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神经外科中的人工智能技术：使用主题建模的系统文献综述。第二部分：研究目标和视角。

Artificial Intelligence Technologies in Neurosurgery: a Systematic Literature Review Using Topic Modeling. Part II: Research Objectives and Perspectives.

机构信息

Scientific Board Secretary; N.N. Burdenko National Medical Research Center for Neurosurgery, Ministry of Health of the Russian Federation, 16, 4 Tverskaya-Yamskaya St., Moscow, 125047, Russia; Head of the Laboratory of Biomedical Informatics and Artificial Intelligence; N.N. Burdenko National Medical Research Center for Neurosurgery, Ministry of Health of the Russian Federation, 16, 4 Tverskaya-Yamskaya St., Moscow, 125047, Russia.

Scientific Consultant, Laboratory of Biomedical Informatics and Artificial Intelligence; N.N. Burdenko National Medical Research Center for Neurosurgery, Ministry of Health of the Russian Federation, 16, 4 Tverskaya-Yamskaya St., Moscow, 125047, Russia.

出版信息

Sovrem Tekhnologii Med. 2021;12(6):111-118. doi: 10.17691/stm2020.12.6.12. Epub 2020 Dec 28.

DOI:10.17691/stm2020.12.6.12
PMID:34796024
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8596229/
Abstract

UNLABELLED: The current increase in the number of publications on the use of artificial intelligence (AI) technologies in neurosurgery indicates a new trend in clinical neuroscience. was to conduct a systematic literature review to highlight the main directions and trends in the use of AI in neurosurgery. METHODS: Using the PubMed search engine, 327 original journal articles published from 1996 to July 2019 and related to the use of AI technologies in neurosurgery, were selected. The typical issues addressed by using AI were identified for each area of neurosurgery. RESULTS: The typical AI applications within each of the five main areas of neurosurgery (neuro-oncology, functional, vascular, spinal neurosurgery, and traumatic brain injury) were defined. CONCLUSION: The article highlights the main areas and trends in the up-to-date AI research in neurosurgery, which might be helpful in planning new scientific projects.

摘要

未加标签：目前，关于在神经外科中使用人工智能（AI）技术的出版物数量不断增加，这表明临床神经科学出现了新趋势。本研究旨在进行系统的文献回顾，以强调 AI 在神经外科中的主要应用方向和趋势。

方法：使用 PubMed 搜索引擎，选择了 1996 年至 2019 年 7 月间发表的 327 篇与神经外科中使用 AI 技术相关的原始期刊文章。确定了每个神经外科领域使用 AI 解决的典型问题。

结果：定义了神经外科五个主要领域（神经肿瘤学、功能神经外科、血管神经外科、脊柱神经外科和创伤性脑损伤）中每个领域的典型 AI 应用。

结论：本文强调了神经外科中最新 AI 研究的主要领域和趋势，这有助于规划新的科学项目。

相似文献

[1]
Artificial Intelligence Technologies in Neurosurgery: a Systematic Literature Review Using Topic Modeling. Part II: Research Objectives and Perspectives.

Sovrem Tekhnologii Med. 2021

[2]
Artificial Intelligence in Neurosurgery: a Systematic Review Using Topic Modeling. Part I: Major Research Areas.

Sovrem Tekhnologii Med. 2021

[3]
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[1]
Machine learning based algorithms for virtual early detection and screening of neurodegenerative and neurocognitive disorders: a systematic-review.

Front Neurol. 2024-12-9

[2]
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Brain Spine. 2024-4-17

[3]
Computer-vision based analysis of the neurosurgical scene - A systematic review.

Brain Spine. 2023-11-7

[4]
Advances in artificial intelligence, robotics, augmented and virtual reality in neurosurgery.

Front Surg. 2023-8-24

[5]
The future of artificial intelligence in neurosurgery: A narrative review.

Surg Neurol Int. 2022-11-18

本文引用的文献

[1]
Machine learning applications to clinical decision support in neurosurgery: an artificial intelligence augmented systematic review.

Neurosurg Rev. 2020-10

[2]
Machine Learning Model to Predict Osteoporotic Spine with Hounsfield Units on Lumbar Computed Tomography.

J Korean Neurosurg Soc. 2019-7

[3]
Artificial Intelligence Based Hierarchical Clustering of Patient Types and Intervention Categories in Adult Spinal Deformity Surgery: Towards a New Classification Scheme that Predicts Quality and Value.

Spine (Phila Pa 1976). 2019-7-1

[4]
Automatic assessment of glioma burden: a deep learning algorithm for fully automated volumetric and bidimensional measurement.

Neuro Oncol. 2019-11-4

[5]
Development of machine learning algorithms for prediction of prolonged opioid prescription after surgery for lumbar disc herniation.

Spine J. 2019-6-9

[6]
Deep Learning-Assisted Diagnosis of Cerebral Aneurysms Using the HeadXNet Model.

JAMA Netw Open. 2019-6-5

[7]
Machine Learning Algorithm Identifies Patients at High Risk for Early Complications After Intracranial Tumor Surgery: Registry-Based Cohort Study.

Neurosurgery. 2019-10-1

[8]
Heart Rate Variability as a Biomarker of Neurocardiogenic Injury After Subarachnoid Hemorrhage.

Neurocrit Care. 2020-2

[9]
Radiomics for predicting hematoma expansion in patients with hypertensive intraparenchymal hematomas.

Eur J Radiol. 2019-4-2

[10]
Classification of foot drop gait characteristic due to lumbar radiculopathy using machine learning algorithms.

Gait Posture. 2019-5-4

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