医学
电流(流体)
脊柱(分子生物学)
医学物理学
生物信息学
生物
电气工程
工程类
作者
Paramesh Karandikar,Elie Massaad,Muhamed Hadzipasic,Ali Kiapour,Rushikesh S. Joshi,Ganesh M. Shankar,John H. Shin
出处
期刊:Neurosurgery
[Oxford University Press]
日期:2022-02-02
卷期号:90 (4): 372-382
被引量:8
标识
DOI:10.1227/neu.0000000000001853
摘要
Recent developments in machine learning (ML) methods demonstrate unparalleled potential for application in the spine. The ability for ML to provide diagnostic faculty, produce novel insights from existing capabilities, and augment or accelerate elements of surgical planning and decision making at levels equivalent or superior to humans will tremendously benefit spine surgeons and patients alike. In this review, we aim to provide a clinically relevant outline of ML-based technology in the contexts of spinal deformity, degeneration, and trauma, as well as an overview of commercial-level and precommercial-level surgical assist systems and decisional support tools. Furthermore, we briefly discuss potential applications of generative networks before highlighting some of the limitations of ML applications. We conclude that ML in spine imaging represents a significant addition to the neurosurgeon's armamentarium—it has the capacity to directly address and manifest clinical needs and improve diagnostic and procedural quality and safety—but is yet subject to challenges that must be addressed before widespread implementation.
科研通智能强力驱动
Strongly Powered by AbleSci AI