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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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机器学习预测cauda equina成像结果 - - 解决问题的解决方案.

Rosa Sun¹, Abdelmageed Abdelrahman Ramadan², Thaaqib Nazar³

¹Department of Neurosurgery, Queen Elizabeth Hospital, Mindelsohn Way, Birmingham, B15 2TH, UK.

European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society

|December 3, 2024

概括

机器学习可以显著减少不必要的MRI扫描可疑的Cauda Equina综合征 (CES) 超过95%. 这种人工智能工具有助于准确分组患者,提高这种罕见的外科紧急情况的诊断效率.

关键词:

人工智能的人工智能是人工智能.马尾 (cauda equina) 是一种动物的尾巴.机器学习是机器学习.神经外科神经外科脊柱外科手术脊柱外科手术

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科学领域:

医疗成像医学成像
医疗保健中的人工智能
神经外科神经外科

背景情况:

尾牙综合征 (CES) 是一种严重的外科紧急情况,对生活质量有严重的影响.
没有单一的症状可以确定CES的确诊,这使得早期治疗变得复杂.
机器学习 (ML) 提供了一种新的方法来提高诊断准确性和患者分拣.

研究的目的:

评估ML算法在诊断疑似CES (CES-S) 的患者中的有效性.
评估ML的潜力,以减少紧急MRI扫描的数量.
探索预测信心 (CoP) 在ML驱动的诊断途径中的作用.

主要方法:

分析了499名疑似CES患者的数据集.
一个ML算法被训练来预测MRI诊断的CES.
算法预测和预测信心 (CoP) 在测试组上进行了评估.

主要成果:

ML算法表现出高准确度,正确分类了476个负的和6个正的CES病例,具有高的CoP.
只有高COP阳性预测和所有低COP病例的扫描策略可以将MRI扫描减少95%以上.
在低COP预测中发现了6个假阴性.

结论:

在疑似CES病例中,ML算法显示出减少不必要的紧急MRI扫描的巨大潜力.
为了广泛的临床采用,需要对大规模前性数据进行进一步的验证.
持续的算法训练对于提高预测准确性和信心至关重要.