Noninvasive intracranial hypertension detection utilizing semisupervised learning.
Noninvasive intracranial hypertension detection utilizing semisupervised learning.
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DOI：
10.1109/tbme.2012.2227477
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发表时间：
2013-04
期刊：
影响因子：
--
通讯作者：
Hu X
中科院分区：
文献类型：
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作者：
Kim S;Hamilton R;Pineles S;Bergsneider M;Hu X
颅内压（ICP）监测是管理具有急性ICP升高风险的患者的既定临床实践，尽管临床上接受的测量ICP的方法仍然是侵入性的。然而，颅内压测量的侵入性使其无法应用于许多临床情况，如特发性颅内高压（IIH）的诊断。我们提出了一种基于脑血流速度（CBFV）波形形态学分析的颅内高压（IH）无创诊断工具。我们主要比较了两种类型的IH检测方法：一种基于传统的监督学习方法，另一种基于半监督学习方法。我们的仿真结果表明，半监督IH检测方法的预测精度（曲线下面积）可以高达92%，而监督IH检测方法的预测精度仅为82%左右。应该注意的是，基于搏动指数（PI）的IH检测方法的预测准确度低至59%。虽然预测准确性是一种广泛使用的准确性测量，但它没有考虑必要和不必要治疗的临床后果。因此，我们采用了决策曲线分析来解决这个问题。决策曲线分析结果表明，半监督IH检测方法不仅更准确，而且比监督IH检测方法或基于PI的IH检测方法在临床上更有用。
Intracranial pressure (ICP) monitoring is an established clinical practice in managing patients with risk of acute ICP elevation although the clinically accepted way of measuring ICP remains invasive. However, the invasive nature of ICP measurement obviates its application in many clinical circumstances such as diagnosis of idiopathic intracranial hypertension (IIH). We propose a noninvasive diagnostic tool for intracranial hypertension (IH) based on the morphological analysis of cerebral blood flow velocity (CBFV) waveforms. We mainly compare two types of IH detection methods: one based on the traditional supervised learning approach and the other based on the semi-supervised learning approach. Our simulation results demonstrate that the predictive accuracy (area-under-the-curve) of the semi-supervised IH detection method can be as high as 92% while that of the supervised IH detection method is only around 82%. It should be noted that the predictive accuracy of the pulsatility index (PI) based IH detection method is as low as 59%. Although the predictive accuracy is a widely used accuracy measurement, it does not consider clinical consequences of necessary and unnecessary treatments. For this reason, we have adopted the decision curve analysis to address this issue. The decision curve analysis results show that the semi-supervised IH detection method is not only more accurate, but also clinically more useful than the supervised IH detection method or the PI-based IH detection method.