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Application neural network to define optimal parameters of laserotherapy in patients after tonsillectomy: pilot study

机译:应用神经网络定义扁桃体切除术后患者激光治疗的最佳参数:初步研究

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After tonsillectomy many patients are required to continue therapy for reaching more effective and quick results. One of the more efficient methods is laserotherapy. It is non-painful, non-invasive and well-tuned with particular patients. But there are no universal algorithms to prescribe parameters for this kind of therapy. Often doctors define these parameters based on their own experience, and they may not be always optimal. To improve the method, the authors decided to use artificial neural network accumulated experience and learning based on actual data.
机译:扁桃体切除术后,许多患者需要继续治疗以达到更有效,更快速的效果。激光治疗是一种更有效的方法。它是非痛苦的,非侵入性的并且针对特定患者进行了很好的调整。但是,尚无通用算法来规定此类疗法的参数。通常,医生会根据自己的经验来定义这些参数,但它们可能并不总是最佳的。为了改进该方法,作者决定使用人工神经网络根据实际数据积累经验和学习。

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