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Yahanda AT, Joseph K, Bui T, Greenberg JK, Ray WZ, Ogunlade JI, Hafez D, Pallotta NA, Neuman BJ, Molina CA. Current Applications and Future Implications of Artificial Intelligence in Spine Surgery and Research: A Narrative Review and Commentary. Global Spine J 2024:21925682241290752. [PMID: 39359113 DOI: 10.1177/21925682241290752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/04/2024] Open
Abstract
STUDY DESIGN Narrative review. OBJECTIVES Artificial intelligence (AI) is being increasingly applied to the domain of spine surgery. We present a review of AI in spine surgery, including its use across all stages of the perioperative process and applications for research. We also provide commentary regarding future ethical considerations of AI use and how it may affect surgeon-industry relations. METHODS We conducted a comprehensive literature review of peer-reviewed articles that examined applications of AI during the pre-, intra-, or postoperative spine surgery process. We also discussed the relationship among AI, spine industry partners, and surgeons. RESULTS Preoperatively, AI has been mainly applied to image analysis, patient diagnosis and stratification, decision-making. Intraoperatively, AI has been used to aid image guidance and navigation. Postoperatively, AI has been used for outcomes prediction and analysis. AI can enable curation and analysis of huge datasets that can enhance research efforts. Large amounts of data are being accrued by industry sources for use by their AI platforms, though the inner workings of these datasets or algorithms are not well known. CONCLUSIONS AI has found numerous uses in the pre-, intra-, or postoperative spine surgery process, and the applications of AI continue to grow. The clinical applications and benefits of AI will continue to be more fully realized, but so will certain ethical considerations. Making industry-sponsored databases open source, or at least somehow available to the public, will help alleviate potential biases and obscurities between surgeons and industry and will benefit patient care.
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Affiliation(s)
- Alexander T Yahanda
- Department of Neurological Surgery, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Karan Joseph
- Department of Neurological Surgery, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Tim Bui
- Department of Neurological Surgery, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Jacob K Greenberg
- Department of Neurological Surgery, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Wilson Z Ray
- Department of Neurological Surgery, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - John I Ogunlade
- Department of Neurological Surgery, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Daniel Hafez
- Department of Neurological Surgery, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Nicholas A Pallotta
- Department of Orthopedic Surgery, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Brian J Neuman
- Department of Orthopedic Surgery, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Camilo A Molina
- Department of Neurological Surgery, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
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Birhiray DG, Chilukuri SV, Witsken CC, Wang M, Scioscia JP, Gehrchen M, Deveza LR, Dahl B. Machine learning identifies clusters of the normal adolescent spine based on sagittal balance. Spine Deform 2024:10.1007/s43390-024-00952-6. [PMID: 39167356 DOI: 10.1007/s43390-024-00952-6] [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: 04/23/2024] [Accepted: 08/11/2024] [Indexed: 08/23/2024]
Abstract
PURPOSE This study applied a machine learning semi-supervised clustering approach to radiographs of adolescent sagittal spines from a single pediatric institution to identify patterns of sagittal alignment in the normal adolescent spine. We sought to explore the inherent variability found in adolescent sagittal alignment using machine learning to remove bias and determine whether clusters of sagittal alignment exist. METHODS Multiple semi-supervised machine learning clustering algorithms were applied to 111 normal adolescent sagittal spines. Sagittal parameters for resultant clusters were determined. RESULTS Machine learning analysis found that the spines did cluster into distinct groups with an optimal number of clusters ranging from 3 to 5. We performed an analysis on both 3 and 5-cluster groups. The 3-cluster groups analysis found good consistency between methods with 96 of 111, while the analysis of 5-cluster groups found consistency with 105 of 111 spines. When assessing for differences in sagittal parameters between the groups for both analyses, there were differences in T4-12 TK, L1-S1 LL, SS, SVA, PI-LL mismatch, and TPA. However, the only parameter that was statistically different for all groups was SVA. CONCLUSIONS Based on machine learning, the adolescent sagittal spine alignments do cluster into distinct groups. While there were distinguishing features with TK and LL, the most important parameter distinguishing these groups was SVA. Further studies may help to understand these findings in relation to spinal deformities.
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Affiliation(s)
- Dion G Birhiray
- Georgetown University School of Medicine, Washington, D.C, USA.
| | | | | | - Maggie Wang
- Baylor College of Medicine, Houston, TX, USA
| | | | - Martin Gehrchen
- Righospitalet and University of Copenhagen, Copenhagen, Europe, Denmark
| | | | - Benny Dahl
- Righospitalet and University of Copenhagen, Copenhagen, Europe, Denmark
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Awuah WA, Adebusoye FT, Wellington J, David L, Salam A, Weng Yee AL, Lansiaux E, Yarlagadda R, Garg T, Abdul-Rahman T, Kalmanovich J, Miteu GD, Kundu M, Mykolaivna NI. Recent Outcomes and Challenges of Artificial Intelligence, Machine Learning, and Deep Learning in Neurosurgery. World Neurosurg X 2024; 23:100301. [PMID: 38577317 PMCID: PMC10992893 DOI: 10.1016/j.wnsx.2024.100301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 07/23/2023] [Accepted: 02/21/2024] [Indexed: 04/06/2024] Open
Abstract
Neurosurgeons receive extensive technical training, which equips them with the knowledge and skills to specialise in various fields and manage the massive amounts of information and decision-making required throughout the various stages of neurosurgery, including preoperative, intraoperative, and postoperative care and recovery. Over the past few years, artificial intelligence (AI) has become more useful in neurosurgery. AI has the potential to improve patient outcomes by augmenting the capabilities of neurosurgeons and ultimately improving diagnostic and prognostic outcomes as well as decision-making during surgical procedures. By incorporating AI into both interventional and non-interventional therapies, neurosurgeons may provide the best care for their patients. AI, machine learning (ML), and deep learning (DL) have made significant progress in the field of neurosurgery. These cutting-edge methods have enhanced patient outcomes, reduced complications, and improved surgical planning.
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Affiliation(s)
| | | | - Jack Wellington
- Cardiff University School of Medicine, Cardiff University, Wales, United Kingdom
| | - Lian David
- Norwich Medical School, University of East Anglia, United Kingdom
| | - Abdus Salam
- Department of Surgery, Khyber Teaching Hospital, Peshawar, Pakistan
| | | | | | - Rohan Yarlagadda
- Rowan University School of Osteopathic Medicine, Stratford, NJ, USA
| | - Tulika Garg
- Government Medical College and Hospital Chandigarh, India
| | | | | | | | - Mrinmoy Kundu
- Institute of Medical Sciences and SUM Hospital, Bhubaneswar, India
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Herzog I, Mendiratta D, Para A, Berg A, Kaushal N, Vives M. Assessing the potential role of ChatGPT in spine surgery research. J Exp Orthop 2024; 11:e12057. [PMID: 38873173 PMCID: PMC11170336 DOI: 10.1002/jeo2.12057] [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: 12/29/2023] [Revised: 05/12/2024] [Accepted: 05/28/2024] [Indexed: 06/15/2024] Open
Abstract
Purpose Since its release in November 2022, Chat Generative Pre-Trained Transformer 3.5 (ChatGPT), a complex machine learning model, has garnered more than 100 million users worldwide. The aim of this study is to determine how well ChatGPT can generate novel systematic review ideas on topics within spine surgery. Methods ChatGPT was instructed to give ten novel systematic review ideas for five popular topics in spine surgery literature: microdiscectomy, laminectomy, spinal fusion, kyphoplasty and disc replacement. A comprehensive literature search was conducted in PubMed, CINAHL, EMBASE and Cochrane. The number of nonsystematic review articles and number of systematic review papers that had been published on each ChatGPT-generated idea were recorded. Results Overall, ChatGPT had a 68% accuracy rate in creating novel systematic review ideas. More specifically, the accuracy rates were 80%, 80%, 40%, 70% and 70% for microdiscectomy, laminectomy, spinal fusion, kyphoplasty and disc replacement, respectively. However, there was a 32% rate of ChatGPT generating ideas for which there were 0 nonsystematic review articles published. There was a 71.4%, 50%, 22.2%, 50%, 62.5% and 51.2% success rate of generating novel systematic review ideas, for which there were also nonsystematic reviews published, for microdiscectomy, laminectomy, spinal fusion, kyphoplasty, disc replacement and overall, respectively. Conclusions ChatGPT generated novel systematic review ideas at an overall rate of 68%. ChatGPT can help identify knowledge gaps in spine research that warrant further investigation, when used under supervision of an experienced spine specialist. This technology can be erroneous and lacks intrinsic logic; so, it should never be used in isolation. Level of Evidence Not applicable.
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Affiliation(s)
- Isabel Herzog
- Rutgers New Jersey Medical SchoolNewarkNew JerseyUSA
| | | | - Ashok Para
- Rutgers New Jersey Medical SchoolNewarkNew JerseyUSA
| | - Ari Berg
- Rutgers New Jersey Medical SchoolNewarkNew JerseyUSA
| | - Neil Kaushal
- Rutgers New Jersey Medical SchoolNewarkNew JerseyUSA
| | - Michael Vives
- Rutgers New Jersey Medical SchoolNewarkNew JerseyUSA
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Arjmandnia F, Alimohammadi E. The value of machine learning technology and artificial intelligence to enhance patient safety in spine surgery: a review. Patient Saf Surg 2024; 18:11. [PMID: 38528562 DOI: 10.1186/s13037-024-00393-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Accepted: 03/15/2024] [Indexed: 03/27/2024] Open
Abstract
Machine learning algorithms have the potential to significantly improve patient safety in spine surgeries by providing healthcare professionals with valuable insights and predictive analytics. These algorithms can analyze preoperative data, such as patient demographics, medical history, and imaging studies, to identify potential risk factors and predict postoperative complications. By leveraging machine learning, surgeons can make more informed decisions, personalize treatment plans, and optimize surgical techniques to minimize risks and enhance patient outcomes. Moreover, by harnessing the power of machine learning, healthcare providers can make data-driven decisions, personalize treatment plans, and optimize surgical interventions, ultimately enhancing the quality of care in spine surgery. The findings highlight the potential of integrating artificial intelligence in healthcare settings to mitigate risks and enhance patient safety in surgical practices. The integration of machine learning holds immense potential for enhancing patient safety in spine surgeries. By leveraging advanced algorithms and predictive analytics, healthcare providers can optimize surgical decision-making, mitigate risks, and personalize treatment strategies to improve outcomes and ensure the highest standard of care for patients undergoing spine procedures. As technology continues to evolve, the future of spine surgery lies in harnessing the power of machine learning to transform patient safety and revolutionize surgical practices. The present review article was designed to discuss the available literature in the field of machine learning techniques to enhance patient safety in spine surgery.
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Affiliation(s)
- Fatemeh Arjmandnia
- Department of Aneasthesiology, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Ehsan Alimohammadi
- Department of Neurosurgery, Kermanshah University of Medical Sciences, Imam Reza Hospital, Kermanshah, Iran.
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Toh ZA, Berg B, Han QYC, Hey HWD, Pikkarainen M, Grotle M, He HG. Clinical Decision Support System Used in Spinal Disorders: Scoping Review. J Med Internet Res 2024; 26:e53951. [PMID: 38502157 PMCID: PMC10988379 DOI: 10.2196/53951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Revised: 01/29/2024] [Accepted: 02/10/2024] [Indexed: 03/20/2024] Open
Abstract
BACKGROUND Spinal disorders are highly prevalent worldwide with high socioeconomic costs. This cost is associated with the demand for treatment and productivity loss, prompting the exploration of technologies to improve patient outcomes. Clinical decision support systems (CDSSs) are computerized systems that are increasingly used to facilitate safe and efficient health care. Their applications range in depth and can be found across health care specialties. OBJECTIVE This scoping review aims to explore the use of CDSSs in patients with spinal disorders. METHODS We used the Joanna Briggs Institute methodological guidance for this scoping review and reported according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) statement. Databases, including PubMed, Embase, Cochrane, CINAHL, Web of Science, Scopus, ProQuest, and PsycINFO, were searched from inception until October 11, 2022. The included studies examined the use of digitalized CDSSs in patients with spinal disorders. RESULTS A total of 4 major CDSS functions were identified from 31 studies: preventing unnecessary imaging (n=8, 26%), aiding diagnosis (n=6, 19%), aiding prognosis (n=11, 35%), and recommending treatment options (n=6, 20%). Most studies used the knowledge-based system. Logistic regression was the most commonly used method, followed by decision tree algorithms. The use of CDSSs to aid in the management of spinal disorders was generally accepted over the threat to physicians' clinical decision-making autonomy. CONCLUSIONS Although the effectiveness was frequently evaluated by examining the agreement between the decisions made by the CDSSs and the health care providers, comparing the CDSS recommendations with actual clinical outcomes would be preferable. In addition, future studies on CDSS development should focus on system integration, considering end user's needs and preferences, and external validation and impact studies to assess effectiveness and generalizability. TRIAL REGISTRATION OSF Registries osf.io/dyz3f; https://osf.io/dyz3f.
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Affiliation(s)
- Zheng An Toh
- National University Hospital, National University Health System, Singapore, Singapore
| | - Bjørnar Berg
- Centre for Intelligent Musculoskeletal Health, Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway
| | | | - Hwee Weng Dennis Hey
- Division of Orthopaedic Surgery, National University Hospital, National University Health System, Singapore, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Minna Pikkarainen
- Department of Rehabilitation and Health Technology, Oslo Metropolitan University, Oslo, Norway
- Martti Ahtisaari Institute, Oulu Business School, Oulu University, Oulu, Finland
- Department of Product Design, Oslo Metropolitan University, Oslo, Norway
| | - Margreth Grotle
- Centre for Intelligent Musculoskeletal Health, Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway
- Department of Research and Innovation, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway
| | - Hong-Gu He
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
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Liawrungrueang W, Cho ST, Sarasombath P, Kim I, Kim JH. Current Trends in Artificial Intelligence-Assisted Spine Surgery: A Systematic Review. Asian Spine J 2024; 18:146-157. [PMID: 38130042 PMCID: PMC10910143 DOI: 10.31616/asj.2023.0410] [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: 12/09/2023] [Revised: 12/12/2023] [Accepted: 12/17/2023] [Indexed: 12/23/2023] Open
Abstract
This systematic review summarizes existing evidence and outlines the benefits of artificial intelligence-assisted spine surgery. The popularity of artificial intelligence has grown significantly, demonstrating its benefits in computer-assisted surgery and advancements in spinal treatment. This study adhered to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), a set of reporting guidelines specifically designed for systematic reviews and meta-analyses. The search strategy used Medical Subject Headings (MeSH) terms, including "MeSH (Artificial intelligence)," "Spine" AND "Spinal" filters, in the last 10 years, and English- from January 1, 2013, to October 31, 2023. In total, 442 articles fulfilled the first screening criteria. A detailed analysis of those articles identified 220 that matched the criteria, of which 11 were considered appropriate for this analysis after applying the complete inclusion and exclusion criteria. In total, 11 studies met the eligibility criteria. Analysis of these studies revealed the types of artificial intelligence-assisted spine surgery. No evidence suggests the superiority of assisted spine surgery with or without artificial intelligence in terms of outcomes. In terms of feasibility, accuracy, safety, and facilitating lower patient radiation exposure compared with standard fluoroscopic guidance, artificial intelligence-assisted spine surgery produced satisfactory and superior outcomes. The incorporation of artificial intelligence with augmented and virtual reality appears promising, with the potential to enhance surgeon proficiency and overall surgical safety.
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Affiliation(s)
| | - Sung Tan Cho
- Department of Orthopaedics, Inje University Ilsan Paik Hospital, Goyang,
Korea
| | - Peem Sarasombath
- Department of Orthopaedics, Faculty of Medicine, Chiang Mai University, Chiang Mai,
Thailand
| | - Inhee Kim
- Department of Orthopaedics, Police National Hospital, Seoul,
Korea
| | - Jin Hwan Kim
- Department of Orthopaedics, Inje University Ilsan Paik Hospital, Goyang,
Korea
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Foley D, Hardacker P, McCarthy M. Emerging Technologies within Spine Surgery. Life (Basel) 2023; 13:2028. [PMID: 37895410 PMCID: PMC10608700 DOI: 10.3390/life13102028] [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: 08/30/2023] [Revised: 10/02/2023] [Accepted: 10/07/2023] [Indexed: 10/29/2023] Open
Abstract
New innovations within spine surgery continue to propel the field forward. These technologies improve surgeons' understanding of their patients and allow them to optimize treatment planning both in the operating room and clinic. Additionally, changes in the implants and surgeon practice habits continue to evolve secondary to emerging biomaterials and device design. With ongoing advancements, patients can expect enhanced preoperative decision-making, improved patient outcomes, and better intraoperative execution. Additionally, these changes may decrease many of the most common complications following spine surgery in order to reduce morbidity, mortality, and the need for reoperation. This article reviews some of these technological advancements and how they are projected to impact the field. As the field continues to advance, it is vital that practitioners remain knowledgeable of these changes in order to provide the most effective treatment possible.
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Affiliation(s)
- David Foley
- Department of Orthopaedic Surgery, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Pierce Hardacker
- Indiana University School of Medicine, Indianapolis, IN 46202, USA;
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Abstract
Today's orthopedic surgery could not be imagined without intraoperative x-ray-based imaging. This enables surgeons to assess operative interim steps as well as the result before wound closure and finishing the procedure. Although there have been mobile C-arms used for decades, there are recent advances that do not only affect the quality of the imaging itself but also the way, the information is processed and presented. These very exciting developments will change the integration of imaging into the surgical workflows, giving options of augmented reality, reduction of radiation dose, automatized acquisition and analysis of images and low-level guidance in procedures. This paper gives a review of current innovations and possible future trends in fluoroscopic 2D and 3D imaging.
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