Abstract：
OBJECTIVE: Discrimination of gastrointestinal tuberculosis (GITB) and Crohn\'s disease (CD) is difficult. Use of artificial intelligence (AI)-based technologies may help in discriminating these two entities.
METHODS: We conducted a systematic review on the use of AI for discrimination of GITB and CD. Electronic databases (PubMed and Embase) were searched on June 6, 2022, to identify relevant studies. We included any study reporting the use of clinical, endoscopic, and radiological information (textual or images) to discriminate GITB and CD using any AI technique. Quality of studies was assessed with MI-CLAIM checklist.
RESULTS: Out of 27 identified results, a total of 9 studies were included. All studies used retrospective databases. There were five studies of only endoscopy-based AI, one of radiology-based AI, and three of multiparameter-based AI. The AI models performed fairly well with high accuracy ranging from 69.6-100%. Text-based convolutional neural network was used in three studies and Classification and regression tree analysis used in two studies. Interestingly, irrespective of the AI method used, the performance of discriminating GITB and CD did not match in discriminating from other diseases (in studies where a third disease was also considered).
CONCLUSIONS: The use of AI in differentiating GITB and CD seem to have acceptable accuracy but there were no direct comparisons with traditional multiparameter models. The use of multiple parameter-based AI models have the potential for further exploration in search of an ideal tool and improve on the accuracy of traditional models.
METHODS: We conducted a systematic review on the use of AI for discrimination of GITB and CD. Electronic databases (PubMed and Embase) were searched on June 6, 2022, to identify relevant studies. We included any study reporting the use of clinical, endoscopic, and radiological information (textual or images) to discriminate GITB and CD using any AI technique. Quality of studies was assessed with MI-CLAIM checklist.
RESULTS: Out of 27 identified results, a total of 9 studies were included. All studies used retrospective databases. There were five studies of only endoscopy-based AI, one of radiology-based AI, and three of multiparameter-based AI. The AI models performed fairly well with high accuracy ranging from 69.6-100%. Text-based convolutional neural network was used in three studies and Classification and regression tree analysis used in two studies. Interestingly, irrespective of the AI method used, the performance of discriminating GITB and CD did not match in discriminating from other diseases (in studies where a third disease was also considered).
CONCLUSIONS: The use of AI in differentiating GITB and CD seem to have acceptable accuracy but there were no direct comparisons with traditional multiparameter models. The use of multiple parameter-based AI models have the potential for further exploration in search of an ideal tool and improve on the accuracy of traditional models.
摘要：
目的：胃肠结核(GITB)和克罗恩病(CD)的鉴别比较困难。使用基于人工智能(AI)的技术可能有助于区分这两个实体。
方法：我们对使用AI区分GITB和CD进行了系统评价。电子数据库（PubMed和Embase）于2022年6月6日进行了搜索，以确定相关研究。我们纳入了任何报告使用临床，内窥镜,和放射学信息（文本或图像），以使用任何AI技术区分GITB和CD。使用MI-CLAIM检查表评估研究质量。
结果：在27个确定的结果中，共纳入9项研究.所有研究均使用回顾性数据库。只有五项基于内窥镜的人工智能研究，一种基于放射学的人工智能，和三个基于多参数的人工智能。AI模型表现得相当好，精度在69.6-100%之间。在三项研究中使用了基于文本的卷积神经网络，在两项研究中使用了分类和回归树分析。有趣的是,无论使用哪种人工智能方法，区分GITB和CD的性能在与其他疾病的区分中不匹配(在还考虑第三种疾病的研究中).
结论：使用AI区分GITB和CD似乎具有可接受的准确性，但与传统多参数模型没有直接比较。使用基于多个参数的AI模型有可能进一步探索寻找理想工具并提高传统模型的准确性。
方法：我们对使用AI区分GITB和CD进行了系统评价。电子数据库（PubMed和Embase）于2022年6月6日进行了搜索，以确定相关研究。我们纳入了任何报告使用临床，内窥镜,和放射学信息（文本或图像），以使用任何AI技术区分GITB和CD。使用MI-CLAIM检查表评估研究质量。
结果：在27个确定的结果中，共纳入9项研究.所有研究均使用回顾性数据库。只有五项基于内窥镜的人工智能研究，一种基于放射学的人工智能，和三个基于多参数的人工智能。AI模型表现得相当好，精度在69.6-100%之间。在三项研究中使用了基于文本的卷积神经网络，在两项研究中使用了分类和回归树分析。有趣的是,无论使用哪种人工智能方法，区分GITB和CD的性能在与其他疾病的区分中不匹配(在还考虑第三种疾病的研究中).
结论：使用AI区分GITB和CD似乎具有可接受的准确性，但与传统多参数模型没有直接比较。使用基于多个参数的AI模型有可能进一步探索寻找理想工具并提高传统模型的准确性。
