Artificial Intelligence and Machine Learning Applications in Spine Surgery

被引：11

作者：

Lee, Nathan J. ^{[1
]}

Lombardi, Joseph M. ^{[1
]}

Lehman, Ronald A. ^{[1
]}

机构：

[1] Columbia Univ, Och Spine Hosp New York Presbyterian, Med Ctr, Dept Orthopaed, New York, NY USA

来源：

INTERNATIONAL JOURNAL OF SPINE SURGERY | 2023年 / 17卷

关键词：

machine learning; artificial intelligence; cervical spine; lumbar spine; adult spinal deformity; predictive model; PREOPERATIVE PREDICTIVE MODEL; LOGISTIC-REGRESSION; NEURAL-NETWORKS; DEFORMITY; COMPLICATIONS;

D O I：

10.14444/8503

中图分类号：

R61 [外科手术学];

学科分类号：

摘要：

The complexity of patients with spine pathology and high rates of complications has driven extensive research directed toward optimizing outcomes and reducing complications. Traditional statistical analysis has been limited both in validity and in the number of predictor variables considered. Over the past decade, artificial intelligence and machine learning have taken center stage as the possible solution to creating more accurate and applicable patient--centered predictive models in spine surgery. This review discusses the current published machine learning applications on preoperative optimization, risk stratification, and predictive modeling for the cervical, lumbar, and adult spinal deformity populations.

引用

页码：S18 / S25

页数：8

共 36 条

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