作者
Jasjit S Suri, Sushant Agarwal, Biswajit Jena, Sanjay Saxena, Ayman El-Baz, Vikas Agarwal, Mannudeep K Kalra, Luca Saba, Klaudija Viskovic, Mostafa Fatemi, Subbaram Naidu
发表日期
2022/5/11
来源
IEEE Transactions on Instrumentation and Measurement
出版商
IEEE
简介
Coronavirus 2019 (COVID-19) has led to a global pandemic infecting 224 million people and has caused 4.6 million deaths. Nearly 80 Artificial Intelligence (AI) articles have been published on COVID-19 diagnosis. The first systematic review on the Deep Learning (DL)-based paradigm for COVID-19 diagnosis was recently published by Suri et al. [IEEE J Biomed Health Inform. 2021]. The above study used AtheroPoint’s “AP(ai)Bias 1.0” using 10 AI attributes in the DL framework. The proposed study uses “AP(ai)Bias 2.0” as part of the three quantitative paradigms for Risk-of-Bias quantification by using the best 40 dedicated Hybrid DL (HDL) studies and utilizing 39 AI attributes. In the first method, the radial-bias map (RBM) was computed for each AI study, followed by the computation of bias value. In the second method, the regional-bias area (RBA) was computed by the area difference between the best and the …
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