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Shlobin NA, Rosseau G. Opportunities and Considerations for the Incorporation of Artificial Intelligence into Global Neurosurgery: A Generative Pretrained Transformer Chatbot-Based Approach. World Neurosurg 2024; 186:e398-e412. [PMID: 38561032 DOI: 10.1016/j.wneu.2024.03.149] [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: 02/29/2024] [Revised: 03/25/2024] [Accepted: 03/26/2024] [Indexed: 04/04/2024]
Abstract
OBJECTIVE Global neurosurgery is a public health focus in neurosurgery that seeks to ensure safe, timely, and affordable neurosurgical care to all individuals worldwide. Although investigators have begun to explore the promise of artificial intelligence (AI) for neurosurgery, its applicability to global neurosurgery has been largely hypothetical. We characterize opportunities and considerations for the incorporation of AI into global neurosurgery by synthesizing key themes yielded from a series of generative pretrained transformers (GPTs), discuss important limitations of GPTs and cautions when using AI in neurosurgery, and develop a framework for the equitable incorporation of AI into global neurosurgery. METHODS ChatGPT, Bing Chat/Copilot, You, Perplexity.ai, and Google Bard were queried with the prompt "How can AI be incorporated into global neurosurgery?" A layered ChatGPT-based thematic analysis was performed. The authors synthesized the results into opportunities and considerations for the incorporation of AI in global neurosurgery. A Pareto analysis was conducted to determine common themes. RESULTS Eight opportunities and 14 important considerations were synthesized. Six opportunities related to patient care, 1 to education, and another to public health planning. Four of the important considerations were deemed specific to global neurosurgery. The Pareto analysis included all 8 opportunities and 5 considerations. CONCLUSIONS AI may be incorporated into global neurosurgery in a variety of capacities requiring numerous considerations. The framework presented in this manuscript may facilitate the incorporation of AI into global neurosurgery initiatives while balancing contextual factors and the reality of limited resources.
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Affiliation(s)
- Nathan A Shlobin
- Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA.
| | - Gail Rosseau
- Department of Neurosurgery, George Washington University School of Medicine and Health Sciences, Washington, District of Columbia, USA; Barrow Global, Barrow Neurological Institute, Phoenix, Arizona, USA
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2
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Staats K, Kayani B, Haddad FS. The impact of the European Union's Medical Device Regulation on orthopaedic implants, technology, and future innovation. Bone Joint J 2024; 106-B:303-306. [PMID: 38555944 DOI: 10.1302/0301-620x.106b4.bjj-2023-1228.r1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/02/2024]
Affiliation(s)
- Kevin Staats
- Department of Trauma and Orthopaedics, University College Hospital, London, UK
| | - Babar Kayani
- Department of Trauma and Orthopaedics, University College Hospital, London, UK
| | - Fares S Haddad
- Department of Trauma and Orthopaedics, University College London NHS Hospitals, London, UK
- Princess Grace Hospital, London, UK
- The NIHR Biomedical Research Centre, UCLH, London, UK
- The Bone & Joint Journal , London, UK
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3
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Maki S, Furuya T, Inoue M, Shiga Y, Inage K, Eguchi Y, Orita S, Ohtori S. Machine Learning and Deep Learning in Spinal Injury: A Narrative Review of Algorithms in Diagnosis and Prognosis. J Clin Med 2024; 13:705. [PMID: 38337399 PMCID: PMC10856760 DOI: 10.3390/jcm13030705] [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: 11/13/2023] [Revised: 01/14/2024] [Accepted: 01/18/2024] [Indexed: 02/12/2024] Open
Abstract
Spinal injuries, including cervical and thoracolumbar fractures, continue to be a major public health concern. Recent advancements in machine learning and deep learning technologies offer exciting prospects for improving both diagnostic and prognostic approaches in spinal injury care. This narrative review systematically explores the practical utility of these computational methods, with a focus on their application in imaging techniques such as computed tomography (CT) and magnetic resonance imaging (MRI), as well as in structured clinical data. Of the 39 studies included, 34 were focused on diagnostic applications, chiefly using deep learning to carry out tasks like vertebral fracture identification, differentiation between benign and malignant fractures, and AO fracture classification. The remaining five were prognostic, using machine learning to analyze parameters for predicting outcomes such as vertebral collapse and future fracture risk. This review highlights the potential benefit of machine learning and deep learning in spinal injury care, especially their roles in enhancing diagnostic capabilities, detailed fracture characterization, risk assessments, and individualized treatment planning.
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Affiliation(s)
- Satoshi Maki
- Department of Orthopaedic Surgery, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan
- Center for Frontier Medical Engineering, Chiba University, Chiba 263-8522, Japan
| | - Takeo Furuya
- Department of Orthopaedic Surgery, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan
| | - Masahiro Inoue
- Department of Orthopaedic Surgery, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan
| | - Yasuhiro Shiga
- Department of Orthopaedic Surgery, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan
| | - Kazuhide Inage
- Department of Orthopaedic Surgery, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan
| | - Yawara Eguchi
- Department of Orthopaedic Surgery, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan
| | - Sumihisa Orita
- Department of Orthopaedic Surgery, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan
- Center for Frontier Medical Engineering, Chiba University, Chiba 263-8522, Japan
| | - Seiji Ohtori
- Department of Orthopaedic Surgery, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan
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Fontalis A, Haddad FS. A leap towards personalized orthopaedic surgery and the prediction of spinopelvic mechanics in total hip arthroplasty. Bone Joint J 2024; 106-B:3-5. [PMID: 38160698 DOI: 10.1302/0301-620x.106b1.bjj-2023-1319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
Affiliation(s)
- Andreas Fontalis
- Department of Trauma and Orthopaedics, University College London NHS Hospitals, London, UK
- Princess Grace Hospital, London, UK
| | - Fares S Haddad
- Department of Trauma and Orthopaedics, University College London NHS Hospitals, London, UK
- Princess Grace Hospital, London, UK
- The NIHR Biomedical Research Centre, UCLH, London, UK, London, UK
- The Bone & Joint Journal , London, UK
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5
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Powling AS, Lisacek-Kiosoglous AB, Fontalis A, Mazomenos E, Haddad FS. Unveiling the potential of artificial intelligence in orthopaedic surgery. Br J Hosp Med (Lond) 2023; 84:1-5. [PMID: 38153019 DOI: 10.12968/hmed.2023.0258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2023]
Abstract
Artificial intelligence is paving the way in contemporary medical advances, with the potential to revolutionise orthopaedic surgical care. By harnessing the power of complex algorithms, artificial intelligence yields outputs that have diverse applications including, but not limited to, identifying implants, diagnostic imaging for fracture and tumour recognition, prognostic tools through the use of electronic medical records, assessing arthroplasty outcomes, length of hospital stay and economic costs, monitoring the progress of functional rehabilitation, and innovative surgical training via simulation. However, amid the promising potential and enthusiasm surrounding artificial intelligence, clinicians should understand its limitations, and caution is needed before artificial intelligence-driven tools are introduced to clinical practice.
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Affiliation(s)
- Amber S Powling
- Barts and The London School of Medicine and Dentistry, School of Medicine London, London, UK
- Department of Trauma and Orthopaedic Surgery, University College London Hospitals NHS Foundation Trust, London, UK
| | - Anthony B Lisacek-Kiosoglous
- Department of Trauma and Orthopaedic Surgery, University College London Hospitals NHS Foundation Trust, London, UK
| | - Andreas Fontalis
- Department of Trauma and Orthopaedic Surgery, University College London Hospitals NHS Foundation Trust, London, UK
- Division of Surgery and Interventional Science, University College London, London, UK
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK
| | - Evangelos Mazomenos
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK
| | - Fares S Haddad
- Department of Trauma and Orthopaedic Surgery, University College London Hospitals NHS Foundation Trust, London, UK
- Division of Surgery and Interventional Science, University College London, London, UK
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Haddad FS. With gratitude to The Bone & Joint Journal family. Bone Joint J 2023; 105-B:1233-1234. [PMID: 38035594 DOI: 10.1302/0301-620x.105b12.bjj-2023-1123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/02/2023]
Affiliation(s)
- Fares S Haddad
- University College London Hospitals, London, UK
- The Princess Grace Hospital, London, UK
- The NIHR Biomedical Research Centre at UCLH, London, UK
- The Bone & Joint Journal , London, UK
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7
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Zsidai B, Hilkert AS, Kaarre J, Narup E, Senorski EH, Grassi A, Ley C, Longo UG, Herbst E, Hirschmann MT, Kopf S, Seil R, Tischer T, Samuelsson K, Feldt R. A practical guide to the implementation of AI in orthopaedic research - part 1: opportunities in clinical application and overcoming existing challenges. J Exp Orthop 2023; 10:117. [PMID: 37968370 PMCID: PMC10651597 DOI: 10.1186/s40634-023-00683-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 10/21/2023] [Indexed: 11/17/2023] Open
Abstract
Artificial intelligence (AI) has the potential to transform medical research by improving disease diagnosis, clinical decision-making, and outcome prediction. Despite the rapid adoption of AI and machine learning (ML) in other domains and industry, deployment in medical research and clinical practice poses several challenges due to the inherent characteristics and barriers of the healthcare sector. Therefore, researchers aiming to perform AI-intensive studies require a fundamental understanding of the key concepts, biases, and clinical safety concerns associated with the use of AI. Through the analysis of large, multimodal datasets, AI has the potential to revolutionize orthopaedic research, with new insights regarding the optimal diagnosis and management of patients affected musculoskeletal injury and disease. The article is the first in a series introducing fundamental concepts and best practices to guide healthcare professionals and researcher interested in performing AI-intensive orthopaedic research studies. The vast potential of AI in orthopaedics is illustrated through examples involving disease- or injury-specific outcome prediction, medical image analysis, clinical decision support systems and digital twin technology. Furthermore, it is essential to address the role of human involvement in training unbiased, generalizable AI models, their explainability in high-risk clinical settings and the implementation of expert oversight and clinical safety measures for failure. In conclusion, the opportunities and challenges of AI in medicine are presented to ensure the safe and ethical deployment of AI models for orthopaedic research and clinical application. Level of evidence IV.
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Affiliation(s)
- Bálint Zsidai
- Sahlgrenska Sports Medicine Center, Gothenburg, Sweden.
- Department of Orthopaedics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
| | - Ann-Sophie Hilkert
- Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, Sweden
- Medfield Diagnostics AB, Gothenburg, Sweden
| | - Janina Kaarre
- Sahlgrenska Sports Medicine Center, Gothenburg, Sweden
- Department of Orthopaedics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Orthopaedic Surgery, UPMC Freddie Fu Sports Medicine Center, University of Pittsburgh, Pittsburgh, USA
| | - Eric Narup
- Sahlgrenska Sports Medicine Center, Gothenburg, Sweden
- Department of Orthopaedics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Eric Hamrin Senorski
- Sahlgrenska Sports Medicine Center, Gothenburg, Sweden
- Department of Health and Rehabilitation, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Sportrehab Sports Medicine Clinic, Gothenburg, Sweden
| | - Alberto Grassi
- Department of Orthopaedics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- IIa Clinica Ortopedica E Traumatologica, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy
| | - Christophe Ley
- Department of Mathematics, University of Luxembourg, Esch-Sur-Alzette, Luxembourg
| | - Umile Giuseppe Longo
- Department of Orthopaedic and Trauma Surgery, Campus Bio-Medico University, Rome, Italy
| | - Elmar Herbst
- Department of Trauma, Hand and Reconstructive Surgery, University Hospital Münster, Münster, Germany
| | - Michael T Hirschmann
- Department of Orthopedic Surgery and Traumatology, Head Knee Surgery and DKF Head of Research, Kantonsspital Baselland, 4101, Bruderholz, Switzerland
| | - Sebastian Kopf
- Center of Orthopaedics and Traumatology, University Hospital Brandenburg a.d.H., Brandenburg Medical School Theodor Fontane, 14770, Brandenburg a.d.H., Germany
- Faculty of Health Sciences Brandenburg, Brandenburg Medical School Theodor Fontane, 14770, Brandenburg a.d.H., Germany
| | - Romain Seil
- Department of Orthopaedic Surgery, Centre Hospitalier Luxembourg and Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Thomas Tischer
- Clinic for Orthopaedics and Trauma Surgery, Malteser Waldkrankenhaus St. Marien, Erlangen, Germany
| | - Kristian Samuelsson
- Sahlgrenska Sports Medicine Center, Gothenburg, Sweden
- Department of Orthopaedics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Orthopaedics, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Robert Feldt
- Department of Orthopaedics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
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Lisacek-Kiosoglous AB, Powling AS, Fontalis A, Gabr A, Mazomenos E, Haddad FS. Artificial intelligence in orthopaedic surgery. Bone Joint Res 2023; 12:447-454. [PMID: 37423607 DOI: 10.1302/2046-3758.127.bjr-2023-0111.r1] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/11/2023] Open
Abstract
The use of artificial intelligence (AI) is rapidly growing across many domains, of which the medical field is no exception. AI is an umbrella term defining the practical application of algorithms to generate useful output, without the need of human cognition. Owing to the expanding volume of patient information collected, known as 'big data', AI is showing promise as a useful tool in healthcare research and across all aspects of patient care pathways. Practical applications in orthopaedic surgery include: diagnostics, such as fracture recognition and tumour detection; predictive models of clinical and patient-reported outcome measures, such as calculating mortality rates and length of hospital stay; and real-time rehabilitation monitoring and surgical training. However, clinicians should remain cognizant of AI's limitations, as the development of robust reporting and validation frameworks is of paramount importance to prevent avoidable errors and biases. The aim of this review article is to provide a comprehensive understanding of AI and its subfields, as well as to delineate its existing clinical applications in trauma and orthopaedic surgery. Furthermore, this narrative review expands upon the limitations of AI and future direction.
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Affiliation(s)
- Anthony B Lisacek-Kiosoglous
- Department of Trauma and Orthopaedic Surgery, University College London Hospitals NHS Foundation Trust, London, UK
| | - Amber S Powling
- Department of Trauma and Orthopaedic Surgery, University College London Hospitals NHS Foundation Trust, London, UK
- Barts and The London School of Medicine and Dentistry, School of Medicine London, London, UK
| | - Andreas Fontalis
- Department of Trauma and Orthopaedic Surgery, University College London Hospitals NHS Foundation Trust, London, UK
- Division of Surgery and Interventional Science, University College London, London, UK
- Wellcome / EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK
| | - Ayman Gabr
- Department of Trauma and Orthopaedic Surgery, University College London Hospitals NHS Foundation Trust, London, UK
| | - Evangelos Mazomenos
- Wellcome / EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK
| | - Fares S Haddad
- Department of Trauma and Orthopaedic Surgery, University College London Hospitals NHS Foundation Trust, London, UK
- Division of Surgery and Interventional Science, University College London, London, UK
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9
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Ryu SJ, So JY, Ha Y, Kuh SU, Chin DK, Kim KS, Cho YE, Kim KH. Risk factors for unplanned reoperation after corrective surgery for adult spinal deformity. Bone Joint Res 2023; 12:245-255. [PMID: 37051826 PMCID: PMC10067324 DOI: 10.1302/2046-3758.124.bjr-2022-0121.r1] [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: 04/04/2023] Open
Abstract
Aims To determine the major risk factors for unplanned reoperations (UROs) following corrective surgery for adult spinal deformity (ASD) and their interactions, using machine learning-based prediction algorithms and game theory. Methods Patients who underwent surgery for ASD, with a minimum of two-year follow-up, were retrospectively reviewed. In total, 210 patients were included and randomly allocated into training (70% of the sample size) and test (the remaining 30%) sets to develop the machine learning algorithm. Risk factors were included in the analysis, along with clinical characteristics and parameters acquired through diagnostic radiology. Results Overall, 152 patients without and 58 with a history of surgical revision following surgery for ASD were observed; the mean age was 68.9 years (SD 8.7) and 66.9 years (SD 6.6), respectively. On implementing a random forest model, the classification of URO events resulted in a balanced accuracy of 86.8%. Among machine learning-extracted risk factors, URO, proximal junction failure (PJF), and postoperative distance from the posterosuperior corner of C7 and the vertical axis from the centroid of C2 (SVA) were significant upon Kaplan-Meier survival analysis. Conclusion The major risk factors for URO following surgery for ASD, i.e. postoperative SVA and PJF, and their interactions were identified using a machine learning algorithm and game theory. Clinical benefits will depend on patient risk profiles. Cite this article: Bone Joint Res 2023;12(4):245–255.
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Affiliation(s)
- Seung-Jun Ryu
- Department of Neurosurgery, National Health Insurance Service Ilsan Hospital, Goyang, South Korea
- Department of Neurosurgery, The Spine and Spinal Cord Institute, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
- Department of Neurosurgery, Daejeon Eulji University Hospital, Eulji University Medical School, Daejeon, South Korea
| | - Jae-Young So
- Department of Neurosurgery, National Health Insurance Service Ilsan Hospital, Goyang, South Korea
- Department of Neurosurgery, The Spine and Spinal Cord Institute, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Yoon Ha
- Department of Neurosurgery, The Spine and Spinal Cord Institute, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Sung-Uk Kuh
- Department of Neurosurgery, The Spine and Spinal Cord Institute, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Dong-Kyu Chin
- Department of Neurosurgery, The Spine and Spinal Cord Institute, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Keun-Su Kim
- Department of Neurosurgery, The Spine and Spinal Cord Institute, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Yong-Eun Cho
- Department of Neurosurgery, The Spine and Spinal Cord Institute, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
- Department of Neurosurgery, Suwon Leon Wiltse Memorial Hospital, Suwon, South Korea
| | - Kyung-Hyun Kim
- Department of Neurosurgery, The Spine and Spinal Cord Institute, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
- Kyung-Hyun Kim. E-mail:
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Zhao Y, Xiang Q, Jiang S, Wang L, Lin J, Sun C, Li W. Prevalence, diagnosis, and impact on clinical outcomes of dural ossification in the thoracic ossification of the ligamentum flavum: a systematic review. EUROPEAN SPINE JOURNAL : OFFICIAL PUBLICATION OF THE EUROPEAN SPINE SOCIETY, THE EUROPEAN SPINAL DEFORMITY SOCIETY, AND THE EUROPEAN SECTION OF THE CERVICAL SPINE RESEARCH SOCIETY 2023; 32:1245-1253. [PMID: 36877368 DOI: 10.1007/s00586-023-07625-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 02/02/2023] [Accepted: 02/22/2023] [Indexed: 03/07/2023]
Abstract
STUDY DESIGN Systematic review. BACKGROUND CONTEXT Thoracic ossification of the ligamentum flavum (TOLF) has become the principal cause of thoracic spinal stenosis. Dural ossification (DO) was a common clinical feature accompanying with TOLF. However, on account of the rarity, we know little about the DO in TOLF so far. PURPOSE This study was conducted to elucidate the prevalence, diagnostic measures, and impact on the clinical outcomes of DO in TOLF by integrating the existing evidence. METHODS PubMed, Embase, and Cochrane Database were comprehensively searched for studies relevant to the prevalence, diagnostic measures, or impact on the clinical outcomes of DO in TOLF. All retrieved studies meeting the inclusion and criterion were included into this systematic review. RESULTS The prevalence of DO in TOLF treated surgically was 27% (281/1046), ranging from 11 to 67%. Eight diagnostic measures have been put forward to predict the DO in TOLF using the CT or MRI modalities, including "tram track sign", "comma sign", "bridge sign", "banner cloud sign", "T2 ring sign", TOLF-DO grading system, CSAOR grading system, and CCAR grading system. DO did not affect the neurological recovery of TOLF patients treated with the laminectomy. The rate of dural tear or CSF leakage in TOLF patients with DO was approximately 83% (149/180). CONCLUSION The prevalence of DO in TOLF treated surgically was 27%. Eight diagnostic measures have been put forward to predict the DO in TOLF. DO did not affect the neurological recovery of TOLF treated with laminectomy but was associated with high risk of complications.
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Affiliation(s)
- Yongzhao Zhao
- Department of Orthopaedics, Peking University Third Hospital, No. 49 NorthGarden Road, Haidian District, Beijing, 100191, China.,Beijing Key Laboratory of Spinal Disease Research, Beijing, 100191, China.,Engineering Research Center of Bone and Joint Precision Medicine, Ministry of Education, Beijing, 100191, China
| | - Qian Xiang
- Department of Orthopaedics, Peking University Third Hospital, No. 49 NorthGarden Road, Haidian District, Beijing, 100191, China.,Beijing Key Laboratory of Spinal Disease Research, Beijing, 100191, China.,Engineering Research Center of Bone and Joint Precision Medicine, Ministry of Education, Beijing, 100191, China
| | - Shuai Jiang
- Department of Orthopaedics, Peking University Third Hospital, No. 49 NorthGarden Road, Haidian District, Beijing, 100191, China.,Beijing Key Laboratory of Spinal Disease Research, Beijing, 100191, China.,Engineering Research Center of Bone and Joint Precision Medicine, Ministry of Education, Beijing, 100191, China
| | - Longjie Wang
- Department of Orthopaedics, Peking University Third Hospital, No. 49 NorthGarden Road, Haidian District, Beijing, 100191, China.,Beijing Key Laboratory of Spinal Disease Research, Beijing, 100191, China.,Engineering Research Center of Bone and Joint Precision Medicine, Ministry of Education, Beijing, 100191, China
| | - Jialiang Lin
- Department of Orthopaedics, Peking University Third Hospital, No. 49 NorthGarden Road, Haidian District, Beijing, 100191, China.,Beijing Key Laboratory of Spinal Disease Research, Beijing, 100191, China.,Engineering Research Center of Bone and Joint Precision Medicine, Ministry of Education, Beijing, 100191, China
| | - Chuiguo Sun
- Department of Orthopaedics, Peking University Third Hospital, No. 49 NorthGarden Road, Haidian District, Beijing, 100191, China.,Beijing Key Laboratory of Spinal Disease Research, Beijing, 100191, China.,Engineering Research Center of Bone and Joint Precision Medicine, Ministry of Education, Beijing, 100191, China
| | - Weishi Li
- Department of Orthopaedics, Peking University Third Hospital, No. 49 NorthGarden Road, Haidian District, Beijing, 100191, China. .,Beijing Key Laboratory of Spinal Disease Research, Beijing, 100191, China. .,Engineering Research Center of Bone and Joint Precision Medicine, Ministry of Education, Beijing, 100191, China.
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11
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Haddad FS. Looking back over the past year. Bone Joint J 2022; 104-B:1279-1280. [DOI: 10.1302/0301-620x.104b12.bjj-2022-1161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
Affiliation(s)
- Fares S. Haddad
- University College London Hospitals, The Princess Grace Hospital, and The NIHR Biomedical Research Centre at UCLH, London, UK
- The Bone & Joint Journal, London, UK
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12
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Prijs J, Liao Z, Ashkani-Esfahani S, Olczak J, Gordon M, Jayakumar P, Jutte PC, Jaarsma RL, IJpma FFA, Doornberg JN. Artificial intelligence and computer vision in orthopaedic trauma : the why, what, and how. Bone Joint J 2022; 104-B:911-914. [PMID: 35909378 DOI: 10.1302/0301-620x.104b8.bjj-2022-0119.r1] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Artificial intelligence (AI) is, in essence, the concept of 'computer thinking', encompassing methods that train computers to perform and learn from executing certain tasks, called machine learning, and methods to build intricate computer models that both learn and adapt, called complex neural networks. Computer vision is a function of AI by which machine learning and complex neural networks can be applied to enable computers to capture, analyze, and interpret information from clinical images and visual inputs. This annotation summarizes key considerations and future perspectives concerning computer vision, questioning the need for this technology (the 'why'), the current applications (the 'what'), and the approach to unlocking its full potential (the 'how'). Cite this article: Bone Joint J 2022;104-B(8):911-914.
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Affiliation(s)
- Jasper Prijs
- Department of Orthopaedic Surgery, Groningen University Medical Centre, Groningen, the Netherlands.,Department of Surgery, Groningen University Medical Centre, Groningen, the Netherlands.,Department of Orthopaedic & Trauma Surgery, Flinders University, Flinders Medical Centre, Adelaide, Australia
| | - Zhibin Liao
- Australian Institute for Machine Learning, Adelaide, Australia
| | | | - Jakub Olczak
- Institute of Clinical Sciences, Danderyd University Hospital, Karolinska Institute, Stockholm, Sweden
| | - Max Gordon
- Institute of Clinical Sciences, Danderyd University Hospital, Karolinska Institute, Stockholm, Sweden
| | - Prakash Jayakumar
- The University of Texas at Austin, Dell Medical School, Austin, Texas, USA
| | - Paul C Jutte
- Department of Orthopaedic Surgery, Groningen University Medical Centre, Groningen, the Netherlands
| | - Ruurd L Jaarsma
- Department of Orthopaedic & Trauma Surgery, Flinders University, Flinders Medical Centre, Adelaide, Australia
| | - Frank F A IJpma
- Department of Orthopaedic Surgery, Groningen University Medical Centre, Groningen, the Netherlands
| | - Job N Doornberg
- Department of Orthopaedic Surgery, Groningen University Medical Centre, Groningen, the Netherlands.,Department of Orthopaedic & Trauma Surgery, Flinders University, Flinders Medical Centre, Adelaide, Australia
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13
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An Artificial Neural Network Model for the Prediction of Perioperative Blood Transfusion in Adult Spinal Deformity Surgery. J Clin Med 2022; 11:jcm11154436. [PMID: 35956053 PMCID: PMC9369471 DOI: 10.3390/jcm11154436] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 07/26/2022] [Accepted: 07/28/2022] [Indexed: 11/17/2022] Open
Abstract
Prediction of blood transfusion after adult spinal deformity (ASD) surgery can identify at-risk patients and potentially reduce its utilization and the complications associated with it. The use of artificial neural networks (ANNs) offers the potential for high predictive capability. A total of 1173 patients who underwent surgery for ASD were identified in the 2017–2019 NSQIP databases. The data were split into 70% training and 30% testing cohorts. Eighteen patient and operative variables were used. The outcome variable was receiving RBC transfusion intraoperatively or within 72 h after surgery. The model was assessed by its sensitivity, positive predictive value, F1-score, accuracy (ACC), and area under the curve (AUROC). Average patient age was 56 years and 63% were female. Pelvic fixation was performed in 21.3% of patients and three-column osteotomies in 19.5% of cases. The transfusion rate was 50.0% (586/1173 patients). The best model showed an overall ACC of 81% and 77% on the training and testing data, respectively. On the testing data, the sensitivity was 80%, the positive predictive value 76%, and the F1-score was 78%. The AUROC was 0.84. ANNs may allow the identification of at-risk patients, potentially decrease the risk of transfusion via strategic planning, and improve resource allocation.
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Abdelhalim H, Berber A, Lodi M, Jain R, Nair A, Pappu A, Patel K, Venkat V, Venkatesan C, Wable R, Dinatale M, Fu A, Iyer V, Kalove I, Kleyman M, Koutsoutis J, Menna D, Paliwal M, Patel N, Patel T, Rafique Z, Samadi R, Varadhan R, Bolla S, Vadapalli S, Ahmed Z. Artificial Intelligence, Healthcare, Clinical Genomics, and Pharmacogenomics Approaches in Precision Medicine. Front Genet 2022; 13:929736. [PMID: 35873469 PMCID: PMC9299079 DOI: 10.3389/fgene.2022.929736] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 05/25/2022] [Indexed: 12/13/2022] Open
Abstract
Precision medicine has greatly aided in improving health outcomes using earlier diagnosis and better prognosis for chronic diseases. It makes use of clinical data associated with the patient as well as their multi-omics/genomic data to reach a conclusion regarding how a physician should proceed with a specific treatment. Compared to the symptom-driven approach in medicine, precision medicine considers the critical fact that all patients do not react to the same treatment or medication in the same way. When considering the intersection of traditionally distinct arenas of medicine, that is, artificial intelligence, healthcare, clinical genomics, and pharmacogenomics—what ties them together is their impact on the development of precision medicine as a field and how they each contribute to patient-specific, rather than symptom-specific patient outcomes. This study discusses the impact and integration of these different fields in the scope of precision medicine and how they can be used in preventing and predicting acute or chronic diseases. Additionally, this study also discusses the advantages as well as the current challenges associated with artificial intelligence, healthcare, clinical genomics, and pharmacogenomics.
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Affiliation(s)
- Habiba Abdelhalim
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Asude Berber
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Mudassir Lodi
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Rihi Jain
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Achuth Nair
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Anirudh Pappu
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Kush Patel
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Vignesh Venkat
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Cynthia Venkatesan
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Raghu Wable
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Matthew Dinatale
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Allyson Fu
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Vikram Iyer
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Ishan Kalove
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Marc Kleyman
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Joseph Koutsoutis
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - David Menna
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Mayank Paliwal
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Nishi Patel
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Thirth Patel
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Zara Rafique
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Rothela Samadi
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Roshan Varadhan
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Shreyas Bolla
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Sreya Vadapalli
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Zeeshan Ahmed
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States.,Department of Medicine, Rutgers Robert Wood Johnson Medical School, Rutgers Biomedical and Health Sciences, New Brunswick, NJ, United States
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