Page 47 - Read Online
P. 47

Page 293                                                       Fuleihan et al. Art Int Surg 2024;4:288-95  https://dx.doi.org/10.20517/ais.2024.39

               DECLARATIONS
               Authors’ contributions
               Conceptualization, design, synthesis, writing, and editing: Fuleihan AA, Menta AK, Azad TD, Theodore N
               Writing and editing: Jiang K, Weber-Levine C, Davidar AD, Hersh AM


               Availability of data and materials
               Not applicable.

               Financial support and sponsorship
               None.

               Conflicts of interest
               Theodore N receives royalties from and owns stock in Globus Medical. He is a consultant for Globus
               Medical and has served on the scientific advisory board/other office for Globus Medical. While the other
               authors have declared that they have no conflicts of interest.


               Ethical approval and consent to participate
               Not applicable.

               Consent for publication
               Not applicable.

               Copyright
               © The Author(s) 2024.

               REFERENCES
               1.       Varghese C, Harrison EM, O’Grady G, Topol EJ. Artificial intelligence in surgery. Nat Med 2024;30:1257-68.  DOI  PubMed
               2.       Holzinger A, Langs G, Denk H, Zatloukal K, Müller H. Causability and explainability of artificial intelligence in medicine. Wiley
                   Interdiscip Rev Data Min Knowl Discov 2019;9:e1312.  DOI  PubMed  PMC
               3.       Sahni NR, Stein G, Zemmel R, Cutler D. The potential impact of artificial intelligence on healthcare spending. In: The economics of
                   artificial intelligence. University of Chicago Press; 2023. Available from: https://www.degruyter.com/document/doi/10.7208/chicago/
                   9780226833125-004/pdf?licenseType=restricted. [Last accessed on 10 Oct 2024].
               4.       Burns JE, Yao J, Summers RM. Vertebral body compression fractures and bone density: automated detection and classification on CT
                   images. Radiology 2017;284:788-97.  DOI  PubMed  PMC
               5.       Al Arif SMMR, Knapp K, Slabaugh G. Fully automatic cervical vertebrae segmentation framework for X-ray images. Comput
                   Methods Programs Biomed 2018;157:95-111.  DOI  PubMed
               6.       Doerr SA, Weber-Levine C, Hersh AM, et al. Automated prediction of the thoracolumbar injury classification and severity score from
                   CT using a novel deep learning algorithm. Neurosurg Focus 2022;52:E5.  DOI  PubMed
               7.       Maier-Hein L, Vedula SS, Speidel S, et al. Surgical data science for next-generation interventions. Nat Biomed Eng 2017;1:691-6.
                   DOI  PubMed
               8.       Kim JS, Merrill RK, Arvind V, et al. Examining the ability of artificial neural networks machine learning models to accurately predict
                   complications following posterior lumbar spine fusion. Spine 2018;43:853-60.  DOI  PubMed  PMC
               9.       Karhade AV, Bongers MER, Groot OQ, et al. Development of machine learning and natural language processing algorithms for
                   preoperative prediction and automated identification of intraoperative vascular injury in anterior lumbar spine surgery. Spine J
                   2021;21:1635-42.  DOI  PubMed
               10.      Wijnberge M, Geerts BF, Hol L, et al. Effect of a machine learning-derived early warning system for intraoperative hypotension vs
                   standard care on depth and duration of intraoperative hypotension during elective noncardiac surgery: the HYPE randomized clinical
                   trial. JAMA 2020;323:1052-60.  DOI  PubMed  PMC
               11.      Kalidasan V, Yang X, Xiong Z, et al. Wirelessly operated bioelectronic sutures for the monitoring of deep surgical wounds. Nat
                   Biomed Eng 2021;5:1217-27.  DOI  PubMed
               12.      Bihorac A, Ozrazgat-Baslanti T, Ebadi A, et al. MySurgeryRisk: development and validation of a machine-learning risk algorithm for
                   major complications and death after surgery. Ann Surg 2019;269:652-62.  DOI  PubMed  PMC
               13.      Hu Z, Simon GJ, Arsoniadis EG, Wang Y, Kwaan MR, Melton GB. Automated detection of postoperative surgical site infections
   42   43   44   45   46   47   48   49   50   51   52