作者
Krithika Rangarajan, Sumanyu Muku, Amit Kumar Garg, Pavan Gabra, Sujay Halkur Shankar, Neeraj Nischal, Kapil Dev Soni, Ashu Seith Bhalla, Anant Mohan, Pawan Tiwari, Sushma Bhatnagar, Raghav Bansal, Atin Kumar, Shivanand Gamanagati, Richa Aggarwal, Upendra Baitha, Ashutosh Biswas, Arvind Kumar, Pankaj Jorwal, Shalimar, A Shariff, Naveet Wig, Rajeshwari Subramanium, Anjan Trikha, Rajesh Malhotra, Randeep Guleria, Vinay Namboodiri, Subhashis Banerjee, Chetan Arora
发表日期
2021/8
期刊
European radiology
卷号
31
页码范围
6039-6048
出版商
Springer Berlin Heidelberg
简介
Objectives
To study whether a trained convolutional neural network (CNN) can be of assistance to radiologists in differentiating Coronavirus disease (COVID)–positive from COVID-negative patients using chest X-ray (CXR) through an ambispective clinical study. To identify subgroups of patients where artificial intelligence (AI) can be of particular value and analyse what imaging features may have contributed to the performance of AI by means of visualisation techniques.
Methods
CXR of 487 patients were classified into [4] categories—normal, classical COVID, indeterminate, and non-COVID by consensus opinion of 2 radiologists. CXR which were classified as “normal” and “indeterminate” were then subjected to analysis by AI, and final categorisation provided as guided by prediction of the network. Precision and recall of the radiologist alone and …
学术搜索中的文章
K Rangarajan, S Muku, AK Garg, P Gabra, SH Shankar… - European radiology, 2021