Engineering (Mar 2024)
From Signal to Knowledge: The Diagnostic Value of Raw Data in the Artificial Intelligence Prediction of Human Data for the First Time
- 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
Affiliations
- Bingxi He
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing 100191, China; Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing 100191, China; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- Yu Guo
- Department of Radiology, The First Hospital of Jilin University, Changchun 130021, China
- Yongbei Zhu
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing 100191, China; Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing 100191, China; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- Lixia Tong
- Neusoft Medical Systems Co. Ltd., Shenyang 110167, China
- Boyu Kong
- Department of Radiology, The First Hospital of Jilin University, Changchun 130021, China
- Kun Wang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- Caixia Sun
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing 100191, China; Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing 100191, China; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- Hailin Li
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing 100191, China; Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing 100191, China; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- Feng Huang
- Neusoft Medical Systems Co. Ltd., Shenyang 110167, China
- Liwei Wu
- Neusoft Research of Intelligent Healthcare Technology Co. Ltd., Shenyang 110167, China
- Meng Wang
- Department of Radiology, The First Hospital of Jilin University, Changchun 130021, China
- Fanyang Meng
- Department of Radiology, The First Hospital of Jilin University, Changchun 130021, China
- Le Dou
- Department of Radiology, The First Hospital of Jilin University, Changchun 130021, China
- Kai Sun
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- Tong Tong
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- Zhenyu Liu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- Ziqi Wei
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- Wei Mu
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing 100191, China; Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing 100191, China; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- Shuo Wang
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing 100191, China; Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing 100191, China; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- Zhenchao Tang
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing 100191, China; Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing 100191, China; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- Shuaitong Zhang
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing 100191, China; Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing 100191, China; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- Jingwei Wei
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- Lizhi Shao
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- Mengjie Fang
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing 100191, China; Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing 100191, China; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- Juntao Li
- School of Life Science and Technology, Xidian University, Xi'an 710126, China; Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi'an 710126, China
- Shouping Zhu
- School of Life Science and Technology, Xidian University, Xi'an 710126, China; Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi'an 710126, China
- Lili Zhou
- Department of Radiology, The First Hospital of Jilin University, Changchun 130021, China
- Shuo Wang
- Department of Radiology, The First Hospital of Jilin University, Changchun 130021, China
- Di Dong
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China; Corresponding authors.
- Huimao Zhang
- Department of Radiology, The First Hospital of Jilin University, Changchun 130021, China; Corresponding authors.
- Jie Tian
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing 100191, China; Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing 100191, China; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; Corresponding authors.
- Journal volume & issue
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Vol. 34
pp. 60 – 69
Abstract
Encouraging and astonishing developments have recently been achieved in image-based diagnostic technology. Modern medical care and imaging technology are becoming 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 (from 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 develop an AI-based signal-to-knowledge diagnostic scheme for lung nodule classification directly from the computed tomography (CT) raw data (the signal). We find that the raw data achieves almost comparable performance with CT, indicating that it is possible to diagnose diseases without reconstructing images. Moreover, the incorporation of raw data through three common convolutional network structures greatly improves the performance of the CT models in all cohorts (with a gain ranging from 0.01 to 0.12), demonstrating that raw data contains diagnostic information that CT does not possess. Our results break new ground and demonstrate the potential for direct signal-to-knowledge domain analysis.
