Page 118 - Read Online
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Page 64                          Ambati et al. Art Int Surg. 2025;5:53-64  https://dx.doi.org/10.20517/ais.2024.45

               50.      Khazanchi R, Bajaj A, Shah RM, et al. Using machine learning and deep learning algorithms to predict postoperative outcomes
                   following anterior cervical discectomy and fusion. Clin Spine Surg. 2023;36:143-9.  DOI
               51.      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
               52.      Karabacak M, Margetis K. A machine learning-based online prediction tool for predicting short-term postoperative outcomes
                   following spinal tumor resections. Cancers. 2023;15:812.  DOI  PubMed  PMC
               53.      Han SS, Azad TD, Suarez PA, Ratliff JK. A machine learning approach for predictive models of adverse events following spine
                   surgery. Spine J. 2019;19:1772-81.  DOI  PubMed
               54.      Park C, Mummaneni PV, Gottfried ON, et al. Which supervised machine learning algorithm can best predict achievement of minimum
                   clinically important difference in neck pain after surgery in patients with cervical myelopathy? A QOD study. Neurosurg Focus.
                   2023;54:E5.  DOI
               55.      Khan O, Badhiwala JH, Witiw CD, Wilson JR, Fehlings MG. Machine learning algorithms for prediction of health-related quality-of-
                   life after surgery for mild degenerative cervical myelopathy. Spine J. 2021;21:1659-69.  DOI  PubMed
               56.      Rushton AB, Jadhakhan F, Verra ML, et al. Predictors of poor outcome following lumbar spinal fusion surgery: a prospective
                   observational study to derive two clinical prediction rules using British Spine Registry data. Eur Spine J. 2023;32:2303-18.  DOI
               57.      Müller D, Haschtmann D, Fekete TF, et al. Development of a machine-learning based model for predicting multidimensional outcome
                   after surgery for degenerative disorders of the spine. Eur Spine J. 2022;31:2125-36.  DOI
               58.      Johnson GW, Chanbour H, Ali MA, et al. Artificial intelligence to preoperatively predict proximal junction kyphosis following adult
                   spinal deformity surgery: soft tissue imaging may be necessary for accurate models. Spine. 2023;48:1688-95.  DOI  PubMed  PMC
               59.      Fiani B, De Stefano F, Kondilis A, Covarrubias C, Reier L, Sarhadi K. Virtual reality in neurosurgery: “Can you see it? World
                   Neurosurg. 2020;141:291-8.  DOI  PubMed
               60.      Goldberg JL, Härtl R, Elowitz E. Challenges hindering widespread adoption of minimally invasive spinal surgery. World Neurosurg.
                   2022;163:228-32.  DOI  PubMed
               61.      Ghanem M, Ghaith AK, El-Hajj VG, et al. Limitations in evaluating machine learning models for imbalanced binary outcome
                   classification in spine surgery: a systematic review. Brain Sci. 2023;13:1723.  DOI  PubMed  PMC
               62.      Brown TB, Mann B, Ryder N, et al. Language models are few-shot learners. arXiv. 2020;arXiv:2005.14165. Available from: https://
                   doi.org/10.48550/arXiv.2005.14165. [accessed 28 Dec 2024]
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