Dataset Information

The Role of Machine Learning in Spine Surgery: The Future Is Now.

ABSTRACT: The recent influx of machine learning centered investigations in the spine surgery literature has led to increased enthusiasm as to the prospect of using artificial intelligence to create clinical decision support tools, optimize postoperative outcomes, and improve technologies used in the operating room. However, the methodology underlying machine learning in spine research is often overlooked as the subject matter is quite novel and may be foreign to practicing spine surgeons. Improper application of machine learning is a significant bioethics challenge, given the potential consequences of over- or underestimating the results of such studies for clinical decision-making processes. Proper peer review of these publications requires a baseline familiarity of the language associated with machine learning, and how it differs from classical statistical analyses. This narrative review first introduces the overall field of machine learning and its role in artificial intelligence, and defines basic terminology. In addition, common modalities for applying machine learning, including classification and regression decision trees, support vector machines, and artificial neural networks are examined in the context of examples gathered from the spine literature. Lastly, the ethical challenges associated with adapting machine learning for research related to patient care, as well as future perspectives on the potential use of machine learning in spine surgery, are discussed specifically.

SUBMITTER: Chang M

PROVIDER: S-EPMC7472375 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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The Role of Machine Learning in Spine Surgery: The Future Is Now.

Chang Michael M Canseco Jose A JA Nicholson Kristen J KJ Patel Neil N Vaccaro Alexander R AR

Frontiers in surgery 20200821

The recent influx of machine learning centered investigations in the spine surgery literature has led to increased enthusiasm as to the prospect of using artificial intelligence to create clinical decision support tools, optimize postoperative outcomes, and improve technologies used in the operating room. However, the methodology underlying machine learning in spine research is often overlooked as the subject matter is quite novel and may be foreign to practicing spine surgeons. Improper applica ...[more]

PMID: 32974382

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Project description:Background: Intraoperative neuromonitoring (IONM) is frequently used during spine surgery to mitigate the risk of neurological injuries. Yet, its role in anterior cervical spine surgery remains controversial. Without consensus on which anterior cervical spine surgeries would benefit the most from IONM, there is a lack of standardized guidelines for its use in such procedures. Purpose: We sought to assess the alerts generated by each IONM modality for 4 commonly performed anterior cervical spinal surgeries: anterior cervical diskectomy and fusion (ACDF), anterior cervical corpectomy and fusion (ACCF), cervical disk replacement (CDR), or anterior diskectomy. In doing so, we sought to determine which IONM modalities (electromyography [EMG], motor evoked potentials [MEP], and somatosensory evoked potentials [SSEP]) are associated with alert status when accounting for procedure characteristics (number of levels, operative level). Methods: We conducted a retrospective review of IONM data collected by Accurate Neuromonitoring, LLC, a company that supports spine surgeries conducted by 400 surgeons in 8 states, in an internally managed database from December 2009 to September 2018. The database was queried for patients who underwent ACCF, ACDF, anterior CDR, or anterior diskectomy in which at least 1 IONM modality was used. The IONM modalities and incidence of alerts were collected for each procedure. The search identified 8854 patients (average age, 50.6 years) who underwent ACCF (n = 209), ACDF (n = 8006), CDR (n = 423), and anterior diskectomy (n = 216) with at least 1 IONM modality. Results: Electromyography was used in 81.3% (n = 7203) of cases, MEP in 64.8% (n = 5735) of cases, and SSEP in 99.9% (n = 8844) of cases. Alerts were seen in 9.3% (n = 671), 0.5% (n = 30), and 2.7% (n = 241) of cases using EMG, MEP and SSEP, respectively. In ACDF, a significant difference was seen in EMG alerts based on the number of spinal levels involved, with 1-level ACDF (6.9%, n = 202) having a lower rate of alerts than 2-level (10.0%, n = 272), 3-level (15.2%, n = 104), and 4-level (23.4%, n = 15). Likewise, 2-level ACDF had a lower rate of alerts than 3-level and 4-level ACDF. A significant difference by operative level was noted in EMG use for single-level ACDF, with C2-C3 having a lower rate of use than other levels. Conclusions: This retrospective review of anterior cervical spinal surgeries performed with at least 1 IONM modality found that SSEP had the highest rate of use across procedure types, whereas MEP had the highest rate of nonuse. Future studies should focus on determining the most useful IONM modalities by procedure type and further explore the benefit of multimodal IONM in spine surgery.

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Dataset Information

The Role of Machine Learning in Spine Surgery: The Future Is Now.

Publications

The Role of Machine Learning in Spine Surgery: The Future Is Now.

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