From signal to knowledge: The diagnostic value of rawdata in artificial intelligence prediction of human data for the first time Article Swipe

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Bingxi He , Yu Guo , Yongbei Zhu , Lixia Tong , Boyu Kong , Kun Wang , Caixia Sun , Hailin Li , Feng Huang , Liwei Wu , Meng Wang , Fanyang Meng , Le Dou , Kai Sun , Tong Tong , Zhenyu Liu , Ziqi Wei , Wei Mu , Shuo Wang , Zhenchao Tang , Shuaitong Zhang , Jingwei Wei , Lizhi Shao , Mengjie Fang , Juntao Li , Shouping Zhu , Lili Zhou , Shuo Wang , Di Dong , Huimao Zhang , Jie Tian ·

YOU? · · 2022 · Open Access · · DOI: https://doi.org/10.1101/2022.08.01.22278299

Recently, image-based diagnostic technology has made encouraging and astonishing development. Modern medical care and imaging technology are increasingly inseparable. However, the current diagnosis pattern of Signal-to-Image-to-Knowledge inevitably leads to information distortion and noise introduction in the procedure of image reconstruction (Signal-to-Image). Artificial intelligence (AI) technologies that can mine knowledge from vast amounts of data offer opportunities to disrupt established workflows. In this prospective study, for the first time, we developed an AI-based Signal-to-Knowledge diagnostic scheme for lung nodule classification directly from the CT rawdata (the signal). We found that the rawdata achieved almost comparable performance with CT indicating that we can diagnose diseases without reconstructing images. Meanwhile, the introduction of rawdata could greatly promote the performance of CT, demonstrating that rawdata contains some diagnostic information that CT does not have. Our results break new ground and demonstrate the potential for direct Signal-to-Knowledge domain analysis.

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Workflow SIGNAL (programming language) Computer science Artificial intelligence Distortion (music) Noise (video) Domain knowledge Image (mathematics) Domain (mathematical analysis) Medical imaging Data science Pattern recognition (psychology) Computer vision Mathematics Telecommunications Mathematical analysis Programming language Amplifier Database Bandwidth (computing)

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Type: preprint
Language: en
Landing Page: https://doi.org/10.1101/2022.08.01.22278299
PDF: https://www.medrxiv.org/content/medrxiv/early/2022/08/03/2022.08.01.22278299.full.pdf
OA Status: green
Cited By: 3
References: 30
Related Works: 10
OpenAlex ID: https://openalex.org/W4289653971

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DOI: https://doi.org/10.1101/2022.08.01.22278299

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Title: From signal to knowledge: The diagnostic value of rawdata in artificial intelligence prediction of human data for the first time

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Type: preprint

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Language: en

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Publication year: 2022

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Publication date: 2022-08-03

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Authors: Bingxi He, Yu Guo, Yongbei Zhu, Lixia Tong, Boyu Kong, Kun Wang, Caixia Sun, Hailin Li, Feng Huang, Liwei Wu, Meng Wang, Fanyang Meng, Le Dou, Kai Sun, Tong Tong, Zhenyu Liu, Ziqi Wei, Wei Mu, Shuo Wang, Zhenchao Tang, Shuaitong Zhang, Jingwei Wei, Lizhi Shao, Mengjie Fang, Juntao Li, Shouping Zhu, Lili Zhou, Shuo Wang, Di Dong, Huimao Zhang, Jie Tian

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Landing page: https://doi.org/10.1101/2022.08.01.22278299

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PDF URL: https://www.medrxiv.org/content/medrxiv/early/2022/08/03/2022.08.01.22278299.full.pdf

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Concepts: Workflow, SIGNAL (programming language), Computer science, Artificial intelligence, Distortion (music), Noise (video), Domain knowledge, Image (mathematics), Domain (mathematical analysis), Medical imaging, Data science, Pattern recognition (psychology), Computer vision, Mathematics, Telecommunications, Mathematical analysis, Programming language, Amplifier, Database, Bandwidth (computing)

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Cited by: 3

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