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Deep learning assistance for tuberculosis diagnosis with chest radiography in low-resource settings
Journal of X-Ray Science and Technology ( IF 3 ) Pub Date : 2021-06-29 , DOI: 10.3233/xst-210894
Mayidili Nijiati 1 , Ziqi Zhang 2 , Abudoukeyoumujiang Abulizi 1 , Hengyuan Miao 2 , Aikebaierjiang Tuluhong 1 , Shenwen Quan 3 , Lin Guo 3 , Tao Xu 2, 4, 5 , Xiaoguang Zou 1
Affiliation  

Tuberculosis (TB) is a major health issue with high mortality rates worldwide. Recently, tremendous researches of artificial intelligence (AI) have been conducted targeting at TB to reduce the diagnostic burden. However, most researches are conducted in the developed urban areas. The feasibility ofapplying AI in low-resource settings remains unexplored. In this study, we apply an automated detection (AI) system to screen a large population in an underdeveloped area and evaluate feasibility and contribution of apply AI to help local radiologists detect and diagnose TB using chest X-ray (CXR) images. First, we divide image data into one training dataset including 2627 TB-positive cases and 7375 TB-negative cases and one testing dataset containing 276 TB-positive cases and 619 TB-negative cases, respectively. Next, in building AI system, the experiment includes image labeling and preprocessing, model training and testing. A segmentation model named TB-UNet is also built to detect diseased regions, which uses ResNeXt as the encoder of U-Net. We use AI-generated confidence score to predict the likelihood of each testing case being TB-positive. Then, we conduct two experiments to compare results between the AI system and radiologists with and without AI assistance. Study results show that AI system yields TB detection accuracy of 85%, which is much higher than detection accuracy of radiologists (62%) without AI assistance. In addition, with AI assistance, the TB diagnostic sensitivity of local radiologists is improved by 11.8%. Therefore, this study demonstrates that AI has great potential to help detection, prevention, and control of TB in low-resource settings, particularly in areas with scant doctors and higher rates of the infected population.

中文翻译:

在资源匮乏的环境中通过胸片诊断肺结核的深度学习辅助

结核病 (TB) 是一个主要的健康问题,在全球范围内具有高死亡率。最近,针对结核病进行了大量的人工智能 (AI) 研究,以减轻诊断负担。然而,大多数研究都是在发达的城市地区进行的。在资源匮乏的环境中应用人工智能的可行性仍未得到探索。在这项研究中,我们应用自动检测 (AI) 系统对欠发达地区的大量人群进行筛查,并评估应用 AI 帮助当地放射科医生使用胸部 X 光 (CXR) 图像检测和诊断结核病的可行性和贡献。首先,我们将图像数据分为一个训练数据集,包括 2627 个 TB 阳性病例和 7375 个 TB 阴性病例,一个测试数据集分别包含 276 个 TB 阳性病例和 619 个 TB 阴性病例。接下来,在构建AI系统中,实验包括图像标注和预处理、模型训练和测试。还建立了一个名为 TB-UNet 的分割模型来检测病变区域,该模型使用 ResNeXt 作为 U-Net 的编码器。我们使用 AI 生成的置信度分数来预测每个测试案例为 TB 阳性的可能性。然后,我们进行了两个实验来比较 AI 系统和放射科医生在有和没有 AI 帮助的情况下的结果。研究结果表明,人工智能系统的结核病检测准确率为 85%,远高于没有人工智能辅助的放射科医生的检测准确率(62%)。此外,在人工智能的帮助下,当地放射科医生的结核病诊断敏感性提高了 11.8%。因此,这项研究表明,人工智能在帮助检测、预防和控制资源匮乏地区的结核病方面具有巨大潜力,
更新日期:2021-07-04
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