A Bibliometric Analysis of Artificial Intelligence Applications in Spine Care

Abstract Background  With the rapid development of science and technology, artificial intelligence (AI) has been widely used in the diagnosis and prognosis of various spine diseases. It has been proved that AI has a broad prospect in accurate diagnosis and treatment of spine disorders. Methods  On May 7, 2022, the Web of Science (WOS) Core Collection database was used to identify the documents on the application of AI in the field of spine care. HistCite and VOSviewer were used for citation analysis and visualization mapping. Results  A total of 693 documents were included in the final analysis. The most prolific authors were Karhade A.V. and Schwab J.H. United States was the most productive country. The leading journal was Spine . The most frequently used keyword was spinal. The most prolific institution was Northwestern University in Illinois, USA. Network visualization map showed that United States was the largest network of international cooperation. The keyword “machine learning” had the strongest total link strengths (TLS) and largest number of occurrences. The latest trends suggest that AI for the diagnosis of spine diseases may receive widespread attention in the future. Conclusions  AI has a wide range of application in the field of spine care, and an increasing number of scholars are committed to research on the use of AI in the field of spine care. Bibliometric analysis in the field of AI and spine provides an overall perspective, and the appreciation and research of these influential publications are useful for future research.

[1]  Zhonggen Yu,et al.  A bibliometric analysis of artificial intelligence chatbots in educational contexts , 2023, Interactive Technology and Smart Education.

[2]  Lianghu Zhang,et al.  Bisphosphates for Osteoporosis: A Bibliometric Analysis of the Most Cited Articles , 2022, Evidence-based complementary and alternative medicine : eCAM.

[3]  M. Palumbo,et al.  Bench to bedside: the ambitious goal of transducing Medicinal Chemistry from the Lab to the Clinic. , 2022, Bioorganic & medicinal chemistry letters.

[4]  Xin Liu,et al.  A Bibliometric Analysis of Personal Protective Equipment and COVID-19 Researches , 2022, Frontiers in Public Health.

[5]  Qiong Bai,et al.  Artificial intelligence in peritoneal dialysis: general overview , 2022, Renal failure.

[6]  S. Li,et al.  Reasoning discriminative dictionary-embedded network for fully automatic vertebrae tumor diagnosis , 2022, Medical Image Anal..

[7]  Arash J. Sayari,et al.  Artificial intelligence in spine care: current applications and future utility , 2022, European Spine Journal.

[8]  Amalie Horstmann Nøddeskou-Fink,et al.  PET/CT imaging of spinal inflammation and microcalcification in patients with low back pain: A pilot study on the quantification by artificial intelligence‐based segmentation , 2022, Clinical physiology and functional imaging.

[9]  Tianxin Lin,et al.  The Global Research of Artificial Intelligence on Prostate Cancer: A 22-Year Bibliometric Analysis , 2022, Frontiers in Oncology.

[10]  P. Tonellato,et al.  Producing personalized statin treatment plans to optimize clinical outcomes using big data and machine learning , 2022, J. Biomed. Informatics.

[11]  Weng Marc Lim,et al.  Past, present, and future of sustainable finance: insights from big data analytics through machine learning of scholarly research , 2022, Annals of operations research.

[12]  D. Furniss,et al.  Perspectives: A surgeon's guide to machine learning. , 2021, International journal of surgery.

[13]  A. Shirazi-Adl,et al.  Biomechanical effects of lumbar fusion surgery on adjacent segments using musculoskeletal models of the intact, degenerated and fused spine , 2021, Scientific Reports.

[14]  N. Zhang,et al.  Top 100 most-cited original articles, systematic reviews/meta-analyses in robotic surgery: A scientometric study. , 2021, Asian journal of surgery.

[15]  Chengquan Ma,et al.  Global Research Trends on Prostate Diseases and Erectile Dysfunction: A Bibliometric and Visualized Study , 2021, Frontiers in Oncology.

[16]  H. Wilke,et al.  Intelligence-Based Spine Care Model: A New Era of Research and Clinical Decision-Making , 2020, Global spine journal.

[17]  Zhongjun Mo,et al.  Application of Simulation Methods in Cervical Spine Dynamics , 2020, Journal of healthcare engineering.

[18]  D. Riew,et al.  Top 100 Cited Articles on Spinal Disc Arthroplasty Research. , 2020, Spine.

[19]  Frank Niemeyer,et al.  A Deep Learning Model for the Accurate and Reliable Classification of Disc Degeneration Based on MRI Data , 2020, Investigative radiology.

[20]  T. Peters,et al.  Identifying Scoliosis in Population-Based Cohorts: Automation of a Validated Method Based on Total Body Dual Energy X-ray Absorptiometry Scans , 2020, Calcified Tissue International.

[21]  J. Raffort,et al.  Fundamentals in artificial intelligence for vascular surgeons. , 2019, Annals of vascular surgery.

[22]  Tobias Kober,et al.  Artificial Intelligence in Musculoskeletal Imaging: Review of Current Literature, Challenges, and Trends , 2019, Seminars in Musculoskeletal Radiology.

[23]  Samir D. Mehta,et al.  Computer-Aided Detection of Incidental Lumbar Spine Fractures from Routine Dual-Energy X-Ray Absorptiometry (DEXA) Studies Using a Support Vector Machine (SVM) Classifier , 2019, Journal of Digital Imaging.

[24]  Adrian B. R. Shatte,et al.  Machine learning in mental health: a scoping review of methods and applications , 2019, Psychological Medicine.

[25]  A. Becker,et al.  Vertebral body insufficiency fractures: detection of vertebrae at risk on standard CT images using texture analysis and machine learning , 2018, European Radiology.

[26]  Jun S. Kim,et al.  Predicting Surgical Complications in Adult Patients Undergoing Anterior Cervical Discectomy and Fusion Using Machine Learning , 2018, Neurospine.

[27]  Arash Shaban-Nejad,et al.  Health intelligence: how artificial intelligence transforms population and personalized health , 2018, npj Digital Medicine.

[28]  David Putrino,et al.  Robotic Rehabilitation and Spinal Cord Injury: a Narrative Review , 2018, Neurotherapeutics.

[29]  Anushikha Singh,et al.  Classification of the trabecular bone structure of osteoporotic patients using machine vision , 2017, Comput. Biol. Medicine.

[30]  José M. Merigó,et al.  A bibliometric analysis of operations research and management science , 2017 .

[31]  Enrique Caro-Osorio,et al.  The 100 most-cited articles in spinal oncology. , 2016, Journal of neurosurgery. Spine.

[32]  Johan A. Wallin,et al.  The bibliometric analysis of scholarly production: How great is the impact? , 2015, Scientometrics.

[33]  I. Bratko,et al.  Elicitation of neurological knowledge with argument-based machine learning , 2013, Artif. Intell. Medicine.

[34]  Michael R. Murray,et al.  The 100 most cited spine articles , 2012, European Spine Journal.

[35]  Ludo Waltman,et al.  Software survey: VOSviewer, a computer program for bibliometric mapping , 2009, Scientometrics.

[36]  E. Garfield Citation analysis as a tool in journal evaluation. , 1972, Science.

[37]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[38]  Florian Roser,et al.  Spinal robotics: current applications and future perspectives. , 2013, Neurosurgery.

[39]  A. Bárta,et al.  ARTIFICIAL INTELLIGENCE IN BIOLOGY , 2013 .

[40]  Chaomei Chen,et al.  CiteSpace II: Visualization and Knowledge Discovery in Bibliographic Databases , 2005, AMIA.