagnosis of odontoid fractures. CONCLUSIONS The ANN models with the KNIME Analytics Platform were successfully used in the computer-assisted diagnosis of odontoid fractures using radiographic X-ray images. This approach can help radiologists in the screening, detection, and diagnosis of acute odontoid fractures.
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Affiliation(s)
| | - Sung Tan Cho
- Department of Orthopaedic Surgery, Seoul Seonam Hospital, Seoul, Korea
| | - Vit Kotheeranurak
- Department of Orthopaedics, King Chulalongkorn Memorial Hospital and Faculty of Medicine, Chulalongkorn University, Bangkok,
Thailand
- Center of Excellence in Biomechanics and Innovative Spine Surgery, Chulalongkorn University, Bangkok,
Thailand
| | - Alvin Pun
- Department of Neurosciences Clinical Institute, Epworth Richmond, Melbourne,
Australia
| | - Khanathip Jitpakdee
- Department of Orthopaedics, Queen Savang Vadhana Memorial Hospital, Sriracha, Chonburi,
Thailand
| | - Peem Sarasombath
- Department of Orthopaedics, Faculty of Medicine, Chiang Mai University, Chiang Mai,
Thailand
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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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Gonzalez-Suarez AD, Rezaii PG, Herrick D, Tigchelaar SS, Ratliff JK, Rusu M, Scheinker D, Jeon I, Desai AM. Using Machine Learning Models to Identify Factors Associated With 30-Day Readmissions After Posterior Cervical Fusions: A Longitudinal Cohort Study. Neurospine 2024; 21:620-632. [PMID: 38768945 PMCID: PMC11224744 DOI: 10.14245/ns.2347340.670] [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: 12/20/2023] [Revised: 03/22/2024] [Accepted: 04/01/2024] [Indexed: 05/22/2024] Open
Abstract
OBJECTIVE Readmission rates after posterior cervical fusion (PCF) significantly impact patients and healthcare, with complication rates at 15%-25% and up to 12% 90-day readmission rates. In this study, we aim to test whether machine learning (ML) models that capture interfactorial interactions outperform traditional logistic regression (LR) in identifying readmission-associated factors. METHODS The Optum Clinformatics Data Mart database was used to identify patients who underwent PCF between 2004-2017. To determine factors associated with 30-day readmissions, 5 ML models were generated and evaluated, including a multivariate LR (MLR) model. Then, the best-performing model, Gradient Boosting Machine (GBM), was compared to the LACE (Length patient stay in the hospital, Acuity of admission of patient in the hospital, Comorbidity, and Emergency visit) index regarding potential cost savings from algorithm implementation. RESULTS This study included 4,130 patients, 874 of which were readmitted within 30 days. When analyzed and scaled, we found that patient discharge status, comorbidities, and number of procedure codes were factors that influenced MLR, while patient discharge status, billed admission charge, and length of stay influenced the GBM model. The GBM model significantly outperformed MLR in predicting unplanned readmissions (mean area under the receiver operating characteristic curve, 0.846 vs. 0.829; p < 0.001), while also projecting an average cost savings of 50% more than the LACE index. CONCLUSION Five models (GBM, XGBoost [extreme gradient boosting], RF [random forest], LASSO [least absolute shrinkage and selection operator], and MLR) were evaluated, among which, the GBM model exhibited superior predictive performance, robustness, and accuracy. Factors associated with readmissions impact LR and GBM models differently, suggesting that these models can be used complementarily. When analyzing PCF procedures, the GBM model resulted in greater predictive performance and was associated with higher theoretical cost savings for readmissions associated with PCF complications.
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Affiliation(s)
| | - Paymon G. Rezaii
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
| | - Daniel Herrick
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
| | | | - John K. Ratliff
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
| | - Mirabela Rusu
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - David Scheinker
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
| | - Ikchan Jeon
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
| | - Atman M. Desai
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
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5
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Huffman N, Pasqualini I, Khan ST, Klika AK, Deren ME, Jin Y, Kunze KN, Piuzzi NS. Enabling Personalized Medicine in Orthopaedic Surgery Through Artificial Intelligence: A Critical Analysis Review. JBJS Rev 2024; 12:01874474-202403000-00006. [PMID: 38466797 DOI: 10.2106/jbjs.rvw.23.00232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
» The application of artificial intelligence (AI) in the field of orthopaedic surgery holds potential for revolutionizing health care delivery across 3 crucial domains: (I) personalized prediction of clinical outcomes and adverse events, which may optimize patient selection, surgical planning, and enhance patient safety and outcomes; (II) diagnostic automated and semiautomated imaging analyses, which may reduce time burden and facilitate precise and timely diagnoses; and (III) forecasting of resource utilization, which may reduce health care costs and increase value for patients and institutions.» Computer vision is one of the most highly studied areas of AI within orthopaedics, with applications pertaining to fracture classification, identification of the manufacturer and model of prosthetic implants, and surveillance of prosthesis loosening and failure.» Prognostic applications of AI within orthopaedics include identifying patients who will likely benefit from a specified treatment, predicting prosthetic implant size, postoperative length of stay, discharge disposition, and surgical complications. Not only may these applications be beneficial to patients but also to institutions and payors because they may inform potential cost expenditure, improve overall hospital efficiency, and help anticipate resource utilization.» AI infrastructure development requires institutional financial commitment and a team of clinicians and data scientists with expertise in AI that can complement skill sets and knowledge. Once a team is established and a goal is determined, teams (1) obtain, curate, and label data; (2) establish a reference standard; (3) develop an AI model; (4) evaluate the performance of the AI model; (5) externally validate the model, and (6) reinforce, improve, and evaluate the model's performance until clinical implementation is possible.» Understanding the implications of AI in orthopaedics may eventually lead to wide-ranging improvements in patient care. However, AI, while holding tremendous promise, is not without methodological and ethical limitations that are essential to address. First, it is important to ensure external validity of programs before their use in a clinical setting. Investigators should maintain high quality data records and registry surveillance, exercise caution when evaluating others' reported AI applications, and increase transparency of the methodological conduct of current models to improve external validity and avoid propagating bias. By addressing these challenges and responsibly embracing the potential of AI, the medical field may eventually be able to harness its power to improve patient care and outcomes.
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Affiliation(s)
- Nickelas Huffman
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
| | | | - Shujaa T Khan
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
| | - Alison K Klika
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
| | - Matthew E Deren
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
| | - Yuxuan Jin
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
| | - Kyle N Kunze
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, New York
| | - Nicolas S Piuzzi
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
- Department of Biomedical Engineering, Cleveland Clinic Foundation, Cleveland, Ohio
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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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Tragaris T, Benetos IS, Vlamis J, Pneumaticos S. Machine Learning Applications in Spine Surgery. Cureus 2023; 15:e48078. [PMID: 38046496 PMCID: PMC10689893 DOI: 10.7759/cureus.48078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/31/2023] [Indexed: 12/05/2023] Open
Abstract
This literature review sought to identify and evaluate the current applications of artificial intelligence (AI)/machine learning (ML) in spine surgery that can effectively guide clinical decision-making and surgical planning. By using specific keywords to maximize search sensitivity, a thorough literature research was conducted in several online databases: Scopus, PubMed, and Google Scholar, and the findings were filtered according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 46 studies met the requirements and were included in this review. According to this study, AI/ML models were sufficiently accurate with a mean overall value of 74.9%, and performed best at preoperative patient selection, cost prediction, and length of stay. Performance was also good at predicting functional outcomes and postoperative mortality. Regression analysis was the most frequently utilized application whereas deep learning/artificial neural networks had the highest sensitivity score (81.5%). Despite the relatively brief history of engagement with AI/ML, as evidenced by the fact that 77.5% of studies were published after 2018, the outcomes have been promising. In light of the Big Data era, the increasing prevalence of National Registries, and the wide-ranging applications of AI, such as exemplified by ChatGPT (OpenAI, San Francisco, California), it is highly likely that the field of spine surgery will gradually adopt and integrate AI/ML into its clinical practices. Consequently, it is of great significance for spine surgeons to acquaint themselves with the fundamental principles of AI/ML, as these technologies hold the potential for substantial improvements in overall patient care.
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Affiliation(s)
- Themistoklis Tragaris
- 1st Department of Orthopaedic Surgery, National and Kapodistrian University of Athens School of Medicine, KAT Hospital, Athens, GRC
| | - Ioannis S Benetos
- 3rd Department of Orthopaedic Surgery, National and Kapodistrian University of Athens School of Medicine, KAT Hospital, Athens, GRC
| | - John Vlamis
- 3rd Department of Orthopaedic Surgery, National and Kapodistrian University of Athens School of Medicine, KAT Hospital, Athens, GRC
| | - Spyridon Pneumaticos
- 3rd Department of Orthopaedic Surgery, National and Kapodistrian University of Athens School of Medicine, KAT Hospital, Athens, GRC
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Rezaii PG, Herrick D, Ratliff JK, Rusu M, Scheinker D, Desai AM. Identification of Factors Associated With 30-day Readmissions After Posterior Lumbar Fusion Using Machine Learning and Traditional Models: A National Longitudinal Database Study. Spine (Phila Pa 1976) 2023; 48:1224-1233. [PMID: 37027190 DOI: 10.1097/brs.0000000000004664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 01/23/2023] [Indexed: 04/08/2023]
Abstract
STUDY DESIGN A retrospective cohort study. OBJECTIVE To identify the factors associated with readmissions after PLF using machine learning and logistic regression (LR) models. SUMMARY OF BACKGROUND DATA Readmissions after posterior lumbar fusion (PLF) place significant health and financial burden on the patient and overall health care system. MATERIALS AND METHODS The Optum Clinformatics Data Mart database was used to identify patients who underwent posterior lumbar laminectomy, fusion, and instrumentation between 2004 and 2017. Four machine learning models and a multivariable LR model were used to assess factors most closely associated with 30-day readmission. These models were also evaluated in terms of ability to predict unplanned 30-day readmissions. The top-performing model (Gradient Boosting Machine; GBM) was then compared with the validated LACE index in terms of potential cost savings associated with the implementation of the model. RESULTS A total of 18,981 patients were included, of which 3080 (16.2%) were readmitted within 30 days of initial admission. Discharge status, prior admission, and geographic division were most influential for the LR model, whereas discharge status, length of stay, and prior admissions had the greatest relevance for the GBM model. GBM outperformed LR in predicting unplanned 30-day readmission (mean area under the receiver operating characteristic curve 0.865 vs. 0.850, P <0.0001). The use of GBM also achieved a projected 80% decrease in readmission-associated costs relative to those achieved by the LACE index model. CONCLUSIONS The factors associated with readmission vary in terms of predictive influence based on standard LR and machine learning models used, highlighting the complementary roles these models have in identifying relevant factors for the prediction of 30-day readmissions. For PLF procedures, GBM yielded the greatest predictive ability and associated cost savings for readmission. LEVEL OF EVIDENCE 3.
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Affiliation(s)
- Paymon G Rezaii
- Department of Neurosurgery, Stanford University, Stanford, CA
| | - Daniel Herrick
- Department of Neurosurgery, Stanford University, Stanford, CA
| | - John K Ratliff
- Department of Neurosurgery, Stanford University, Stanford, CA
| | - Mirabela Rusu
- Department of Radiology, Stanford University, Stanford, CA
| | - David Scheinker
- Department of Neurosurgery, Stanford University, Stanford, CA
| | - Atman M Desai
- Department of Neurosurgery, Stanford University, Stanford, CA
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Shah AA, Devana SK, Lee C, Olson TE, Upfill-Brown A, Sheppard WL, Lord EL, Shamie AN, van der Schaar M, SooHoo NF, Park DY. Development and External Validation of a Risk Calculator for Prediction of Major Complications and Readmission After Anterior Cervical Discectomy and Fusion. Spine (Phila Pa 1976) 2023; 48:460-467. [PMID: 36730869 PMCID: PMC10023283 DOI: 10.1097/brs.0000000000004531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 10/22/2022] [Indexed: 02/04/2023]
Abstract
STUDY DESIGN A retrospective, case-control study. OBJECTIVE We aim to build a risk calculator predicting major perioperative complications after anterior cervical fusion. In addition, we aim to externally validate this calculator with an institutional cohort of patients who underwent anterior cervical discectomy and fusion (ACDF). SUMMARY OF BACKGROUND DATA The average age and proportion of patients with at least one comorbidity undergoing ACDF have increased in recent years. Given the increased morbidity and cost associated with perioperative complications and unplanned readmission, accurate risk stratification of patients undergoing ACDF is of great clinical utility. METHODS This is a retrospective cohort study of adults who underwent anterior cervical fusion at any nonfederal California hospital between 2015 and 2017. The primary outcome was major perioperative complication or 30-day readmission. We built standard and ensemble machine learning models for risk prediction, assessing discrimination, and calibration. The best-performing model was validated on an external cohort comprised of consecutive adult patients who underwent ACDF at our institution between 2013 and 2020. RESULTS A total of 23,184 patients were included in this study; there were 1886 cases of major complication or readmissions. The ensemble model was well calibrated and demonstrated an area under the receiver operating characteristic curve of 0.728. The variables most important for the ensemble model include male sex, medical comorbidities, history of complications, and teaching hospital status. The ensemble model was evaluated on the validation cohort (n=260) with an area under the receiver operating characteristic curve of 0.802. The ensemble algorithm was used to build a web-based risk calculator. CONCLUSION We report derivation and external validation of an ensemble algorithm for prediction of major perioperative complications and 30-day readmission after anterior cervical fusion. This model has excellent discrimination and is well calibrated when tested on a contemporaneous external cohort of ACDF cases.
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Affiliation(s)
- Akash A. Shah
- Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, CA
| | - Sai K. Devana
- Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, CA
| | - Changhee Lee
- Department of Artificial Intelligence, Chung-Ang University, Seoul, South Korea
| | - Thomas E. Olson
- Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, CA
| | - Alexander Upfill-Brown
- Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, CA
| | - William L. Sheppard
- Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, CA
| | - Elizabeth L. Lord
- Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, CA
| | - Arya N. Shamie
- Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, CA
| | - Mihaela van der Schaar
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom
| | - Nelson F. SooHoo
- Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, CA
| | - Don Y. Park
- Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, CA
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11
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Noh SH, Cho PG, Kim KN, Kim SH, Shin DA. Artificial Intelligence for Neurosurgery : Current State and Future Directions. J Korean Neurosurg Soc 2023; 66:113-120. [PMID: 36124365 PMCID: PMC10009243 DOI: 10.3340/jkns.2022.0130] [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: 06/08/2022] [Accepted: 09/12/2022] [Indexed: 11/27/2022] Open
Abstract
Artificial intelligence (AI) is a field of computer science that equips machines with human-like intelligence and enables them to learn, reason, and solve problems when presented with data in various formats. Neurosurgery is often at the forefront of innovative and disruptive technologies, which have similarly altered the course of acute and chronic diseases. In diagnostic imaging, such as X-rays, computed tomography, and magnetic resonance imaging, AI is used to analyze images. The use of robots in the field of neurosurgery is also increasing. In neurointensive care units, AI is used to analyze data and provide care to critically ill patients. Moreover, AI can be used to predict a patient's prognosis. Several AI applications have already been introduced in the field of neurosurgery, and many more are expected in the near future. Ultimately, it is our responsibility to keep pace with this evolution to provide meaningful outcomes and personalize each patient's care. Rather than blindly relying on AI in the future, neurosurgeons should gain a thorough understanding of it and use it to enhance their patient care.
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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
| | - Pyung Goo Cho
- Department of Neurosurgery, Ajou University College of Medicine, Suwon, Korea
| | - Keung Nyun Kim
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Korea.,Department of Neurosurgery, Spine and Spinal Cord Institute, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Sang Hyun Kim
- Department of Neurosurgery, Ajou University College of Medicine, Suwon, Korea
| | - Dong Ah Shin
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Korea.,Department of Neurosurgery, Spine and Spinal Cord Institute, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
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12
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Liawrungrueang W, Kim P, Kotheeranurak V, Jitpakdee K, Sarasombath P. Automatic Detection, Classification, and Grading of Lumbar Intervertebral Disc Degeneration Using an Artificial Neural Network Model. Diagnostics (Basel) 2023; 13:diagnostics13040663. [PMID: 36832151 PMCID: PMC9955414 DOI: 10.3390/diagnostics13040663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Revised: 02/02/2023] [Accepted: 02/07/2023] [Indexed: 02/12/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Intervertebral disc degeneration (IDD) is a common cause of symptomatic axial low back pain. Magnetic resonance imaging (MRI) is currently the standard for the investigation and diagnosis of IDD. Deep learning artificial intelligence models represent a potential tool for rapidly and automatically detecting and visualizing IDD. This study investigated the use of deep convolutional neural networks (CNNs) for the detection, classification, and grading of IDD. METHODS Sagittal images of 1000 IDD T2-weighted MRI images from 515 adult patients with symptomatic low back pain were separated into 800 MRI images using annotation techniques to create a training dataset (80%) and 200 MRI images to create a test dataset (20%). The training dataset was cleaned, labeled, and annotated by a radiologist. All lumbar discs were classified for disc degeneration based on the Pfirrmann grading system. The deep learning CNN model was used for training in detecting and grading IDD. The results of the training with the CNN model were verified by testing the grading of the dataset using an automatic model. RESULTS The training dataset of the sagittal intervertebral disc lumbar MRI images found 220 IDDs of grade I, 530 of grade II, 170 of grade III, 160 of grade IV, and 20 of grade V. The deep CNN model was able to detect and classify lumbar IDD with an accuracy of more than 95%. CONCLUSION The deep CNN model can reliably automatically grade routine T2-weighted MRIs using the Pfirrmann grading system, providing a quick and efficient method for lumbar IDD classification.
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Affiliation(s)
| | - Pyeoungkee Kim
- Department of Computer Engineering, Silla University, Busan 46958, Republic of Korea
| | - Vit Kotheeranurak
- Department of Orthopaedics, Faculty of Medicine, King Chulalongkorn Memorial Hospital, Chulalongkorn University, Bangkok 10330, Thailand
- Center of Excellence in Biomechanics and Innovative Spine Surgery, Chulalongkorn University, Bangkok 10330, Thailand
| | - Khanathip Jitpakdee
- Department of Orthopedics, Queen Savang Vadhana Memorial Hospital, Chonburi 20110, Thailand
| | - Peem Sarasombath
- Department of Orthopaedics, Faculty of Medicine, Chiang Mai University, Chiang Mai 50200, Thailand
- Correspondence: ; Tel.: +66-8164-0444-5
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13
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Chen KA, Joisa CU, Stitzenberg KB, Stem J, Guillem JG, Gomez SM, Kapadia MR. Development and Validation of Machine Learning Models to Predict Readmission After Colorectal Surgery. J Gastrointest Surg 2022; 26:2342-2350. [PMID: 36070116 PMCID: PMC10081888 DOI: 10.1007/s11605-022-05443-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 08/18/2022] [Indexed: 01/31/2023]
Abstract
BACKGROUND Readmission after colorectal surgery is common and often implies complications for patients and costs for hospitals. Previous works have created predictive models using logistic regression for this outcome but have shown limited accuracy. Machine learning has shown promise in improving predictions by identifying non-linear patterns in data. We sought to create a more accurate predictive model for readmission after colorectal surgery using machine learning. METHODS Patients who underwent colorectal surgery were identified in the National Quality Improvement Program (NSQIP) database including years 2012-2019 and split into training, validation, and test sets. The primary outcome was readmission within 30 days of surgery. Three types of machine learning models were created, including random forest (RF), gradient boosting (XGB), and neural network (NN). A logistic regression (LR) model was also created for comparison. Model performance was evaluated using area under the receiver operating characteristic curve (AUROC). RESULTS The dataset included 213,827 patients after application of exclusion criteria. A total of 23,083 (10.8%) of patients experienced readmission. NN obtained an AUROC of 0.751 (95% CI 0.743-0.759), compared with 0.684 (95% CI 0.676-0.693) for LR. RF and XGB performed similarly with AUROCs of 0.749 (95% CI 0.741-0.757) and 0.745 (95% CI 0.737-0.753) respectively. Ileus, index admission length of stay, organ-space surgical site infection present at time of surgery, and ostomy placement were identified as the most contributory variables. CONCLUSIONS Machine learning approaches outperformed traditional statistical methods in the prediction of readmission after colorectal surgery. After external validation, this improved prediction model could be used to target interventions to reduce readmission rate.
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Affiliation(s)
- Kevin A Chen
- Department of Surgery, University of North Carolina, 100 Manning Drive, Burnett Womack Building, Suite 4038, Chapel Hill, NC, 27599, USA
| | - Chinmaya U Joisa
- Joint Department of Biomedical Engineering, University of North Carolina, 10202C Mary Ellen Jones Building, Chapel Hill, NC, 27599, USA
| | - Karyn B Stitzenberg
- Department of Surgery, University of North Carolina, 100 Manning Drive, Burnett Womack Building, Suite 4038, Chapel Hill, NC, 27599, USA
| | - Jonathan Stem
- Department of Surgery, University of North Carolina, 100 Manning Drive, Burnett Womack Building, Suite 4038, Chapel Hill, NC, 27599, USA
| | - Jose G Guillem
- Department of Surgery, University of North Carolina, 100 Manning Drive, Burnett Womack Building, Suite 4038, Chapel Hill, NC, 27599, USA
| | - Shawn M Gomez
- Joint Department of Biomedical Engineering, University of North Carolina, 10202C Mary Ellen Jones Building, Chapel Hill, NC, 27599, USA
| | - Muneera R Kapadia
- Department of Surgery, University of North Carolina, 100 Manning Drive, Burnett Womack Building, Suite 4038, Chapel Hill, NC, 27599, USA.
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14
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Shah AA, Devana SK, Lee C, Bugarin A, Hong MK, Upfill-Brown A, Blumstein G, Lord EL, Shamie AN, van der Schaar M, SooHoo NF, Park DY. A Risk Calculator for the Prediction of C5 Nerve Root Palsy After Instrumented Cervical Fusion. World Neurosurg 2022; 166:e703-e710. [PMID: 35872129 PMCID: PMC10410645 DOI: 10.1016/j.wneu.2022.07.082] [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: 07/14/2022] [Accepted: 07/17/2022] [Indexed: 12/15/2022]
Abstract
BACKGROUND C5 palsy is a common postoperative complication after cervical fusion and is associated with increased health care costs and diminished quality of life. Accurate prediction of C5 palsy may allow for appropriate preoperative counseling and risk stratification. We primarily aim to develop an algorithm for the prediction of C5 palsy after instrumented cervical fusion and identify novel features for risk prediction. Additionally, we aim to build a risk calculator to provide the risk of C5 palsy. METHODS We identified adult patients who underwent instrumented cervical fusion at a tertiary care medical center between 2013 and 2020. The primary outcome was postoperative C5 palsy. We developed ensemble machine learning, standard machine learning, and logistic regression models predicting the risk of C5 palsy-assessing discrimination and calibration. Additionally, a web-based risk calculator was built with the best-performing model. RESULTS A total of 1024 patients were included, with 52 cases of C5 palsy. The ensemble model was well-calibrated and demonstrated excellent discrimination with an area under the receiver-operating characteristic curve of 0.773. The following features were the most important for ensemble model performance: diabetes mellitus, bipolar disorder, C5 or C4 level, surgical approach, preoperative non-motor neurologic symptoms, degenerative disease, number of fused levels, and age. CONCLUSIONS We report a risk calculator that generates patient-specific C5 palsy risk after instrumented cervical fusion. Individualized risk prediction for patients may facilitate improved preoperative patient counseling and risk stratification as well as potential intraoperative mitigating measures. This tool may also aid in addressing potentially modifiable risk factors such as diabetes and obesity.
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Affiliation(s)
- Akash A Shah
- Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, California, USA.
| | - Sai K Devana
- Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | - Changhee Lee
- Department of Artificial Intelligence, Chung-Ang University, Seoul, South Korea
| | - Amador Bugarin
- Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | - Michelle K Hong
- Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | - Alexander Upfill-Brown
- Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | - Gideon Blumstein
- Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, Illinois, USA
| | - Elizabeth L Lord
- Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | - Arya N Shamie
- Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | - Mihaela van der Schaar
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom; Department of Electrical & Computer Engineering, UCLA, Los Angeles, California, USA
| | - Nelson F SooHoo
- Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | - Don Y Park
- Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
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15
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Lopez CD, Boddapati V, Lombardi JM, Lee NJ, Mathew J, Danford NC, Iyer RR, Dyrszka MD, Sardar ZM, Lenke LG, Lehman RA. Artificial Learning and Machine Learning Applications in Spine Surgery: A Systematic Review. Global Spine J 2022; 12:1561-1572. [PMID: 35227128 PMCID: PMC9393994 DOI: 10.1177/21925682211049164] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
OBJECTIVES This current systematic review sought to identify and evaluate all current research-based spine surgery applications of AI/ML in optimizing preoperative patient selection, as well as predicting and managing postoperative outcomes and complications. METHODS A comprehensive search of publications was conducted through the EMBASE, Medline, and PubMed databases using relevant keywords to maximize the sensitivity of the search. No limits were placed on level of evidence or timing of the study. Findings were reported according to the PRISMA guidelines. RESULTS After application of inclusion and exclusion criteria, 41 studies were included in this review. Bayesian networks had the highest average AUC (.80), and neural networks had the best accuracy (83.0%), sensitivity (81.5%), and specificity (71.8%). Preoperative planning/cost prediction models (.89,82.2%) and discharge/length of stay models (.80,78.0%) each reported significantly higher average AUC and accuracy compared to readmissions/reoperation prediction models (.67,70.2%) (P < .001, P = .005, respectively). Model performance also significantly varied across postoperative management applications for average AUC and accuracy values (P < .001, P < .027, respectively). CONCLUSIONS Generally, authors of the reviewed studies concluded that AI/ML offers a potentially beneficial tool for providers to optimize patient care and improve cost-efficiency. More specifically, AI/ML models performed best, on average, when optimizing preoperative patient selection and planning and predicting costs, hospital discharge, and length of stay. However, models were not as accurate in predicting postoperative complications, adverse events, and readmissions and reoperations. An understanding of AI/ML-based applications is becoming increasingly important, particularly in spine surgery, as the volume of reported literature, technology accessibility, and clinical applications continue to rapidly expand.
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Affiliation(s)
- Cesar D. Lopez
- Department of Orthopaedic Surgery, The Spine Hospital, New York-Presbyterian/Columbia University Irving Medical Center, New York, NY, USA
| | - Venkat Boddapati
- Department of Orthopaedic Surgery, The Spine Hospital, New York-Presbyterian/Columbia University Irving Medical Center, New York, NY, USA,Venkat Boddapati, MD, Columbia University Irving Medical Center, 622 W. 168th St., PH-11, New York, NY 10032, USA.
| | - Joseph M. Lombardi
- Department of Orthopaedic Surgery, The Spine Hospital, New York-Presbyterian/Columbia University Irving Medical Center, New York, NY, USA
| | - Nathan J. Lee
- Department of Orthopaedic Surgery, The Spine Hospital, New York-Presbyterian/Columbia University Irving Medical Center, New York, NY, USA
| | - Justin Mathew
- Department of Orthopaedic Surgery, The Spine Hospital, New York-Presbyterian/Columbia University Irving Medical Center, New York, NY, USA
| | - Nicholas C. Danford
- Department of Orthopaedic Surgery, The Spine Hospital, New York-Presbyterian/Columbia University Irving Medical Center, New York, NY, USA
| | - Rajiv R. Iyer
- Department of Orthopaedic Surgery, The Spine Hospital, New York-Presbyterian/Columbia University Irving Medical Center, New York, NY, USA
| | - Marc D. Dyrszka
- Department of Orthopaedic Surgery, The Spine Hospital, New York-Presbyterian/Columbia University Irving Medical Center, New York, NY, USA
| | - Zeeshan M. Sardar
- Department of Orthopaedic Surgery, The Spine Hospital, New York-Presbyterian/Columbia University Irving Medical Center, New York, NY, USA
| | - Lawrence G. Lenke
- Department of Orthopaedic Surgery, The Spine Hospital, New York-Presbyterian/Columbia University Irving Medical Center, New York, NY, USA
| | - Ronald A. Lehman
- Department of Orthopaedic Surgery, The Spine Hospital, New York-Presbyterian/Columbia University Irving Medical Center, New York, NY, USA
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16
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Abi-Rafeh J, Safran T, Winocour S, Dionisopoulos T, Davison P, Vorstenbosch J. Complications of Capsulectomies: An Analysis of the American College of Surgeons National Surgical Quality Improvement Program (NSQIP) Database. AESTHETIC SURGERY JOURNAL OPEN FORUM 2022; 4:ojac025. [PMID: 35747463 PMCID: PMC9212085 DOI: 10.1093/asjof/ojac025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Background Although plastic surgeons commonly perform capsulectomies for a variety of peri-prosthetic capsular conditions, the safety of capsulectomy remains unknown, and the literature lacks evidence describing its morbidity and complication rates for patients inquiring about its associated risks. Objectives The present study aims to identify and define the complication rates associated with capsulectomies. Methods An analysis of the American College of Surgeons National Surgical Quality Improvement Program (NSQIP) database was performed between the years 2015 and 2018. All information pertaining to demographics, patient-related information, surgical indications, procedure-related information, outcomes, and complications were assessed. Results The study identified 2231 cases of surgeon-reported capsulectomies; indications most commonly reported included capsular contracture (n = 638, 28.6%) and breast implant rupture (n = 403, 18.1%). In total, 141 patients (6.32%) were hospitalized for longer than 1 postoperative day (range, 2-28 days), while the overall complication rate was 3.0% (n = 67/2231 patients). Incidence of minor complications, representing superficial surgical site infections, was 0.8%, while the major complication rate was 2.24%. These included 7 cases of deep surgical site infections (0.3%), 19 organ space infections (0.9%), and 8 cases of wound dehiscence (0.4%). Eight patients developed sepsis (0.4%); 6 patients required transfusions (0.3%); 1 case of postoperative pneumonia and 1 myocardial infarction were also identified (n = 1 each, 0.0%). The overall reoperation and readmission rates were 2.0%, representing a readmission rate of 66% among patients with complications. Conclusions The present study provides the first estimate of the incidence of complications associated with capsulectomies. Although the NSQIP database contains significant limitations, the data presented herein describe a complication profile that plastic surgeons can share with their patients during informed consent. Level of Evidence: 4
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Affiliation(s)
- Jad Abi-Rafeh
- Division of Plastic and Reconstructive Surgery, McGill University Health Center, Montreal, Quebec, Canada
| | - Tyler Safran
- Division of Plastic and Reconstructive Surgery, McGill University Health Center, Montreal, Quebec, Canada
| | - Sebastian Winocour
- Division of Plastic and Reconstructive Surgery, Baylor College of Medicine, Houston, TX, USA
| | - Tassos Dionisopoulos
- Division of Plastic and Reconstructive Surgery, McGill University Health Center, Montreal, Quebec, Canada
| | - Peter Davison
- Division of Plastic and Reconstructive Surgery, McGill University Health Center, Montreal, Quebec, Canada
| | - Joshua Vorstenbosch
- Division of Plastic and Reconstructive Surgery, McGill University Health Center, Montreal, Quebec, Canada
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Saravi B, Hassel F, Ülkümen S, Zink A, Shavlokhova V, Couillard-Despres S, Boeker M, Obid P, Lang GM. Artificial Intelligence-Driven Prediction Modeling and Decision Making in Spine Surgery Using Hybrid Machine Learning Models. J Pers Med 2022; 12:jpm12040509. [PMID: 35455625 PMCID: PMC9029065 DOI: 10.3390/jpm12040509] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Revised: 03/18/2022] [Accepted: 03/19/2022] [Indexed: 12/22/2022] Open
Abstract
Healthcare systems worldwide generate vast amounts of data from many different sources. Although of high complexity for a human being, it is essential to determine the patterns and minor variations in the genomic, radiological, laboratory, or clinical data that reliably differentiate phenotypes or allow high predictive accuracy in health-related tasks. Convolutional neural networks (CNN) are increasingly applied to image data for various tasks. Its use for non-imaging data becomes feasible through different modern machine learning techniques, converting non-imaging data into images before inputting them into the CNN model. Considering also that healthcare providers do not solely use one data modality for their decisions, this approach opens the door for multi-input/mixed data models which use a combination of patient information, such as genomic, radiological, and clinical data, to train a hybrid deep learning model. Thus, this reflects the main characteristic of artificial intelligence: simulating natural human behavior. The present review focuses on key advances in machine and deep learning, allowing for multi-perspective pattern recognition across the entire information set of patients in spine surgery. This is the first review of artificial intelligence focusing on hybrid models for deep learning applications in spine surgery, to the best of our knowledge. This is especially interesting as future tools are unlikely to use solely one data modality. The techniques discussed could become important in establishing a new approach to decision-making in spine surgery based on three fundamental pillars: (1) patient-specific, (2) artificial intelligence-driven, (3) integrating multimodal data. The findings reveal promising research that already took place to develop multi-input mixed-data hybrid decision-supporting models. Their implementation in spine surgery may hence be only a matter of time.
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Affiliation(s)
- Babak Saravi
- Department of Orthopedics and Trauma Surgery, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79108 Freiburg, Germany; (S.Ü.); (P.O.); (G.M.L.)
- Department of Spine Surgery, Loretto Hospital, 79100 Freiburg, Germany; (F.H.); (A.Z.)
- Institute of Experimental Neuroregeneration, Spinal Cord Injury and Tissue Regeneration Center Salzburg (SCI-TReCS), Paracelsus Medical University, 5020 Salzburg, Austria;
- Correspondence:
| | - Frank Hassel
- Department of Spine Surgery, Loretto Hospital, 79100 Freiburg, Germany; (F.H.); (A.Z.)
| | - Sara Ülkümen
- Department of Orthopedics and Trauma Surgery, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79108 Freiburg, Germany; (S.Ü.); (P.O.); (G.M.L.)
- Department of Spine Surgery, Loretto Hospital, 79100 Freiburg, Germany; (F.H.); (A.Z.)
| | - Alisia Zink
- Department of Spine Surgery, Loretto Hospital, 79100 Freiburg, Germany; (F.H.); (A.Z.)
| | - Veronika Shavlokhova
- Department of Oral and Maxillofacial Surgery, University Hospital Heidelberg, 69120 Heidelberg, Germany;
| | - Sebastien Couillard-Despres
- Institute of Experimental Neuroregeneration, Spinal Cord Injury and Tissue Regeneration Center Salzburg (SCI-TReCS), Paracelsus Medical University, 5020 Salzburg, Austria;
- Austrian Cluster for Tissue Regeneration, 1200 Vienna, Austria
| | - Martin Boeker
- Intelligence and Informatics in Medicine, Medical Center Rechts der Isar, School of Medicine, Technical University of Munich, 81675 Munich, Germany;
| | - Peter Obid
- Department of Orthopedics and Trauma Surgery, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79108 Freiburg, Germany; (S.Ü.); (P.O.); (G.M.L.)
| | - Gernot Michael Lang
- Department of Orthopedics and Trauma Surgery, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79108 Freiburg, Germany; (S.Ü.); (P.O.); (G.M.L.)
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Karhade AV, Lavoie-Gagne O, Agaronnik N, Ghaednia H, Collins AK, Shin D, Schwab JH. Natural language processing for prediction of readmission in posterior lumbar fusion patients: which free-text notes have the most utility? Spine J 2022; 22:272-277. [PMID: 34407468 DOI: 10.1016/j.spinee.2021.08.002] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 07/19/2021] [Accepted: 08/09/2021] [Indexed: 02/03/2023]
Abstract
BACKGROUND CONTEXT The increasing volume of free-text notes available in electronic health records has created an opportunity for natural language processing (NLP) algorithms to mine this unstructured data in order to detect and predict adverse outcomes. Given the volume and diversity of documentation available in spine surgery, it remains unclear which types of documentation offer the greatest value for prediction of adverse outcomes. STUDY DESIGN/SETTING Retrospective review of medical records at two academic and three community hospitals. PURPOSE The purpose of this study was to conduct an exploratory analysis in order to examine the utility of free-text notes generated during the index hospitalization for lumbar spine fusion for prediction of 90-day unplanned readmission. PATIENT SAMPLE Adult patients 18 years or older undergoing lumbar spine fusion for lumbar spondylolisthesis or lumbar spinal stenosis between January 1, 2016 and December 31, 2020. OUTCOME MEASURES The primary outcome was inpatient admission within 90-days of discharge from the index hospitalization. METHODS The predictive performance of NLP algorithms developed by using discharge summary notes, operative notes, nursing notes, physical therapy notes, case management notes, medical doctor (MD) (resident or attending), and allied practice professional (APP) (nurse practitioner or physician assistant) notes were assessed by discrimination, calibration, overall performance. RESULTS Overall, 708 patients were included in the study and 83 (11.7%) had 90-day inpatient readmission. In the independent testing set of patients (n=141) not used for model development, the area under the receiver operating curve of NLP algorithms for prediction of 90-day readmission using discharge summary notes, operative notes, nursing notes, physical therapy notes, case management notes, MD/APP notes was 0.70, 0.57, 0.57, 0.60, 0.60, and 0.49 respectively. CONCLUSION In this exploratory analysis, discharge summary, physical therapy, and case management notes had the most utility and daily MD/APP progress notes had the least utility for prediction of 90-day inpatient readmission in lumbar fusion patients among the free-text documentation generated during the index hospitalization.
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Affiliation(s)
- Aditya V Karhade
- Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Harvard Combined Orthopaedic Residency Program, Boston, MA, USA
| | - Ophelie Lavoie-Gagne
- Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Nicole Agaronnik
- Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Hamid Ghaednia
- Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Austin K Collins
- Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - David Shin
- Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Joseph H Schwab
- Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Harvard Combined Orthopaedic Residency Program, Boston, MA, USA.
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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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Huang J, Shlobin NA, DeCuypere M, Lam SK. Deep Learning for Outcome Prediction in Neurosurgery: A Systematic Review of Design, Reporting, and Reproducibility. Neurosurgery 2022; 90:16-38. [PMID: 34982868 DOI: 10.1227/neu.0000000000001736] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 08/18/2021] [Indexed: 02/06/2023] Open
Abstract
Deep learning (DL) is a powerful machine learning technique that has increasingly been used to predict surgical outcomes. However, the large quantity of data required and lack of model interpretability represent substantial barriers to the validity and reproducibility of DL models. The objective of this study was to systematically review the characteristics of DL studies involving neurosurgical outcome prediction and to assess their bias and reporting quality. Literature search using the PubMed, Scopus, and Embase databases identified 1949 records of which 35 studies were included. Of these, 32 (91%) developed and validated a DL model while 3 (9%) validated a pre-existing model. The most commonly represented subspecialty areas were oncology (16 of 35, 46%), spine (8 of 35, 23%), and vascular (6 of 35, 17%). Risk of bias was low in 18 studies (51%), unclear in 5 (14%), and high in 12 (34%), most commonly because of data quality deficiencies. Adherence to transparent reporting of a multivariable prediction model for individual prognosis or diagnosis reporting standards was low, with a median of 12 transparent reporting of a multivariable prediction model for individual prognosis or diagnosis items (39%) per study not reported. Model transparency was severely limited because code was provided in only 3 studies (9%) and final models in 2 (6%). With the exception of public databases, no study data sets were readily available. No studies described DL models as ready for clinical use. The use of DL for neurosurgical outcome prediction remains nascent. Lack of appropriate data sets poses a major concern for bias. Although studies have demonstrated promising results, greater transparency in model development and reporting is needed to facilitate reproducibility and validation.
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Affiliation(s)
- Jonathan Huang
- Ann and Robert H. Lurie Children's Hospital, Division of Pediatric Neurosurgery, Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
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Katsuura Y, Colón LF, Perez AA, Albert TJ, Qureshi SA. A Primer on the Use of Artificial Intelligence in Spine Surgery. Clin Spine Surg 2021; 34:316-321. [PMID: 34050043 DOI: 10.1097/bsd.0000000000001211] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Accepted: 04/14/2021] [Indexed: 11/26/2022]
Abstract
DESIGN This was a narrative review. PURPOSE Summarize artificial intelligence (AI) fundamentals as well as current and potential future uses in spine surgery. SUMMARY OF BACKGROUND DATA Although considered futuristic, the field of AI has already had a profound impact on many industries, including health care. Its ability to recognize patterns and self-correct to improve over time mimics human cognitive function, but on a much larger scale. METHODS Review of literature on AI fundamentals and uses in spine pathology. RESULTS Machine learning (ML), a subset of AI, increases in hierarchy of complexity from classic ML to unsupervised ML to deep leaning, where Language Processing and Computer Vision are possible. AI-based tools have been developed to segment spinal structures, acquire basic spinal measurements, and even identify pathology such as tumor or degeneration. AI algorithms could have use in guiding clinical management through treatment selection, patient-specific prognostication, and even has the potential to power neuroprosthetic devices after spinal cord injury. CONCLUSION While the use of AI has pitfalls and should be adopted with caution, future use is promising in the field of spine surgery and medicine as a whole. LEVEL OF EVIDENCE Level IV.
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Affiliation(s)
| | - Luis F Colón
- Department of Orthopedic Surgery, University of Tennessee College of Medicine in Chattanooga, Chattanooga, TN
| | - Alberto A Perez
- School of Medicine and Public Health, University of Wisconsin, Madison, WI
| | - Todd J Albert
- Hospital for Special Surgery
- Weill Cornell Medical College, New York, NY
| | - Sheeraz A Qureshi
- Hospital for Special Surgery
- Weill Cornell Medical College, New York, NY
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Stephens ME, O'Neal CM, Westrup AM, Muhammad FY, McKenzie DM, Fagg AH, Smith ZA. Utility of machine learning algorithms in degenerative cervical and lumbar spine disease: a systematic review. Neurosurg Rev 2021; 45:965-978. [PMID: 34490539 DOI: 10.1007/s10143-021-01624-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 06/28/2021] [Accepted: 08/09/2021] [Indexed: 10/20/2022]
Abstract
Machine learning is a rapidly evolving field that offers physicians an innovative and comprehensive mechanism to examine various aspects of patient data. Cervical and lumbar degenerative spine disorders are commonly age-related disease processes that can utilize machine learning to improve patient outcomes with careful patient selection and intervention. The aim of this study is to examine the current applications of machine learning in cervical and lumbar degenerative spine disease. A systematic review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A search of PubMed, Embase, Medline, and Cochrane was conducted through May 31st, 2020, using the following terms: "artificial intelligence" OR "machine learning" AND "neurosurgery" AND "spine." Studies were included if original research on machine learning was utilized in patient care for degenerative spine disease, including radiographic machine learning applications. Studies focusing on robotic applications in neurosurgery, navigation, or stereotactic radiosurgery were excluded. The literature search identified 296 papers, with 35 articles meeting inclusion criteria. There were nine studies involving cervical degenerative spine disease and 26 studies on lumbar degenerative spine disease. The majority of studies for both cervical and lumbar spines utilized machine learning for the prediction of postoperative outcomes, with 5 (55.6%) and 15 (61.5%) studies, respectively. Machine learning applications focusing on degenerative lumbar spine greatly outnumber the current volume of cervical spine studies. The current research in lumbar spine also demonstrates more advanced clinical applications of radiographic, diagnostic, and predictive machine learning models.
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Affiliation(s)
- Mark E Stephens
- Department of Neurosurgery, University of Oklahoma Health Sciences Center, 1000 N Lincoln Blvd, Suite 4000, Oklahoma City, OK, 73104, USA
| | - Christen M O'Neal
- Department of Neurosurgery, University of Oklahoma Health Sciences Center, 1000 N Lincoln Blvd, Suite 4000, Oklahoma City, OK, 73104, USA
| | - Alison M Westrup
- Department of Neurosurgery, University of Oklahoma Health Sciences Center, 1000 N Lincoln Blvd, Suite 4000, Oklahoma City, OK, 73104, USA
| | - Fauziyya Y Muhammad
- Department of Neurosurgery, University of Oklahoma Health Sciences Center, 1000 N Lincoln Blvd, Suite 4000, Oklahoma City, OK, 73104, USA
| | - Daniel M McKenzie
- Department of Neurosurgery, University of Oklahoma Health Sciences Center, 1000 N Lincoln Blvd, Suite 4000, Oklahoma City, OK, 73104, USA
| | - Andrew H Fagg
- School of Computer Science, University of Oklahoma, Norman, OK, USA
| | - Zachary A Smith
- Department of Neurosurgery, University of Oklahoma Health Sciences Center, 1000 N Lincoln Blvd, Suite 4000, Oklahoma City, OK, 73104, USA.
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Shah AA, Devana SK, Lee C, Bugarin A, Lord EL, Shamie AN, Park DY, van der Schaar M, SooHoo NF. Prediction of Major Complications and Readmission After Lumbar Spinal Fusion: A Machine Learning-Driven Approach. World Neurosurg 2021; 152:e227-e234. [PMID: 34058366 PMCID: PMC8338911 DOI: 10.1016/j.wneu.2021.05.080] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 05/18/2021] [Accepted: 05/19/2021] [Indexed: 02/05/2023]
Abstract
BACKGROUND Given the significant cost and morbidity of patients undergoing lumbar fusion, accurate preoperative risk-stratification would be of great utility. We aim to develop a machine learning model for prediction of major complications and readmission after lumbar fusion. We also aim to identify the factors most important to performance of each tested model. METHODS We identified 38,788 adult patients who underwent lumbar fusion at any California hospital between 2015 and 2017. The primary outcome was major perioperative complication or readmission within 30 days. We build logistic regression and advanced machine learning models: XGBoost, AdaBoost, Gradient Boosting, and Random Forest. Discrimination and calibration were assessed using area under the receiver operating characteristic curve and Brier score, respectively. RESULTS There were 4470 major complications (11.5%). The XGBoost algorithm demonstrates the highest discrimination of the machine learning models, outperforming regression. The variables most important to XGBoost performance include angina pectoris, metastatic cancer, teaching hospital status, history of concussion, comorbidity burden, and workers' compensation insurance. Teaching hospital status and concussion history were not found to be important for regression. CONCLUSIONS We report a machine learning algorithm for prediction of major complications and readmission after lumbar fusion that outperforms logistic regression. Notably, the predictors most important for XGBoost differed from those for regression. The superior performance of XGBoost may be due to the ability of advanced machine learning methods to capture relationships between variables that regression is unable to detect. This tool may identify and address potentially modifiable risk factors, helping risk-stratify patients and decrease complication rates.
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Affiliation(s)
- Akash A Shah
- Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, California, USA.
| | - Sai K Devana
- Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | - Changhee Lee
- Department of Electrical and Computer Engineering, University of California, Los Angeles, California, USA
| | - Amador Bugarin
- Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | - Elizabeth L Lord
- Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | - Arya N Shamie
- Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | - Don Y Park
- Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | - Mihaela van der Schaar
- Department of Electrical and Computer Engineering, University of California, Los Angeles, California, USA; Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom
| | - Nelson F SooHoo
- Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
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Raju B, Jumah F, Ashraf O, Narayan V, Gupta G, Sun H, Hilden P, Nanda A. Big data, machine learning, and artificial intelligence: a field guide for neurosurgeons. J Neurosurg 2021; 135:373-383. [PMID: 33007750 DOI: 10.3171/2020.5.jns201288] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Accepted: 05/27/2020] [Indexed: 11/06/2022]
Abstract
Big data has transformed into a trend phrase in healthcare and neurosurgery, becoming a pervasive and inescapable phrase in everyday life. The upsurge in big data applications is a direct consequence of the drastic boom in information technology as well as the growing number of internet-connected devices called the Internet of Things in healthcare. Compared with business, marketing, and other sectors, healthcare applications are lagging due to a lack of technical knowledge among healthcare workers, technological limitations in acquiring and analyzing the data, and improper governance of healthcare big data. Despite these limitations, the medical literature is flooded with big data-related articles, and most of these are filled with abstruse terminologies such as machine learning, artificial intelligence, artificial neural network, and algorithm. Many of the recent articles are restricted to neurosurgical registries, creating a false impression that big data is synonymous with registries. Others advocate that the utilization of big data will be the panacea to all healthcare problems and research in the future. Without a proper understanding of these principles, it becomes easy to get lost without the ability to differentiate hype from reality. To that end, the authors give a brief narrative of big data analysis in neurosurgery and review its applications, limitations, and the challenges it presents for neurosurgeons and healthcare professionals naive to this field. Awareness of these basic concepts will allow neurosurgeons to understand the literature regarding big data, enabling them to make better decisions and deliver personalized care.
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Affiliation(s)
- Bharath Raju
- 1Department of Neurosurgery, Rutgers-Robert Wood Johnson Medical School and University Hospital; and
| | - Fareed Jumah
- 1Department of Neurosurgery, Rutgers-Robert Wood Johnson Medical School and University Hospital; and
| | - Omar Ashraf
- 1Department of Neurosurgery, Rutgers-Robert Wood Johnson Medical School and University Hospital; and
| | - Vinayak Narayan
- 1Department of Neurosurgery, Rutgers-Robert Wood Johnson Medical School and University Hospital; and
| | - Gaurav Gupta
- 1Department of Neurosurgery, Rutgers-Robert Wood Johnson Medical School and University Hospital; and
| | - Hai Sun
- 1Department of Neurosurgery, Rutgers-Robert Wood Johnson Medical School and University Hospital; and
| | - Patrick Hilden
- 2Rutgers Neurosurgery Health Outcomes, Policy, and Economics (HOPE) Center, New Brunswick, New Jersey
| | - Anil Nanda
- 1Department of Neurosurgery, Rutgers-Robert Wood Johnson Medical School and University Hospital; and
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Huang Y, Talwar A, Chatterjee S, Aparasu RR. Application of machine learning in predicting hospital readmissions: a scoping review of the literature. BMC Med Res Methodol 2021; 21:96. [PMID: 33952192 PMCID: PMC8101040 DOI: 10.1186/s12874-021-01284-z] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2020] [Accepted: 04/15/2021] [Indexed: 12/18/2022] Open
Abstract
Background Advances in machine learning (ML) provide great opportunities in the prediction of hospital readmission. This review synthesizes the literature on ML methods and their performance for predicting hospital readmission in the US. Methods This review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Reviews (PRISMA-ScR) Statement. The extraction of items was also guided by the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS). Electronic databases PUBMED, MEDLINE, and EMBASE were systematically searched from January 1, 2015, through December 10, 2019. The articles were imported into COVIDENCE online software for title/abstract screening and full-text eligibility. Observational studies using ML techniques for hospital readmissions among US patients were eligible for inclusion. Articles without a full text available in the English language were excluded. A qualitative synthesis included study characteristics, ML algorithms utilized, and model validation, and quantitative analysis assessed model performance. Model performances in terms of Area Under the Curve (AUC) were analyzed using R software. Quality in Prognosis Studies (QUIPS) tool was used to assess the quality of the reviewed studies. Results Of 522 citations reviewed, 43 studies met the inclusion criteria. A majority of the studies used electronic health records (24, 56%), followed by population-based data sources (15, 35%) and administrative claims data (4, 9%). The most common algorithms were tree-based methods (23, 53%), neural network (NN) (14, 33%), regularized logistic regression (12, 28%), and support vector machine (SVM) (10, 23%). Most of these studies (37, 85%) were of high quality. A majority of these studies (28, 65%) reported ML algorithms with an AUC above 0.70. There was a range of variability within AUC reported by these studies with a median of 0.68 (IQR: 0.64–0.76; range: 0.50–0.90). Conclusions The ML algorithms involving tree-based methods, NN, regularized logistic regression, and SVM are commonly used to predict hospital readmission in the US. Further research is needed to compare the performance of ML algorithms for hospital readmission prediction. Supplementary Information The online version contains supplementary material available at 10.1186/s12874-021-01284-z.
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Affiliation(s)
- Yinan Huang
- Department of Pharmaceutical Health Outcomes and Policy, College of Pharmacy, University of Houston, 4849 Calhoun Road, Health & Sciences Bldg 2, Houston, TX, 77204, USA
| | - Ashna Talwar
- Department of Pharmaceutical Health Outcomes and Policy, College of Pharmacy, University of Houston, 4849 Calhoun Road, Health & Sciences Bldg 2, Houston, TX, 77204, USA
| | - Satabdi Chatterjee
- Department of Pharmaceutical Health Outcomes and Policy, College of Pharmacy, University of Houston, 4849 Calhoun Road, Health & Sciences Bldg 2, Houston, TX, 77204, USA
| | - Rajender R Aparasu
- Department of Pharmaceutical Health Outcomes and Policy, College of Pharmacy, University of Houston, 4849 Calhoun Road, Health & Sciences Bldg 2, Houston, TX, 77204, USA.
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Lubelski D, Hersh A, Azad TD, Ehresman J, Pennington Z, Lehner K, Sciubba DM. Prediction Models in Degenerative Spine Surgery: A Systematic Review. Global Spine J 2021; 11:79S-88S. [PMID: 33890803 PMCID: PMC8076813 DOI: 10.1177/2192568220959037] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
STUDY DESIGN Systematic review. OBJECTIVES To review the existing literature of prediction models in degenerative spinal surgery. METHODS Review of PubMed/Medline and Embase databases was conducted to identify articles between January 1, 2000 and March 1, 2020 that reported prediction model performance for outcomes following elective degenerative spine surgery. RESULTS Thirty-one articles were included. Twenty studies were of thoracolumbar, 5 were of cervical, and 6 included all spine patients. Five studies were externally validated. Prediction models were developed using machine learning (42%) and logistic regression (42%) as well as other techniques. Web-based calculators were included in 45% of published articles. Various outcomes were investigated, including complications, infection, length of stay, discharge disposition, reoperation, readmission, disability score, back pain, leg pain, return to work, and opioid dependence. CONCLUSIONS Significant heterogeneity exists in methods used to develop prediction models of postoperative outcomes after degenerative spine surgery. Most internally validate their scores, but a few have been externally validated. Areas under the curve for most models range from 0.6 to 0.9. Techniques for development are becoming increasingly sophisticated with different machine learning tools. With further external validation, these models can be deployed online for patient, physician, and administrative use, and have the potential to optimize outcomes and maximize value in spine surgery.
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Affiliation(s)
- Daniel Lubelski
- Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Andrew Hersh
- Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Tej D. Azad
- Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jeff Ehresman
- Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | | | - Kurt Lehner
- Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Daniel M. Sciubba
- Johns Hopkins University School of Medicine, Baltimore, MD, USA,Daniel M. Sciubba, Department of Neurosurgery, Johns Hopkins University School of Medicine, 600 North Wolfe Street, Meyer 5-185A, Baltimore, MD 21287, USA.
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Kuris EO, Veeramani A, McDonald CL, DiSilvestro KJ, Zhang AS, Cohen EM, Daniels AH. Predicting Readmission After Anterior, Posterior, and Posterior Interbody Lumbar Spinal Fusion: A Neural Network Machine Learning Approach. World Neurosurg 2021; 151:e19-e27. [PMID: 33744425 DOI: 10.1016/j.wneu.2021.02.114] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 02/22/2021] [Accepted: 02/26/2021] [Indexed: 12/15/2022]
Abstract
BACKGROUND Readmission after spine surgery is costly and a relatively common occurrence. Previous research identified several risk factors for readmission; however, the conclusions remain equivocal. Machine learning algorithms offer a unique perspective in analysis of risk factors for readmission and can help predict the likelihood of this occurrence. This study evaluated a neural network (NN), a supervised machine learning technique, to determine whether it could predict readmission after 3 lumbar fusion procedures. METHODS The American College of Surgeons National Surgical Quality Improvement Program database was queried between 2009 and 2018. Patients who had undergone anterior, lateral, and/or posterior lumbar fusion were included in the study. The Python scikit Learn package was used to run the NN algorithm. A multivariate regression was performed to determine risk factors for readmission. RESULTS There were 63,533 patients analyzed (12,915 anterior lumbar interbody fusion, 27,212 posterior lumbar interbody fusion, and 23,406 posterior spinal fusion cases). The NN algorithm was able to successfully predict 30-day readmission for 94.6% of anterior lumbar interbody fusion, 94.0% of posterior lumbar interbody fusion, and 92.6% of posterior spinal fusion cases with area under the curve values of 0.64-0.65. Multivariate regression indicated that age >65 years and American Society of Anesthesiologists class >II were linked to increased risk for readmission for all 3 procedures. CONCLUSIONS The accurate metrics presented indicate the capability for NN algorithms to predict readmission after lumbar arthrodesis. Moreover, the results of this study serve as a catalyst for further research into the utility of machine learning in spine surgery.
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Affiliation(s)
- Eren O Kuris
- Department of Orthopedic Surgery, Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island, USA
| | - Ashwin Veeramani
- Division of Applied Mathematics, Brown University, Providence, Rhode Island, USA
| | - Christopher L McDonald
- Department of Orthopedic Surgery, Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island, USA
| | - Kevin J DiSilvestro
- Department of Orthopedic Surgery, Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island, USA
| | - Andrew S Zhang
- Department of Orthopedic Surgery, Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island, USA
| | - Eric M Cohen
- Department of Orthopedic Surgery, Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island, USA
| | - Alan H Daniels
- Department of Orthopedic Surgery, Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island, USA.
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Tadavarthi Y, Vey B, Krupinski E, Prater A, Gichoya J, Safdar N, Trivedi H. The State of Radiology AI: Considerations for Purchase Decisions and Current Market Offerings. Radiol Artif Intell 2020; 2:e200004. [PMID: 33937846 DOI: 10.1148/ryai.2020200004] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Revised: 06/16/2020] [Accepted: 06/25/2020] [Indexed: 01/02/2023]
Abstract
Purpose To provide an overview of important factors to consider when purchasing radiology artificial intelligence (AI) software and current software offerings by type, subspecialty, and modality. Materials and Methods Important factors for consideration when purchasing AI software, including key decision makers, data ownership and privacy, cost structures, performance indicators, and potential return on investment are described. For the market overview, a list of radiology AI companies was aggregated from the Radiological Society of North America and the Society for Imaging Informatics in Medicine conferences (November 2016-June 2019), then narrowed to companies using deep learning for imaging analysis and diagnosis. Software created for image enhancement, reporting, or workflow management was excluded. Software was categorized by task (repetitive, quantitative, explorative, and diagnostic), modality, and subspecialty. Results A total of 119 software offerings from 55 companies were identified. There were 46 algorithms that currently have Food and Drug Administration and/or Conformité Européenne approval (as of November 2019). Of the 119 offerings, distribution of software targets was 34 of 70 (49%), 21 of 70 (30%), 14 of 70 (20%), and one of 70 (1%) for diagnostic, quantitative, repetitive, and explorative tasks, respectively. A plurality of companies are focused on nodule detection at chest CT and two-dimensional mammography. There is very little activity in certain subspecialties, including pediatrics and nuclear medicine. A comprehensive table is available on the website hitilab.org/pages/ai-companies. Conclusion The radiology AI marketplace is rapidly maturing, with an increase in product offerings. Radiologists and practice administrators should educate themselves on current product offerings and important factors to consider before purchase and implementation.© RSNA, 2020See also the invited commentary by Sala and Ursprung in this issue.
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Affiliation(s)
- Yasasvi Tadavarthi
- Department of Radiology, Medical College of Georgia at Augusta University, 1120 15th St, Augusta, GA 30912 (Y.T.); and Department of Radiology, Emory University, Atlanta, Ga (B.V., E.K., A.P., J.G., N.S., H.T.)
| | - Brianna Vey
- Department of Radiology, Medical College of Georgia at Augusta University, 1120 15th St, Augusta, GA 30912 (Y.T.); and Department of Radiology, Emory University, Atlanta, Ga (B.V., E.K., A.P., J.G., N.S., H.T.)
| | - Elizabeth Krupinski
- Department of Radiology, Medical College of Georgia at Augusta University, 1120 15th St, Augusta, GA 30912 (Y.T.); and Department of Radiology, Emory University, Atlanta, Ga (B.V., E.K., A.P., J.G., N.S., H.T.)
| | - Adam Prater
- Department of Radiology, Medical College of Georgia at Augusta University, 1120 15th St, Augusta, GA 30912 (Y.T.); and Department of Radiology, Emory University, Atlanta, Ga (B.V., E.K., A.P., J.G., N.S., H.T.)
| | - Judy Gichoya
- Department of Radiology, Medical College of Georgia at Augusta University, 1120 15th St, Augusta, GA 30912 (Y.T.); and Department of Radiology, Emory University, Atlanta, Ga (B.V., E.K., A.P., J.G., N.S., H.T.)
| | - Nabile Safdar
- Department of Radiology, Medical College of Georgia at Augusta University, 1120 15th St, Augusta, GA 30912 (Y.T.); and Department of Radiology, Emory University, Atlanta, Ga (B.V., E.K., A.P., J.G., N.S., H.T.)
| | - Hari Trivedi
- Department of Radiology, Medical College of Georgia at Augusta University, 1120 15th St, Augusta, GA 30912 (Y.T.); and Department of Radiology, Emory University, Atlanta, Ga (B.V., E.K., A.P., J.G., N.S., H.T.)
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Goshtasbi K, Yasaka TM, Zandi-Toghani M, Djalilian HR, Armstrong WB, Tjoa T, Haidar YM, Abouzari M. Machine learning models to predict length of stay and discharge destination in complex head and neck surgery. Head Neck 2020; 43:788-797. [PMID: 33142001 DOI: 10.1002/hed.26528] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2020] [Revised: 10/13/2020] [Accepted: 10/23/2020] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND This study develops machine learning (ML) algorithms that use preoperative-only features to predict discharge-to-nonhome-facility (DNHF) and length-of-stay (LOS) following complex head and neck surgeries. METHODS Patients undergoing laryngectomy or composite tissue excision followed by free tissue transfer were extracted from the 2005 to 2017 NSQIP database. RESULTS Among the 2786 included patients, DNHF and mean LOS were 421 (15.1%) and 11.7 ± 8.8 days. Four classification models for predicting DNHF with high specificities (range, 0.80-0.84) were developed. The generalized linear and gradient boosting machine models performed best with receiver operating characteristic (ROC), accuracy, and negative predictive value (NPV) of 0.72-0.73, 0.75-0.76, and 0.88-0.89. Four regression models for predicting LOS in days were developed, where all performed similarly with mean absolute error and root mean-squared errors of 3.95-3.98 and 5.14-5.16. Both models were developed into an encrypted web-based interface: https://uci-ent.shinyapps.io/head-neck/. CONCLUSION Novel and proof-of-concept ML models to predict DNHF and LOS were developed and published as web-based interfaces.
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Affiliation(s)
- Khodayar Goshtasbi
- Department of Otolaryngology-Head and Neck Surgery, University of California, Irvine, California, USA
| | - Tyler M Yasaka
- Department of Otolaryngology-Head and Neck Surgery, University of California, Irvine, California, USA
| | - Mehdi Zandi-Toghani
- Department of Otolaryngology-Head and Neck Surgery, University of California, Irvine, California, USA
| | - Hamid R Djalilian
- Department of Otolaryngology-Head and Neck Surgery, University of California, Irvine, California, USA.,Department of Biomedical Engineering, University of California, Irvine, California, USA
| | - William B Armstrong
- Department of Otolaryngology-Head and Neck Surgery, University of California, Irvine, California, USA
| | - Tjoson Tjoa
- Department of Otolaryngology-Head and Neck Surgery, University of California, Irvine, California, USA
| | - Yarah M Haidar
- Department of Otolaryngology-Head and Neck Surgery, University of California, Irvine, California, USA
| | - Mehdi Abouzari
- Department of Otolaryngology-Head and Neck Surgery, University of California, Irvine, California, USA
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Romero-Brufau S, Wyatt KD, Boyum P, Mickelson M, Moore M, Cognetta-Rieke C. Implementation of Artificial Intelligence-Based Clinical Decision Support to Reduce Hospital Readmissions at a Regional Hospital. Appl Clin Inform 2020; 11:570-577. [PMID: 32877943 DOI: 10.1055/s-0040-1715827] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
BACKGROUND Hospital readmissions are a key quality metric, which has been tied to reimbursement. One strategy to reduce readmissions is to direct resources to patients at the highest risk of readmission. This strategy necessitates a robust predictive model coupled with effective, patient-centered interventions. OBJECTIVE The aim of this study was to reduce unplanned hospital readmissions through the use of artificial intelligence-based clinical decision support. METHODS A commercially vended artificial intelligence tool was implemented at a regional hospital in La Crosse, Wisconsin between November 2018 and April 2019. The tool assessed all patients admitted to general care units for risk of readmission and generated recommendations for interventions intended to decrease readmission risk. Similar hospitals were used as controls. Change in readmission rate was assessed by comparing the 6-month intervention period to the same months of the previous calendar year in exposure and control hospitals. RESULTS Among 2,460 hospitalizations assessed using the tool, 611 were designated by the tool as high risk. Sensitivity and specificity for risk assignment were 65% and 89%, respectively. Over 6 months following implementation, readmission rates decreased from 11.4% during the comparison period to 8.1% (p < 0.001). After accounting for the 0.5% decrease in readmission rates (from 9.3 to 8.8%) at control hospitals, the relative reduction in readmission rate was 25% (p < 0.001). Among patients designated as high risk, the number needed to treat to avoid one readmission was 11. CONCLUSION We observed a decrease in hospital readmission after implementing artificial intelligence-based clinical decision support. Our experience suggests that use of artificial intelligence to identify patients at the highest risk for readmission can reduce quality gaps when coupled with patient-centered interventions.
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Affiliation(s)
- Santiago Romero-Brufau
- Mayo Clinic Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota, United States.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard University, Cambridge, Massachusetts, United States
| | - Kirk D Wyatt
- Division of Pediatric Hematology/Oncology, Department of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, Minnesota, United States
| | - Patricia Boyum
- Mayo Clinic Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota, United States
| | - Mindy Mickelson
- Mayo Clinic Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota, United States
| | - Matthew Moore
- Mayo Clinic Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota, United States
| | - Cheristi Cognetta-Rieke
- Department of Nursing, Mayo Clinic Health System, La Crosse, La Crosse, Wisconsin, United States
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Chang M, Canseco JA, Nicholson KJ, Patel N, Vaccaro AR. The Role of Machine Learning in Spine Surgery: The Future Is Now. Front Surg 2020; 7:54. [PMID: 32974382 PMCID: PMC7472375 DOI: 10.3389/fsurg.2020.00054] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Accepted: 07/13/2020] [Indexed: 12/12/2022] Open
Abstract
The recent influx of machine learning centered investigations in the spine surgery literature has led to increased enthusiasm as to the prospect of using artificial intelligence to create clinical decision support tools, optimize postoperative outcomes, and improve technologies used in the operating room. However, the methodology underlying machine learning in spine research is often overlooked as the subject matter is quite novel and may be foreign to practicing spine surgeons. Improper application of machine learning is a significant bioethics challenge, given the potential consequences of over- or underestimating the results of such studies for clinical decision-making processes. Proper peer review of these publications requires a baseline familiarity of the language associated with machine learning, and how it differs from classical statistical analyses. This narrative review first introduces the overall field of machine learning and its role in artificial intelligence, and defines basic terminology. In addition, common modalities for applying machine learning, including classification and regression decision trees, support vector machines, and artificial neural networks are examined in the context of examples gathered from the spine literature. Lastly, the ethical challenges associated with adapting machine learning for research related to patient care, as well as future perspectives on the potential use of machine learning in spine surgery, are discussed specifically.
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Affiliation(s)
- Michael Chang
- Department of Orthopaedic Surgery, Thomas Jefferson University, Philadelphia, PA, United States
- Rothman Orthopaedic Institute, Philadelphia, PA, United States
| | - Jose A. Canseco
- Department of Orthopaedic Surgery, Thomas Jefferson University, Philadelphia, PA, United States
- Rothman Orthopaedic Institute, Philadelphia, PA, United States
| | | | - Neil Patel
- Department of Orthopaedic Surgery, Thomas Jefferson University, Philadelphia, PA, United States
- Rothman Orthopaedic Institute, Philadelphia, PA, United States
| | - Alexander R. Vaccaro
- Department of Orthopaedic Surgery, Thomas Jefferson University, Philadelphia, PA, United States
- Rothman Orthopaedic Institute, Philadelphia, PA, United States
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Staartjes VE, Kernbach JM. Letter to the Editor. Importance of calibration assessment in machine learning-based predictive analytics. J Neurosurg Spine 2020; 32:985-987. [PMID: 32084640 DOI: 10.3171/2019.12.spine191503] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Victor E Staartjes
- 1Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, University Hospital Zurich, Clinical Neuroscience Centre, University of Zurich, Switzerland
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Azimi P, Yazdanian T, Benzel EC, Aghaei HN, Azhari S, Sadeghi S, Montazeri A. A Review on the Use of Artificial Intelligence in Spinal Diseases. Asian Spine J 2020; 14:543-571. [PMID: 32326672 PMCID: PMC7435304 DOI: 10.31616/asj.2020.0147] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Accepted: 04/12/2020] [Indexed: 12/13/2022] Open
Abstract
Artificial neural networks (ANNs) have been used in a wide variety of real-world applications and it emerges as a promising field across various branches of medicine. This review aims to identify the role of ANNs in spinal diseases. Literature were searched from electronic databases of Scopus and Medline from 1993 to 2020 with English publications reported on the application of ANNs in spinal diseases. The search strategy was set as the combinations of the following keywords: “artificial neural networks,” “spine,” “back pain,” “prognosis,” “grading,” “classification,” “prediction,” “segmentation,” “biomechanics,” “deep learning,” and “imaging.” The main findings of the included studies were summarized, with an emphasis on the recent advances in spinal diseases and its application in the diagnostic and prognostic procedures. According to the search strategy, a set of 3,653 articles were retrieved from Medline and Scopus databases. After careful evaluation of the abstracts, the full texts of 89 eligible papers were further examined, of which 79 articles satisfied the inclusion criteria of this review. Our review indicates several applications of ANNs in the management of spinal diseases including (1) diagnosis and assessment of spinal disease progression in the patients with low back pain, perioperative complications, and readmission rate following spine surgery; (2) enhancement of the clinically relevant information extracted from radiographic images to predict Pfirrmann grades, Modic changes, and spinal stenosis grades on magnetic resonance images automatically; (3) prediction of outcomes in lumbar spinal stenosis, lumbar disc herniation and patient-reported outcomes in lumbar fusion surgery, and preoperative planning and intraoperative assistance; and (4) its application in the biomechanical assessment of spinal diseases. The evidence suggests that ANNs can be successfully used for optimizing the diagnosis, prognosis and outcome prediction in spinal diseases. Therefore, incorporation of ANNs into spine clinical practice may improve clinical decision making.
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Affiliation(s)
- Parisa Azimi
- Department of Neurosurgery, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | | | - Edward C Benzel
- Department of Neurosurgery, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Hossein Nayeb Aghaei
- Department of Neurosurgery, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Shirzad Azhari
- Department of Neurosurgery, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Sohrab Sadeghi
- Department of Neurosurgery, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ali Montazeri
- Mental Health Research Group, Health Metrics Research Centre, Iranian Institute for Health Sciences Research, ACECR, Tehran, Iran
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Abouzari M, Goshtasbi K, Sarna B, Khosravi P, Reutershan T, Mostaghni N, Lin HW, Djalilian HR. Prediction of vestibular schwannoma recurrence using artificial neural network. Laryngoscope Investig Otolaryngol 2020; 5:278-285. [PMID: 32337359 PMCID: PMC7178452 DOI: 10.1002/lio2.362] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Revised: 01/28/2020] [Accepted: 02/08/2020] [Indexed: 12/12/2022] Open
Abstract
OBJECTIVES To compare two statistical models, namely logistic regression and artificial neural network (ANN), in prediction of vestibular schwannoma (VS) recurrence. METHODS Seven hundred eighty-nine patients with VS diagnosis completed an online survey. Potential predictors for recurrence were derived from univariate analysis by reaching the cut off P value of .05. Those nine potential predictors were years since treatment, surgeon's specialty, resection amount, and having incomplete eye closure, dry eye, double vision, facial pain, seizure, and voice/swallowing problem as a complication following treatment. Multivariate binary logistic regression model was compared with a four-layer 9-5-10-1 feedforward backpropagation ANN for prediction of recurrence. RESULTS The overall recurrence rate was 14.5%. Significant predictors of recurrence in the regression model were years since treatment and resection amount (both P < .001). The regression model did not show an acceptable performance (area under the curve [AUC] = 0.64; P = .27). The regression model's sensitivity and specificity were 44% and 69%, respectively and correctly classified 56% of cases. The ANN showed a superior performance compared to the regression model (AUC = 0.79; P = .001) with higher sensitivity (61%) and specificity (81%), and correctly classified 70% of cases. CONCLUSION The constructed ANN model was superior to logistic regression in predicting patient-answered VS recurrence in an anonymous survey with higher sensitivity and specificity. Since artificial intelligence tools such as neural networks can have higher predictive abilities compared to logistic regression models, continuous investigation into their utility as complementary clinical tools in predicting certain surgical outcomes is warranted.
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Affiliation(s)
- Mehdi Abouzari
- Division of Neurotology and Skull Base Surgery, Department of Otolaryngology‐Head and Neck SurgeryUniversity of CaliforniaIrvineCalifornia
- Division of Pediatric OtolaryngologyChildren's Hospital of Orange CountyOrangeCalifornia
| | - Khodayar Goshtasbi
- Division of Neurotology and Skull Base Surgery, Department of Otolaryngology‐Head and Neck SurgeryUniversity of CaliforniaIrvineCalifornia
| | - Brooke Sarna
- Division of Neurotology and Skull Base Surgery, Department of Otolaryngology‐Head and Neck SurgeryUniversity of CaliforniaIrvineCalifornia
| | - Pooya Khosravi
- Division of Neurotology and Skull Base Surgery, Department of Otolaryngology‐Head and Neck SurgeryUniversity of CaliforniaIrvineCalifornia
- Department of Biomedical EngineeringUniversity of CaliforniaIrvineCalifornia
| | - Trevor Reutershan
- Division of Neurotology and Skull Base Surgery, Department of Otolaryngology‐Head and Neck SurgeryUniversity of CaliforniaIrvineCalifornia
- Department of Biomedical EngineeringUniversity of CaliforniaIrvineCalifornia
| | - Navid Mostaghni
- Division of Neurotology and Skull Base Surgery, Department of Otolaryngology‐Head and Neck SurgeryUniversity of CaliforniaIrvineCalifornia
- Department of Biomedical EngineeringUniversity of CaliforniaIrvineCalifornia
| | - Harrison W. Lin
- Division of Neurotology and Skull Base Surgery, Department of Otolaryngology‐Head and Neck SurgeryUniversity of CaliforniaIrvineCalifornia
| | - Hamid R. Djalilian
- Division of Neurotology and Skull Base Surgery, Department of Otolaryngology‐Head and Neck SurgeryUniversity of CaliforniaIrvineCalifornia
- Department of Biomedical EngineeringUniversity of CaliforniaIrvineCalifornia
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