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Automatic detection of coagulation and carbonization in laser applications using machine learning techniques

机译:采用机器学习技术自动检测激光应用中的激光应用中的凝结和碳化

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Laser-induced thermal treatment (LITT) technique is considered to be the most effective method for minimally invasive surgical procedures. However, the parameters of laser systems used in surgical operations need to be adjusted very precisely. Otherwise, irreversible situations may occur especially in the operation of sensitive organs such as the brain. In order to avoid such undesirable situations, artificial intelligence algorithms are used in the fields of engineering, aerospace and automotive, especially in medicine and biomedical. In this study, it was aimed to automatically detect Coagulation (Co) and Carbonization (Ca) states using a total of 140 data obtained as a result of LITT experimental applications (1070 nm and seven different power density values) onex vivoliver tissue. In the first part of the study, which consists of two main stages, the data set was presented to seven different expert systems based on artificial intelligence (C4.5, KNN, ANN, RF, MLR, SMO-SVM and LWLDS), and automatically Co and Ca formations were determined. Among the proposed expert systems, the C4.5 algorithm showed the best performance with 100% accuracy and 0% error rate. In the second stage of the study, the same data set was given to Attribute Information Gain (AIG) ranking system, which was used to determine the minimum number of feature that would provide the maximum performance, and the gain values of the attributes were calculated. As a result of this application, the most effective feature was found to be Normalized Depth of Thermal Damage (NDeTD) with 100% accuracy rate. Thus, the purpose of using the AIG system has been successfully achieved. As a result, thanks to the expert systems presented within the scope of the study, Co and Ca formations were determined automatically with 100% success rate according to the required parameters without laser application onex vivoliver tissue. The fact that the aim of the study was realized with the highest success rate is thought to be the first in terms of contributing to the literature. Through this study, as a result of the laser treatment to be applied on cancerous tissue, it can be predicted whether the tissue will suffer irreversible damage and necessary precautions can be taken beforehand.
机译:激光诱导的热处理(LITT)技术被认为是最有效的侵入性外科手术的方法。然而,在外科手术中使用的激光系统的参数需要非常精确地调整。否则,可能在诸如大脑的敏感器官的操作中发生不可逆情况。为了避免这种不良情况,人工智能算法用于工程,航空航天和汽车领域,特别是医学和生物医学。在该研究中,旨在通过LITT实验应用(1070nm和七种不同功率密度值)单次诱导组织,使用总共140个数据来自动检测凝血(CO)和碳化(CA)状态。在该研究的第一部分包括两个主要阶段,数据集被呈现给基于人工智能的七种不同专家系统(C4.5,KNN,ANN,RF,MLR,SMO-SVM和LWLD),以及确定自动CO和CA形成。在拟议的专家系统中,C4.5算法显示了具有100%精度和0%的错误率的最佳性能。在该研究的第二阶段中,对属性信息增益(AIG)排名系统的相同数据集用于确定将提供最大性能的最小特征数,并且计算属性的增益值。由于本申请,发现最有效的特征是具有100%精度率的热损坏(NDETD)的标准化深度。因此,已经成功实现了使用AIG系统的目的。因此,由于在研究范围内提出的专家系统,根据所需参数,在没有激光应用单位viviver组织的所需参数,根据所需参数自动测定CO和CA组。这项研究的目的是以最高成功率实现的目标是认为是对文献的贡献之一。通过这项研究,由于激光治疗适用于癌组织,可以预测组织是否会遭受不可逆转的损坏,并且可以预先采取必要的预防措施。

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