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[神经外科中的人工智能]

[Artificial intelligence in neurosurgery].

作者信息

Bonsanto M M, Tronnier V M

机构信息

Klinik für Neurochirurgie, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, 23538, Lübeck, Deutschland.

出版信息

Chirurg. 2020 Mar;91(3):229-234. doi: 10.1007/s00104-020-01131-9.

DOI:10.1007/s00104-020-01131-9
PMID:32052108
Abstract

BACKGROUND

Artificial intelligence (AI) in neurosurgery is becoming increasingly more important as the technology advances. This development can be measured by the increase of publications on AI in neurosurgery over the last years.

OBJECTIVE

This article provides insights into the current possibilities of using AI in neurosurgery.

MATERIAL AND METHODS

A review of the literature was carried out with a focus on exemplary work on the use of AI in neurosurgery.

RESULTS

The current neurosurgical publications on the use of AI show the diversity of the topic in this field. The main areas of application are diagnostics, outcome and treatment models.

CONCLUSION

The various areas of application of AI in the field of neurosurgery with a refined preoperative diagnostics and outcome predictions will significantly influence the future of neurosurgery. Neurosurgeons will continue to make the decisions on the indications for surgery but an optimized statement on diagnosis, treatment options and on the risk of surgery will be made by neurosurgeons with the help of AI in the future.

摘要

背景

随着技术的进步,人工智能(AI)在神经外科领域正变得越来越重要。这一发展可以通过近年来神经外科领域关于AI的出版物数量的增加来衡量。

目的

本文深入探讨了目前在神经外科中使用AI的可能性。

材料与方法

对文献进行综述,重点关注神经外科中使用AI的典型工作。

结果

目前关于神经外科中使用AI的出版物展示了该领域主题的多样性。主要应用领域是诊断、预后和治疗模型。

结论

AI在神经外科领域的各种应用,以及精确的术前诊断和预后预测,将显著影响神经外科的未来。神经外科医生将继续对手术适应症做出决策,但未来神经外科医生将借助AI对诊断、治疗方案和手术风险做出优化说明。

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Examining the Ability of Artificial Neural Networks Machine Learning Models to Accurately Predict Complications Following Posterior Lumbar Spine Fusion.探讨人工神经网络机器学习模型准确预测后路腰椎融合术后并发症的能力。
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Machine Learning and Neurosurgical Outcome Prediction: A Systematic Review.
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人工智能、机器学习和深度学习在神经外科领域的近期成果与挑战
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Image-guided preoperative prediction of pyramidal tract side effect in deep brain stimulation: proof of concept and application to the pyramidal tract side effect induced by pallidal stimulation.基于影像引导的脑深部电刺激术中锥体束副作用的术前预测:概念验证及在苍白球刺激诱发锥体束副作用中的应用
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Methodological Issues in Predicting Pediatric Epilepsy Surgery Candidates Through Natural Language Processing and Machine Learning.通过自然语言处理和机器学习预测小儿癫痫手术候选者的方法学问题
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