Prasitpuriprecha Chutinun, Pitakaso Rapeepan, Gonwirat Sarayut, Enkvetchakul Prem, Preeprem Thanawadee, Jantama Sirima Suvarnakuta, Kaewta Chutchai, Weerayuth Nantawatana, Srichok Thanatkij, Khonjun Surajet, Nanthasamroeng Natthapong
Faculty of Pharmaceutical Sciences, Ubon Ratchathani University, Ubon Ratchathani 34190, Thailand.
Department of Industrial Engineering, Ubon Ratchathani University, Ubon Ratchathani 34190, Thailand.
Diagnostics (Basel). 2022 Nov 28;12(12):2980. doi: 10.3390/diagnostics12122980.
A person infected with drug-resistant tuberculosis (DR-TB) is the one who does not respond to typical TB treatment. DR-TB necessitates a longer treatment period and a more difficult treatment protocol. In addition, it can spread and infect individuals in the same manner as regular TB, despite the fact that early detection of DR-TB could reduce the cost and length of TB treatment. This study provided a fast and effective classification scheme for the four subtypes of TB: Drug-sensitive tuberculosis (DS-TB), drug-resistant tuberculosis (DR-TB), multidrug-resistant tuberculosis (MDR-TB), and extensively drug-resistant tuberculosis (XDR-TB). The drug response classification system (DRCS) has been developed as a classification tool for DR-TB subtypes. As a classification method, ensemble deep learning (EDL) with two types of image preprocessing methods, four convolutional neural network (CNN) architectures, and three decision fusion methods have been created. Later, the model developed by EDL will be included in the dialog-based object query system (DBOQS), in order to enable the use of DRCS as the classification tool for DR-TB in assisting medical professionals with diagnosing DR-TB. EDL yields an improvement of 1.17-43.43% over the existing methods for classifying DR-TB, while compared with classic deep learning, it generates 31.25% more accuracy. DRCS was able to increase accuracy to 95.8% and user trust to 95.1%, and after the trial period, 99.70% of users were interested in continuing the utilization of the system as a supportive diagnostic tool.
感染耐药结核病（DR-TB）的人是对典型结核病治疗无反应的人。耐药结核病需要更长的治疗期和更复杂的治疗方案。此外，尽管早期发现耐药结核病可以降低结核病治疗的成本和时长，但它仍能以与普通结核病相同的方式传播并感染他人。本研究为结核病的四种亚型提供了一种快速有效的分类方案：药物敏感结核病（DS-TB）、耐药结核病（DR-TB）、耐多药结核病（MDR-TB）和广泛耐药结核病（XDR-TB）。药物反应分类系统（DRCS）已被开发为一种用于耐药结核病亚型的分类工具。作为一种分类方法，已经创建了具有两种图像预处理方法、四种卷积神经网络（CNN）架构和三种决策融合方法的集成深度学习（EDL）。之后，由EDL开发的模型将被纳入基于对话的对象查询系统（DBOQS），以便能够将DRCS用作耐药结核病的分类工具，协助医学专业人员诊断耐药结核病。与现有的耐药结核病分类方法相比，EDL的准确率提高了1.17%-43.43%，与经典深度学习相比，其准确率提高了31.25%。DRCS能够将准确率提高到95.8%，将用户信任度提高到95.1%，在试验期后，99.70%的用户有兴趣继续使用该系统作为辅助诊断工具。
